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Security (65)

In my last post, I explored how OTA updates are typically performed using Amazon Web Services and FreeRTOS. OTA updates are critically important to developers with connected devices. In today’s post, we are going to explore several best practices developers should keep in mind with implementing their OTA solution. Most of these will be generic although I will point out a few AWS specific best practices.

Best Practice #1 – Name your S3 bucket with afr-ota

There is a little trick with creating S3 buckets that I was completely oblivious to for a long time. Thankfully when I checked in with some colleagues about it, they also had not been aware of it so I’m not sure how long this has been supported but it can help an embedded developer from having to wade through too many AWS policies and simplify the process a little bit.

Anyone who has attempted to create an OTA Update with AWS and FreeRTOS knows that you have to setup several permissions to allow an OTA Update Job to access the S3 bucket. Well if you name your S3 bucket so that it begins with “afr-ota”, then the S3 bucket will automatically have the AWS managed policy AmazonFreeRTOSOTAUpdate attached to it. (See Create an OTA Update service role for more details). It’s a small help, but a good best practice worth knowing.

Best Practice #2 – Encrypt your firmware updates

Embedded software must be one of the most expensive things to develop that mankind has ever invented! It’s time consuming to create and test and can consume a large percentage of the development budget. Software though also drives most features in a product and can dramatically different a product. That software is intellectual property that is worth protecting through encryption.

Encrypting a firmware image provides several benefits. First, it can convert your firmware binary into a form that seems random or meaningless. This is desired because a developer shouldn’t want their binary image to be easily studied, investigated or reverse engineered. This makes it harder for someone to steal intellectual property and more difficult to understand for someone who may be interested in attacking the system. Second, encrypting the image means that the sender must have a key or credential of some sort that matches the device that will decrypt the image. This can be looked at a simple source for helping to authenticate the source, although more should be done than just encryption to fully authenticate and verify integrity such as signing the image.

Best Practice #3 – Do not support firmware rollbacks

There is often a debate as to whether firmware rollbacks should be supported in a system or not. My recommendation for a best practice is that firmware rollbacks be disabled. The argument for rollbacks is often that if something goes wrong with a firmware update then the user can rollback to an older version that was working. This seems like a good idea at first, but it can be a vulnerability source in a system. For example, let’s say that version 1.7 had a bug in the system that allowed remote attackers to access the system. A new firmware version, 1.8, fixes this flaw. A customer updates their firmware to version 1.8, but an attacker knows that if they can force the system back to 1.7, they can own the system. Firmware rollbacks seem like a convenient and good idea, in fact I’m sure in the past I used to recommend them as a best practice. However, in today’s connected world where we perform OTA updates, firmware rollbacks are a vulnerability so disable them to protect your users.

Best Practice #4 – Secure your bootloader

Updating firmware Over-the-Air requires several components to ensure that it is done securely and successfully. Often the focus is on getting the new image to the device and getting it decrypted. However, just like in traditional firmware updates, the bootloader is still a critical piece to the update process and in OTA updates, the bootloader can’t just be your traditional flavor but must be secure.

There are quite a few methods that can be used with the onboard bootloader, but no matter the method used, the bootloader must be secure. Secure bootloaders need to be capable of verifying the authenticity and integrity of the firmware before it is ever loaded. Some systems will use the application code to verify and install the firmware into a new application slot while others fully rely on the bootloader. In either case, the secure bootloader needs to be able to verify the authenticity and integrity of the firmware prior to accepting the new firmware image.

It’s also a good idea to ensure that the bootloader is built into a chain of trust and cannot be easily modified or updated. The secure bootloader is a critical component in a chain-of-trust that is necessary to keep a system secure.

Best Practice #5 – Build a Chain-of-Trust

A chain-of-trust is a sequence of events that occur while booting the device that ensures each link in the chain is trusted software. For example, I’ve been working with the Cypress PSoC 64 secure MCU’s recently and these parts come shipped from the factory with a hardware-based root-of-trust to authenticate that the MCU came from a secure source. That Root-of-Trust (RoT) is then transferred to a developer, who programs a secure bootloader and security policies onto the device. During the boot sequence, the RoT verifying the integrity and authenticity of the bootloader, which then verifies the integrity and authenticity of any second stage bootloader or software which then verifies the authenticity and integrity of the application. The application then verifies the authenticity and integrity of its data, keys, operational parameters and so on.

This sequence creates a Chain-Of-Trust which is needed and used by firmware OTA updates. When the new firmware request is made, the application must decrypt the image and verify that authenticity and integrity of the new firmware is intact. That new firmware can then only be used if the Chain-Of-Trust can successfully make its way through each link in the chain. The bottom line, a developer and the end user know that when the system boots successfully that the new firmware is legitimate. 

Conclusions

OTA updates are a critical infrastructure component to nearly every embedded IoT device. Sure, there are systems out there that once deployed will never update, however, those are probably a small percentage of systems. OTA updates are the go-to mechanism to update firmware in the field. We’ve examined several best practices that developers and companies should consider when they start to design their connected systems. In fact, the bonus best practice for today is that if you are building a connected device, make sure you explore your OTA update solution sooner rather than later. Otherwise, you may find that building that Chain-Of-Trust necessary in today’s deployments will be far more expensive and time consuming to implement.

Originally posted here.

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4 key questions to ask tech vendors

Posted by Terri Hiskey

Without mindful and strategic investments, a company’s supply chain could become wedged in its own proverbial Suez Canal, ground to a halt by outside forces and its inflexible, complex systems.

 

It’s a dramatic image, but one that became reality for many companies in the last year. Supply chain failures aren’t typically such high-profile events as the Suez Canal blockage, but rather death by a thousand inefficiencies, each slowing business operations and affecting the customer experience.

Delay by delay and spreadsheet by spreadsheet, companies are at risk of falling behind more nimble, cloud-enabled competitors. And as we emerge from the pandemic with a new understanding of how important adaptable, integrated supply chains are, company leaders have critical choices to make.

The Hannover Messe conference (held online from April 12-16) gives manufacturing and supply chain executives around the world a chance to hear perspectives from industry leaders and explore the latest manufacturing and supply chain technologies available.

Technology holds great promise. But if executives don’t ask key strategic questions to supply chain software vendors, they could unknowingly introduce a range of operational and strategic obstacles into their company’s future.

If you’re attending Hannover Messe, here are a few critical questions to ask:

Are advanced technologies like machine learning, IoT, and blockchain integrated into your supply chain applications and business processes, or are they addressed separately?

It’s important to go beyond the marketing. Is the vendor actually promoting pilots of advanced technologies that are simply customized use cases for small parts of an overall business process hosted on a separate platform? If so, it may be up to your company to figure out how to integrate it with the rest of that vendor’s applications and to maintain those integrations.

To avoid this situation, seek solutions that have been purpose-built to leverage advanced technologies across use cases that address the problems you hope to solve. It’s also critical that these solutions come with built-in connections to ensure easy integration across your enterprise and to third party applications.

Are your applications or solutions written specifically for the cloud?

If a vendor’s solution for a key process (like integrated business planning or plan to produce, for example) includes applications developed over time by a range of internal development teams, partners, and acquired companies, what you’re likely to end up with is a range of disjointed applications and processes with varying user interfaces and no common data model. Look for a cloud solution that helps connect and streamline your business processes seamlessly.

Update schedules for the various applications could also be disjointed and complicated, so customers can be tempted to skip updates. But some upgrades may be forced, causing disruption in key areas of your business at various times.

And if some of the applications in the solution were written for the on-premises world, business processes will likely need customization, making them hard-wired and inflexible. The convenience of cloud solutions is that they can take frequent updates more easily, resulting in greater value driven by the latest innovations.

Are your supply chain applications fully integrated—and can they be integrated with other key applications like ERP or CX?

A lack of integration between and among applications within the supply chain and beyond means that end users don’t have visibility into the company’s operations—and that directly affects the quality and speed of business decisions. When market disruptions or new opportunities occur, unintegrated systems make it harder to shift operations—or even come to an agreement on what shift should happen.

And because many key business processes span multiple areas—like manufacturing forecast to plan, order to cash, and procure to pay—integration also increases efficiency. If applications are not integrated across these entire processes, business users resort to pulling data from the various systems and then often spend time debating whose data is right.

Of course, all of these issues increase operational costs and make it harder for a company to adapt to change. They also keep the IT department busy with maintenance tasks rather than focusing on more strategic projects.

Do you rely heavily on partners to deliver functionality in your supply chain solutions?

Ask for clarity on which products within the solution belong to the vendor and which were developed by partners. Is there a single SLA for the entire solution? Will the two organizations’ development teams work together on a roadmap that aligns the technologies? Will their priority be on making a better solution together or on enhancements to their own technology? Will they focus on enabling data to flow easily across the supply chain solution, as well as to other systems like ERP? Will they be able to overcome technical issues that arise and streamline customer support?

It’s critical for supply chain decision-makers to gain insight into these crucial questions. If the vendor is unable to meet these foundational needs, the customer will face constant obstacles in their supply chain operations.

Originally posted here.

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By Ricardo Buranello

What Is the Concept of a Virtual Factory?

For a decade, the first Friday in October has been designated as National Manufacturing Day. This day begins a month-long events schedule at manufacturing companies nationwide to attract talent to modern manufacturing careers.

For some period, manufacturing went out of fashion. Young tech talents preferred software and financial services career opportunities. This preference has changed in recent years. The advent of digital technologies and robotization brought some glamour back.

The connected factory is democratizing another innovation — the virtual factory. Without critical asset connection at the IoT edge, the virtual factory couldn’t have been realized by anything other than brand-new factories and technology implementations.

There are technologies that enable decades-old assets to communicate. Such technologies allow us to join machine data with physical environment and operational conditions data. Benefits of virtual factory technologies like digital twin are within reach for greenfield and legacy implementations.

Digital twin technologies can be used for predictive maintenance and scenario planning analysis. At its core, the digital twin is about access to real-time operational data to predict and manage the asset’s life cycle. It leverages relevant life cycle management information inside and outside the factory. The possibilities of bringing various data types together for advanced analysis are promising.

I used to see a distinction between IoT-enabled greenfield technology in new factories and legacy technology in older ones. Data flowed seamlessly from IoT-enabled machines to enterprise systems or the cloud for advanced analytics in new factories’ connected assets. In older factories, while data wanted to move to the enterprise systems or the cloud, it hit countless walls. Innovative factories were creating IoT technologies in proof of concepts (POCs) on legacy equipment, but this wasn’t the norm.

No matter the age of the factory or equipment, everything looks alike. When manufacturing companies invest in machines, the expectation is this asset will be used for a decade or more. We had to invent something inclusive to new and legacy machines and systems.

We had to create something to allow decades-old equipment from diverse brands and types (PLCs, CNCs, robots, etc.) to communicate with one another. We had to think in terms of how to make legacy machines to talk to legacy systems. Connecting was not enough. We had to make it accessible for experienced developers and technicians not specialized in systems integration.

If plant managers and leaders have clear and consumable data, they can use it for analysis and measurement. Surfacing and routing data has enabled innovative use cases in processes controlled by aged equipment. Prescriptive and predictive maintenance reduce downtime and allow access to data. This access enables remote operation and improved safety on the plant floor. Each line flows better, improving supply chain orchestration and worker productivity.

Open protocols aren’t optimized for connecting to each machine. You need tools and optimized drivers to connect to the machines, cut latency time and get the data to where it needs to be in the appropriate format to save costs. These tools include:

  • Machine data collection
  • Data transformation and visualization
  • Device management
  • Edge logic
  • Embedded security
  • Enterprise integration
This digital copy of the entire factory floor brings more promise for improving productivity, quality, downtime, throughput and lending access to more data and visibility. It enables factories to make small changes in the way machines and processes operate to achieve improvements.

Plants are trying to get and use data to improve overall equipment effectiveness. OEE applications can calculate how many good and bad parts were produced compared to the machine’s capacity. This analysis can go much deeper. Factories can visualize how the machine works down to sub-processes. They can synchronize each movement to the millisecond and change timing to increase operational efficiency.

The technology is here. It is mature. It’s no longer a question of whether you want to use it — you have it to get to what’s next. I think this makes it a fascinating time for smart manufacturing.

Originally posted here.

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By Tony Pisani

For midstream oil and gas operators, data flow can be as important as product flow. The operator’s job is to safely move oil and natural gas from its extraction point (upstream), to where it’s converted to fuels (midstream), to customer delivery locations (downstream). During this process, pump stations, meter stations, storage sites, interconnection points, and block valves generate a substantial volume and variety of data that can lead to increased efficiency and safety.

“Just one pipeline pump station might have 6 Programmable Logic Controllers (PLCs), 12 flow computers, and 30 field instruments, and each one is a source of valuable operational information,” said Mike Walden, IT and SCADA Director for New Frontier Technologies, a Cisco IoT Design-In Partner that implements OT and IT systems for industrial applications. Until recently, data collection from pipelines was so expensive that most operators only collected the bare minimum data required to comply with industry regulations. That data included pump discharge pressure, for instance, but not pump bearing temperature, which helps predict future equipment failures.

A turnkey solution to modernize midstream operations

Now midstream operators are modernizing their pipelines with Industrial Internet of Things (IIoT) solutions. Cisco and New Frontier Technologies have teamed up to offer a solution combining the Cisco 1100 Series Industrial Integrated Services Router, Cisco Edge Intelligence, and New Frontier’s know-how. Deployed at edge locations like pump stations, the solution extracts data from pipeline equipment and is sent via legacy protocols, transforming data at the edge to a format that analytics and other enterprise applications understand. The transformation also minimizes bandwidth usage.

Mike Walden views the Cisco IR1101 as a game-changer for midstream operators. He shared with me that “Before the Cisco IR1101, our customers needed four separate devices to transmit edge data to a cloud server—a router at the pump station, an edge device to do protocol conversion from the old to the new, a network switch, and maybe a firewall to encrypt messages…With the Cisco IR1101, we can meet all of those requirements with one physical device.”

Collect more data, at almost no extra cost

Using this IIoT solution, midstream operators can for the first time:

  • Collect all available field data instead of just the data on a polling list. If the maintenance team requests a new type of data, the operations team can meet the request using the built-in protocol translators in Edge Intelligence. “Collecting a new type of data takes almost no extra work,” Mike said. “It makes the operations team look like heroes.”
  • Collect data more frequently, helping to spot anomalies. Recording pump discharge pressure more frequently, for example, makes it easier to detect leaks. Interest in predicting (rather than responding to) equipment failure is also growing. The life of pump seals, for example, depends on both the pressure that seals experience over their lifetime and the peak pressures. “If you only collect pump pressure every 30 minutes, you probably missed the spike,” Mike explained. “If you do see the spike and replace the seal before it fails, you can prevent a very costly unexpected outage – saving far more than the cost of a new seal.”
  • Protect sensitive data with end-to-end security. Security is built into the IR1101, with secure boot, VPN, certificate-based authentication, and TLS encryption.
  • Give IT and OT their own interfaces so they don’t have to rely on the other team. The IT team has an interface to set up network templates to make sure device configuration is secure and consistent. Field engineers have their own interface to extract, transform, and deliver industrial data from Modbus, OPC-UA, EIP/CIP, or MQTT devices.

As Mike summed it up, “It’s finally simple to deploy a secure industrial network that makes all field data available to enterprise applications—in less time and using less bandwidth.”

Originally posted here.

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By GE Digital

“The End of Cloud Computing.” “The Edge Will Eat The cloud.” “Edge Computing—The End of Cloud Computing as We Know It.”  

Such headlines grab attention, but don’t necessarily reflect reality—especially in Industrial Internet of Things (IoT) deployments. To be sure, edge computing is rapidly emerging as a powerful force in turning industrial machines into intelligent machines, but to paraphrase Mark Twain: “The reports of the death of cloud are greatly exaggerated.” 

The Tipping Point: Edge Computing Hits Mainstream

We’ve all heard the stats—billions and billions of IoT devices, generating inconceivable amounts of big data volumes, with trillions and trillions of U.S. dollars to be invested in IoT over the next several years. Why? Because industrials have squeezed every ounce of productivity and efficiency out of operations over the past couple of decades, and are now looking to digital strategies to improve production, performance, and profit. 

The Industrial Internet of Things (IIoT) represents a world where human intelligence and machine intelligence—what GE Digital calls minds and machines—connect to deliver new value for industrial companies. 

In this new landscape, organizations use data, advanced analytics, and machine learning to drive digital industrial transformation. This can lead to reduced maintenance costs, improved asset utilization, and new business model innovations that further monetize industrial machines and the data they create. 

Despite the “cloud is dead” headlines, GE believes the cloud is still very important in delivering on the promise of IIoT, powering compute-intense workloads to manage massive amounts of data generated by machines. However, there’s no question that edge computing is quickly becoming a critical factor in the total IIoT equation.

“The End of Cloud Computing.” “The Edge Will Eat The cloud.” “Edge Computing—The End of Cloud Computing as We Know It.”  

Such headlines grab attention, but don’t necessarily reflect reality—especially in Industrial Internet of Things (IoT) deployments. To be sure, edge computing is rapidly emerging as a powerful force in turning industrial machines into intelligent machines, but to paraphrase Mark Twain: “The reports of the death of cloud are greatly exaggerated.”

The Tipping Point: Edge Computing Hits Mainstream

We’ve all heard the stats—billions and billions of IoT devices, generating inconceivable amounts of big data volumes, with trillions and trillions of U.S. dollars to be invested in IoT over the next several years. Why? Because industrials have squeezed every ounce of productivity and efficiency out of operations over the past couple of decades, and are now looking to digital strategies to improve production, performance, and profit. 

The Industrial Internet of Things (IIoT) represents a world where human intelligence and machine intelligence—what GE Digital calls minds and machines—connect to deliver new value for industrial companies. 

In this new landscape, organizations use data, advanced analytics, and machine learning to drive digital industrial transformation. This can lead to reduced maintenance costs, improved asset utilization, and new business model innovations that further monetize industrial machines and the data they create. 

Despite the “cloud is dead” headlines, GE believes the cloud is still very important in delivering on the promise of IIoT, powering compute-intense workloads to manage massive amounts of data generated by machines. However, there’s no question that edge computing is quickly becoming a critical factor in the total IIoT equation. 

What is edge computing? 

The “edge” of a network generally refers to technology located adjacent to the machine which you are analyzing or actuating, such as a gas turbine, a jet engine, or magnetic resonance (MR) scanner. 

Until recently, edge computing has been limited to collecting, aggregating, and forwarding data to the cloud. But what if instead of collecting data for transmission to the cloud, industrial companies could turn massive amounts of data into actionable intelligence, available right at the edge? Now they can. 

This is not just valuable to industrial organizations, but absolutely essential.

Edge computing vs. Cloud computing 

Cloud and edge are not at war … it’s not an either/or scenario. Think of your two hands. You go about your day using one or the other or both depending on the task. The same is true in Industrial Internet workloads. If the left hand is edge computing and the right hand is cloud computing, there will be times when the left hand is dominant for a given task, instances where the right hand is dominant, and some cases where both hands are needed together. 

Scenarios in which edge computing will take a leading position include things such as low latency, bandwidth, real-time/near real-time actuation, intermittent or no connectivity, etc. Scenarios where cloud will play a more prominent role include compute-heavy tasks, machine learning, digital twins, cross-plant control, etc. 

The point is you need both options working in tandem to provide design choices across edge to cloud that best meet business and operational goals.

Edge Computing and Cloud Computing: Balance in Action 

Let’s look at a couple of illustrations. In an industrial context, examples of intelligent edge machines abound—pumps, motors, sensors, blowout preventers and more benefit from the growing capabilities of edge computing for real-time analytics and actuation. 

Take locomotives. These modern 200 ton digital machines carry more than 200 sensors that can pump one billion instructions per second. Today, applications can not only collect data locally and respond to changes on that data, but they can also perform meaningful localized analytics. GE Transportation’s Evolution Series Tier 4 Locomotive uses on-board edge computing to analyze data and apply algorithms for running smarter and more efficiently. This improves operational costs, safety, and uptime. 

Sending all that data created by the locomotive to the cloud for processing, analyzing, and actuation isn’t useful, practical, or cost-effective. 

Now let’s switch gears (pun intended) and talk about another mode of transportation—trucking. Here’s an example where edge plays an important yet minor role, while cloud assumes a more dominant position. In this example, the company has 1,000 trucks under management. There are sensors on each truck tracking performance of the vehicle such as engine, transmission, electrical, battery, and more. 

But in this case, instead of real-time analytics and actuation on the machine (like our locomotive example), the data is being ingested, then stored and forwarded to the cloud where time series data and analytics are used to track performance of vehicle components. The fleet operator then leverages a fleet management solution for scheduled maintenance and cost analysis. This gives him or her insights such as the cost over time per part type, or the median costs over time, etc. The company can use this data to improve uptime of its vehicles, lower repair costs, and improve the safe operation of the vehicle.

What’s next in edge computing 

While edge computing isn’t a new concept, innovation is now beginning to deliver on the promise—unlocking untapped value from the data being created by machines. 

GE has been at the forefront of bridging minds and machines. Predix Platform supports a consistent execution environment across cloud and edge devices, helping industrials achieve new levels of performance, production, and profit.

Originally posted here.

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Computer vision is fundamental to capturing real-world data within the IoT. Arm technology provides a secure ecosystem for smart cameras in business, industrial and home applications

By Mohamed Awad, VP IoT & Embedded, Arm

Computer vision leverages artificial intelligence (AI) to enable devices such as smart cameras to interpret and understand what is happening in an image. Recreating a sensor as powerful as the human eye with technology opens up a wide and varied range of use cases for computers to perform tasks that previously required human sight – so it’s no wonder that computer vision is quickly becoming one of the most important ways to capture and act on real-world data within the Internet of Things (IoT).

Smart cameras now use computer vision in a range of business and industrial applications, from counting cars in parking lots to monitoring footfall in retail stores or spotting defects on a production line. And in the home, smart cameras can tell us when a package has been delivered, whether the dog escaped from the back yard or when our baby is awake.

Across the business and consumer worlds, the adoption of smart camera technology is growing exponentially. In its 2020 report “Cameras and Computing for Surveillance and Security”, market research and strategy consulting company Yole Développement estimates that for surveillance alone, there are approximately one billion cameras across the world. That number of installations is expected to double by 2024.

This technology features key advancements in security, heterogeneous computing, image processing and cloud services – enabling future computer vision products that are more capable than ever.

Smart camera security is top priority for computer vision

IoT security is a key priority and challenge for the technology industry. It’s important that all IoT devices are secure from exploitation by malicious actors, but it’s even more critical when that device captures and stores image data about people, places and high-value assets.

Unauthorized access to smart cameras tasked with watching over factories, hospitals, schools or homes would not only be a significant breach of privacy, it could also lead to untold harm—from plotting crimes to the leaking of confidential information. Compromising a smart camera could also provide a gateway, giving a malicious actor access to other devices within the network – from door, heating and lighting controls to control over an entire smart factory floor.

We need to be able to trust smart cameras to maintain security for us all, not open up new avenues for exploitation. Arm has embraced the importance of security in IoT devices for many years through its product portfolio offerings such as Arm TrustZone for both Cortex-A and Cortex-M.

In the future, smart camera chips based on the Armv9 architecture will add further security enhancements for computer vision products through the Arm Confidential Compute Architecture (CCA).

Further to this, Arm promotes common standards of security best practice such as PSA Certified and PARSEC. These are designed to ensure that all future smart camera deployments have built-in security, from the point the image sensor first records the scene to storage, whether that data is stored locally or in the cloud by using advanced security and data encryption techniques.

Endpoint AI powers computer vision in smart camera devices

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The combination of image sensor technology and endpoint AI is enabling smart cameras to infer increasingly complex insights from the vast amounts of computer vision data they capture. New machine learning capabilities within smart camera devices meet a diverse range of use cases – such as detecting individual people or animals, recognizing specific objects and reading license plates. All of these applications for computer vision require ML algorithms running on the endpoint device itself, rather than sending data to the cloud for inference. It’s all about moving compute closer to data.

For example, a smart camera employed at a busy intersection could use computer vision to determine the number and type of vehicles waiting at a red signal at various hours throughout the day. By processing its own data and inferring meaning using ML, the smart camera could automatically adjust its timings in order to reduce congestion and limit build-up of emissions automatically without human involvement.

Arm’s investment in AI for applications in endpoints and beyond is demonstrated through its range of Ethos machine learning processors: highly scalable and efficient NPUs capable of supporting a range of 0.1 to 10 TOP/s through many-core technologies. Software also plays a vital role in ML and this is why Arm continues to support the open-source community through the Arm NN SDK and TensorFlow Lite for Microcontrollers (TFLM) open-source frameworks.

These machine learning workload frameworks are based on existing neural networks and power-efficient Arm Cortex-A CPUs, Mali GPUs and Ethos NPUs as well as Arm Compute library and CMSIS-NN – a collection of low-level machine learning functions optimized for Cortex-A CPU, Cortex-M CPU and Mali GPU architectures.

The Armv9 architecture supports enhanced AI capabilities, too, by providing accessible vector arithmetic (individual arrays of data that can be computed in parallel) via Scalable Vector Extension 2 (SVE2). This enables scaling of the hardware vector length without having to rewrite or recompile code. In the future, extensions for matrix multiplication (a key element in enhancing ML) will push the AI envelope further.

Smart cameras connected in the cloud

Cloud and edge computing is also helping to expedite the adoption of smart cameras. Traditional CCTV architectures saw camera data stored on-premises via a Network Video Recorder (NVR) or a Digital Video Recorder (DVR). This model had numerous limitations, from the vast amount of storage required to the limited number of physical connections on each NVR.

Moving to a cloud-native model simplifies the rollout of smart cameras enormously: any number of cameras can be provisioned and managed via a configuration file downloaded to the device. There’s also a virtuous cycle at play: Data from smart cameras can be now used to train the models in the cloud for specific use-cases so that cameras become even smarter. And the smarter they become, the less data they need to send upstream.

The use of cloud computing also enables automation of processes via AI sensor fusion by combining computer vision data from multiple smart cameras. Taking our earlier example of the smart camera placed at a road intersection, cloud AI algorithms could combine data from multiple cameras to constantly adjust traffic light timings holistically across an entire city, keeping traffic moving.

Arm enables the required processing continuum from cloud to endpoint. Cortex-M microcontrollers and Cortex-A processors power smart cameras, with Cortex-A processors also powering edge gateways. Cloud and edge servers harness the capabilities of the Neoverse platform.

New hardware and software demands on smart cameras

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The compute needs for computer vision devices continue to grow year over year, with ultra-high resolution video capture (8K 60fps) and 64-bit (Armv8-A) processing marking the current standard for high-end smart camera products.

As a result, the system-on-chip (SoC) within next-generation smart cameras will need to embrace heterogenous architectures, combining CPUs, GPUs, NPUs alongside dedicated hardware for functions like computer vision, image processing, video encoding and decoding.

Storage, too, is a key concern: While endpoint AI can reduce storage requirements by processing images locally on the camera, many use cases will require that data be retained somewhere for safety and security – whether on the device, in edge servers or in the cloud.

To ensure proper storage of high-resolution computer vision data, new video encoding and decoding standards such as H.265 and AV1 are becoming the de facto standard.

New use cases driving continuous innovation

Overall, the demands from the new use cases are driving the need for continuous improvement in computing and imaging technologies across the board.

When we think about image-capturing devices such as CCTV cameras today, we should no longer imagine grainy images of barely recognizable faces passing by a camera. Advancements in computer vision – more efficient and powerful compute coupled with the intelligence of AI and machine learning – are making smart cameras not just image sensors but image interpreters. This bridge between the analog and digital worlds is opening up new classes of applications and use cases that were unimaginable a few years ago.

Originally posted here.

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TinyML focuses on optimizing machine learning (ML) workloads so that they can be processed on microcontrollers no bigger than a grain of rice and consuming only milliwatts of power.

By Arm Blueprint staff
 

TinyML focuses on the optimization of machine learning (ML) workloads so that they can be processed on microcontrollers no bigger than a grain of rice and consuming only a few milliwatts of power.

TinyML gives tiny devices intelligence. We mean tiny in every sense of the word: as tiny as a grain of rice and consuming tiny amounts of power. Supported by Arm, Google, Qualcomm and others, tinyML has the potential to transform the Internet of Things (IoT), where billions of tiny devices, based on Arm chips, are already being used to provide greater insight and efficiency in sectors including consumer, medical, automotive and industrial.

Why target microcontrollers with tinyML?

Microcontrollers such as the Arm Cortex-M family are an ideal platform for ML because they’re already used everywhere. They perform real-time calculations quickly and efficiently, so they’re reliable and responsive, and because they use very little power, can be deployed in places where replacing the battery is difficult or inconvenient. Perhaps even more importantly, they’re cheap enough to be used just about anywhere. The market analyst IDC reports that 28.1 billion microcontrollers were sold in 2018, and forecasts that annual shipment volume will grow to 38.2 billion by 2023.

TinyML on microcontrollers gives us new techniques for analyzing and making sense of the massive amount of data generated by the IoT. In particular, deep learning methods can be used to process information and make sense of the data from sensors that do things like detect sounds, capture images, and track motion.

Advanced pattern recognition in a very compact format

Looking at the math involved in machine learning, data scientists found they could reduce complexity by making certain changes, such as replacing floating-point calculations with simple 8-bit operations. These changes created machine learning models that work much more efficiently and require far fewer processing and memory resources.

TinyML technology is evolving rapidly thanks to new technology and an engaged base of committed developers. Only a few years ago, we were celebrating our ability to run a speech-recognition model capable of waking the system if it detects certain words on a constrained Arm Cortex-M3 microcontroller using just 15 kilobytes (KB) of code and 22KB of data.

Since then, Arm has launched new machine learning (ML) processors, called the Ethos-U55 and Ethos-U65, a microNPU specifically designed to accelerate ML inference in embedded and IoT devices.

The Ethos-U55, combined with the AI-capable Cortex-M55 processor, will provide a significant uplift in ML performance and improvement in energy efficiency over the already impressive examples we are seeing today.

TinyML takes endpoint devices to the next level

The potential use cases of tinyML are almost unlimited. Developers are already working with tinyML to explore all sorts of new ideas: responsive traffic lights that change signaling to reduce congestion, industrial machines that can predict when they’ll need service, sensors that can monitor crops for the presence of damaging insects, in-store shelves that can request restocking when inventory gets low, healthcare monitors that track vitals while maintaining privacy. The list goes on.

TinyML can make endpoint devices more consistent and reliable, since there’s less need to rely on busy, crowded internet connections to send data back and forth to the cloud. Reducing or even eliminating interactions with the cloud has major benefits including reduced energy use, significantly reduced latency in processing data and security benefits, since data that doesn’t travel is far less exposed to attack. 

It’s worth nothing that these tinyML models, which perform inference on the microcontroller, aren’t intended to replace the more sophisticated inference that currently happens in the cloud. What they do instead is bring specific capabilities down from the cloud to the endpoint device. That way, developers can save cloud interactions for if and when they’re needed. 

TinyML also gives developers a powerful new set of tools for solving problems. ML makes it possible to detect complex events that rule-based systems struggle to identify, so endpoint AI devices can start contributing in new ways. Also, since ML makes it possible to control devices with words or gestures, instead of buttons or a smartphone, endpoint devices can be built more rugged and deployable in more challenging operating environments. 

TinyML gaining momentum with an expanding ecosystem

Industry players have been quick to recognize the value of tinyML and have moved rapidly to create a supportive ecosystem. Developers at every level, from enthusiastic hobbyists to experienced professionals, can now access tools that make it easy to get started. All that’s needed is a laptop, an open-source software library and a USB cable to connect the laptop to one of several inexpensive development boards priced as low as a few dollars.

In fact, at the start of 2021, Raspberry Pi released its very first microcontroller board, one of the most affordable development board available in the market at just $4. Named Raspberry Pi Pico, it’s powered by the RP2040 SoC, a surprisingly powerful dual Arm Cortex-M0+ processor. The RP2040 MCU is able to run TensorFlow Lite Micro and we’re expecting to see a wide range of ML use cases for this board over the coming months.

Arm is a strong proponent of tinyML because our microcontroller architectures are so central to the IoT, and because we see the potential of on-device inference. Arm’s collaboration with Google is making it even easier for developers to deploy endpoint machine learning in power-conscious environments.

The combination of Arm CMSIS-NN libraries with Google’s TensorFlow Lite Micro (TFLu) framework, allows data scientists and software developers to take advantage of Arm’s hardware optimizations without needing to become experts in embedded programming.

On top of this, Arm is investing in new tools derived from Keil MDK to help developers get from prototype to production when deploying ML applications.

TinyML would not be possible without a number of early influencers. Pete Warden, a “founding father” of tinyML and a technical lead of TensorFlow Lite Micro at Google,&nbspArm Innovator, Kwabena Agyeman, who developed OpenMV, a project dedicated to low-cost, extensible, Python-powered machine-vision modules that support machine learning algorithms, and Arm Innovator, Daniel Situnayake a founding tinyML engineer and developer from Edge Impulse, a company that offers a full tinyML pipeline that covers data collection, model training and model optimization. Also, Arm partners such as Cartesiam.ai, a company that offers NanoEdge AI, a tool that creates software models on the endpoint based on the sensor behavior observed in real conditions have been pushing the possibilities of tinyML to another level. 

Arm, is also a partner of the TinyML Foundation, an open community that coordinates meet-ups to help people connect, share ideas, and get involved. There are many localised tinyML meet-ups covering UK, Israel and Seattle to name a few, as well as a global series of tinyML Summits. For more information, visit the tinyML foundation website.

Originally posted here.

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What is 5G NR (New Radio)?

by Gus Vos

Unless you have been living under a rock, you have been seeing and hearing a lot about&nbsp5G these days. In addition, if you are at all involved in Internet of Things (IoT) or other initiatives at your organization that use cellular networking technologies, you have also likely heard about 5G New Radio, otherwise known as 5G NR, the new 5G radio access technology specification.

However, all the jargon, hype, and sometimes contradictory statements made by solution providers, the media, and analysts regarding 5G and 5G NR can make it difficult to understand what 5G NR actually is, how it works, what its advantages are, to what extent it is different than other cellular radio access technologies, and perhaps most importantly, how your organization can use this new radio access technology.

In this blog, we will provide you with an overview on 5G NR, offering you answers to these and other basic 5G NR questions – with a particular focus on what these answers mean for those in the IoT industry. 

We can’t promise to make you a 5G NR expert with this blog – but we can say that if you are confused about 5G NR before reading it, you will come away afterward with a better understanding of what 5G NR is, how it works, and how it might transform your industry.

What is the NR in 5G NR?

As its name implies, 5G New Radio or 5G NR is the new radio access technology specification found in the 5G standard. 

Set by the 3rd Generation Partnership Project (3GPP) telecommunications standards group, the 5G NR specification defines how 5G NR edge devices (smart phones, embedded modules, routers, and gateways) and 5G NR network infrastructure (base stations, small cells, and other Radio Access Network equipment) wirelessly transmit data. To put it another way, 5G NR describes how 5G NR edge devices and 5G NR network infrastructure use radio waves to talk to each other. 

5G NR is a very important part of 5G. After all, it describes how 5G solutions will use radio waves to wirelessly transmit data faster and with less latency than previous radio access technology specifications. However, while 5G NR is a very important part of the new 5G standard, it does not encompass everything related to 5G. 

For example, 5G includes a new core network architecture standard (appropriately named 5G Core Network or 5GCN) that specifies the architecture of the network that collects, processes, and routes data from edge devices and then sends this data to the cloud, other edge devices, or elsewhere. The 5GCN will improve 5G networks’ operational capacity, efficiency, and performance.

However, 5GCN is not a radio access technology like 5G NR, but rather a core network technology. In fact, networks using the 5GCN core network will be able to work with previous types of radio access technologies – like LTE. 

Is 5G NR one of 5G’s most important new technological advancements? Yes. But it is not the only technological advancement to be introduced by 5G.  

How does 5G NR work?

Like all radio access communications technology specifications, the 5G NR specification describes how edge devices and network infrastructure transmit data to each other using electromagnetic radio waves. Depending on the frequency of the electromagnetic waves (how long the wave is), it occupies a different part of the wireless spectrum.

Some of the waves that 5G NR uses have frequencies of between 400 MHz and 6 GHz. These waves occupy what is called sub-6 spectrum (since their frequencies are all under 6 GHz).

This sub-6 spectrum is used by other cellular radio access technologies, like LTE, as well. In the past, using different cellular radio access technologies like this over the same spectrum would lead to unmanageable interference problems, with the different technologies radio waves interfering with each other. 

One of 5G NR’s many advantages is that it’s solved this problem, using a technology called Dynamic Spectrum Sharing (DSS). This DSS technology allows 5G NR signals to use the same band of spectrum as LTE and other cellular technologies, like LTE-M and NB-IoT. This allows 5G NR networks to be rolled out without shutting down LTE or other networks that support existing LTE smart phones or IoT devices. You can learn more about DSS, and how it speeds the rollout of 5G NR while also extending the life of IoT devices, here.

One of 5G NR’s other major advancements is that it does not just use waves in the sub-6 spectrum to transmit data. The 5G NR specification also specifies how edge devices and network infrastructure can use radio waves in bands between 24 GHz and 52 GHz to transmit data.

These millimeter wave (mmWave) bands greatly expand the amount of spectrum available for wireless data communications. The lack of spectrum capacity has been a problem in the past, as there is a limited number of bands of sub-6 spectrum available for organizations to use for cellular communications, and many of these bands are small. Lack of available capacity and narrow spectrum bands led to network congestion, which limits the amount of data that can be transmitted over networks that use sub-6 spectrum. 

mmWave opens up a massive amount of new wireless spectrum, as well as much broader bands of wireless spectrum for cellular data transmission. This additional spectrum and these broader spectrum bands increase the capacity (amount of data) that can be transmitted over these bands, enabling 5G NR mmWave devices to achieve data speeds that are four or more times faster than devices that use just sub-6 spectrum. 

The additional wireless capacity provided by mmWave also reduces latency (the time between when device sends a signal and when it receives a response). By reducing latency from 10 milliseconds with sub-6 devices to 3-4 milliseconds or lower with 5G NR mmWave devices, 5G enables new industrial automation, autonomous vehicle and immersive gaming use cases, as well as Virtual Reality (VR), Augmented Reality (AR), and similar Extended Reality (XR) use cases, all of which require very low latency. 

On the other hand, these new mmWave devices and network infrastructure come with new technical requirements, as well as drawbacks associated with their use of mmWave spectrum. For example, mmWave devices use more power and generate more heat than sub-6 devices. In addition, mmWave signals have less range and do not penetrate walls and other physical objects as easily as sub-6 waves. 5G NR includes some technologies, such as beamforming and massive Multiple Input Multiple Output (MIMO) that lessen some of these range and obstacle penetration limitations – but they do not eliminate them. 

To learn more about the implications of 5G NR mmWave on the design of IoT and other products, read our blog, Seven Tips For Designing 5G NR mmWave Products.

In addition, there has been a lot written on these two different “flavors” (sub-6 and mmWave) of 5G NR. If you are interested in learning more about the differences between sub-6 5G NR and mmWave 5G NR, and how together they enable both evolutionary and revolutionary changes for Fixed Wireless Access (FWA), mobile broadband, IoT and other wireless applications, read our previous blog A Closer Look at the Five Waves of 5G.

What is the difference between 5G NR and LTE?

Though sub-6 and mmWave are very different, both types of 5G NR provide data transfer speed, latency, and other performance improvements compared to LTE, the previous radio access technology specification used for cellular communications. 

For example, outside of its use of mmWave, 5G NR features other technical advancements designed to improve network performance, including:

• Flexible numerology, which enables 5G NR network infrastructure to set the spacing between subcarriers in a band of wireless spectrum at 15, 30, 60, 120 and 240 kHz, rather than only use 15 kHz spacing, like LTE. This flexible numerology is what allows 5G NR to use mmWave spectrum in the first place. It also improves the performance of 5G NR devices that use higher sub-6 spectrum, such as 3.5 GHz C-Band spectrum, since the network can adjust the subcarrier spacing to meet the particular spectrum and use case requirements of the data it is transmitting. For example, when low latency is required, the network can use wider subcarrier spacing to help improve the latency of the transmission.
• Beamforming, in which massive MIMO (multiple-input and multiple-output) antenna technologies are used to focus wireless signal and then sweep them across areas till they make a strong connection. Beamforming helps extend the range of networks that use mmWave and higher sub-6 spectrum.  
• Selective Hybrid Automatic Repeat Request (HARQ), which allows 5G NR to break large data blocks into smaller blocks, so that when there is an error, the retransmission is smaller and results in higher data transfer speeds than LTE, which transfers data in larger blocks. 
• Faster Time Division Duplexing (TDD), which enables 5G NR networks to switch between uplink and downlink faster, reducing latency. 
• Pre-emptive scheduling, which lowers latency by allowing higher-priority data to overwrite or pre-empt lower-priority data, even if the lower-priority data is already being transmitted. 
• Shorter scheduling units that trim the minimum scheduling unit to just two symbols, improving latency.
• A new inactive state for devices. LTE devices had two states – idle and connected. 5G NR includes a new state – inactive – that reduces the time needed for an edge device to move in and out of its connected state (the state used for transmission), making the device more responsive. 

These and the other technical advancements made to 5G NR are complicated, but the result of these advancements is pretty simple – faster data speeds, lower latency, more spectrum agility, and otherwise better performance than LTE. 

Are LPWA radio access technology specifications, like NB-IoT and LTE-M, supported by 5G?

Though 5G features a new radio access technology, 5G NR, 5G supports other radio access technologies as well. This includes the Low Power Wide Area (LPWA) technologies, Narrowband IoT (NB-IoT), and Long Term Evolution for Machines (LTE-M). In fact, these LPWA standards are the standards that 5G uses to address one of its three main use cases – Massive, Machine-Type Communications (mMTC). 

Improvements have been and continue to be made to these 5G LPWA standards to address these mMTC use cases – improvements that further lower the cost of LPWA devices, reduce these devices’ power usage, and enable an even larger number of LPWA devices to connect to the network in a given area.

What are the use cases for 5G NR and 5G LPWA Radio Access Technologies?

Today, LTE supports three basic use cases:

• Voice: People today can use LTE to talk to each other using mobile devices. 
• Mobile broadband (MBB): People can use smartphones, tablets, mobile and other edge devices to view videos, play games, and use other applications that require broadband data speeds.
• IoT: People can use cellular modules, routers, and other gateways embedded in practically anything – a smart speaker, a dog collar, a commercial washing machine, a safety shoe, an industrial air purifier, a liquid fertilizer storage tank – to transmit data from the thing to the cloud or a private data center and back via the internet.  

5G NR, as well as 5G’s LPWA radio access technologies (NB-IoT and LTE-M) will continue to support these existing IoT and voice use cases. 

However, 5G also expands on the MBB use case with a new Enhanced Mobile Broadband (eMBB) use case. These eMBB use cases leverage 5G NR’s higher peak and average speeds and lower latency to enable smart phones and other devices to support high-definition cloud-based immersive video games, high quality video calls and new VR, AR, and other XR applications.

In addition, 5G NR also supports a new use case, called Ultra-Reliable, Low-Latency Communications (URLLC). 5G NR enables devices to create connections that are ultra-reliable with very low latency. With these new 5G NR capabilities, as well as 5G NR’s support for very fast handoffs and high mobility, organizations can now deploy new factory automation, smart city 2.0 and other next generation Industrial IoT (IIoT) applications, as well as Vehicle-to-everything (V2X) applications, such as autonomous vehicles. 

As we mentioned above, 5G will also support the new mMTC use case, which represents an enhancement of the existing IoT use case. However, in the case of mMTC, new use cases will be enabled by improvements to LTE-M and NB-IoT radio access technology standards, not 5G NR. Examples of these types of new mMTC use cases include large-scale deployments of small, low cost edge devices (like sensors) for smart city, smart logistics, smart grid, and similar applications.

But this is not all. 3GPP is looking at additional new use cases (and new technologies for these use cases), as discussed in this recent blog on Release 17 of the 5G standard. One of these new technologies is a new Reduced Capability (RedCap) device – sometimes referred to as NR Light – for IoT or MTC use cases that require faster data speeds than LPWA devices can provide, but also need devices that are less expensive than the 5G NR devices being deployed today.

3GPP is also examining standard changes to NR, LTE-M, and NB-IoT in 5G Release 17 that would make it possible for satellites to use these technologies for Non-Terrestrial Network (NTN) communications. This new NTN feature would help enable the deployment of satellites able to provide NR, LTE-M, and NB-IoT coverage in very remote areas, far away from cellular base stations.

What should you look for in a 5G NR module, router or gateway solution?

While all 5G NR edge devices use the 5G NR technology specification, they are not all created equal. In fact, the flexibility, performance, quality, security, and other capabilities of a 5G NR edge device can make the difference between a successful 5G NR application rollout and a failed one. 

As they evaluate 5G NR edge devices for their application, organizations should ask themselves the following questions:

• Is the edge device multi-mode? 
While Mobile Network Operators (MNOs) are rapidly expanding their 5G NR networks, there are still many areas where 5G NR coverage is not available. Multi-mode edge devices that can support LTE, or even 3G, help ensure that wherever the edge device is deployed, it will be able to connect to a MNO’s network – even if this connection does not provide the data speed, latency, or other performance needed to maximize the value of the 5G NR application. 

In addition, many MNOs are rolling out non-standalone (NSA) 5G NR networks at first. These NSA 5G NR networks need a LTE connection in addition to a 5G NR connection to transmit data from and to 5G NR devices. If your edge device does not include support for LTE, it will not be able to use 5G NR on these NSA networks. 

• How secure are the edge devices? 
Data is valuable and sensitive – and the data transmitted by 5G NR devices is no different. To limit the risk that this data is exposed, altered, or destroyed, organizations need to adopt a Defense in Depth approach to 5G NR cybersecurity, with layers of security implemented at the cloud, network, and edge device levels. 

At the edge device level, organizations should ensure their devices have security built-in with features such as HTTPS, secure socket, secure boot, and free unlimited firmware over-the-air (FOTA) updates. 

Organizations will also want to use edge devices from trustworthy companies that are headquartered in countries that have strict laws in place to protect customer data. In doing so you will ensure these companies are committed to working with you to prevent state or other malicious actors from gaining access to your 5G NR data.

• Are the 5G NR devices future-proof? 
Over time, organizations are likely to want to upgrade their applications. In addition, the 5G NR specification is not set in stone, and updates to it are made periodically. Organizations will want to ensure their 5G NR edge devices are futureproof, with capabilities that include the ability to update them with new firmware over the air, so they can upgrade their applications and take advantage of new 5G NR capabilities in the future. 

• Can the 5G NR device do edge processing? 
While 5G NR increases the amount of data that can be transmitted over cellular wireless networks, in many cases organizations will want to filter, prioritize, or otherwise process some of their 5G NR application’s data at the edge. This edge processing can enable these organizations to lower their data transmission costs, improve application performance, and lower their devices energy use. 

5G NR edge devices that offer organizations the ability to easily process data at the edge allow them to lower their data transmission expenses, optimize application performance, and maximize their devices’ battery lives. 

Originally posted here.

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Look around you, and you’ll find at least 4 objects in your house with the word ‘smart’. Your TV, your phone, maybe even your fridge. All of our appliances are increasingly being connected to the internet.

This interconnection of ‘smart’ objects is referred to as the Internet of Things (IoT). In less than a decade, we’ve pushed communication technology to its limits.

Now you can talk to your microwave from your phone. With over 20 billion devices part of the IoT phenomenon, our lives are more connected than ever before. But, there's a flip side to this technological revolution.

There are a lot of IoT security risks that arise from the vulnerabilities of the devices. Over 200 million individual IoT attacks were tracked in 2020 alone.

Hackers can take advantage of vulnerabilities to steal your data, or to use your devices to conduct attacks. This blog will take you through the procedures you can adopt to secure your life from cybercriminals.

Overview of IoT

Internet Protocol Version 4 (IPv4) is the protocol that is the process through which our devices connect with the internet.

When the internet is accessed, a unique identity protocol is generated Recently, however, IPv4 has been running out of physical addresses. To fix this, IPv6 was introduced that has the capacity for trillions of trillions of physical addresses. It also offers improvements to connectivity, performance, and security. 

IoT has already been used extensively to provide ease to human life, with some of the many applications being innocuous.

  • Home Automation - Phones are now being used to connect with most homes to your electric circuits, your TV, AC Fridge, and more. This has simplified lives while improving productivity and efficiency. Many of these devices also optimise themselves, for instance, an AC that adjusts temperature itself based on the environment.
  • Smart Cities - By far the most awaited feature of IoT. Smart Cities promise to revolutionize the way you live. Not only will they reduce costs, but they would also improve efficiency. Road signals would be able to manage traffic congestions, and parking sensors would inform you of empty spots.
  • Drones - Drones are increasingly being used to simplify our lives. Right from being used by Amazon as delivery agents, to being used by government bodies for firefighting activities, drones are revolutionizing the tech space.
  • Medical Applications - Smartwatches monitor a person’s health and can call them an ambulance when at risk. Ambulances that can connect to the road signals and clear a path. 
  • Smart Phones - Your phone will most likely be the key to controlling the IoT. Already many appliances connect to your phone, and many more may soon follow. VR and AR have made great leaps forward, allowing people to do much more than simply call people with their phones.

Threats of IoT


IoT suffers from similar vulnerabilities that hackers attempt to use to their advantage. They use these devices to either steal personal data or to connect them to a botnet. According to Symantec, IoT attacks have increased a 1000% since 2016, with routers and security cameras being the most attacked.

  • Botnet - A botnet is essentially a large collection of IoT that hackers can be used to make large coordinated attacks on other services. Botnets are easy to create, with IoT being a favored target due to their weaker security.
  • Shadow IoT - Losing control of your devices can be terrifying. Researchers have already proved it possible to stall the engines of smart cars too. Many industries use SCADA systems, a massive computer overseer. Were this to be compromised, it could lead to catastrophe, including nuclear meltdowns in the worst case. 
  • Data Theft - The most common threat. Data can be extracted not only from the compromised device but all on the network. This can be catastrophic for your business. Losing the data of millions of users can erode their trust in your services. 

Applying Penetration Testing to circumvent IoT Attacks


Thankfully, there are ways in which firms protect themselves from IoT Security risks. Using penetration testing (pen testing), a simulated attack on your device, you can identify vulnerabilities and fix them before an actual attack hit. Pen testing will search for a range of vulnerabilities, ranging from but not limited to,

  1. Weak passwords: passwords that can be guessed or opened through trial and error.
  2. Hardcoded passwords: Public passwords that cannot be changed, such as firmware backdoors and client software.  
  3. Network services: This helps the devices on the network to communicate and share information. 
  4. Ecosystem Interfaces: Authentication, encryption issues, and input/output filtering problems arising from the device’s connections. These could be the internet, the backend API, the cloud, or other devices.
  5. Updates: The ability to receive and apply updates to firmware, security, and provide warnings for security changes. 
  6. Components: Insecure or outdated components such as software, libraries, customization, third-party apps, etc.  
  7. Privacy: Data that can be leaked due to a device on the network not being configured with the proper controls.
  8. Data Security: Data encryption and access control during storage, transit, and processing to prevent hijacks between connections. 
  9. Default Settings: Insecure default settings that might still be on the device.
  10. Physical Hardening: Physical hardening measures to prevent hackers from scoping devices or taking local control of devices. 

Your business must be secure and Pen testing is a great way of identifying these vulnerabilities, which you can then rectify.

Here's how firms can go about it. 

  • Identify Devices on The Network - The first step to protecting yourself is to identify which devices are actually on the network. As the network gets bigger, it can be harder to keep track of the devices. Don’t hesitate to pull back, take stock, and remove devices you don’t think need to be on the network.
  • Implement Strong Passwords - Without saying, this is the single most important tip. Always ensure your password is not something related to your personal information, such as your full name. Vary your password with numbers and symbols. And don’t use the same password everywhere!
  • Download The Latest Security Updates - As attacks increase, so does the defines. Many manufacturers release new updates that patch IoT vulnerabilities. Staying up to date can prevent older attacks from getting through.
  • Install Firewalls - Firewalls will prevent unauthorized access through the network. Intrusion detection systems/intrusion prevention systems (IDS/IPS) should be run to monitor and analyse network traffic. Firewalls also have the added benefit of warning you of unauthorized access. This can be the first sign of a breach in security.

IoT has a lot of potential for making human life convenient and efficient. However, it is advisable to take your time with implementing new technologies, make sure you have all the risks covered.

Rigorous testing should be your motto before introducing anything new to your business. Security should be your main priority. 

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WEBINAR SERIES:
 
Fast and Fearless - The Future of IoT Software Development
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SUMMARY

The IoT is transforming the software landscape. What was a relatively straightforward embedded software stack, has been revolutionized due to the IoT where developers juggle specialized workloads, security, machine learning, real-time connectivity, managing devices in the field - the list goes on.

How can our industry help developers prototype ‘fearlessly’ because the tools and platforms allow them to navigate varying IoT components? How can developers move to production quickly, capitalizing on innovation opportunities in emerging IoT markets? 

This webinar series will take you through the fundamental steps, tools and opportunities for simplifying IoT development. Each webinar will be a panel discussion with industry experts who will share their experience and development tips on the below topics.

 

Part One of Four: The IoT Software Developer Experience

Date: Tuesday, May 11, 2021

Webinar Recording Available Here
 

Part Two of Four: AI and IoT Innovation

Date: Tuesday, June 29, 2021

Time: 8:00 am PDT/ 3:00 pm UTC

Duration: 60 minutes

Click Here to Register for Part Two
 

Part Three of Four: Making the Most of IoT Connectivity

Date: Tuesday, September 28, 2021

Time: 8:00 am PDT/ 3:00 pm UTC

Duration: 60 minutes

Click Here to Register for Part Three
 

Part Four of Four: IoT Security Solidified and Simplified

Date: Tuesday, November 16, 2021

Time: 8:00 am PDT/ 3:00 pm UTC

Duration: 60 minutes

Click Here to Register for Part Four
 
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It’s no secret that I love just about everything to do with what we now refer to as STEM; that is, science, technology, engineering, and math. When I was a kid, my parents gifted me with what was, at that time, a state-of-the-art educational electronics kit containing a collection of basic components (resistors, capacitors, inductors), a teensy loudspeaker, some small (6-volt) incandescent bulbs… that sort of thing. Everything was connected using a patch-board of springs (a bit like the 130-in-1 Electronic Playground from SparkFun).

The funny thing is, now that I come to look back on it, most electronics systems in the real world at that time weren’t all that much more sophisticated than my kit. In our house, for example, we had one small vacuum tube-based black-and-white television in the family room and one rotary-dial telephone that was hardwired to the wall in the hallway. We never even dreamed of color televisions and I would have laughed my socks off if you’d told me that the day would come when we’d have high-definition color televisions in almost every room in the house, smart phones so small you could carry them your pocket and use them to take photos and videos and make calls around the world, smart devices that you could control with your voice and that would speak back to you… the list goes on.

Now, of course, we have the Internet of Things (IoT), which boasts more “things” than you can throw a stick at (according to Statista, there were ~22 billion IoT devices in 2018, there will be ~38 billion in 2025, and there are expected to be ~50 billion by 2030).

One of the decisions required when embarking on an IoT deployment pertains to connectivity. Some devices are hardwired, many use Bluetooth or Wi-Fi or some form of wireless mesh, and many more employ cellular technology as their connectivity solution of choice.

In order to connect to a cellular network, the IoT device must include some form of subscriber identity module (SIM). Over the years, the original SIMs (which originated circa 1991) evolved in various ways. A few years ago, the industry saw the introduction of embedded SIM (eSIM) technology. Now, the next-generation integrated SIM (iSIM) is poised to shake the IoT world once more.

“But what is iSIM,” I hear you cry. Well, I’m glad you asked because, by some strange quirk of fate, I’ve been invited to host a panel discussion — Accelerating Innovation on the IoT Edge with Integrated SIM (iSIM) — which is being held under the august auspices of IotCentral.io

In this webinar — which will be held on Thursday 20 May 2021 from 10:00 a.m. to 11:00 a.m. CDT — I will be joined by four industry gurus to discuss how cellular IoT is changing and how to navigate through the cornucopia of SIM, eSIM, and iSIM options to decide what’s best for your product. As part of this, we will see quick-start tools and cool demos that can move you from concept to product. Also (and of particular interest to your humble narrator), we will experience the supercharge potential of TinyML and iSIM.

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Panel members Loic Bonvarlet (upper left), Brian Partridge (upper right),

Dr. Juan Nogueira (lower left), and Jan Jongboom (bottom right)

The gurus in question (and whom I will be questioning) are Loic Bonvarlet, VP Product and Marketing at Kigen; Brian Partridge, Research Director for Infrastructure and Cloud Technologies at 451 Research; Dr. Juan Nogueira, Senior Director, Connectivity, Global Technology Team at FLEX; and Jan Jongboom, CTO and Co-Founder at Edge Impulse.

So, what say you? Dare I hope that we will have the pleasure of your company and that you will be able to join us to (a) tease your auditory input systems with our discussions and (b) join our question-and-answer free-for-all at the end?

 

Video recording available:

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By Bee Hayes-Thakore

The Android Ready SE Alliance, announced by Google on March 25th, paves the path for tamper resistant hardware backed security services. Kigen is bringing the first secure iSIM OS, along with our GSMA certified eSIM OS and personalization services to support fast adoption of emerging security services across smartphones, tablets, WearOS, Android Auto Embedded and Android TV.

Google has been advancing their investment in how tamper-resistant secure hardware modules can protect not only Android and its functionality, but also protect third-party apps and secure sensitive transactions. The latest android smartphone device features enable tamper-resistant key storage for Android Apps using StrongBox. StrongBox is an implementation of the hardware-backed Keystore that resides in a hardware security module.

To accelerate adoption of new Android use cases with stronger security, Google announced the formation of the Android Ready SE Alliance. Secure Element (SE) vendors are joining hands with Google to create a set of open-source, validated, and ready-to-use SE Applets. On March 25th, Google launched the General Availability (GA) version of StrongBox for SE.

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Hardware based security modules are becoming a mainstay of the mobile world. Juniper Research’s latest eSIM research, eSIMs: Sector Analysis, Emerging Opportunities & Market Forecasts 2021-2025, independently assessed eSIM adoption and demand in the consumer sector, industrial sector, and public sector, and predicts that the consumer sector will account for 94% of global eSIM installations by 2025. It anticipates that established adoption of eSIM frameworks from consumer device vendors such as Google, will accelerate the growth of eSIMs in consumer devices ahead of the industrial and public sectors.


Consumer sector will account for 94% of global eSIM installations by 2025

Juniper Research, 2021.

Expanding the secure architecture of trust to consumer wearables, smart TV and smart car

What’s more? A major development is that now this is not just for smartphones and tablets, but also applicable to WearOS, Android Auto Embedded and Android TV. These less traditional form factors have huge potential beyond being purely companion devices to smartphones or tablets. With the power, size and performance benefits offered by Kigen’s iSIM OS, OEMs and chipset vendors can consider the full scope of the vast Android ecosystem to deliver new services.

This means new secure services and innovations around:

🔐 Digital keys (car, home, office)

🛂 Mobile Driver’s License (mDL), National ID, ePassports

🏧 eMoney solutions (for example, Wallet)

How is Kigen supporting Google’s Android Ready SE Alliance?

The alliance was created to make discrete tamper resistant hardware backed security the lowest common denominator for the Android ecosystem. A major goal of this alliance is to enable a consistent, interoperable, and demonstrably secure applets across the Android ecosystem.

Kigen believes that enabling the broadest choice and interoperability is fundamental to the architecture of digital trust. Our secure, standards-compliant eSIM and iSIM OS, and secure personalization services are available to all chipset or device partners in the Android Ready SE Alliance to leverage the benefits of iSIM for customer-centric innovations for billions of Android users quickly.

Vincent Korstanje, CEO of Kigen

Kigen’s support for the Android Ready SE Alliance will allow our industry partners to easily leapfrog to the enhanced security and power efficiency benefits of iSIM technology or choose a seamless transition from embedded SIM so they can focus on their innovation.

We are delighted to partner with Kigen to further strengthen the security of Android through StrongBox via Secure Element (SE). We look forward to widespread adoption by our OEM partners and developers and the entire Android ecosystem.

Sudhi Herle, Director of Android Platform Security 

In the near term, the Google team is prioritizing and delivering the following Applets in conjunction with corresponding Android feature releases:

  • Mobile driver’s license and Identity Credentials
  • Digital car keys

Kigen brings the ability to bridge the physical embedded security hardware to a fully integrated form factor. Our Kigen standards-compliant eSIM OS (version 2.2. eUICC OS) is available to support chipsets and device makers now. This announcement is a start to what will bring a whole host of new and exciting trusted services offering better experience for users on Android.

Kigen’s eSIM (eUICC) OS brings

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The smallest operating system, allowing OEMs to select compact, cost-effective hardware to run it on.

Kigen OS offers the highest level of logical security when employed on any SIM form factor, including a secure enclave.

On top of Kigen OS, we have a broad portfolio of Java Card™ Applets to support your needs for the Android SE Ready Alliance.

Kigen’s Integrated SIM or iSIM (iUICC) OS further this advantage

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Integrated at the heart of the device and securely personalized, iSIM brings significant size and battery life benefits to cellular Iot devices. iSIM can act as a root of trust for payment, identity, and critical infrastructure applications

Kigen’s iSIM is flexible enough to support dual sim capability through a single profile or remote SIM provisioning mechanisms with the latter enabling out-of-the-box connectivity, secure and remote profile management.

For smartphones, set top boxes, android auto applications, auto car display, Chromecast or Google Assistant enabled devices, iSIM can offer significant benefits to incorporate Artificial intelligence at the edge.

Kigen’s secure personalization services to support fast adoption

SIM vendors have in-house capabilities for data generation but the eSIM and iSIM value chains redistribute many roles and responsibilities among new stakeholders for the personalization of operator credentials along different stages of production or over-the-air when devices are deployed.

Kigen can offer data generation as a service to vendors new to the ecosystem.

Partner with us to provide cellular chipset and module makers with the strongest security, performance for integrated SIM leading to accelerate these new use cases.

Security considerations for eSIM and iSIM enabled secure connected services

Designing a secure connected product requires considerable thought and planning and there really is no ‘one-size-fits-all’ solution. How security should be implemented draws upon a multitude of factors, including:

  • What data is being stored or transmitted between the device and other connected apps?
  • Are there regulatory requirements for the device? (i.e. PCI DSS, HIPAA, FDA, etc.)
  • What are the hardware or design limitations that will affect security implementation?
  • Will the devices be manufactured in a site accredited by all of the necessary industry bodies?
  • What is the expected lifespan of the device?

End-to-end ecosystem and services thinking needs to be a design consideration from the very early stage especially when considering the strain on battery consumption in devices such as wearables, smart watches and fitness devices as well as portable devices that are part of the connected consumer vehicles.

Originally posted here.

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IoT in Mining

Flowchart of IoT in Mining

by Vaishali Ramesh

Introduction – Internet of Things in Mining

The Internet of things (IoT) is the extension of Internet connectivity into physical devices and everyday objects. Embedded with electronics, Internet connectivity, and other forms of hardware; these devices can communicate and interact with others over the Internet, and they can be remotely monitored and controlled. In the mining industry, IoT is used as a means of achieving cost and productivity optimization, improving safety measures and developing their artificial intelligence needs.

IoT in the Mining Industry

Considering the numerous incentives it brings, many large mining companies are planning and evaluating ways to start their digital journey and digitalization in mining industry to manage day-to-day mining operations. For instance:

  • Cost optimization & improved productivity through the implementation of sensors on mining equipment and systems that monitor the equipment and its performance. Mining companies are using these large chunks of data – 'big data' to discover more cost-efficient ways of running operations and also reduce overall operational downtime.
  • Ensure the safety of people and equipment by monitoring ventilation and toxicity levels inside underground mines with the help of IoT on a real-time basis. It enables faster and more efficient evacuations or safety drills.
  • Moving from preventive to predictive maintenance
  • Improved and fast-decision making The mining industry faces emergencies almost every hour with a high degree of unpredictability. IoT helps in balancing situations and in making the right decisions in situations where several aspects will be active at the same time to shift everyday operations to algorithms.

IoT & Artificial Intelligence (AI) application in Mining industry

Another benefit of IoT in the mining industry is its role as the underlying system facilitating the use of Artificial Intelligence (AI). From exploration to processing and transportation, AI enhances the power of IoT solutions as a means of streamlining operations, reducing costs, and improving safety within the mining industry.

Using vast amounts of data inputs, such as drilling reports and geological surveys, AI and machine learning can make predictions and provide recommendations on exploration, resulting in a more efficient process with higher-yield results.

AI-powered predictive models also enable mining companies to improve their metals processing methods through more accurate and less environmentally damaging techniques. AI can be used for the automation of trucks and drills, which offers significant cost and safety benefits.

Challenges for IoT in Mining 

Although there are benefits of IoT in the mining industry, implementation of IoT in mining operations has faced many challenges in the past.

  • Limited or unreliable connectivity especially in underground mine sites
  • Remote locations may struggle to pick up 3G/4G signals
  • Declining ore grade has increased the requirements to dig deeper in many mines, which may increase hindrances in the rollout of IoT systems

Mining companies have overcome the challenge of connectivity by implementing more reliable connectivity methods and data-processing strategies to collect, transfer and present mission critical data for analysis. Satellite communications can play a critical role in transferring data back to control centers to provide a complete picture of mission critical metrics. Mining companies worked with trusted IoT satellite connectivity specialists such as ‘Inmarsat’ and their partner eco-systems to ensure they extracted and analyzed their data effectively.

 

Cybersecurity will be another major challenge for IoT-powered mines over the coming years

 As mining operations become more connected, they will also become more vulnerable to hacking, which will require additional investment into security systems.

 

Following a data breach at Goldcorp in 2016, that disproved the previous industry mentality that miners are not typically targets, 10 mining companies established the Mining and Metals Information Sharing and Analysis Centre (MM-ISAC) to share cyber threats among peers in April 2017.

In March 2019, one of the largest aluminum producers in the world, Norsk Hydro, suffered an extensive cyber-attack, which led to the company isolating all plants and operations as well as switching to manual operations and procedures. Several of its plants suffered temporary production stoppages as a result. Mining companies have realized the importance of digital security and are investing in new security technologies.

Digitalization of Mining Industry - Road Ahead

Many mining companies have realized the benefits of digitalization in their mines and have taken steps to implement them. There are four themes that are expected to be central to the digitalization of the mining industry over the next decade are listed below:

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The above graph demonstrates the complexity of each digital technology and its implementation period for the widespread adoption of that technology. There are various factors, such as the complexity and scalability of the technologies involved in the adoption rate for specific technologies and for the overall digital transformation of the mining industry.

The world can expect to witness prominent developments from the mining industry to make it more sustainable. There are some unfavorable impacts of mining on communities, ecosystems, and other surroundings as well. With the intention to minimize them, the power of data is being harnessed through different IoT statements. Overall, IoT helps the mining industry shift towards resource extraction, keeping in mind a particular time frame and footprint that is essential.

Originally posted here.

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Image Source: SEGGER.com

Nearly every embedded software developer working in the IoT space is now building secure devices. Developers have been mostly focused on how to handle secure applications and the basic microcontroller technologies such as how to use Arms TrustZone or leverage multicore processors. A looming problem that many companies and teams are overlooking is that figuring out how to develop secure applications is just the first step. There are three stages to secure product lifecycle management and in today’s post, we will review what is involved in each stage.

As a quick overview, the stages, which can be seen in the diagram below, are:

  • Development
  • Test and Production Deployment
  • Maintenance and In-field Servicing

Let us look at each of these stages in a little more detail. 

Stage #1 – Development

Development is probably the area that most developers are the most familiar with, but at the same time, the area that they are learning to adapt to the most. Many developers have designed and built systems without ever having to take security into account. Development involves a lot more than just deciding which components to isolate and how to separate the software into secure and non-secure regions.

For example, during the development phase developers now need to learn how to develop in the environment where a secure bootloader is in place. They need to consider how to handle firmware fallbacks, if they are allowed and if so, under what conditions. Firmware images may need to be compressed on top of the need for authentication.

While the development stage has become more complicated, developers should not struggle too much to extrapolate their past experiences to developing secure firmware successfully.

Stage #2 – Test and Production Deployment

The area that developers will probably struggle with the most is the test and production deployment stage. Testing secure software requires additional steps to be taken that authenticate debug hardware so that the developer can access secure memory regions to test their code and successfully debug it. Even more importantly, care must be taken to install that secure software onto a product during production.

There are several ways this can be done, but one method is to use a secure flashing device like SEGGERS Flasher Secure. These devices can follow a multistep process that involves validating a user ID which allows the secure firmware to be installed on the device. The devices themselves limit how many and on what devices the firmware can be installed which helps to protect a team’s intellectual property and prevents unauthorized production of a product.

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Stage #3 – Maintenance and In-field Servicing

Finally, there is the maintenance and in-field servicing stage which is a partial continuation of the development phase. Once a product has been deployed into the field, it needs to be securely updated. Updates can be done manually in-field, or they can be done using an over-the-air update process. This involves a device being able to contact a secure firmware server that can compress and encrypt the image and transport it to the device. Once the device has received the image, it must decrypt, decompress and validate the contents of the image. If everything looks good, the image can then be loaded as the primary firmware for the device.

Conclusions

 There is much more to designing and deploying a secure device than simply developing a secure application. The entire process is broken up into three main stages that we have looked at in greater detail today. Unfortunately, we have only just scratched the surface!

Orignally posted here.

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by Stephanie Overby

What's next for edge computing, and how should it shape your strategy? Experts weigh in on edge trends and talk workloads, cloud partnerships, security, and related issues


All year, industry analysts have been predicting that that edge computing – and complimentary 5G network offerings ­­– will see significant growth, as major cloud vendors are deploying more edge servers in local markets and telecom providers pushing ahead with 5G deployments.

The global pandemic has not significantly altered these predictions. In fact, according to IDC’s worldwide IT predictions for 2021, COVID-19’s impact on workforce and operational practices will be the dominant accelerator for 80 percent of edge-driven investments and business model change across most industries over the next few years.

First, what exactly do we mean by edge? Here’s how Rosa Guntrip, senior principal marketing manager, cloud platforms at Red Hat, defines it: “Edge computing refers to the concept of bringing computing services closer to service consumers or data sources. Fueled by emerging use cases like IoT, AR/VR, robotics, machine learning, and telco network functions that require service provisioning closer to users, edge computing helps solve the key challenges of bandwidth, latency, resiliency, and data sovereignty. It complements the hybrid computing model where centralized computing can be used for compute-intensive workloads while edge computing helps address the requirements of workloads that require processing in near real time.”

Moving data infrastructure, applications, and data resources to the edge can enable faster response to business needs, increased flexibility, greater business scaling, and more effective long-term resilience.

“Edge computing is more important than ever and is becoming a primary consideration for organizations defining new cloud-based products or services that exploit local processing, storage, and security capabilities at the edge of the network through the billions of smart objects known as edge devices,” says Craig Wright, managing director with business transformation and outsourcing advisory firm Pace Harmon.

“In 2021 this will be an increasing consideration as autonomous vehicles become more common, as new post-COVID-19 ways of working require more distributed compute and data processing power without incurring debilitating latency, and as 5G adoption stimulates a whole new generation of augmented reality, real-time application solutions, and gaming experiences on mobile devices,” Wright adds.

8 key edge computing trends in 2021


Noting the steady maturation of edge computing capabilities, Forrester analysts said, “It’s time to step up investment in edge computing,” in their recent Predictions 2020: Edge Computing report. As edge computing emerges as ever more important to business strategy and operations, here are eight trends IT leaders will want to keep an eye on in the year ahead.

1. Edge meets more AI/ML


Until recently, pre-processing of data via near-edge technologies or gateways had its share of challenges due to the increased complexity of data solutions, especially in use cases with a high volume of events or limited connectivity, explains David Williams, managing principal of advisory at digital business consultancy AHEAD. “Now, AI/ML-optimized hardware, container-packaged analytics applications, frameworks such as TensorFlow Lite and tinyML, and open standards such as the Open Neural Network Exchange (ONNX) are encouraging machine learning interoperability and making on-device machine learning and data analytics at the edge a reality.” 

Machine learning at the edge will enable faster decision-making. “Moreover, the amalgamation of edge and AI will further drive real-time personalization,” predicts Mukesh Ranjan, practice director with management consultancy and research firm Everest Group.

“But without proper thresholds in place, anomalies can slowly become standards,” notes Greg Jones, CTO of IoT solutions provider Kajeet. “Advanced policy controls will enable greater confidence in the actions made as a result of the data collected and interpreted from the edge.” 

 

2. Cloud and edge providers explore partnerships


IDC predicts a quarter of organizations will improve business agility by integrating edge data with applications built on cloud platforms by 2024. That will require partnerships across cloud and communications service providers, with some pairing up already beginning between wireless carriers and the major public cloud providers.

According to IDC research, the systems that organizations can leverage to enable real-time analytics are already starting to expand beyond traditional data centers and deployment locations. Devices and computing platforms closer to end customers and/or co-located with real-world assets will become an increasingly critical component of this IT portfolio. This edge computing strategy will be part of a larger computing fabric that also includes public cloud services and on-premises locations.

In this scenario, edge provides immediacy and cloud supports big data computing.

 

3. Edge management takes center stage


“As edge computing becomes as ubiquitous as cloud computing, there will be increased demand for scalability and centralized management,” says Wright of Pace Harmon. IT leaders deploying applications at scale will need to invest in tools to “harness step change in their capabilities so that edge computing solutions and data can be custom-developed right from the processor level and deployed consistently and easily just like any other mainstream compute or storage platform,” Wright says.

The traditional approach to data center or cloud monitoring won’t work at the edge, notes Williams of AHEAD. “Because of the rather volatile nature of edge technologies, organizations should shift from monitoring the health of devices or the applications they run to instead monitor the digital experience of their users,” Williams says. “This user-centric approach to monitoring takes into consideration all of the components that can impact user or customer experience while avoiding the blind spots that often lie between infrastructure and the user.”

As Stu Miniman, director of market insights on the Red Hat cloud platforms team, recently noted, “If there is any remaining argument that hybrid or multi-cloud is a reality, the growth of edge solidifies this truth: When we think about where data and applications live, they will be in many places.”

“The discussion of edge is very different if you are talking to a telco company, one of the public cloud providers, or a typical enterprise,” Miniman adds. “When it comes to Kubernetes and the cloud-native ecosystem, there are many technology-driven solutions competing for mindshare and customer interest. While telecom giants are already extending their NFV solutions into the edge discussion, there are many options for enterprises. Edge becomes part of the overall distributed nature of hybrid environments, so users should work closely with their vendors to make sure the edge does not become an island of technology with a specialized skill set.“

 

4. IT and operational technology begin to converge


Resiliency is perhaps the business term of the year, thanks to a pandemic that revealed most organizations’ weaknesses in this area. IoT-enabled devices (and other connected equipment) drive the adoption of edge solutions where infrastructure and applications are being placed within operations facilities. This approach will be “critical for real-time inference using AI models and digital twins, which can detect changes in operating conditions and automate remediation,” IDC’s research says.

IDC predicts that the number of new operational processes deployed on edge infrastructure will grow from less than 20 percent today to more than 90 percent in 2024 as IT and operational technology converge. Organizations will begin to prioritize not just extracting insight from their new sources of data, but integrating that intelligence into processes and workflows using edge capabilities.

Mobile edge computing (MEC) will be a key enabler of supply chain resilience in 2021, according to Pace Harmon’s Wright. “Through MEC, the ecosystem of supply chain enablers has the ability to deploy artificial intelligence and machine learning to access near real-time insights into consumption data and predictive analytics as well as visibility into the most granular elements of highly complex demand and supply chains,” Wright says. “For organizations to compete and prosper, IT leaders will need to deliver MEC-based solutions that enable an end-to-end view across the supply chain available 24/7 – from the point of manufacture or service  throughout its distribution.”

 

5. Edge eases connected ecosystem adoption


Edge not only enables and enhances the use of IoT, but it also makes it easier for organizations to participate in the connected ecosystem with minimized network latency and bandwidth issues, says Manali Bhaumik, lead analyst at technology research and advisory firm ISG. “Enterprises can leverage edge computing’s scalability to quickly expand to other profitable businesses without incurring huge infrastructure costs,” Bhaumik says. “Enterprises can now move into profitable and fast-streaming markets with the power of edge and easy data processing.”

 

6. COVID-19 drives innovation at the edge


“There’s nothing like a pandemic to take the hype out of technology effectiveness,” says Jason Mann, vice president of IoT at SAS. Take IoT technologies such as computer vision enabled by edge computing: “From social distancing to thermal imaging, safety device assurance and operational changes such as daily cleaning and sanitation activities, computer vision is an essential technology to accelerate solutions that turn raw IoT data (from video/cameras) into actionable insights,” Mann says. Retailers, for example, can use computer vision solutions to identify when people are violating the store’s social distance policy.

 

7. Private 5G adoption increases


“Use cases such as factory floor automation, augmented and virtual reality within field service management, and autonomous vehicles will drive the adoption of private 5G networks,” says Ranjan of Everest Group. Expect more maturity in this area in the year ahead, Ranjan says.

 

8. Edge improves data security


“Data efficiency is improved at the edge compared with the cloud, reducing internet and data costs,” says ISG’s Bhaumik. “The additional layer of security at the edge enhances the user experience.” Edge computing is also not dependent on a single point of application or storage, Bhaumik says. “Rather, it distributes processes across a vast range of devices.”

As organizations adopt DevSecOps and take a “design for security” approach, edge is becoming a major consideration for the CSO to enable secure cloud-based solutions, says Pace Harmon’s Wright. “This is particularly important where cloud architectures alone may not deliver enough resiliency or inherent security to assure the continuity of services required by autonomous solutions, by virtual or augmented reality experiences, or big data transaction processing,” Wright says. “However, IT leaders should be aware of the rate of change and relative lack of maturity of edge management and monitoring systems; consequently, an edge-based security component or solution for today will likely need to be revisited in 18 to 24 months’ time.”

Originally posted here.

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Security has long been a worry for the Internet of Things projects, and for many organizations with active or planned IoT deployments, security concerns have hampered digital ambitions. By implementing IoT security best practices, however, risk can be minimized.

Fortunately, IoT security best practices can help organizations reduce the risks facing their deployments and broader digital transformation initiatives. These same best practices can also reduce legal liability and protect an organization’s reputation.

Technological fragmentation is not just one of the biggest barriers to IoT adoption, but it also complicates the goal of securing connected devices and related services. With IoT-related cyberattacks on the rise, organizations must become more adept at managing cyber-risk or face potential reputational and legal consequences. This article summarizes best practices for enterprise and industrial IoT projects.

Key takeaways from this article include the following:

  • Data security remains a central technology hurdle related to IoT deployments.
  • IoT security best practices also can help organizations curb the risk of broader digital transformation initiatives.
  • Securing IoT projects requires a comprehensive view that encompasses the entire life cycle of connected devices and relevant supply chains.

Fragmentation and security have long been two of the most significant barriers to the Internet of Things adoption. The two challenges are also closely related.

Despite the Internet of Things (IoT) moniker, which implies a synthesis of connected devices, IoT technologies vary considerably based on their intended use. Organizations deploying IoT thus rely on an array of connectivity types, standards and hardware. As a result, even a simple IoT device can pose many security vulnerabilities, including weak authentication, insecure cloud integration, and outdated firmware and software.

For many organizations with active or planned IoT deployments, security concerns have hampered digital ambitions. An IoT World Today August 2020 survey revealed data security as the top technology hurdle for IoT deployments, selected by 46% of respondents.

Fortunately, IoT security best practices can help organizations reduce the risks facing their deployments and broader digital transformation initiatives. These same best practices can also reduce legal liability and protect an organization’s reputation.

But to be effective, an IoT-focused security strategy requires a broad view that encompasses the entire life cycle of an organization’s connected devices and projects in addition to relevant supply chains.

Know What You Have and What You Need

Asset management is a cornerstone of effective cyber defence. Organizations should identify which processes and systems need protection. They should also strive to assess the risk cyber attacks pose to assets and their broader operations.

In terms of enterprise and industrial IoT deployments, asset awareness is frequently spotty. It can be challenging given the array of industry verticals and the lack of comprehensive tools to track assets across those verticals. But asset awareness also demands a contextual understanding of the computing environment, including the interplay among devices, personnel, data and systems, as the National Institute of Standards and Technology (NIST) has observed.

There are two fundamental questions when creating an asset inventory: What is on my network? And what are these assets doing on my network?

Answering the latter requires tracking endpoints’ behaviours and their intended purpose from a business or operational perspective. From a networking perspective, asset management should involve more than counting networking nodes; it should focus on data protection and building intrinsic security into business processes.

Relevant considerations include the following:

  • Compliance with relevant security and privacy laws and standards.
  • Interval of security assessments.
  • Optimal access of personnel to facilities, information and technology, whether remote or in-person.
  • Data protection for sensitive information, including strong encryption for data at rest and data in transit.
  • Degree of security automation versus manual controls, as well as physical security controls to ensure worker safety.

IoT device makers and application developers also should implement a vulnerability disclosure program. Bug bounty programs are another option that should include public contact information for security researchers and plans for responding to disclosed vulnerabilities.

Organizations that have accurately assessed current cybersecurity readiness need to set relevant goals and create a comprehensive governance program to manage and enforce operational and regulatory policies and requirements. Governance programs also ensure that appropriate security controls are in place. Organizations need to have a plan to implement controls and determine accountability for that enforcement. Another consideration is determining when security policies need to be revised.

An effective governance plan is vital for engineering security into architecture and processes, as well as for safeguarding legacy devices with relatively weak security controls. Devising an effective risk management strategy for enterprise and industrial IoT devices is a complex endeavour, potentially involving a series of stakeholders and entities. Organizations that find it difficult to assess the cybersecurity of their IoT project should consider third-party assessments.

Many tools are available to help organizations evaluate cyber-risk and defences. These include the vulnerability database and the Security and Privacy Controls for Information Systems and Organizations document from the National Institute of Standards and Technology. Another resource is the list of 20 Critical Security Controls for Effective Cyber Defense. In terms of studying the threat landscape, the MITRE ATT&CK is one of the most popular frameworks for adversary tactics and techniques.

At this stage of the process, another vital consideration is the degree of cybersecurity savviness and support within your business. Three out of ten organizations deploying IoT cite lack of support for cybersecurity as a hurdle, according to August 2020 research from IoT World Today. Security awareness is also frequently a challenge. Many cyberattacks against organizations — including those with an IoT element — involve phishing, like the 2015 attack against Ukraine’s electric grid.

IoT Security Best Practices

Internet of Things projects demands a secure foundation. That starts with asset awareness and extends into responding to real and simulated cyberattacks.

Step 1: Know what you have.

Building an IoT security program starts with achieving a comprehensive understanding of which systems need to be protected.

Step 2: Deploy safeguards.

Shielding devices from cyber-risk requires a thorough approach. This step involves cyber-hygiene, effective asset control and the use of other security controls.

Step 3: Identify threats

Spotting anomalies can help mitigate attacks. Defenders should hone their skills through wargaming.

Step 4: Respond effectively.

Cyberattacks are inevitable but should provide feedback that feeds back to step 1.

Exploiting human gullibility is one of the most common cybercriminal strategies. While cybersecurity training can help individuals recognize suspected malicious activities, such programs tend not to be entirely effective. “It only takes one user and one-click to introduce an exploit into a network,” wrote Forrester analyst Chase Cunningham in the book “Cyber Warfare.” Recent studies have found that, even after receiving cybersecurity training, employees continue to click on phishing links about 3% of the time.

Security teams should work to earn the support of colleagues, while also factoring in the human element, according to David Coher, former head of reliability and cybersecurity for a major electric utility. “You can do what you can in terms of educating folks, whether it’s as a company IT department or as a consumer product manufacturer,” he said. But it is essential to put controls in place that can withstand user error and occasionally sloppy cybersecurity hygiene.

At the same time, organizations should also look to pool cybersecurity expertise inside and outside the business. “Designing the controls that are necessary to withstand user error requires understanding what users do and why they do it,” Coher said. “That means pulling together users from throughout your organization’s user chain — internal and external, vendors and customers, and counterparts.”

Those counterparts are easier to engage in some industries than others. Utilities, for example, have a strong track record in this regard, because of the limited market competition between them. Collaboration “can be more challenging in other industries, but no less necessary,” Coher added.

Deploy Appropriate Safeguards

Protecting an organization from cyberattacks demands a clear framework that is sensitive to business needs. While regulated industries are obligated to comply with specific cybersecurity-related requirements, consumer-facing organizations tend to have more generic requirements for privacy protections, data breach notifications and so forth. That said, all types of organizations deploying IoT have leeway in selecting a guiding philosophy for their cybersecurity efforts.

A basic security principle is to minimize networked or vulnerable systems’ attack surface — for instance, closing unused network ports and eliminating IoT device communication over the open internet. Generally speaking, building security into the architecture of IoT deployments and reducing attackers’ options to sabotage a system is more reliable than adding layers of defence to an unsecured architecture. Organizations deploying IoT projects should consider intrinsic security functionality such as embedded processors with cryptographic support.

But it is not practical to remove all risk from an IT system. For that reason, one of the most popular options is defence-in-depth, a military-rooted concept espousing the use of multiple layers of security. The basic idea is that if one countermeasure fails, additional security layers are available.

While the core principle of implementing multiple layers of security remains popular, defence in depth is also tied to the concept of perimeter-based defence, which is increasingly falling out of favour. “The defence-in-depth approach to cyber defence was formulated on the basis that everything outside of an organization’s perimeter should be considered ‘untrusted’ while everything internal should be inherently ‘trusted,’” said Andrew Rafla, a Deloitte Risk & Financial Advisory principal. “Organizations would layer a set of boundary security controls such that anyone trying to access the trusted side from the untrusted side had to traverse a set of detection and prevention controls to gain access to the internal network.”

Several trends have chipped away at the perimeter-based model. As a result, “modern enterprises no longer have defined perimeters,” Rafla said. “Gone are the days of inherently trusting any connection based on where the source originates.” Trends ranging from the proliferation of IoT devices and mobile applications to the popularity of cloud computing have fueled interest in cybersecurity models such as zero trust. “At its core, zero trust commits to ‘never trusting, always verifying’ as it relates to access control,” Rafla said. “Within the context of zero trusts, security boundaries are created at a lower level in the stack, and risk-based access control decisions are made based on contextual information of the user, device, workload or network attempting to gain access.”

Zero trust’s roots stretch back to the 1970s when a handful of computer scientists theorized on the most effective access control methods for networks. “Every program and every privileged user of the system should operate using the least amount of privilege necessary to complete the job,” one of those researchers, Jerome Saltzer, concluded in 1974.

While the concept of least privilege sought to limit trust among internal computing network users, zero trusts extend the principle to devices, networks, workloads and external users. The recent surge in remote working has accelerated interest in the zero-trust model. “Many businesses have changed their paradigm for security as a result of COVID-19,” said Jason Haward-Grau, a leader in KPMG’s cybersecurity practice. “Many organizations are experiencing a surge to the cloud because businesses have concluded they cannot rely on a physically domiciled system in a set location.”

Based on data from Deloitte, 37.4% of businesses accelerated their zero trust adoption plans in response to the pandemic. In contrast, more than one-third, or 35.2%, of those embracing zero trusts stated that the pandemic had not changed the speed of their organization’s zero-trust adoption.

“I suspect that many of the respondents that said their organization’s zero-trust adoption efforts were unchanged by the pandemic were already embracing zero trusts and were continuing with efforts as planned,” Rafla said. “In many cases, the need to support a completely remote workforce in a secure and scalable way has provided a tangible use case to start pursuing zero-trust adoption.”

A growing number of organizations are beginning to blend aspects of zero trust and traditional perimeter-based controls through a model known as secure access service edge (SASE), according to Rafla. “In this model, traditional perimeter-based controls of the defence-in-depth approach are converged and delivered through a cloud-based subscription service,” he said. “This provides a more consistent, resilient, scalable and seamless user experience regardless of where the target application a user is trying to access may be hosted. User access can be tightly controlled, and all traffic passes through multiple layers of cloud-based detection and prevention controls.”

Regardless of the framework, organizations should have policies in place for access control and identity management, especially for passwords. As Forrester’s Cunningham noted in “Cyber Warfare,” the password is “the single most prolific means of authentication for enterprises, users, and almost any system on the planet” — is the lynchpin of failed security in cyberspace. Almost everything uses a password at some stage.” Numerous password repositories have been breached, and passwords are frequently recycled, making the password a common security weakness for user accounts as well as IoT devices.

A significant number of consumer-grade IoT devices have also had their default passwords posted online. Weak passwords used in IoT devices also fueled the growth of the Mirai botnet, which led to widespread internet outages in 2016. More recently, unsecured passwords on IoT devices in enterprise settings have reportedly attracted state-sponsored actors’ attention.

IoT devices and related systems also need an effective mechanism for device management, including tasks such as patching, connectivity management, device logging, device configuration, software and firmware updates and device provisioning. Device management capabilities also extend to access control modifications and include remediation of compromised devices. It is vital to ensure that device management processes themselves are secure and that a system is in place for verifying the integrity of software updates, which should be regular and not interfere with device functionality.

Organizations must additionally address the life span of devices and the cadence of software updates. Many environments allow IT pros to identify a specific end-of-life period and remove or replace expired hardware. In such cases, there should be a plan for device disposal or transfer of ownership. In other contexts, such as in industrial environments, legacy workstations don’t have a defined expiration date and run out-of-date software. These systems should be segmented on the network. Often, such industrial systems cannot be easily patched like IT systems are, requiring security professionals to perform a comprehensive security audit on the system before taking additional steps.

Identify Threats and Anomalies

In recent years, attacks have become so common that the cybersecurity community has shifted its approach from preventing breaches from assuming a breach has already happened. The threat landscape has evolved to the point that cyberattacks against most organizations are inevitable.

“You hear it everywhere: It’s a matter of when, not if, something happens,” said Dan Frank, a principal at Deloitte specializing in privacy and data protection. Matters have only become more precarious in 2020. The FBI has reported a three- to four-fold increase in cybersecurity complaints after the advent of COVID-19.

Advanced defenders have taken a more aggressive stance known as threat hunting, which focuses on proactively identifying breaches. Another popular strategy is to study adversary behaviour and tactics to classify attack types. Models such as the MITRE ATT&CK framework and the Common Vulnerability Scoring System (CVSS) are popular for assessing adversary tactics and vulnerabilities.

While approaches to analyzing vulnerabilities and potential attacks vary according to an organization’s maturity, situational awareness is a prerequisite at any stage. The U.S. Army Field Manual defines the term like this: “Knowledge and understanding of the current situation which promotes timely, relevant and accurate assessment of friendly, enemy and other operations within the battlespace to facilitate decision making.”

In cybersecurity as in warfare, situational awareness requires a clear perception of the elements in an environment and their potential to cause future events. In some cases, the possibility of a future cyber attack can be averted by merely patching software with known vulnerabilities.

Intrusion detection systems can automate some degree of monitoring of networks and operating systems. Intrusion detection systems that are based on detecting malware signatures also can identify common attacks. They are, however, not effective at recognizing so-called zero-day malware, which has not yet been catalogued by security researchers. Intrusion detection based on malware signatures is also ineffective at detecting custom attacks, (i.e., a disgruntled employee who knows just enough Python or PowerShell to be dangerous. Sophisticated threat actors who slip through defences to gain network access can become insiders, with permission to view sensitive networks and files. In such cases, situational awareness is a prerequisite to mitigate damage.

Another strategy for intrusion detection systems is to focus on context and anomalies rather than malware signatures. Such systems could use machine learning to learn legitimate commands, use of messaging protocols and so forth. While this strategy overcomes the reliance on malware signatures, it can potentially trigger false alarms. Such a system can also detect so-called slow-rate attacks, a type of denial of service attack that gradually robs networking bandwidth but is more difficult to detect than volumetric attacks.

Respond Effectively to Cyber-Incidents

The foundation for successful cyber-incident response lies in having concrete security policies, architecture and processes. “Once you have a breach, it’s kind of too late,” said Deloitte’s Frank. “It’s what you do before that matters.”

That said, the goal of warding off all cyber-incidents, which range from violations of security policies and laws to data breaches, is not realistic. It is thus essential to implement short- and long-term plans for managing cybersecurity emergencies. Organizations should have contingency plans for addressing possible attacks, practising how to respond to them through wargaming exercises to improve their ability to mitigate some cyberattacks and develop effective, coordinated escalation measures for successful breaches.

There are several aspects of the zero trust model that enhance organizations’ ability to respond and recover from cyber events. “Network and micro-segmentation, for example, is a concept by which trust zones are created by organizations around certain classes or types of assets, restricting the blast radius of potentially destructive cyberattacks and limiting the ability for an attacker to move laterally within the environment,” Rafla said. Also, efforts to automate and orchestrate zero trust principles can enhance the efficiency of security operations, speeding efforts to mitigate attacks. “Repetitive and manual tasks can now be automated and proactive actions to isolate and remediate security threats can be orchestrated through integrated controls,” Rafla added.

Response to cyber-incidents involves coordinating multiple stakeholders beyond the security team. “Every business function could be impacted — marketing, customer relations, legal compliance, information technology, etc.,” Frank said.

A six-tiered model for cyber incident response from the SANS Institute contains the following steps:

  • Preparation: Preparing the team to react to events ranging from cyberattacks to hardware failure and power outages.
  • Identification: Determining if an operational anomaly should be classified as a cybersecurity incident, and how to respond to it.
  • Containment: Segmenting compromised devices on the network long enough to limit damage in the event of a confirmed cybersecurity incident. Conversely, long-term containment measures involve hardening effective systems to allow them to enable normal operations.
  • Eradication: Removing or restoring compromised systems. If a security team detects malware on an IoT device, for instance, this phase could involve reimaging its hardware to prevent reinfection.
  • Recovery: Integrating previously compromised systems back into production and ensuring they operate normally after that. In addition to addressing the security event directly, recovery can involve crisis communications with external stakeholders such as customers or regulators.
  • Lessons Learned: Documenting and reviewing the factors that led to the cyber-incident and taking steps to avoid future problems. Feedback from this step should create a feedback loop providing insights that support future preparation, identification, etc.

While the bulk of the SANS model focuses on cybersecurity operations, the last step should be a multidisciplinary process. Investing in cybersecurity liability insurance to offset risks identified after ongoing cyber-incident response requires support from upper management and the legal team. Ensuring compliance with the evolving regulatory landscape also demands feedback from the legal department.

A central practice that can prove helpful is documentation — not just for security incidents, but as part of ongoing cybersecurity assessment and strategy. Organizations with mature security documentation tend to be better positioned to deal with breaches.

“If you fully document your program — your policies, procedures, standards and training — that might put you in a more favourable position after a breach,” Frank explained. “If you have all that information summarized and ready, in the event of an investigation by a regulatory authority after an incident, it shows the organization has robust programs in place.”

Documenting security events and controls can help organizations become more proactive and more capable of embracing automation and machine learning tools. As they collect data, they should repeatedly ask how to make the most of it. KPMG’s Haward-Grau said cybersecurity teams should consider the following questions:

  • What data should we focus on?
  • What can we do to improve our operational decision making?
  • How do we reduce our time and costs efficiently and effectively, given the nature of the reality in which we’re operating?

Ultimately, answering those questions may involve using machine learning or artificial intelligence technology, Haward- Grau said. “If your business is using machine learning or AI, you have to digitally enable them so that they can do what they want to do,” he said.

Finally, documenting security events and practices as they relate to IoT devices and beyond can be useful in evaluating the effectiveness of cybersecurity spending and provide valuable feedback for digital transformation programs. “Security is a foundational requirement that needs to be ingrained holistically in architecture and processes and governed by policies,” said Chander Damodaran, chief architect at Brillio, a digital consultancy firm. ”Security should be a common denominator.”

IoT Security

Recent legislation requires businesses to assume responsibility for protecting the Internet of Things (IoT) devices. “Security by Design” approaches are essential since successful applications deploy millions of units and analysts predict billions of devices deployed in the next five to ten years. The cost of fixing compromised devices later could overwhelm a business.

Security risks can never be eliminated: there is no single solution for all concerns, and the cost to counter every possible threat vector is prohibitively expensive. The best we can do is minimize the risk, and design devices and processes to be easily updatable.

It is best to assess damage potential and implement security methods accordingly. For example, for temperature and humidity sensors used in environmental monitoring, data protection needs are not as stringent as devices transmitting credit card information. The first may require anonymization for privacy, and the second may require encryption to prevent unauthorized access.

Overall Objectives

Senders and receivers must authenticate. IoT devices must transmit to the correct servers and ensure they receive messages from the correct servers.

Mission-critical applications, such as vehicle crash notification or medical alerts, may fail if the connection is not reliable. Lack of communication itself is a lack of security.

Connectivity errors can make good data unreliable, and actions on the content may be erroneous. It is best to select connectivity providers with strong security practices—e.g., whitelisting access and traffic segregation to prevent unauthorized communication.

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IoT Security: 360-Degree Approach

Finally, only authorized recipients should access the information. In particular, privacy laws require extra care in accessing the information on individuals.

Data Chain

Developers should implement security best practices at all points in the chain. However, traditional IT security protects servers with access controls, intrusion detection, etc., the farther away from the servers that best practices are implemented, the less impact that remote IoT device breaches have on the overall application.

For example, compromised sensors might send bad data, and servers might take incorrect actions despite data filtering. Thus, gateways offer an ideal location for security with compute capacity for encryption and implement over-the-air (OTA) updates for security fixes.

Servers often automate responses on data content. Simplistic and automated responses to bad data could cascade into much greater difficulty. If devices transmit excessively, servers could overload and fail to provide timely responses to transmissions—retry algorithms resulting from network unavailability often create data storms.

IoT devices often use electrical power rather than batteries, and compromised units could continue to operate for years. Implementing over-the-air (OTA) functions for remotely disabling devices could be critical.

When a breach requires device firmware updates, OTA support is vital when devices are inaccessible or large numbers of units must be modified rapidly. All devices should support OTA, even if it increases costs—for example, adding memory for managing multiple “images” of firmware for updates.

In summary, IoT security best practices of authentication, encryption, remote device disable, and OTA for security fixes, along with traditional IT server protection, offers the best chance of minimizing risks of attacks on IoT applications.

Originally posted here.

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The benefits of IoT data are widely touted. Enhanced operational visibility, reduced costs, improved efficiencies and increased productivity have driven organizations to take major strides towards digital transformation. With countless promising business opportunities, it’s no surprise that IoT is expanding rapidly and relentlessly. It is estimated that there will be 75.4 billion IoT devices by 2025. As IoT grows, so do the volumes of IoT data that need to be collected, analyzed and stored. Unfortunately, significant barriers exist that can limit or block access to this data altogether.

Successful IoT data acquisition starts and ends with reliable and scalable IoT connectivity. Selecting the right communications technology is paramount to the long-term success of your IoT project and various factors must be considered from the beginning to build a functional wireless infrastructure that can support and manage the influx of IoT data today and in the future.

Here are five IoT architecture must-haves for unlocking IoT data at scale.

1. Network Ownership

For many businesses, IoT data is one of their greatest assets, if not the most valuable. This intensifies the demand to protect the flow of data at all costs. With maximum data authority and architecture control, the adoption of privately managed networks is becoming prevalent across industrial verticals.

Beyond the undeniable benefits of data security and privacy, private networks give users more control over their deployment with the flexibility to tailor their coverage to the specific needs of their campus style network. On a public network, users risk not having the reliable connectivity needed for indoor, underground and remote critical IoT applications. And since this network is privately owned and operated, users also avoid the costly monthly access, data plans and subscription costs imposed by public operators, lowering the overall total-cost-of-ownership. Private networks also provide full control over network availability and uptime to ensure users have reliable access to their data at all times.

2. Minimal Infrastructure Requirements

Since the number of end devices is often fixed to your IoT use cases, choosing a wireless technology that requires minimal supporting infrastructure like base stations and repeaters, as well as configuration and optimization is crucial to cost-effectively scale your IoT network.

Wireless solutions with long range and excellent penetration capability, such as next-gen low-power wide area networks, require fewer base stations to cover a vast, structurally dense industrial or commercial campuses. Likewise, a robust radio link and large network capacity allow an individual base station to effectively support massive amounts of sensors without comprising performance to ensure a continuous flow of IoT data today and in the future.

3. Network and Device Management

As IoT initiatives move beyond proofs-of-concept, businesses need an effective and secure approach to operate, control and expand their IoT network with minimal costs and complexity.

As IoT deployments scale to hundreds or even thousands of geographically dispersed nodes, a manual approach to connecting, configuring and troubleshooting devices is inefficient and expensive. Likewise, by leaving devices completely unattended, users risk losing business-critical IoT data when it’s needed the most. A network and device management platform provides a single-pane, top-down view of all network traffic, registered nodes and their status for streamlined network monitoring and troubleshooting. Likewise, it acts as the bridge between the edge network and users’ downstream data servers and enterprise applications so users can streamline management of their entire IoT project from device to dashboard.

4. Legacy System Integration

Most traditional assets, machines, and facilities were not designed for IoT connectivity, creating huge data silos. This leaves companies with two choices: building entirely new, greenfield plants with native IoT technologies or updating brownfield facilities for IoT connectivity. Highly integrable, plug-and-play IoT connectivity is key to streamlining the costs and complexity of an IoT deployment. Businesses need a solution that can bridge the gap between legacy OT and IT systems to unlock new layers of data that were previously inaccessible. Wireless IoT connectivity must be able to easily retrofit existing assets and equipment without complex hardware modifications and production downtime. Likewise, it must enable straightforward data transfer to the existing IT infrastructure and business applications for data management, visualization and machine learning.

5. Interoperability

Each IoT system is a mashup of diverse components and technologies. This makes interoperability a prerequisite for IoT scalability, to avoid being saddled with an obsolete system that fails to keep pace with new innovation later on. By designing an interoperable architecture from the beginning, you can avoid fragmentation and reduce the integration costs of your IoT project in the long run. 

Today, technology standards exist to foster horizontal interoperability by fueling global cross-vendor support through robust, transparent and consistent technology specifications. For example, a standard-based wireless protocol allows you to benefit from a growing portfolio of off-the-shelf hardware across industry domains. When it comes to vertical interoperability, versatile APIs and open messaging protocols act as the glue to connect the edge network with a multitude of value-deriving backend applications. Leveraging these open interfaces, you can also scale your deployment across locations and seamlessly aggregate IoT data across premises.  

IoT data is the lifeblood of business intelligence and competitive differentiation and IoT connectivity is the crux to ensuring reliable and secure access to this data. When it comes to building a future-proof wireless architecture, it’s important to consider not only existing requirements, but also those that might pop up down the road. A wireless solution that offers data ownership, minimal infrastructure requirements, built-in network management and integration and interoperability will not only ensure access to IoT data today, but provide cost-effective support for the influx of data and devices in the future.

Originally posted here.

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by Olivier Pauzet

Over the past year, we have seen the Industrial IoT (IIoT) take an important step forward, crossing the chasm that previously separated IIoT early adopters from the majority of companies.

New solutions like Octave, Sierra Wireless’ edge-to-cloud solution for connecting industrial assets, have greatly simplified the IIoT, making it possible now for practically any company to securely extract, transmit, and act on data from bio-waste collectors, liquid fertilizer tanks, water purifiers, hot water heaters and other industrial equipment.

So, what IIoT trends will these 2020 developments lead to in 2021? I expect that they will drive greater adoption of the IIoT next year, as manufacturing, utility, healthcare, and other organizations further realize that they can help their previously silent industrial assets speak using the APIs integrated in new IoT solutions. At the same time, I expect we will start to see the development of some revolutionary IIoT applications that use 5G’s Ultra-Reliable, Low-Latency Communications (URLLC) capabilities to change the way our factories, electric grid, and healthcare systems operate.

In 2021, Industrial Equipment APIs Will Give Quiet Equipment A Voice

Cloud APIs have transformed the tech industry, and with it, our digital economy. By enabling SaaS and other cloud-based applications to easily and securely talk to each other, cloud APIs have vastly expanded the value of these applications to users. These APIs have also spawned billion-dollar companies like Stripe, Tableau, and Twilio, whose API-focused business models have transformed the online payments, data visualization, and customer service markets.

2021 will be the year industrial companies begin seeing their markets transformed by APIs, as more of these companies begin using industrial equipment APIs built into new IIoT solutions to enable their industrial assets to talk to the cloud.

Using new edge-to-cloud solutions - like Octave -with built-in Industrial equipment APIs for Modbus and other industrial communications protocols, these companies will be able to securely connect these assets to the cloud almost as easily as if this equipment was a cloud-based application.

In fact, by simply plugging a low-cost IoT gateway with these IIoT APIs into their industrial equipment, they will be able to deploy IIoT applications that allow them to remotely monitor, maintain, and control this equipment. Then, using these applications, they can lower equipment downtime, reduce maintenance costs, launch new Equipment-as-a-Service business models, and innovate faster.

Industrial companies have been trying to connect their assets to the cloud for years, but have been stymied by the complexity, time, and expense involved in doing so. In 2021, industrial equipment APIs will provide these companies with a way to simply, quickly, and cheaply connect this equipment to the cloud. By giving a voice to billions of pieces of industrial equipment, these Industrial IoT APIs will help bring about the productivity, sustainability, and other benefits Industry 4.0 has long promised.

In 2021 Manufacturing, Utility and Healthcare Will Drive Growth of the Industrial IoT

Until recently, the consumer sector, and especially the smart home market, has led the way in adopting the IoT, as the success of the Google Nest smart thermostat, the Amazon Echo smart speaker and Ring smart doorbell, and the Phillips Hue smart lights demonstrate. However, in 2021 another IIoT trend we can expect to see is the industrial sector starting to catch up to the consumer market regarding the IoT, with the manufacturing, utility, and healthcare markets leading the way.

For example, new IIoT solutions now make it possible for Original Equipment Manufacturers (OEMs) and other manufacturing companies to simply plug their equipment into the IIoT and begin acting on data from this equipment almost immediately. This has lowered the time to value for IIoT applications to the point where companies can begin reaping financial benefits greater than the total cost for their IIoT application in a few short months.

At this point, manufacturers who don’t have a plan to integrate the IIoT into their assets are, to put it bluntly, leaving money on the table – money their competitors will happily snap up with their own new connected industrial equipment offerings if they do not.

Like manufacturing companies, utilities will ramp up their use of the IIoT in 2021, as they seek to improve their operational efficiency, customer engagement, reliability, and sustainability. For example, utilities will increasingly use the IIoT to perform remote diagnostics and predictive maintenance on their grid infrastructure, reducing this equipment’s downtime while also lowering maintenance costs. In addition, a growing number of utilities will use the IIoT to collect and analyze data on their wind, solar and other renewable energy generation portfolios, allowing them to reduce greenhouse gas emissions while still balancing energy supply and demand on the grid.

Along with manufacturing and utilities, healthcare is the third market sector I expect to lead the way in adopting the IIoT in 2021. The COVID-19 pandemic has demonstrated to healthcare providers how connectivity – such as Internet-based telemedicine solutions -- can improve patient outcomes while reducing their costs. In 2021 they will increase their use of the IIoT, as they work to extend this connectivity to patient monitors, scanners and other medical devices. With the Internet of Medical Things (IoMT), healthcare providers will be better able to prepare patient treatments, remotely monitor and respond to changes to their patients’ conditions, and generate health care treatment documents.

Revolutionary Ultra-Reliable, Low-Latency 5G Applications Will Begin to Be Developed

There is a lot of buzz regarding 5G New Radio (NR) in the IIoT market. However, having been designed to co-exist with 4G LTE, most of 5G NR’s impact in this market is still evolutionary, not revolutionary. Companies are beginning to adopt 5G to wring better performance out of their existing IIoT applications, or to future-proof their connectivity strategies. But they are doing this while continuing to use LTE, as well as Low Power Wide Area (LPWA) 5G technologies, like LTE-M and NB-IoT, for now.

In 2021 however I think we will begin to see companies starting to develop revolutionary new IIoT application proof of concepts designed to take advantage of 5G NR’s Ultra-Reliable, Low-Latency Communications (URLLC) capabilities. These URLLC applications – including smart Automated Guided Vehicle (AGVs) for manufacturing, self-healing energy grids for utilities and remote surgery for health care – are simply not possible with existing wireless technologies.

Thanks to its ability to deliver ultra-high reliability and latencies as low as one millisecond, 5G NR enables companies to finally build URLLC applications – especially when 5G NR is used in conjunction with new edge computing technologies.

It will be a long time before any of these URLLC application proof-of-concepts are commercialized. But as far as 5G Wave 5+, next year is when we will first begin seeing this wave forming out at sea. And when it does eventually reach shore, it will have a revolutionary impact on our connected economy.

Originally posted here.

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