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Software updates have become a key component of our connected day-to-day lives. It usually takes no more than a touch of a button to keep apps on our smartphones or software on our computers up to date.

Software updates are also playing an increasingly pivotal role in the Industrial IoT (IIoT). They enable device manufacturers to fix bugs, ensure the availability of devices, and offer new features and services to their customers. Over-the-air updates are the delivery means of choice when it comes making the entire process as efficient and convenient as possible for everyone. Device manufacturers do not have to send technicians out to install updates; customers are not compelled to take their gear in to the shop.

Rolling out software and firmware updates over-the-air (SOTA/FOTA) is certainly convenient, however, it is still a complicated process with a number of factors to consider. In this blog, we’ll discuss five challenges related to SOTA/FOTA and look at some potential solutions.

Software updates for distributed device fleets

In many IIoT use cases, devices are distributed all over the world – sometimes even in places that are fairly inaccessible. Just think of all that agricultural and construction machinery that usually operates in rough terrain. Manual updates would be most inefficient in these scenarios, if not altogether impossible. Over-the-air updates are a far more feasible option – provided that these updates can be delivered to the target devices quickly and reliably.

This is where a content delivery network (CDN) consisting of regionally distributed servers plays an important role. Rather than sending updates from a central location to anywhere in the world, they are delivered via several geographically distributed nodes. This brings the devices and respective updates closer together and ensures that new software versions are delivered that much faster. Even major software rollouts can be swiftly executed with this method.

Different components require compatible software updates

Modern machines and devices consist of a multitude of components that require compatible software updates. The automotive industry provides a prime example of that: With so many customization options, practically no two vehicles are alike. Even two of the same model can have very different features. The same holds true for some household appliances. The label may indicate the same model of washing machine, but even then the installed components can vary from appliance to appliance.

How do manufacturers keep track of installed components and ensure that each gets the right updates? One way is with a digital twin – that is, a virtual copy of the product. Digital twins reveal which components are installed at what location, which makes it a lot easier to manage devices and roll out software updates.

Rolling out software for a host of devices

Devices’ geographical distribution is an important factor when it comes to software updates, but the number of deployed devices also matters. The larger the fleet, the more complicated the rollout will be. But what happens if an error occurs during the update? Manufacturers need a solution that enables them to respond flexibly to those kinds of situations and take remedial action with a reasonable amount of effort.

At Bosch.IO we employ campaign management to address this challenge. The idea here is to break the fleet of devices down into smaller batches that get updated successively. If a problem crops up during the update, it will not affect the entire fleet. Device manufacturers can then reset the update process for specific batches and troubleshoot the error in a targeted manner. Manufacturers also rely on campaign management to set rules for when an update may be carried out. This goes to prevent software rollouts from disrupting ongoing operations.

Keeping a lid on costs

Software updates are more important than ever, and now they are coming at ever shorter intervals. Although over-the-air updates are more convenient and less time-consuming, the cost of rolling out software is still an issue. Device manufacturers have to keep a lid on their infrastructural and data transmission expenses.

Delta updates can help with that. This type of update addresses only those parts of the code that have actually changed. This leaves device manufacturers with much smaller updates and a lot less data to transfer. And it makes the whole update process more scalable.

Ensuring secure updating

Security is a big concern for over-the-air updates. Device manufacturers have to be sure that only trusted code is installed on the devices.

A key management system helps them do this. It generates a certificate enabling each software artifact to authenticate itself on the device. This way, device manufacturers can be sure that no unauthorized third-party code finds its way onto the device.

Originally posted here.

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IoT Sustainability, Data At The Edge.

Recently I've written quite a bit about IOT, and one thing you may have picked up on is that the Internet of Things is made up of a lot of very large numbers.

For starters, the number of connected things is measured in the tens of billions, nearly 100's of billions. Then, behind that very large number is an even bigger number, the amount of data these billions of devices is predicted to generate.

As FutureIoT pointed out, IDC forecasted that the amount of data generated by IoT devices by 2025 is expected to be in excess of 79.4 zettabytes (ZB).

How much is Zettabyte!?

A zettabyte is a very large number indeed, but how big? How can you get your head around it? Does this help...?

A zettabyte is 1,000,000,000,000,000,000,000 bytes. Hmm, that's still not very easy to visualise.

So let's think of it in terms of London busses. Let's image a byte is represented as a human on a bus, a London bus can take 80 people, so you'd need 993 quintillion busses to accommodate 79.4 zettahumans.

I tried to calculate how long 993 quintillion busses would be. Relating it to the distance to the moon, Mars or the Sun wasn't doing it justice, the only comparable scale is the size of the Milky Way. Even with that, our 79.4 zettahumans lined up in London busses, would stretch across the entire Milky Way ... and a fair bit further!

Sustainability Of Cloud Storage For 993 Quintillion Busses Of Data

Everything we do has an impact on the planet. Just by reading this article, you're generating 0.2 grams of Carbon Dioxide (CO2) emissions per second ... so I'll try to keep this short.

Using data from the Stanford Magazine that suggests every 100 gigabytes of data stored in the Cloud could generate 0.2 tons of CO2 per year. Storing 79.4 zettabytes of data in the Cloud could be responsible for the production of 158.8 billion tons of greenhouse gases.

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Putting that number into context, using USA Today numbers, the total emissions for China, USA, India, Russia, Japan and Germany accounted for a little over 21 billion tons in 2019.

So if we just go ahead and let all the IoT devices stream data to the Cloud, those billions of little gadgets would indirectly generate more than seven times the air pollution than the six most industrial countries, combined.

Save The Planet, Store Data At The Edge

As mentioned in a previous article, not all data generated by IoT devices needs to be stored in the Cloud.

Speaking with an expert in data storage, ObjectBox, they say their users on average cut their Cloud data storage by 60%. So how does that work, then? 

First, what does The Edge mean?

The term "Edge" refers to the edge of the network, in other words the last piece of equipment or thing connected to the network closest to the point of usage.

Let me illustrate in rather over-simplified diagram.

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How Can Edge Data Storage Improve Sustainability?

In an article about computer vision and AI on the edge, I talked about how vast amounts of network data could be saved if the cameras themselves could detect what an important event was, and to just send that event over the network, not the entire video stream.

In that example, only the key events and meta data, like the identification marks of a vehicle crossing a stop light, needed to be transmitted across the network. However, it is important to keep the raw content at the edge, so it can be used for post processing, for further learning of the AI or even to be retrieved at a later date, e.g. by law-enforcement.

Another example could be sensors used to detect gas leaks, seismic activity, fires or broken glass. These sensors are capturing volumes of data each second, but they only want to alert someone when something happens - detection of abnormal gas levels, a tremor, fire or smashed window.

Those alerts are the primary purpose of those devices, but the data in between those events can also hold significant value. In this instance, keeping it locally at the edge, but having it as and when needed is an ideal way to reduce network traffic, reduce Cloud storage and save the planet (well, at least a little bit).

Accessible Data At The Edge

Keeping your data at the edge is a great way to save costs and increase performance, but you still want to be able to get access to it, when you need it.

ObjectBox have created not just one of the most efficient ways to store data at the edge, but they've also built a sophisticated and powerful method to synchronise data between edge devices, the Cloud and other edge devices.

Synchronise Data At The Edge - Fog Computing.

Fog Computing (which is computing that happens between the Cloud and the Edge) requires data to be exchanged with devices connected to the edge, but without going all the way to/from the servers in the Cloud. 

In the article on making smarter, safer cities, I talked about how by having AI-equipped cameras share data between themselves they could become smarter, more efficient. 

A solution like that could be using ObjectBox's synchronisation capabilities to efficiently discover and collect relevant video footage from various cameras to help either identify objects or even train the artificial intelligence algorithms running on the AI-equipped cameras at the edge.

Storing Data At The Edge Can Save A Bus Load CO2

Edge computing has a lot of benefits to offer, in this article I've just looked at what could often be overlooked - the cost of transferring data. I've also not really delved into the broader benefits of ObjectBox's technology, for example, from their open source benchmarks, ObjectBox seems to offer a ten times performance benefit compared to other solutions out there, and is being used by more than 300,000 developers.  

The team behind ObjectBox also built technologies currently used by internet heavy-weights like Twitter, Viber and Snapchat, so they seem to be doing something right, and if they can really cut down network traffic by 60%, they could be one of sustainable technology companies to watch.  

Originally posted here.

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Edge Impulse has joined 1% for Planet, pledging to donate 1% of our revenue to support nonprofit organizations focused on the environment. To complement this effort we launched the ElephantEdge competition, aiming to create the world’s best elephant tracking device to protect elephant populations that would otherwise be impacted by poaching. In this similar vein, this blog will detail how Lacuna Space, Edge Impulse, a microcontroller and LoraWAN can promote the conservation of endangered species by monitoring bird calls in remote areas.

Over the past years, The Things Networks has worked around the democratization of the Internet of Things, building a global and crowdsourced LoraWAN network carried by the thousands of users operating their own gateways worldwide. Thanks to Lacuna Space’ satellites constellation, the network coverage goes one step further. Lacuna Space uses LEO (Low-Earth Orbit) satellites to provide LoRaWAN coverage at any point around the globe. Messages received by satellites are then routed to ground stations and forwarded to LoRaWAN service providers such as TTN. This technology can benefit several industries and applications: tracking a vessel not only in harbors but across the oceans, monitoring endangered species in remote areas. All that with only 25mW power (ISM band limit) to send a message to the satellite. This is truly amazing!

Most of these devices are typically simple, just sending a single temperature value, or other sensor reading, to the satellite - but with machine learning we can track much more: what devices hear, see, or feel. In this blog post we'll take you through the process of deploying a bird sound classification project using an Arduino Nano 33 BLE Sense board and a Lacuna Space LS200 development kit. The inferencing results are then sent to a TTN application.

Note: Access to the Lacuna Space program and dev kit is closed group at the moment. Get in touch with Lacuna Space for hardware and software access. The technical details to configure your Arduino sketch and TTN application are available in our GitHub repository.

 

Our bird sound model classifies house sparrow and rose-ringed parakeet species with a 92% accuracy. You can clone our public project or make your own classification model following our different tutorials such as Recognize sounds from audio or Continuous Motion Recognition.

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Once you have trained your model, head to the Deployment section, select the Arduino library and Build it.

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Import the library within the Arduino IDE, and open the microphone continuous example sketch. We made a few modifications to this example sketch to interact with the LS200 dev kit: we added a new UART link and we transmit classification results only if the prediction score is above 0.8.

Connect with the Lacuna Space dashboard by following the instructions on our application’s GitHub ReadMe. By using a web tracker you can determine when the next good time a Lacuna Space satellite will be flying in your location, then you can receive the signal through your The Things Network application and view the inferencing results on the bird call classification:

    {
       "housesparrow": "0.91406",
       "redringedparakeet": "0.05078",
       "noise": "0.03125",
       "satellite": true,
   }

No Lacuna Space development kit yet? No problem! You can already start building and verifying your ML models on the Arduino Nano 33 BLE Sense or one of our other development kits, test it out with your local LoRaWAN network (by pairing it with a LoRa radio or LoRa module) and switch over to the Lacuna satellites when you get your kit.

Originally posted on the Edge Impulse blog by Aurelien Lequertier - Lead User Success Engineer at Edge Impulse, Jenny Plunkett - User Success Engineer at Edge Impulse, & Raul James - Embedded Software Engineer at Edge Impulse

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The possibilities of what you can do with digital twin technology are only as limited as your imagination

Today, forward-thinking companies across industries are implementing digital twin technology in increasingly fascinating and ground-breaking ways. With Internet of Things (IoT) technology improving every day and more and more compute power readily available to organizations of all sizes, the possibilities of what you can do with digital twin technology are only as limited as your imagination.

What Is a Digital Twin?

A digital twin is a virtual representation of a physical asset that is practically indistinguishable from its physical counterpart. It is made possible thanks to IoT sensors that gather data from the physical world and send it to be virtually reconstructed. This data includes design and engineering details that describe the asset’s geometry, materials, components, and behavior or performance.

When combined with analytics, digital twin data can unlock hidden value for an organization and provide insights about how to improve operations, increase efficiency or discover and resolve problems before the real-world asset is affected.

These 4 Steps Are Critical for Digital Twin Success:

Involve the Entire Product Value Chain

It’s critical to involve stakeholders across the product value chain in your design and implementation. Each department faces diverse business challenges in their day-to-day operations, and a digital twin provides ready solutions to problems such as the inability to coordinate across end-to-end supply chain processes, minimal or no cross-functional collaboration, the inability to make data-driven decisions, or clouded visibility across the supply chain. Decision-makers at each level of the value chain have extensive knowledge on critical and practical challenges. Including their inputs will ensure a better and more efficient design of the digital twin and ensure more valuable and relevant insights.

Establish Well-Documented Practices

Standardized and well-documented design practices help organizations communicate ideas across departments, or across the globe, and make it easier for multiple users of the digital twin to build or alter the model without destroying existing components or repeating work. Best-in-class modelling practices increase transparency while simplifying and streamlining collaborative work.

Include Data From Multiple Sources

Data from multiple sources—both internal and external—is an essential part of creating realistic and helpful simulations. 3D modeling and geometry is sufficient to show how parts fit together and how a product works, but more input is required to model how various faults or errors might occur somewhere in the product’s lifecycle. Because many errors and problems can be nearly impossible to accurately predict by humans alone, a digital twin needs a vast amount of data and a robust analytics program to be able to run algorithms to make accurate forecasts and prevent downtime.

Ensure Long Access Lifecycles 

Digital twins implemented using proprietary design software have a risk of locking owners into a single vendor, which ties the long-term viability of the digital twin to the longevity of the supplier’s product. This risk is especially significant for assets with long lifecycles such as buildings, industrial machinery, airplanes, etc., since the lifecycles of these assets are usually much longer than software lifecycles. This proprietary dependency only becomes riskier and less sustainable over time. To overcome these risks, IT architects and digital twin owners need to carefully set terms with software vendors to ensure data compatibility is maintained and vendor lock-in can be avoided.

Common Pitfalls to Digital Twin Implementation

Digital twin implementation requires an extraordinary investment of time, capital, and engineering might, and as with any project of this scale, there are several common pitfalls to implementation success.

Pitfall 1: Using the Same Platform for Different Applications

Although it’s tempting to try and repurpose a digital twin platform, doing so can lead to incorrect data at best and catastrophic mistakes at worst. Each digital twin is completely unique to a part or machine, therefore assets with unique operating conditions and configurations cannot share digital twin platforms.

Pitfall 2: Going Too Big, Too Fast

In the long run, a digital twin replica of your entire production line or building is possible and could provide incredible insights, but it is a mistake to try and deploy digital twins for all of your pieces of equipment or programs all at once. Not only is doing too much, too fast costly, but it might cause you to rush and miss critical data and configurations along the way. Rather than rushing to do it all at once, perfect a few critical pieces of machinery first and work your way up from there.

Pitfall 3: Inability to Source Quality Data

Data collected in the field is subject to quality errors due to human mistakes or duplicate entries. The insights your digital twin provides you are only as valuable as the data it runs off of. Therefore, it is imperative to standardize data collection practices across your organization and to regularly cleanse your data to remove duplicate and erroneous entries.

Pitfall 4: Lack of Device Communication Standards

If your IoT devices do not speak a common language, miscommunications can muddy your processes and compromise your digital twin initiative. Build an IT framework that allows your IoT devices to communicate with one another seamlessly to ensure success.

Pitfall 5: Failing to Get User Buy-In

As mentioned earlier in this eBook, a successful digital twin strategy includes users from across your product value chain. It is critical that your users understand and appreciate the value your digital twin brings to them individually and to your organization as a whole. Lack of buy-in due to skepticism, lack of confidence, or resistance can lead to a lack of user participation, which can undermine all of your efforts.

The Challenge of Measuring Digital Twin Success

Each digital twin is unique and completely separate in its function and end-goal from others on the market, which can make measuring success challenging. Depending on the level of the twin implemented, businesses need to create KPIs for each individual digital twin as it relates to larger organizational goals.

The configuration of digital twins is determined by the type of input data, number of data sources and the defined metrics. The configuration determines the value an organization can extract from the digital twin. Therefore, a twin with a higher configuration can yield better predictions than can a twin with a lower configuration. The reality is that success can be relative, and it is impossible to compare the effectiveness of two different digital twins side by side.

Conclusion

It’s possible — probable even — that in the future all people, enterprises, and even cities will have a digital twin. With the enormous growth predicted in the digital twin market in the coming years, it’s evident that the technology is here to stay. The possible applications of digital twins are truly limitless, and as IoT technology becomes more advanced and widely accessible, we’re likely to see many more innovative and disruptive use cases.

However, a technology with this much potential must be carefully and thoughtfully implemented in order to ensure its business value and long-term viability. Before embracing a digital twin, an organization must first audit its maturity, standardize processes, and prepare its culture and staff for this radical change in operations. Is your organization ready?

Originally posted here.

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Five IoT retail trends for 2021

In 2020 we saw retailers hard hit by the economic effects of the COVID-19 pandemic with dozens of retailers—Neiman Marcus, J.C. Penney, and Brooks Brothers to name a few— declaring bankruptcy. During the unprecedented chaos of lockdowns and social distancing, consumers accelerated their shift to online shopping. Retailers like Target and Best Buy saw online sales double while Amazon’s e–commerce sales grew 39 percent.1 Retailers navigated supply chain disruptions due to COVID-19, climate change events, trade tensions, and cybersecurity events.  

After the last twelve tumultuous months, what will 2021 bring for the retail industry? I spoke with Microsoft Azure IoT partners to understand how they are planning for 2021 and compiled insights about five retail trends. One theme we’re seeing is a focus on efficiency. Retailers will look to pre-configured digital platforms that leverage cloud-based technologies including the Internet of Things (IoT), artificial intelligence (AI), and edge computing to meet their business goals. 

a group of people standing in front of a mirror posing for the camera

Empowering frontline workers with real-time data

In 2021, retailers will increase efficiency by empowering frontline workers with real-time data. Retail employees will be able to respond more quickly to customers and expand their roles to manage curbside pickups, returns, and frictionless kiosks.  

In H&M Mitte Garten in Berlin, H&M empowered employee ambassadors with fashionable bracelets connected to the Azure cloud. Ambassadors were able to receive real-time requests via their bracelets when customers needed help in fitting rooms or at a cash desk. The ambassadors also received visual merchandising instructions and promotional updates. 

Through the app built on Microsoft partner Turnpike’s wearable SaaS platform leveraging Azure IoT Hub, these frontline workers could also communicate with their peers or their management team during or after store hours. With the real-time data from the connected bracelets, H&M ambassadors were empowered to delivered best-in-class service.   

Carl Norberg, Founder, Turnpike explained, “We realized that by connecting store IoT sensors, POS systems, and AI cameras, store staff can be empowered to interact at the right place at the right time.” 

Leveraging live stream video to innovate omnichannel

Livestreaming has been exploding in China as influencers sell through their social media channels. Forbes recently projected that nearly 40 percent of China’s population will have viewed livestreams during 2020.2 Retailers in the West are starting to leverage live stream technology to create innovative omnichannel solutions.  

For example, Kjell & Company, one of Scandinavia’s leading consumer electronics retailers, is using a solution from Bambuser and Ombori called Omni-queue built on top of the Ombori Grid. Omni-queue enables store employees to handle a seamless combination of physical and online visitors within the same queue using one-to-one live stream video for online visitors.  

Kjell & Company ensures e-commerce customers receive the same level of technical expertise and personalized service they would receive in one of their physical locations. Omni-queue also enables its store employees to be utilized highly efficiently with advanced routing and knowledge matching. 

Maryam Ghahremani, CEO of Bambuser explains, “Live video shopping is the future, and we are so excited to see how Kjell & Company has found a use for our one-to-one solution.” Martin Knutson, CTO of Kjell & Company added “With physical store locations heavily affected due to the pandemic, offering a new and innovative way for customers to ask questions—especially about electronics—will be key to Kjell’s continued success in moving customers online.” 

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Augmenting omnichannel with dark stores and micro-fulfillment centers  

In 2021, retailers will continue experimenting with dark stores—traditional retail stores that have been converted to local fulfillment centers—and micro-fulfillment centers. These supply chain innovations will increase efficiency by bringing products closer to customers. 

Microsoft partner Attabotics, a 3D robotics supply chain company, works with an American luxury department store retailer to reduce costs and delivery time using a micro-fulfillment center. Attabotics’ unique use of both horizontal and vertical space reduces warehouse needs by 85 percent. Attabotics’ structure and robotic shuttles leveraged Microsoft Azure Edge Zones, Azure IoT Central, and Azure Sphere.

The luxury retailer leverages the micro-fulfillment center to package and ship multiple beauty products together. As a result, customers experience faster delivery times. The retailer also reduces costs related to packaging, delivery, and warehouse space.  

Scott Gravelle, Founder, CEO, and CTO of Attabotics explained, “Commerce is at a crossroads, and for retailers and brands to thrive, they need to adapt and take advantage of new technologies to effectively meet consumers’ growing demands. Supply chains have not traditionally been set up for e-commerce. We will see supply chain innovations in automation and modulation take off in 2021 as they bring a wider variety of products closer to the consumer and streamline the picking and shipping to support e-commerce.” 

a group of people wearing costumes

Helping keep warehouse workers safe

What will this look like? Cognizant’s recent work with an athletic apparel retailer offers a blueprint. During the peak holiday season, the retailer needed to protect its expanding warehouse workforce while minimizing absenteeism. To implement physical distancing and other safety measures, the retailer  leveraged Cognizant’s Safe Buildings solution built with Azure IoT Edge and IoT Hub services.   

With this solution, employees maintain physical distancing using smart wristbands. When two smart wristbands were within a pre-defined distance of each other for more than a pre-defined time, the worker’s bands buzzed to reinforce safe behaviors. The results drove nearly 98 percent distancing compliance in the initial pilot. As the retailer plans to scale-up its workforce at other locations, implementing additional safety modules are being considered:   

  • Touchless temperature checks.  
  • Occupancy sensors communicate capacity information to the management team for compliance records.  
  • Air quality sensors provide environmental data so the facility team could help ensure optimal conditions for workers’ health.  

“For organizations to thrive during and post-pandemic, enterprise-grade workplace safety cannot be compromised. Real-time visibility of threats is providing essential businesses an edge in minimizing risks proactively while building employee trust and empowering productivity in a safer workplace,” Rajiv Mukherjee, Cognizant’s IoT Practice Director for Retail and Consumer Goods.  

Optimizing inventory management with real-time edge data

In 2021, retailers will ramp up the adoption of intelligent edge solutions to optimize inventory management with real-time data. Most retailers have complex inventory management systems. However, no matter how good the systems are, there can still be data gaps due to grocery pick-up services, theft, and sweethearting. The key to addressing these gaps is to combine real-time data from applications running on edge cameras and other edge devices in the physical store with backend enterprise resource planning (ERP) data.  

Seattle Goodwill worked with Avanade to implement a new Microsoft-based Dynamics platform across its 24 stores. The new system provided almost real-time visibility into the movement of goods from the warehouses to the stores. 

Rasmus Hyltegård, Director of Advanced Analytics at Avanade explained, “To ensure inventory moves quickly off the shelves, retailers can combine real-time inventory insights from Avanade’s smart inventory accelerator with other solutions across the customer journey to meet customer expectations.” Hyltegård continued, “Customers can check online to find the products they want, find the stores with product in stock, and gain insight into which stores have the shortest queues, which is important during the pandemic and beyond. Once a customer is in the store, digital signage allows for endless aisle support.” 

a person standing in front of a building

Summary

The new year 2021 holds a wealth of opportunities for retailers. We foresee retail leaders reimagining their businesses by investing in platforms that integrate IoT, AI, and edge computing technologies. Retailers will focus on increasing efficiencies to reduce costs. Modular platforms supported by an ecosystem of strong partner solutions will empower frontline workers with data, augment omnichannel fulfillment with dark stores and micro-fulfillment, leverage livestream video to enhance omnichannel, prioritize warehouse worker safety, and optimize inventory management with real-time data. 

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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Skoltech researchers and their colleagues from Russia and Germany have designed an on-chip printed "electronic nose" that serves as a proof of concept for this kind of low-cost and sensitive devices to be used in portable electronics and healthcare. The paper was published in the journal ACS Applied Materials Interfaces.

The rapidly growing fields of Internet of Things (IoT) and advanced medical diagnostics require small, cost-effective, low-powered yet reasonably sensitive and selective gas-analytical systems like so-called "electronic noses." These systems can be used for noninvasive diagnostics of human breath, such as diagnosing chronic obstructive pulmonary disease (COPD) with a compact sensor system also designed at Skoltech. Some of these sensors work a lot like actual noses—say, yours—by using an array of sensors to better detect the complex signal of a gaseous compound.

One approach to creating these sensors is by additive manufacturing technologies, which have achieved enough power and precision to be able to produce the most intricate devices. Skoltech senior research scientist Fedor Fedorov, Professor Albert Nasibulin, research scientist Dmitry Rupasov and their collaborators created a multisensor "electronic nose" by printing nanocrystalline films of eight different metal oxides onto a multielectrode chip (they were manganese, cerium, zirconium, zinc, chromium, cobalt, tin, and titanium). The Skoltech team came up with the idea for this project.

"For this work, we used microplotter printing and true solution inks. There are a few things that make it valuable. First, the resolution of the printing is close to the distance between electrodes on the chip which is optimized for more convenient measurements. We show these technologies are compatible. Second, we managed to use several different oxides which enables more orthogonal signal from the chip resulting in improved selectivity. We can also speculate that this technology is reproducible and easy to be implemented in industry to obtain chips with similar characteristics, and that is really important for the 'e-nose' industry," Fedorov explained.

In subsequent experiments, the device was able to sniff out the difference between different alcohol vapors (methanol, ethanol, isopropanol, and n-butanol), which are chemically very similar and hard to tell apart, at low concentrations in the air. Since methanol is extremely toxic, detecting it in beverages and differentiating between methanol and ethanol can even save lives. To process the data, the team used linear discriminant analysis (LDA), a pattern recognition algorithm, but other machine learning algorithms could also be used for this task.

So far the device operates at rather high temperatures of 200-400 degrees Celsius, but the researchers believe that new quasi-2-D materials such as MXenes, graphene and so on could be used to increase the sensitivity of the array and ultimately allow it to operate at room temperature. The team will continue working in this direction, optimizing the materials used to lower power consumption.

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 Ariane Elena Fuchs

Solar power, wind energy, micro cogeneration power plants: energy from renewable sources has become indispensable, but it makes power generation and distribution far more complex. How the Internet of Things is helping make energy management sustainable.

It feels like Ground Hog Day yet again – in 2020 it happened on August 22. That was the point when the demand for raw materials exceeded the Earth’s supply and capacity to reproduce these natural resources. All reserves that are consumed from that date on cannot be regenerated in the current year. In other words, humanity is living above its means, consuming around 50 percent more energy than the Earth provides naturally.

To conserve these precious resources and reduce climate-damaging CO2 emissions, the energy we need must come from renewable sources such as wind, sun and water. This is the only way to reduce both greenhouse gases and our fossil fuel use. Fortunately, a start has now been made: In 2019, renewable energies – predominantly from wind and sun – will already cover almost 43 percent of Germany's energy requirements and the trend is rising.

DECENTRALIZING ENERGY PRODUCTION

This also means, however, that the traditional energy management model – a few power plants supplying a lot of consumers – is outdated. After all, the phasing out of large nuclear and coal-fired power plants doesn’t just have consequences for Germany’s CO2 balance. Shifting electricity production to wind, solar and smaller cogeneration plants reverses the previous pattern of energy generation and distribution from a highly centralized to an increasingly decentralized organizational structure. Instead of just a few large power plants sending electricity to the grid, there are now many smaller energy sources such as solar panels and wind turbines. This has made the management of it all – including the optimal distribution of the electricity – far more complex as a result. It’s up to the energy sector to wrangle this challenging transformation. As the country’s energy is becoming more sustainable, it’s also becoming harder to organize, since the energy generated from wind and sun cannot be planned in advance as easily as coal and nuclear power can. What’s more, there are thousands of wind turbines and solar panels making electricity and feeding it into the grid. This has made the management of the power network extremely difficult. In particular, there’s a lack of real-time information about the amount of electricity being generated.

KEY TECHNOLOGY IOT: FROM ENERGY FLOW TO DATA STREAM

This is where the Internet of Things comes into play: IoT can supply exactly this data from every power generator and send it to a central location. Once there, it can be evaluated before ideally enabling the power grid to be controlled automatically. The result is an IoT ecosystem. In order to operate wind farms more efficiently and reliably, a project team is currently developing an IoT-supported system that bundles and processes all relevant parameters and readings at a wind farm. They can then reconstruct the current operating and maintenance status of individual turbines. This information can be used to detect whether certain components are about to wear out and replace them before a turbine fails.

POTENTIAL FOR NEW BUSINESS MODELS

According to a recent Gartner study, the Internet of Things (IoT) is becoming a key technology for monitoring and orchestrating the complex energy and water ecosystem. In addition, consumers want more control over energy prices and more environmentally friendly power products. With the introduction of smart metering, data from so-called prosumers is becoming increasingly important. These energy producing consumers act like operators of the photovoltaic systems on their roofs. IoT sensors are used to collect the necessary power generation information. Although they are only used locally and for specific purposes, they provide energy companies with a lot of data. In order to be able to use the potential of this information for the expansion of renewable energy, it must be combined and evaluated intelligently. According to Gartner, IoT has the potential to change the energy value chain in four key areas: It enables new business models, optimizes asset management, automates operations and digitalizes the entire value chain from energy source to kWh.

ENERGY TRANSITION REQUIRES TECHNOLOGICAL CHANGE

Installing smaller power-generating systems will soon no longer pose the greatest challenge for operators. In the near future, coherently linking, integrating and controlling them will be the order of the day. The energy transition is therefore spurring technological change on a grand scale. For example, smart grids will only function properly and increase overall capacity when data on generation, consumption and networks is available in real-time. The Internet of Things enables the necessary fast data processing, even from the smallest consumers and prosumers on the grid. With the help of the Internet of Things, more and more household appliances can communicate with the Internet. These devices are then in turn connected to a smart meter gateway, i.e. a hub for the intelligent management of consumers, producers and storage locations at private households and commercial enterprises. To be able to use the true potential of this information, however, all the data must flow together into a common data platform, so that it can be analyzed intelligently.

FROM INDIVIDUAL APPLICATIONS TO AN ECOSYSTEM

For the transmission of data from the Internet of Things, Germany has national fixed-line and mobile networks available. New technology such as the 5G mobile standard allows data to be securely and reliably transferred to the cloud either directly via the 5G network or a 5G campus networks. Software for data analytics and AI tailored to energy firms are now available – including monitoring, analysis, forecasting and optimization tools. Any analyzed data can be accessed via web browsers and in-house data centers. Taken together, it all provides the energy sector with a comprehensive IoT ecosystem for the future.

Originally posted here.

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Earlier, Artificial Intelligence was just a notion we experienced in Sci-Fi movies and documentaries, but now it's making a huge impact on society as well as the business world. 

Today, modern technologies like Artificial Intelligence and Machine Learning are no longer trendy words. It has been around us for more than a decade, but yes, we are harnessing its power for the last couple of years. Due to its more incredible data processing speed and advanced prediction capabilities, Artificial Intelligence is making its way into our daily lives. 

Artificial Intelligence is a vast term that refers to any software or application that engages in studying human preferences during interactions. There are several real-life artificial intelligence applications such as Netflix's movie recommendations, Apple's Siri, Amazon's shopping personalized mails, and Google's Deep Mind. This way, AI is changing business dynamics and allowing them to upscale their game in a positive way. 

AI is implemented in most businesses. As per the report published by PwC, AI will contribute $15.7 trillion by 2030 to the global economy. Businesses that were still considering that AI is a futuristic technology are now investing in it because it will reap many benefits and improve their business offerings. 

From assisting customers to improve their overall experience to automate certain business tasks to crafting personalized solutions, AI has the potential to revamp your modern business outlet. 

5 Ways AI is Accelerating Modern Business World

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According to Salesforce's report, usage of AI is no longer limited to large corporations but SMBs are also using AI technology to achieve higher business growth. 

We live in an age of technology. Businesses are getting evolved as per consumer's demand. Small businesses are slowly automating their business operations in order to capture huge market share while large companies are revamping their existing strategies to establish their brand. 

It means everyone in the business world is now enjoying their proportionate revenue share. Thanks to technologies such as AI and ML, it is now only improving customer relationships and enabling owners to make informed decisions with accurate insights. From SIRI to self-driving cars, AI is transforming our lives in such a way that stimulates human behavior to another level. 

AI is nothing but the technology that can solve problems without human interference and come up with rational solutions taken with the help of 

  • Knowledge
  • Reasoning
  • Perception
  • Learning
  • Planning 

Popular brands like Amazon, Apple, Tesla, etc., are using this technology to transform our current and future lives. The biggest benefit AI has given to businesses is that it has eliminated human intervention in tedious or mundane tasks. Here we have addressed five ways how AI is revolutionizing modern businesses and improving business operations. 

AI Ameliorate Customer Experience

These days, customers are expecting lightning-fast responses from brands, and AI-based advancements allow businesses to integrate voice search or chatbots into their strategy. AI-based chatbots improve customer experience and resolve their queries in seconds without getting frustrated. Moreover, chatbots will also help to find the best suitable products based on customer's preferences. 

Built using smart technologies, chatbots are getting more attention, especially in eCommerce and on-demand business. The online food delivery business has experienced major growth, and entrepreneurs are now integrating chatbots in their restaurant's online ordering system so that customers can frequently ask questions related to orders and get them resolved in minutes. Along with good customer support, chatbots and virtual assistants have several capabilities such as: 

  1. NLP that can interpret voice-based interactions with customers
  2. Resolve customer's queries through accurate insights
  3. Provide personalized offerings to customers

In short, chatbots interact with your customers, assist them round the clock, and provide a personalized experience without getting frustrated. 

Cut Down on Recruiting and Onboarding Cost

According to Deloitte, more than 40% of companies are now using AI in their human resource operations in order to gain long-term benefits. Therefore, more and more organizations are now using AI-based technologies while others are still on their way to adopt this transformation. 

There are few ways in which AI can play a major role in HR operations are: 

  • Onboarding
  • Talent acquisition 
  • HR management 
  • Sentiment analysis
  • Performance management 

Overall, implementing AI in HR speeds up the hiring process, cuts down administrative costs, scans thousands of candidates in just a couple of hours, and reduces bias against candidates. 

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According ot Oracle, HR departments are more likely to use AI to source the best talent as it automates certain tasks and comes up with best results without any bias. Usage of AI is not limited to delivering quality customer support, but it has significantly impacted the organization's recruitment and onboarding process. The companies already using AI have admitted that it has resulted in noticeable benefits. 

Generate Sales

According to Gartner, it is projected that by 2021, 30% of businesses will use AI in their existing sales strategies. Businesses that are not integrating AI in their existing CRM software will be left behind since AI can do wonders and improve customer experiences and sales altogether.

When you leverage AI into your organization's CRM:

  • It studies customer's data
  • Based on that data, your sales team can put efforts
  • It would help to predict whether the customer is interested or not 

Based on the customer's browsing history, personal details, and behavior, you can turn this visitor into a lead with personalized marketing strategies like promotional emails and offerings that ultimately boost sales and customer engagement ratio. Moreover, you can also get started with paid advertising campaigns based on demographics and insights. 

Improve Recommendations for Customers

Leveraging AI, brands can more smartly analyze data to predict customer behavior and craft their marketing strategy based on their preferences and interest. This level of personalization delivers a seamless customer experience, and they feel valued. But for that, you need to understand the customer's demands first. 

For example, Starbucks, using "My Starbucks Barista", which utilizes AI to enable customers to place orders with voice technology or messaging. This level of personalization helps brands to suggest better products and connect with customers. 

Help You Re-target Online Ads

Running paid advertising campaigns on Google or social media are cost-effective and powerful ways to grab users' attention. Hence, targeting audiences' right set using AI and ML algorithms helps you study user preferences for better conversions.  

For instance, if you have an online delivery business, machine learning studies your audience, behavior, and sentiments and helps you re-target with best offerings. Moreover, an advanced level of AI algorithms helps you target customers at the right time so that it will encourage them to make a decision. 

Summing Up

The introduction of AI-powered technology in the modern business world has allowed enterprises to implement crafted and well-researched methods to avail long-term business goals. Artificial Intelligence in the business world plays a crucial role in resolving customer's issues in real-time with innovative solutions and increases businesses' productivity. 

Therefore, we can conclude that AI's capabilities speed-up the decision-making process and solve real problems with smart solutions. Indeed, AI is here to stay for a long time and reap multiple benefits to the business.

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by Philipp Richert

New digital and IoT use cases are becoming more and more important. When it comes to the adoption of these new technologies, there are several different maturity levels, depending on the domain. Within the retail industry, and specifically food retail, we are currently seeing the emergence of a host of IoT use cases.

Two forces are driving this: a technology push, in which suppliers in the retail domain have technologies available to build retail IoT use cases within a connected store; and a market pull by their customers, who are boosting the demand for such use cases.

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However, we also need to ask the following questions: What are IoT use cases good for? And what are they aiming at? We currently see three different fields of application:

  • Increasing efficiency and optimizing processes
  • Increasing customer satisfaction
  • Increasing revenues with new business models

No matter what is most important for your organization or whatever your focus, it is crucial to set up a process that provides guidance for identifying the right use cases. In the following section, we share some insights on how retailers can best design this process. We collated these insights together with the team from the Food Tech Campus.

How to identify the right retail IoT use cases

When identifying the right use cases for their stores, retailers should make sure to look into all phases within the entire innovation process: from problem description and idea collation to solution concept and implementation. Within this process, it is also essential to consider the so-called innovator’s trilemma and ensure that use cases are:

  • Desirable ones that your customer really needs
  • Technically feasible
  • Profitable for your sustainable business development

Before we can actually start identifying retail IoT use cases, we need to define search fields so that we can work on one topic with greater dedication and focus. We must then open up the problem space in order to extract the most relevant problems and pain points. Starting with prioritized and selected pain points, we then open up the solution space in order to define several solution concepts. Once these have been validated, the result should be a well-defined problem statement that concisely describes one singular pain point.

In the following, we want to take a deep dive into the different phases of the process while giving concrete examples, tips and our top-rated tools. Enjoy!

Search fields

Retailers possess expertise and face challenges at various stages along their complex process chains. It helps here to focus on a specific target group in order to avoid distraction. Target groups are typically users or customers in a defined environment. A good example would be to focus your search on processes that happen inside a store location and are relevant to the customer (e.g., the food shopper).

Understand and observe problems

User research, observation and listening are keys to a well-defined problem statement that allows for further ideation. Embedding yourself in various situations and conducting interviews with all the stakeholders visiting or operating a store should be the first steps. Join employees around the store for a day or two and support them during their everyday tasks. Empathize, look for any friction and ask questions. Take your key findings into workshops and spend some time isolating specific causes. Use personas based on your user research and make use of frameworks and canvas templates in order to structure your findings. Use working titles to name the specific problem statements. One example might be: Long queueing as a major nuisance for customers.

Synthesize findings

Are your findings somehow connected? Single-purpose processes and their owners within a store environment are prone to isolated views. Creating a common problem space increases the chances of adoption of any solution later. So it is worth taking the time to map out all findings and take a look at projects in the past and their outcome. In our example, queueing is linked to staff planning, lack of communication and unpredictable customer behavior.

Prioritize problems and pain points

Ask users or stakeholders to give their view on defined problem statements and let them vote. Challenge their view and make them empathize and broaden their view towards a more holistic benefit. Once the quality of a problem statement has been assessed, evaluate the economic implications. In our example, this could mean that queueing affects most employees in the store, directly or indirectly. This problem might be solved through technology and should be further explored.

The result of a well-structured problem statement list should consist of a few new insights that might result in quick gains; one or two major known pain points, where the solution might be viable and feasible; and a list with additional topics that exist but are not too pressing at the moment.

Define opportunity areas

Map technologies and problems together. Are there any strategic goals that these problem statements might be assigned to? Have things changed in terms of technical feasibility (e.g., has the cost of a technology dropped over the past three years?). Can problems be validated within a larger setup easily or are we talking about singular use cases? All these considerations should lead towards the most attractive problem to solve. Again, in our example, this might be: Queuing is a major problem in most locations, satisfying our customers should be our main goal, existing solutions are too expensive or inflexible.

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When identifying the right use cases for their stores, retailers should make sure to look into all phases within the entire innovation process: from problem description and idea collation to solution concept and implementation.

Ideate and explore use cases

When conducting an ideation session, it is very helpful to bring in trends that are relevant to the defined problem areas so as to help boost creativity. In our example, for instance, this might be technology trends such as frictionless checkout for retail, hybrid checkout concepts, bring your own device (BYOD) and sensor approaches. It is always important to keep the following in mind: What do these trends mean for the customer journey in-store and how can they be integrated in (legacy) environments?

Define solutions concepts

In the process of further defining the solution concepts, it is essential to evaluate the market potential and to consider customer and user feedback. Depending on the solution, it might be necessary to ask the various stakeholders – from store managers to personnel to customers – in order to get a clearer picture. When talking to customers or users, it is also helpful to bring along scribbles, pictures or prototypes in order to increase immersion. The insights gathered in this way help to validate assumptions and to pilot the concept accordingly.

Set metrics and KPIs to prove success

Defining data-based metrics and KPIs is essential for a successful solution. When setting up metrics and KPIs, you need to consider two aspects:

  • Use existing data – e.g., checkout frequency – in order to demonstrate the impact of the new solution. This offers a very inexpensive way of validating the business potential of the solution early on.
  • Use new data – e.g. measure waiting time – from the solution and evaluate it on a regular basis. This helps to get a better understanding of whether you are collecting the right data and to derive measures that help to improve your solution.

Prototype for quick insights

In terms of technology, practically everything is feasible today. However, the value proposition of a use case (in terms of business and users) can remain unclear and requires testing. Instead of building a technical prototype, it can be helpful to evaluate the value proposition of the solution with humans (empathy prototyping). This could be a person triggering an alarm based on the information at hand instead of an automatic action. Insights and lessons learnt from this phase can be used alongside the technical realization (proof-of-concept) in order to tweak specific features of the solution.

Initiate a PoC for technical feasibility

When it comes to technical feasibility, a clear picture of the objectives and key results (OKRs) for the PoC is essential. This helps to set the boundaries for a lean process with respect to the installation of hardware, an efficient timeline and minimum costs. Furthermore, a well-defined test setup fosters short testing timespans that often yield all needed results.

How IoT platforms can help build retail IoT use cases

The strong trend towards digitization within the retail industry opens up new use cases for the (food) retail industry. In order to make the most of this trend and to build on IoT, it is crucial first of all to determine which use cases to start with. Every retailer has a different focus and needs for their stores.

In the course of our retail projects, we have identified some of the recurring use cases that food retailers are currently implementing. We have also learnt a lot about how they can best leverage IoT in order to build a connected store. We share these insights in our white paper “The connected retail store.”

Originally posted here.

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By Sanjay Tripathi, Lauren Luellwitz, and Kevin Egge

There are petabytes of data generated by intelligent, interconnected and autonomous systems of Industry 4.0. When combined with artificial intelligence tools that provide actionable insight, it has the potential to improve every function within a plant, i.e. operations, engineering, quality, reliability and maintenance.

The maintenance function, while crucial to the smooth functioning of a plant has, until recently not seen much innovation. Many among us have experienced the equipment downtime, process drifts, massive hits to yield, and decline in product reliability because of maintenance performed poorly or late. Yet, Enterprise Asset Management (EAM) systems – ERP systems that help maintain assets – remained as systems of record that typically generated work-orders and recorded maintenance performed. Even as production processes became mind-numbingly complex, EAM systems remained much the same.

IBM Maximo 8.0, or Maximo Application Suite, is one example of a system that combines artificial intelligent (AI), big data and cloud computing technologies with domain expertise from operating technologies (OT) to simplify maintenance and deliver production resilience.

Maximo 8.0 leverages AI to visually inspect gas pipelines, rail tracks, bridges and tunnels; AI guides technicians as they conduct complex repairs; it provides maintenance supervisors real-time visibility into the health and safety of their technicians. Domain expertise is incorporated in the form of data to train AI models. These capabilities improve the ability to avoid unscheduled downtime, improve first-time-fix rate, and reduce safety incidents.

Maintenance records residing in Maximo are combined with real-time operational data from production assets and their associated asset model to better predict when maintenance is required. In this example, asset models embody domain expertise. These models characterize how a production asset such as a power generator or catalytic converter should perform in the context of where it is installed in the process.

The Maximo application itself is encapsulated (containerized) using Red Hat’s OpenShift technology. Containerization allows the application to be easily deployed on-premises, on private clouds or hybrid clouds. This flexibility in deployment benefits IT organizations that need to continually evolve their infrastructure, which is almost every organization.

Maximo 8.0 is available as a suite that includes both core and advanced capabilities. A single software entitlement provides access to all capabilities. The entitlement provides access to the core EAM functionality of work and resource scheduling, asset management, industry-specific customizations, EHS guidelines, and mobile functionality. And it provides access to advanced functionality such as Maximo Monitor, which automatically detects anomalies in how an asset may be performing; Maximo Health, which measures equipment health; Maximo Predict, which, as the name suggests, predicts when maintenance is required; and Maximo Assist which assists technicians conduct repairs.

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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When analyzing whether a machine learning model works well, we rely on accuracy numbers, F1 scores and confusion matrices - but they don't give any insight into why a machine learning model misclassifies data. Is it because data looks very similar, is it because data is mislabeled, or is it because preprocessing parameters are chosen incorrectly? To answer these questions we have now added the feature explorer to all neural network blocks in Edge Impulse. The feature explorer shows your complete dataset in one 3D graph, and shows you whether data was classified correct or incorrect.

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Showing exactly which data samples are misclassified in the feature explorer.

If you haven't used the feature explorer before: it's one of the most interesting options in the Edge Impulse. The axes are the output of the signal processing process (we heavily rely on signal processing to extract interesting features beforehand, making smaller and more reliable ML models), and they can let you quickly validate whether your data separates nicely. In addition the feature explorer is integrated in Live classification, where you can compare incoming test data directly with your training set.

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Redesign of the neural network pages.

This work has been part of a redesign of our neural network pages. These pages are now more compact, giving you full insight in both your neural network architecture, and the training performance - and giving you an easy way to compare models with different optimization options (like comparing an int8 quantized model vs. an unoptimized model) and show accurate on-device performance metrics for a wide variety of targets.

Next steps

Currently the feature explorer shows the performance of your training set, but over the next weeks we'll also integrate the feature explorer and the new confusion matrix to the Model testing page in Edge Impulse. This will give you direct insight in the performance of your test set in the same way, so keep an eye out for that!

Want to try the new feature explorer out? Just head to any neural network block in your Edge Impulse project and retrain. Don't have a project yet?! Followone of our tutorials on building embedded machine learning models on real sensor data, it takes 30 minutes and you can even use your phone as a sensor.

Article originally written by Jan Jongboom, the CTO and co-founder of Edge Impulse. He loves pretty pictures, colors, and insight in his ML models.

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By Sanjay Tripathi, Kevin Egge, and Shane Kehoe

Each Industrial Revolution has been catalyzed by the convergence of technologies from multiple domains. Industry 4.0 is no different.

Machines were first introduced into a manual manufacturing process between 1760 and 1820.  But, it was the concurrent introduction of means to power machines that led to the First Industrial Revolution. An example is the first commercially viable Textile Power Loom which was introduced by Edmund Cartwright in England. It used water-power at first. But in two short years water-powered looms were replaced with looms powered with the steam-engines created by James Watts. The relatively smaller steam-engines allowed textile looms to be deployed in many sites enabling persons to be employed in factories.

Multiple innovations such as new manufacturing methods, electricity, steel, and machine tools ushered in the era of mass manufacturing and the Second Industrial Revolution. Henry Ford’s River Rouge Complex in Michigan, completed in 1928, deployed these modern inventions and was the largest integrated factory in the world at that time. The era of mass manufacturing subsequently brought about an explosion in the consumption of goods by households.

The Third Industrial Revolution improved Automation and Controls across many industries through the use of Programmable Logic Controllers (PLCs). PLCs were first introduced by Modicon in 1969. PLC-based automation and controls were introduced to a mostly mechanical world, and helped improve yields and decrease manufacturing costs. This revolution helped provide cheaper products.

Fast forward to the Industry 4.0 Revolution made possible by the synergistic combination of expertise from the worlds of Operating Technologies (OT) and Information Technologies (IT). The current revolution is bringing about intelligent, interconnected and autonomous manufacturing equipment and systems. This is by augmenting deep domain expertise within OT companies with IT technologies such as artificial intelligence (AI), big data, cloud computing and ubiquitous connectivity.

The widespread use of open protocols across heterogeneous equipment makes it feasible to optimize horizontally across previously disjointed processes. In addition, owner/operators of assets can more easily link the shop-floor to the top-floor. Connections across multiple layers of the ISA-95/Purdue Model stack provides greater vertical visibility and added ability to optimize processes.

The increased integration brings together both OT data (from sensors, PLCs, DCS, SCADA systems) and IT data (from MES, ERP systems). However, this integration has different impacts on different functions such as operations, engineering, quality, reliability, and maintenance.

To learn more about how the integration positively impacts the organization, read the next installment in this series to see how you can bridge the gap between OT and IT teams to improve production resilience.

Originally posted here.

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As the Internet of Things (IoT) grows rapidly, huge amounts of wireless sensor networks emerged monitoring a wide range of infrastructure, in various domains such as healthcare, energy, transportation, smart city, building automation, agriculture, and industry producing continuously streamlines of data. Big Data technologies play a significant role within IoT processes, as visual analytics tools, generating valuable knowledge in real-time in order to support critical decision making. This paper provides a comprehensive survey of visualization methods, tools, and techniques for the IoT. We position data visualization inside the visual analytics process by reviewing the visual analytics pipeline. We provide a study of various chart types available for data visualization and analyze rules for employing each one of them, taking into account the special conditions of the particular use case. We further examine some of the most promising visualization tools. Since each IoT domain is isolated in terms of Big Data approaches, we investigate visualization issues in each domain. Additionally, we review visualization methods oriented to anomaly detection. Finally, we provide an overview of the major challenges in IoT visualizations.

Internet of Things (IoT) has become one of the most emerging and powerful technologies that is used to improve the quality of life. IoT connects together a great number of heterogeneous devices in order to dynamically acquire various types of data from the real-world environment. IoT data is used to mine useful information that may be used, by context-aware applications, in order to improve people’s daily life. As data is typically featured with contextual information (time, location, status, etc), IoT turns into a valuable and voluminous source of contextual data with variety (several sources), velocity (real-time collection), veracity (uncertainty of data) and value. The cooperation of Big Data and IoT has initiated the development of smart services for many complex infrastructures. As IoT develops rapidly, Big Data technologies play a critical role, as visual analytics tools, producing valuable knowledge in real-time, within the IoT infrastructures, aiming in supporting critical decision making. Large-scale IoT applications employ a large number of sensors resulting in a very large amount of collected data. In the context of IoT data analysis, two tasks are of relevance: exploring the large amounts of data to find subsets and patterns of interest, and; analyzing the available data to make assessments and predictions. This paper will exploit ways to gain insight from IoT Data using meaningful visualizations. Visual analytics is an analysis technique that can assist the exploration of vast amounts of data by utilizing data mining, statistics, and visualization. Interactive visualization tools combine automated analysis and human interaction allowing user control during the data analysis process, aiming in producing valuable insight for decision making. They involve custom data visualization methods that enable the operator to interact with them, in order to view data through different perspectives and focus on details of interest. Data analytics methods involve machine learning and AI methods, to automatically extract patterns from data and make predictions. AI methods are usually untrustworthy to their operators, due to their black-box operation that does not provide insight into the accuracy of their results. Visual analytics can be used to make AI methods more transparent and explainable, visualizing both their results and the way they work.

Visual Analytics

Visual Analytics is a data analysis method that employs data mining, statistics, and visualization. Besides automated analysis, implementations of visual analytics tools combine human interaction allowing user control and judgment during data analysis, in order to produce valuable insight for decision making. Over the years, numerous research studies on visual analytics were conducted. Most of them deal with a conventional visual analytics pipeline originally presented by Keim et al which depicts the visual analytics process. As figure1 illustrates, the visual analytics process starts performing data transformation subprocesses, such as filtering and sampling, that modify the data set into representations enabled for further exploration. To create knowledge, the pipeline adopts either a visual exploration method or an automatic analysis method, depending on the specific use case. In the case of automatic analysis, data mining methods are applied to assist the characterization of the data. The visual interface is operated by analysts and decision-makers, to explore and analyze the data. The framework of the Visual Analytics Pipeline has four core concepts: Data, Models, Visualization, and Knowledge. The Data module is responsible for the collection and pre-processing of the raw and heterogeneous data. As data acquisition is done in real-time through sensors, raw data sets are usually incomplete, noisy, or inconsistent making it impossible for them to be used directly in the Visualization or in the Models module. In order to eliminate these difficulties, some data pre-processing has to be applied to the original data sets. Data pre-processing is a flexible process, depending on the quality of raw data. This module includes pre-processing techniques such as data parsing, data integration, data cleaning (elimination of redundancy, errors, and invalid data), data transformation (normalization), and data reduction. The Models module, is responsible for converting data to information. This module includes conversion methods such as feature selection and generation, model building, selection, and validation.

Visual Analytics Pipeline

The Visualization module is responsible for visualizing and abstractly transforming the data. This module includes techniques for visual mapping (parallel coordinates, force-directed graphs, chord graphs, scatter matrices), view generation and coordination, human-computer interaction. The Knowledge module is responsible for driving the process of transforming information into meaningful insight using human machine interaction methods. 3 Visualization Charts Rules and Tools Data visualization places data in an appropriate visual context that triggers people’s understanding of its significance. This reduces the overall effort to manually analyze the data. As a result, visualization and recognition of patterns within the IoT generated data, play a significant role in the insight-gaining process, and enhance the decision-making process. Visualizing data plays a major role in data analytics since it manifests the presentation of findings and its patterns concurrently with the original data. Data visualization helps to interpret the results by correlating the findings to the goals. It also exposes hidden patterns, trends, and correlations, that otherwise would be undetected, in an impactful and perceptible manner. As a result, it assists the creation of good storytelling in terms of data and data pattern understanding. In this section, we will address different types of data charting. Also, we will analyze chart selection rules, that take into account special conditions that hold for a particular use case. Moreover, we will present the most popular IoT visualization tools. 

Different Tools For IoT Data Visualization

IoT Data Visualization Tools Visualization tools assist the decision-making process since they provide strong data analytics that help interpreting big data acquired from the various IoT devices. IoT data visualization systems involve custom dashboard design that, given a set of measurements acquired by several geographically scattered IoT sensors, and several AI models applied to the data, allows the operator to explore the available raw measurements and gain insight about the models’ operation. The main aim of these systems is to enhance the operator’s trust in the models. A flexible visualization system should maintain some core characteristics such as the ability to update in real-time, interactivity, transparency, and explainability. Since, IoT measurements are highly dynamic, with new measurements being collected in real-time, dashboards should be able to update in real-time as new measurements become available. The dashboard should provide an interactive user interface allowing operators to engage with the data and explore them. The dashboard should also provide means of looking into the applied AI models and visualize their internals, to enhance the transparency and explainability of the models. Many proposed visualization platforms are designed based on SOA (Service Oriented Architecture) with four key services: Data Collection Service, that receives data; Data Visualization Service, that observes the data intuitively; Dynamic Dashboard Service, providing an interface that organizes and displays various information such as text, the value of the machine, or the visualization result; and Data Analytic Service, that delivers statistical analysis tools and consists of three main layers Big Data Infrastructure as a Service, Big Data Platform as a Service, and Big Data Analytics Software as a Service. The most widely used IoT Data visualization tools, across several industries globally, will be summarized in this section. Each one was compared against the following criteria: open-source tool, the ability to integrate with popular data sources (MapR Hadoop Hive, Salesforce, Google Analytics, Cloudera Hadoop, etc.), interactive visualization, client-type (desktop, online or mobile app), availability of APIs for customization and embedding purposes.

Tableau is a fast and flexible data visualization tool, allowing user interaction. Its user interface provides a wide range of fixed and custom visualizations employing a great variety of intuitive charts. In-depth analyses may be accomplished by R-scripting. It supports most data formats and connections to various servers such as Amazon Aurora, Cloudera Hadoop, and Salesforce. Tableau’s online service is publicly available but it supports limited storage. Server and desktop versions are available under commercial licenses. ThingsBoard is an open-source IoT platform containing modules for device management, data collection, processing, and visualization. The platform allows the creation of custom IoT dashboards containing widgets that visualize sensor data collected through multiple devices. It contains a set of features including line and bar chart modules for both historical and real-time data visualizations. It also contains map widgets enabling object tracking on online maps. Its complex stack technology (Java, Python, C++, JavaScript) provides error-free performance and real-time data analytics. It supports standard IoT protocols for device connectivity (e.g. MQTT, CoAP, and HTTP). It can be integrated with Node-Red, a flow-based programming platform for IoT, through a custom function. Plotly is an online cloud-based public data visualization service. It is built using Python and Django frameworks. It provides various data storage services and modules for IoT visualization and analytics. It allows the creation of online dashboards employing a wide range of charts such as statistical, scientific, 3D, multiple axes charts, etc. It provides Python, R, MATLAB and Julia based APIs for in-depth analyses. Also, graphics libraries such as ggplot2, matplotlib, and MATLAB chart conversion techniques enhance the visualizations. Its internal tool Web Plot Digitizer (WPD) may automatically grab data from static images. It is publicly available with limited chart features and storage while its full set of chart features are available through a professional membership license. IBM Watson IoT Platform is a cloud platform as a service supporting several programming languages, services, and integrated DevOps in order to deploy and manage cloud applications. It features a set of built-in web applications while it provides support for 3rd-party software integration via REST APIs. The visualization of static and dynamic data is provided through effortless creation of custom diagrams, graphs, and tables. It provides access to device properties and alert management. Node-RED may be used for IoT device connection, APIs, and online services. Sensor data, stored in Cloudant NoSQL DB, may be processed for further data analysis. Power BI is a powerful business analytics service based on the cloud. It provides a rich set of interactive visualizations and detailed analysis reports for large enterprises. It is designed to trace and visualize various sensor gathered data. The platform works in cooperation with Azure cloud-based analytics and cognitive services. It consists of 3 basic components: Power BI Desktop, report generator; Service (SaaS), report publisher; and Apps, report viewer, and dashboard. Numerous types of source integrations are supported while rich data visualizations are also provided. Among other methods, data may be queried using the natural language query feature. Data analysis is accomplished both in real-time streaming and static historic data. Power BI provides sub-components that enable IoT integration.  

These days, Immersive Virtual Reality is recognized as one of the most promising technologies that enables virtual interactions with physical systems. The user is situated within a 3D environment where data visualizations and physical space are matched in a sense that it provides users the ability to orient, navigate, and interact naturally. These frameworks utilize hybrid collaborative multi-modal methods to enable collaboration between users and provide intuitive and natural interaction within a specific virtual environment. As users remain immersed within a 3D virtual environment, immersive reality applications require sophisticated approaches for interacting with the IoT data analytics visualization. As such, immersive analytics is the visualization outcome within IoT infrastructures. Immersive analytics frameworks promote a better understanding of the IoT Services and enhance decision-making. To ensure such a collaborative virtual environment presupposes highly responsive connectivity that may be accomplished by employing high-speed 5G network infrastructures, which provide ultra-low-delay and ultra-high-reliable communications. Similarly, a Cyber-Physical System (CPS) is a set of physical devices, connected through a communication network, that communicates with its virtual cyberspace. Each physical object is associated with a cyber model that stores all information and knowledge of it. This cyber model is called “Digital Twin”. It allows data transfer from the physical to the cyber part. However, in a specific CPS, where every physical object has a digital twin counterpart, the spatiotemporal relations between the individual digital twins are far more valuable than the actual digital twin. The generation of Digital twins may be accomplished using 3D technologies through AR/VR/MR or even hologram devices. Digital twins integrate various technologies such as Haptics, Humanoid Robotics as well as Soft Robotics, 5G and Tactile Internet, Cloud Computing Offloading, Wearable technology, IoT Services, and AI.

IoT domains and Visualization

IoT technologies have already entered into various significant domains of our life. The growing market competition and inexpensive connectivity have emerged the Internet of things (IoT) across many domains. Connected sensors, devices, and machines via the Internet are the “things” in IoT. The enormous volume of IoT data provides the information needed to be analyzed to gain knowledge. Visual analytics, involving data analysis methods, artificial intelligence, and visualization, aims to improve domain operations with concerning efficiency, flexibility, and safety. The employment of IoT smart devices facilitates the transformation of traditional domains into modern, smart, and autonomous domains. Over the past recent years, many traditional domains such as healthcare, energy, industry, transportation, city and building management, and agriculture have become IoT-based with intelligent human-to-machine (H2M) and machine-to-machine (M2M) communication.  

Challenges and Future Work

Visual analytics main objective is to discover knowledge and produce actionable insight. This is succeeded by processing large and complex data sets through by integrating techniques from various fields such as data analysis, data management, visualization, knowledge discovery, analytical reasoning, human perception, and human-computer interaction. Even though visualization is an important entity in Big IoT data analytics, most visualization tools exhibit poor performance results in terms of functionality, scalability, interaction, infrastructure, insight creation, and evaluation.

Conclusion

The emergence of IoT Services increased drastically the growth rate of data production creating large and complex data sets. The integration of human judgment within the data analysis process enables visual analytics in discovering knowledge and gaining valuable insight from these data sets. In this process, every piece of IoT data is considered crucial for the extraction of information and useful patterns. Human cognitive and perceptual capabilities identify patterns efficiently when data is represented visually. Data visualization methods face several challenges in handling the voluminous and streaming IoT data without compromising performance and response time matters.

 

 

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Then it seemed that overnight, millions of workers worldwide were told to isolate and work from home as best as they could. Businesses were suddenly forced to enable remote access for hundreds or thousands of users, all at once, from anywhere across the globe. Many companies that already offered VPN services to a small group of remote workers scurried to extend those capabilities to the much larger workforce sequestering at home. It was a decision made in haste out of necessity, but now it’s time to consider, is VPN the best remote access technology for the enterprise, or can other technologies provide a better long-term solution?

Long-term Remote Access Could Be the Norm for Some Time

Some knowledge workers are trickling back to their actual offices, but many more are still at home and will be for some time. Global Workplace Analytics estimates that 25-30% of the workforce will still be working from home multiple days a week by the end of 2021. Others may never return to an official office, opting to remain a work-from-home (WFH) employee for good.

Consequently, enterprises need to find a remote access solution that gives home-based workers a similar experience as they would have in the office, including ease of use, good performance, and a fully secure network access experience. What’s more, the solution must be cost effective and easy to administer without the need to add more technical staff members.

VPNs are certainly one option, but not the only one. Other choices include appliance-based SD-WAN and SASE. Let’s have a look at each approach.

VPNs Weren’t Designed to Support an Entire Workforce

While VPNs are a useful remote access solution for a small portion of the workforce, they are an inefficient technology for giving remote access to a very large number of workers. VPNs are designed for point-to-point connectivity, so each secure connection between two points – presumably a remote worker and a network access server (NAS) in a datacenter – requires its own VPN link. Each NAS has a finite capacity for simultaneous users, so for a large remote user base, some serious infrastructure may be needed in the datacenter.

Performance can be an issue. With a VPN, all communication between the user and the VPN is encrypted. The encryption process takes time, and depending on the type of encryption used, this may add noticeable latency to Internet communications. More important, however, is the latency added when a remote user needs access to IaaS and SaaS applications and services. The traffic path is convoluted because it must travel between the end user and the NAS before then going out to the cloud, and vice versa on the way back.

An important issue with VPNs is that they provide overly broad access to the entire network without the option of controlling granular user access to specific resources. Stolen VPN credentials have been implicated in several high-profile data breaches. By using legitimate credentials and connecting through a VPN, attackers were able to infiltrate and move freely through targeted company networks. What’s more, there is no scrutiny of the security posture of the connecting device, which could allow malware to enter the network via insecure user devices.

SD-WAN Brings Intelligence into Routing Remote Users’ Traffic

Another option for providing remote access for home-based workers is appliance-based SD-WAN. It brings a level of intelligence to the connectivity that VPNs don’t have. Lee Doyle, principal analyst with Doyle Research, outlines the benefits of using SD-WAN to connect home office users to their enterprise network:

  • Prioritization for mission-critical and latency-sensitive applications
  • Accelerated access to cloud-based services
  • Enhanced security via encryption, VPNs, firewalls and integration with cloud-based security
  • Centralized management tools for IT administrators

One thing to consider about appliance-based SD-WAN is that it’s primarily designed for branch office connectivity—though it can accommodate individual users at home as well. However, if a company isn’t already using SD-WAN, this isn’t a technology that is easy to implement and setup for hundreds or thousands of home-based users. What’s more, a significant investment must be made in the various communication and security appliances.

SASE Provides a Simpler, More Secure, Easily Scalable Solution

Cato’s Secure Access Service Edge (or SASE) platform provides a great alternative to VPN for remote access by many simultaneous workers. The platform offers scalable access, optimized connectivity, and integrated threat prevention that are needed to support continuous large-scale remote access.

Companies that enable WFH using Cato’s platform can scale quickly to any number of remote users with ease. There is no need to set up regional hubs or VPN concentrators. The SASE service is built on top of dozens of globally distributed Points of Presence (PoPs) maintained by Cato to deliver a wide range of security and networking services close to all locations and users. The complexity of scaling is all hidden in the Cato-provided PoPs, so there is no infrastructure for the organization to purchase, configure or deploy. Giving end users remote access is as simple as installing a client agent on the user’s device, or by providing clientless access to specific applications via a secure browser.

Cato’s SASE platform employs Zero Trust Network Access in granting users access to the specific resources and applications they need to use. This granular-level security is part of the identity-driven approach to network access that SASE demands. Since all traffic passes through a full network security stack built into the SASE service, multi-factor authentication, full access control, and threat prevention are applied to traffic from remote users. All processing is done within the PoP closest to the users while enforcing all corporate network and security policies. This eliminates the “trombone effect” associated with forcing traffic to specific security choke points on a network. Further, admins have consistent visibility and control of all traffic throughout the enterprise WAN.

SASE Supports WFH in the Short-term and Long-term

While some workers are venturing back to their offices, many more are still working from home—and may work from home permanently. The Cato SASE platform is the ideal way to give them access to their usual network environment without forcing them to go through insecure and inconvenient VPNs.

Originally posted here

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Today the world is obsessed with the IoT, as if this is a new concept. We've been building the IoT for decades, but it was only recently some marketing "genius" came up with the new buzz-acronym.

Before there was an IoT, before there was an Internet, many of us were busy networking. For the Internet itself was a (brilliant) extension of what was already going on in the industry.

My first experience with networking was in 1971 at the University of Maryland. The school had a new computer, a $10 million Univac 1108 mainframe. This was a massive beast that occupied most of the first floor of a building. A dual-processor machine it was transistorized, though the control console did have some ICs. Rows of big tape drives mirrored the layman's idea of computers in those days. Many dishwasher-sized disk drives were placed around the floor and printers, card readers and other equipment were crammed into every corner. Two Fastrand drum memories, each consisting of a pair of six-foot long counterrotating drums, stored a whopping 90 MB each. Through a window you could watch the heads bounce around.

The machine was networked. It had a 300 baud modem with which it could contact computers at other universities. A primitive email system let users create mail which was queued till nightfall. Then, when demands on the machine were small, it would call the appropriate remote computer and forward mail. The system operated somewhat like today's "hot potato" packets, where the message might get delivered to the easiest machine available, which would then attempt further forwarding. It could take a week to get an email, but at least one saved the $0.08 stamp that the USPS charged.

The system was too slow to be useful. After college I lost my email account but didn't miss it at all.

By the late 70s many of us had our own computers. Mine was a home-made CP/M machine with a Z80 processor and a small TV set as a low-res monitor. Around this time Compuserve came along and I, like so many others, got an account with them. Among other features, users had email addresses. Pretty soon it was common to dial into their machines over a 300 baud modem and exchange email and files. Eventually Compuserve became so ubiquitous that millions were connected, and at my tools business during the 1980s it was common to provide support via this email. The CP/M machine gave way to a succession of PCs, Modems ramped up to 57 K baud.

My tools business expanded rapidly and soon we had a number of employees. Sneakernet was getting less efficient so we installed an Arcnet network using Windows 3.11. That morphed into Ethernet connections, though the cursing from networking problems multiplied about as fast as the data transfers. Windows was just terrible at maintaining reliable connectivity.

In 1992 Mike Lee, a friend from my Boys Night Out beer/politics/sailing/great friends group, which still meets weekly (though lately virtually) came by the office with his laptop. "You have GOT to see this" he intoned, and he showed me the world-wide web. There wasn't much to see as there were few sites. But the promise was shockingly clear. I was stunned.

The tools business had been doing well. Within a month we spent $100k on computers, modems and the like and had a new business: Softaid Internet Services. SIS was one of Maryland's first ISPs and grew quickly to several thousand customers. We had a T1 connection to MAE-EAST in the DC area which gave us a 1.5 Mb/s link… for $5000/month. Though a few customers had ISDN connections to us, most were dialup, and our modem shelf grew to over 100 units with many big fans keeping the things cool.

The computers all ran BSD Unix, which was my first intro to that OS.

I was only a few months back from a failed attempt to singlehand my sailboat across the Atlantic and had written a book-length account of that trip. I hastily created a web page of that book to learn about using the web. It is still online and has been read several million times in the intervening years. We put up a site for the tools business which eventually became our prime marketing arm.

The SIS customers were sometimes, well, "interesting." There was the one who claimed to be a computer expert, but who tried to use the mouse by waving it around over the desk. Many had no idea how to connect a modem. Others complained about our service because it dropped out when mom would pick up the phone to make a call over the modem's beeping. A lot of handholding and training was required.

The logs showed a shocking (to me at the time) amount of porn consumption. Over lunch an industry pundit explained how porn drove all media, from the earliest introduction of printing hundreds of years earlier.

The woman who ran the ISP was from India. She was delightful and had a wonderful marriage. She later told me it had been arranged; they met  their wedding day. She came from a remote and poor village and had had no exposure to computers, or electricity, till emigrating to the USA.

Meanwhile many of our tools customers were building networking equipment. We worked closely with many of them and often had big routers, switches and the like onsite that our engineers were working on. We worked on a lot of what we'd now call IoT gear: sensors et al connected to the net via a profusion of interfaces.

I sold both the tools and Internet businesses in 1997, but by then the web and Internet were old stories.

Today, like so many of us, I have a fast (250 Mb/s) and cheap connection into the house with four wireless links and multiple computers chattering to each other. Where in 1992 the web was incredibly novel and truly lacking in useful functionality, now I can't imagine being deprived of it. Remember travel agents? Ordering things over the phone (a phone that had a physical wire connecting it to Ma Bell)? Using 15 volumes of an encyclopedia? Physically mailing stuff to each other?

As one gets older the years spin by like microseconds, but it is amazing to stop and consider just how much this world has changed. My great grandfather lived on a farm in a world that changed slowly; he finally got electricity in his last year of life. His daughter didn't have access to a telephone till later in life, and my dad designed spacecraft on vellum and starched linen using a slide rule. My son once saw a typewriter and asked me what it was; I mumbled that it was a predecessor of Microsoft Word.

That he understood. I didn't have the heart to try and explain carbon paper.

Originally posted HERE.

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