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For IoT and M2M device security assurance, it's critical to introduce automated software development tools into the development lifecycle. Although software tools' roles in quality assurance is important, it becomes even more so when security becomes part of a new or existing product's requirements.

Automated Software Development Tools

There are three broad categories of automated software development tools that are important for improving quality and security in embedded IoT products:

  • Application lifecycle management (ALM): Although not specific to security, these tools cover requirements analysis, design, coding, testing and integration, configuration management, and many other aspects of software development. However, with a security-first embedded development approach, these tools can help automate security engineering as well. For example, requirements analysis tools (in conjunction with vulnerability management tools) can ensure that security requirements and known vulnerabilities are tracked throughout the lifecycle.  Design automation tools can incorporate secure design patterns and then generate code that avoids known security flaws (e.g. avoiding buffer overflows or checking input data for errors). Configuration management tools can insist on code inspection or static analysis reports before checking in code. Test automation tools can be used to test for "abuse" cases against the system. In general, there is a role for ALM tools in the secure development just as there is for the entire project.
  • Dynamic Application Security Testing (DAST): Dynamic testing tools all require program execution in order to generate useful results. Examples include unit testing tools, test coverage, memory analyzers, and penetration test tools. Test automation tools are important for reducing the testing load on the development team and, more importantly, detecting vulnerabilities that manual testing may miss.
  • Static Application Security Testing (SAST): Static analysis tools work by analyzing source code, bytecode (e,g, compiled Java), and binary executable code. No code is executed in static analysis, but rather the analysis is done by reasoning about the potential behavior of the code. Static analysis is relatively efficient at analyzing a codebase compared to dynamic tools. Static analysis tools also analyze code paths that are untested by other methods and can trace execution and data paths through the code. Static analysis can be incorporated early during the development phase for analyzing existing, legacy, and third-party source and binaries before incorporating them into your product. As new source is added, incremental analysis can be used in conjunction with configuration management to ensure quality and security throughout. 

Figure 1: The application of various tool classes in the context of the software development lifecycle.

Although adopting any class of tools helps productivity, security, and quality, using a combination of these is recommended. No single class of tools is the silver bullet[1]. The best approach is one that automates the use of a combination of tools from all categories, and that is based on a risk-based rationale for achieving high security within budget.

The role of static analysis tools in a security-first approach

Static analysis tools provide critical support in the coding and integration phases of development. Ensuring continuous code quality, both in the development and maintenance phases, greatly reduces the costs and risks of security and quality issues in software. In particular, it provides some of the following benefits:

  • Continuous source code quality and security assurance: Static analysis is often applied initially to a large codebase as part of its initial integration as discussed below. However, where it really shines is after an initial code quality and security baseline is established. As each new code block is written (file or function), it can be scanned by the static analysis tools, and developers can deal with the errors and warnings quickly and efficiently before checking code into the build system. Detecting errors and vulnerabilities (and maintaining secure coding standards, discussed below) in the source at the source (developers themselves) yields the biggest impact from the tools.
  • Tainted data detection and analysis: Analysis of the data flows from sources (i.e. interfaces) to sinks (where data gets used in a program) is critical in detecting potential vulnerabilities from tainted data. Any input, whether from a user interface or network connection, if used unchecked, is a potential security vulnerability.  Many attacks are mounted by feeding specially-crafted data into inputs, designed to subvert the behavior of the target system. Unless data is verified to be acceptable both in length and content, it can be used to trigger error conditions or worse. Code injection and data leakage are possible outcomes of these attacks, which can have serious consequences.
  • Third-party code assessment: Most projects are not greenfield development and require the use of existing code within a company or from a third party. Performing testing and dynamic analysis on a large existing codebase is hugely time consuming and may exceed the limits on the budget and schedule. Static analysis is particularly suited to analyzing large code bases and providing meaningful errors and warnings that indicate both security and quality issues. GrammaTech CodeSonar binary analysis can analyze binary-only libraries and provide similar reports as source analysis when source is not available. In addition, CodeSonar binary analysis can work in a mixed source and binary mode to detect errors in the usage of external binary libraries from the source code. 
  • Secure coding standard enforcement: Static analysis tools analyze source syntax and can be used to enforce coding standards. Various code security guidelines are available such as SEI CERT C [2] and Microsoft's Secure Coding Guidelines [3]. Coding standards are good practice because they prevent risky code from becoming future vulnerabilities. As mentioned above, integrating these checks into the build and configuration management system improves the quality and security of code in the product.

As part of a complete tools suite, static analysis provides key capabilities that other tools cannot. The payback for adopting static analysis is the early detection of errors and vulnerabilities that traditional testing tools may miss. This helps ensure a high level of quality and security on an on-going basis.

Conclusion

Machine to machine and IoT device manufacturers incorporating a security-first design philosophy with formal threat assessments, leveraging automated tools, produce devices better secured against the accelerating threats on the Internet. Modifying an existing successful software development process that includes security at the early stages of product development is key. Smart use of automated tools to develop new code and analyze existing and third party code allows development teams to meet strict budget and schedule constraints. Static analysis of both source and binaries plays a key role in a security-first development toolset. 

References

  1. No Silver Bullet – Essence and Accident in Software Engineering, Fred Brooks, 1986
  2. SEI CERT C Coding Standard,
  3. Outsource Code Development Driving Automated Test Tool Market, VDC Research, IoT & Embedded Blog, October 22, 2013

 

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Using Mattermarks’s list of the Top 100 IoT startups in 2015 (ranked by funding, published in Forbes Oct 25, 2015) Ipqwery has looked behind the analytics to reveal the nature of the intellectual property (IP) behind these innovative companies. Our infographic presents a general summary of the IP within the group as a whole, and illustrates the trailing 5-year trends related to IP filing activity.

The vast majority of these companies have both patents (84%) and trademarks (85%) in their IP portfolio. There was a sharp and mostly linear increase in filings for both patents and trademarks, from 2011 through to 2014, with a slight decrease showing in 2015. 2016 looks to be on pace to meet or exceed last year’s filing activity as well. All this is consistent with the ever-expanding number of companies operating within the IoT ecosystem.

A closer look at the top 5 patent class descriptions amongst all patents granted or published yields close results between these classes. This is not surprising given the similar technologies behind many IoT products, such that their patents will incorporate the same or similar descriptions within their claims. Comparatively, there is a wider variance in the Top 5 Trademark classes used, but this speaks more to the wider variety of marketing and branding potential than to the underlying IoT technologies. 

What’s striking in Mattermark’s original analysis of the Top 100 IoT Startups is that 30% of all funding raised by this group as a whole has been concentrated in only the top 5 companies; Jawbone, Genband, Silver Spring Networks, View Glass and Jasper Technologies. Ipqwery’s analysis further reveals that only two of these companies (Silver Spring and Jasper) have Top 5 inventors within the group. In fact, Jasper actually has 2 of the Top 5 inventors. The other top inventors come from Hello and Kineto Wireless.=

The broad-strokes approach of IPqwery’s infographic doesn’t directly illustrate the IP held by any one company, but certainly hints at where exactly this type of analysis could be very useful indeed. For where Mattermark sought to pinpoint where the greatest growth potential (momentum) was within the group of companies by looking at the overall IoT funding environment, IPqwery’s analysis of the general IP trends within this group sheds additional light on the matter, and perhaps raises some additional issues. Wouldn’t potential correlations between IP and funding also be a useful measure of momentum across metrics, and thus shouldn’t IP data be generally more integrated into business growth analytics, from the get go?

Here's a link to a new infographic by IPqwery summarizing the intellectual property held by the Top 100 IoT Startups (2015). 

 

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A smart, highly optimized distributed neural network, based on Intel Edison "Receptive" Nodes

Training ‘complex multi-layer’ neural networks is referred to as deep-learning as these multi-layer neural architectures interpose many neural processing layers between the input data and the predicted output results – hence the use of the word deep in the deep-learning catchphrase.

While the training procedure of large scale network is computationally expensive, evaluating the resulting trained neural network is not, which explains why trained networks can be extremely valuable as they have the ability to very quickly perform complex, real-world pattern recognition tasks on a variety of low-power devices.

These trained networks can perform complex pattern recognition tasks for real-world applications ranging from real-time anomaly detection in Industrial IoT to energy performance optimization in complex industrial systems. The high-value, high accuracy recognition (sometimes better than human) trained models have the ability to be deployed nearly everywhere, which explains the recent resurgence in machine-learning, in particular in deep-learning neural networks.

These architectures can be efficiently implemented on Intel Edison modules to process information quickly and economically, especially in Industrial IoT application.

Our architectural model is based on a proprietary algorithm, called Hierarchical LSTM, able to capture and learn the internal dynamics of physical systems, simply observing the evolution of related time series.

To train efficiently the system, we implemented a greedy, layer based parameter optimization approach, so each device can train one layer at a time, and send the encoded feature to the upper level device, to learn higher levels of abstraction on signal dinamic.

Using Intel Edison as layers "core computing units", we can perform higher sampling rates and frequent retraining, near the system we are observing without the need of a complex cloud architecture, sending just a small amount of encoded data to the cloud.

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5 Innovative Ways IoT Can Help Farms

With the global population expected to reach 9.7 billion people by 2050, innovation in the agricultural industry is more important than ever. This growth brings with it a need for increased food production and a dwindling availability of arable land. Projections show that feeding a population near 9 billion people would require raising global food production by some 70%. To provide for such steep demands, old farming techniques are simply no longer adequate. Thankfully, the agriculture industry is a burgeoning sector within the Internet of Things, and farmers globally are ready to reap the benefits.

Let’s look at a few ways IoT is helping the agriculture industry around the world.

 

  1. Smart Ag Is Environmentally Friendly

Agriculture is responsible for a significant environmental footprint, accounting for 80% to 90% of US water consumption, releasing massive quantities of chemical pesticides, and producing more greenhouse gas emissions with animal agriculture alone than all combined transportation. IoT technology can maximize production capabilities and minimize required resources in order to reduce the industry’s environmental harm. Sensors can be implemented to test agricultural factors such as soil for moisture and nutrient levels to ensure resources are being used as efficiently as possible. This way water and pesticides can be reserved from unnecessary use and alternates can be implemented.

  1. IoT Provides Precision Control for Farmers

The agriculture industry reaches far and wide, and each sector involves too much labor for one person to accomplish alone. As a result, much of the agriculture industry relies heavily on trusting intuition and human judgment. This also means that workers are nearly irreplaceable during illness or absence. Implementation of IoT technology can allow for real-time access to information that otherwise would take too much time or effort to obtain. Managers can have remotely controlled decision-making capabilities at their fingertips rather than having to wait for reports and then send out orders to workers. For example, rather than using valuable workers, drones are now available to monitor crops or apply treatment to specific areas. Also, companies like Lecia have developed GPS-guided combines and other agricultural IoT technologies. Aeris is helping to make solutions like these possible through cellular connectivity, perfect for remote real-time access to critical data.

  1. Farms Are More Productive With IoT

As agricultural businesses gain more insight and control over their operations, they are able to make better business decisions and thus increase productivity. If a farm can use drones or sensors to monitor fields or cattle, for example, then the experience of the farmer can be utilized to make decisions while the manual labor previously needed to monitor these areas can be better repurposed elsewhere. IoT technology can also be applied to agricultural machinery, allowing for preventative maintenance and more accurate reports in the case of a malfunction, saving time and money. As smarter decisions are made regarding resources, productivity will improve.

  1. Smart Farming Saves Money

According to the USDA, some of the top expenses of agricultural businesses include feed, labor, fuel, chemicals, and farm supplies and repairs, all expenditures that can be reduced with the help of IoT. Implementing IoT technology can allow businesses to make better decisions about efficiently using resources including assets, labor, and capital, significantly reducing operational costs. Replacing and improving past techniques will be the only way to maintain a competitive advantage with rising demands and ever-improving technology.

  1. IoT Provides Transparency for Consumers

As more data is made available to the public, consumers demand high quality products more emphatically than ever. Concerns continue to emerge regarding the environmental footprint, personal health effects, and other details surrounding food production. Utilizing IoT technology in production is the only way to provide consumers with the data and transparency that is now the standard expectation, and thus maintain a competitive advantage in the industry.

When you’re ready to connect your agriculture devices to the Internet of Things, contact Aeris for a customized IoT solution.

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Originally Posted by: Mimi Spier

The Internet of Things (IoT) is here to stay—and rapidly evolving. As we try to make sense of IoT’s impact on our lives and businesses, we also continue grappling with the security challenges.

As the IoT security landscape evolves, here are five key insights for designing and implementing IoT deployments for your enterprise.

5 IoT insights vmware airwatch

1. Protect Your People

IoT has opened up a world of possibilities in business, but it has also opened up a host of ways to potentially harm employees and customers. A security breach is not limited to stealing credit card data, anymore. Anyone with the right access could breach firewalls or steal health records. A key challenge of the IoT world is providing the right access to the right people at the right time.

[Related: 5 Real Ways to Enable IoT Success in Your Enterprise]

2. Watch Your Things

As millions of “things” start joining the enterprise network, it also expands the surface area for hackers to breach your system. All these devices will be leveraging public Wi-Fi, cloud, Bluetooth networks, etc., which will create multiple points of vulnerabilities. Your system needs to be designed for security from the bottom up to account for:

A) Device level: better quality devices

B) Data level: encryption and cryptology

C) Network level: certificates and firewalls

D) Application level: login/authorized access

3. Poor Quality of Things

The standards for IoT hardware and software are still evolving, which means until we have any established guidelines, we need to account for a vast range in the quality of “things.” Some of these may be very sophisticated and hardy, while others may be of the cheap disposable variety. Which devices you pick may depend upon factors like cost, usage and the use case itself. However, be warned that lower-quality devices have been used to gain entry to a secure network.

“By 2020, more than 25% of identified attacks in enterprises will involve the Internet of Things (IoT), although the IoT will account for less than 10% of the IT security budget.” Gartner

4. Is Your Network Ready?

One of the biggest challenge for any IT department implementing company-wide IoT projects will be assessing and managing bandwidth. As millions of devices join your network at increasing rates, scaling your network’s bandwidth will be an ongoing struggle. Your bandwidth needs must remain elastic, so you can support your enterprise needs, while minimizing costs. It is critical to minimize exposure of your networks by using, for example, micro-segmentation.

5. Data Is Your Friend

As with protecting any system, predictive maintenance is the way to stay a step ahead of breaches. The usual ways of pushing out timely security patches and software upgrades will continue to be helpful. However, one big advantage of IoT is the sheer amount of data it generates. You can track operational data to create alerts based on anomalies in the system. For example, if someone logs into the system from Atlanta and then, 30 minutes later, logs in again from Palo Alto, the system should raise a red flag.

You can view the original post by clicking Here.

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A Different Kind of IoT Competition

Two months ago, we made the claim that IoT needs a new programming language. That was not a light statement. It was backed by 3 years of heads-down innovation on a fundamental technology: TQL (Thing Query Language). Next month, we will see TQLers’ submitting IoT projects from all over the globe for the TQLOne Competition. Imagine learning a foreign language. Before you become proficient, you are invited to join a poetry contest, against the native speakers. What would happen? In fact, your poem would win for its creativity, albeit with a few spelling errors!!
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Security-First Design for IoT Devices

Machine to Machine (M2M) and Internet of Things (IoT) realities mean that more and more devices are being deployed and connected to each other. This connectivity is both the promise of IoT (data gathering, intelligent control, analytics, etc.) and its Achilles’ heel. With ubiquitous connectivity comes security threats -- the reason security has received such a high profile in recent discussions of IoT.
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Original article is published at Forbes: link

Have heard about the magic pill? Not sure how it works, but it helps you lose 20 pounds in a week while consuming the same calories as before. And you’ve probably also heard about the scary side effects of that pill. The need for magic pills is appearing in the IoT market as well. Thanks to the explosion of sensors to measure everything imaginable within the Internet of Things, enterprises are confronted with a never-ending buffet of tempting data.

Typically data has been consumed like food: first it is grown, harvested, and prepared. Then this enjoyable meal is ingested into a data warehouse and digested through analytics. Finally we extract the nutritional value and put it to work to improve some part of our operations. Enterprises have evolved to consume data from CRM, ERP, and even the Web that is high in signal nutrition in this genteel, managed manner from which they can project trends or derive useful BI.

sensory

The IoT and its superabundance of sensors completely changes that paradigm and we need to give serious consideration to our data dietary habits if we want to succeed in this new data food chain. Rather than being served nicely prepared data meals, sensor data is the equivalent of opening your mouth in front of some kind of cartoon food fire hose. Data comes in real-time, completely raw, and in such sustained volume that all you can do is keep stuffing it down.

And, as you would expect, your digestion will be compromised. You won’t benefit from that overload of raw IoT data. In fact, we’ll need to change our internal plumbing, our data pipelines, to get the full nutritional benefit of IoT sensor data.

That will require work, but if you can process the data and extract the value, that’s where the real power comes in.  In fact, you can attain something like superpowers. You can have the eyesight of eagles (self-driving cars), the sonar wave perception of dolphins (for detecting objects in the water), and the night vision of owls (for surveillance cameras).If we can digest all this sensor data and use it in creative ways, the potential is enormous. But how can we adapt to handle this sort of data? Doing so demands a new infrastructure with massive storage, real-time ingestion, and multi-genre analytics.

If we can digest all this sensor data and use it in creative ways, the potential is enormous. But how can we adapt to handle this sort of data? Doing so demands a new infrastructure with massive storage, real-time ingestion, and multi-genre analytics.

Massive storage. More than five years ago, Stephen Brobst predicted that the volume of sensor data would soon crush the amount of unstructured data generated by social media(remember when that seemed like a lot?). Sensor data demands extreme scalability.

Real-time ingestion. The infrastructure needs to be able to ingest raw data and determine moment by moment where to land it. Some data demands immediate reaction and should move into memory. Other data is needed in the data warehouse for operational reporting and analytics. Still other data will add benefit as part of a greater aggregation using Hadoop. Instant decisions will help parse where cloud resources are appropriate versus other assets.

Multi-genre analytics. When you have data that you’ve never seen before, you need to transform data and apply different types of algorithms. Some may require advanced analytics and some may just require a standard deviation. Multi-genre analytics allows you to apply multiple analytics models in various forms so that you can quickly discern the value of the data.

The self-driving car is a helpful metaphor. I’ve heard estimates that each vehicle has 60,000 sensors generating terabytes of data per hour. Consider the variety of that data. Data for obstacle detection requires millisecond response and must be recognized as such if it is to be useful. A sensor on the battery to predict replacement requires aggregation to predict a trend over time and does not require real-time responsiveness. Nevertheless both types of data are being created constantly and must be directed appropriately based on the use case.

How does this work at scale? Consider video games. Real-time data is critical to everything from in game advertising, which depends on near instant delivery of the right ad at a contextually appropriate moment, to recommendations and game features that are critical to the user experience and which are highly specific to moments within the game. At the same time, analyzing patterns at scale is critical to understanding and controlling churn and appeal. This is a lot of data to parse on the fly in order to operate effectively.

From a data perspective, we’re going to need a new digestive system if we are to make the most of the data coming in from the IoT. We’ll need vision and creativity as well. It’s an exciting time to be in analytics.

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The technology sector is buzzing with predictions and hype about the Internet of Things (IoT), but many people are still confused about what it means, what the real world opportunities are and why businesses should be looking into IoT.

At a fundamental and simplistic level the Internet of Things refers to 'physical objects which linked via wired or wireless networks'

These physical objects could be anything (such as medical machines, vehicles, building systems, signage, toasters, smoke alarms, temperature sensors, weather monitors, intelligent tags or rubbish bins for example). Almost any object, in any sector, in any location could potentially join the Internet of Things, so its no wonder that Gartner predict there will be 50 billion devices connected by 2020 (and other analysts estimate several orders of magnitude more).  

Typically the Internet of Things is used to gather data and insight, find efficiency, automate tasks or improve an experience or service. At Smarter Technology Solutions (STS) we put this down to a simple formula, with greater insight, comes better decisions.

I know what you're thinking, why would you connect an object like a rubbish bin to the Internet?

Well its a simple example but it has tremendous flow on effects. Simply tracking the fill level of a rubbish bin using a smart sensor, councils and waste providers can find out a few important facts such as fill-level trends, how often the bin really needs emptying and when, to better plan waste collection services (eg timing of bin collection near food outlets to avoid lunchtimes) and to identify areas that may need more/less bins (to assist with city/service planning).
By collecting just the fill level data of a waste bin the following benefits could be attained:

  1. Reduction in cost as less bin collections = less waste trucks on the road, no unnecessary collections for a bin that's 20% full, less labour to complete waste collection. This also provides a level of operational efficiency and optimized processes.
  2. Environmental benefit - where waste is not overflowing and truck usage is reduced, flow on environmental impact, pollution and fuel consumption is minimized. By ensuring waste bins are placed in convenient locations, littering and scattered waste is also minimized.
  3. Service improvements - truck collection routes can be optimized, waste bins can be collected at convenient times and planning of future/additional services can be amended as the data to trend and verify assumptions is available. 

More complex examples of IoT include:

  • Intelligent transport systems which update digital signage on the highway and adjusts the traffic lights in real time to divert traffic, optimise traffic flow and reduce congestion;
  • A farm which uses sensors to measure soil moisture, chemical levels and weather patterns, adjusting the watering and treatment schedules accordingly;
  • The building which draws the blinds to block out the afternoon sun, reducing the need to consume more power cooling the building and to keep the environment comfortable;
  • Health-care devices which monitor patients and auto-alert medical practitioners once certain symptoms or attributes are detected; 
  • Trucks which automatically detect mechanical anomalies and auto schedule themselves in for preventative maintenance once they reach certain thresholds; 
  • Asset tracking of fleet vehicles within a services company which provides operations staff with fleet visibility to quickly dispatch the closest resource to a job based on proximity to the next task;
  • Water/gas/electric meters which sends in their own reading in on a monthly basis and trends analysis which can detect potential water/gas leaks; or
  • A retail store which analyses your in-store behavior or purchasing patterns and recommend products to you based on previous choices and your personal preferences.

At Smarter Technology Solutions we specialize in consulting with organizations  to understand the benefits of IoT, design best fit solutions, engineer and implement solutions as well as supporting the ongoing support needs of the organization. This results in 3 key outcomes:

  • Discovery of New Opportunities - With better visibility, trends, opportunities, correlations and inefficiencies can be understood. From this, products, services and business models can be adjusted or changed to achieve competitive advantage.
  • Improved Efficiency - By identifying inefficiencies in existing business practices, work-flows can be improved and more automated services can be provided.
  • Improved Services - With trends and real time data businesses are able make smarter decisions and alter the way you services are delivered.

www.smartertechnologysolutions.com.au

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The Internet of Things is changing the world, heralded as one of the most pivotal technology trends of the modern era. We are getting ready to enter a time where everything, quite literally, is connected to the Internet.

For the industrial sector, this is a new area of exploration. Factories have smart infrastructures that use sensors to relay data about machine performance. Cities have smart grids that monitor everything from traffic to the energy used by streetlights. Hospitals can monitor the health of high-risk, at-home patients.

In other words, we are entering a hacker's dream world.

Recent attacks, like the Christmas 2015 attack on the Ukraine power grid, have shown that the Internet of Things possesses severe vulnerabilities. These weak points can be everything from back doors that allow a hacker access to a system to lack of proper use by untrained workers. If your business uses IoT devices, there’s a good chance they are not secure.

Why are so many systems left vulnerable? Weaknesses often come from the same set of five drivers:

 

Source: Allerin

Whether your company is struggling because your devices were deployed too quickly or operational costs constraints got in the way, your team must take measures to fix security risks. Here are four security flaws:

1. Lack of Encryption

Any device that is connected to the Internet to relay data needs encryption. When communication between devices and facility machines are now encrypted, it provides a doorway for hackers to send malicious updates, steal data, and even take control of the system. 

In 2014, an Israeli security firm took control of cars using a specific connected telematics device that failed to use proper encryption.

2. Failing to Install Updates

Once you have a machine-to-machine communication​ system working properly, it can be easy to forget to install the necessary updates to keep the network secure. 

Yet, hackers are constantly updating their strategies and tactics. Failing to install updates and patches leaves your system vulnerable. 

Even if you’re worried about breaking integrations between systems, you should at the least install every security update released by the vendor. These updates are specifically designed to address vulnerabilities discovered in your devices. After all, if your vendor releases a security update, it’s because they found a problem.

You also should know that updates and patches are not always the final solution to security vulnerabilities. Unfortunately, many manufacturers are not able or willing to provide the necessary support to continue updating their devices. 

To avoid this risk, shop carefully for systems that provide updates and are backed by a trusted company.

3. Poorly Built Networks

The modern industrial network is designed to get tasks done. If the design focuses too much on completing that task, it will leave weak points in security. Things that are obvious when building IT networks are sometimes less obvious when creating industrial DNP3 and other network architecture.

The solution to this risk is fairly simple. Those tasked with building industrial networks need to ensure they are partnering with IT professionals to build networks that are safer from attacks. Security features, like deep packet inspection and network segmentation, should be in place from the beginning.

4. Sensors Outside of the Company's Control

Most of the sensors and other connected pieces that make up a network are controlled by the company. But for some companies, that is not the case. For example, power companies have sensors in their customer's homes. 

Sensors outside of the company's immediate control are hard to secure, which gives hackers access. Currently, cloud-based security using public key services to authenticate devices may be the best solution to this problem.

Don't Take The Risk

Industrial security breaches can cause devastating consequences.​ Therefore, the above risks need to be addressed.

As more industrial facilities rely on the Internet of Things, it's important for company teams to be aware of the potential vulnerabilities. Take security into full consideration.

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The Internet of Things (IoT) concept promises to improve our lives by embedding billions of cheap purpose-built sensors into devices, objects and structures that surround us (appliances, homes, clothing, wearables, vehicles, buildings, healthcare tech, industrial equipment, manufacturing, etc.).

IoT Market Map -- Goldman Sachs

What this means is that billions of sensors, machines and smart devices will simultaneously collect volumes of big data, while processing real-time fast data from almost everything and... almost everyone!!!

IoT vision is not net reality

Simply stated, the Internet of Things is all about the power of connections.

Consumers, for the moment anyway, seem satisfied to have access to gadgets, trendy devices and apps which they believe will make them more efficient (efficient doesn't necessarily mean productive), improve their lives and promote general well-being.

Corporations on the other hand, have a grand vision that convergence of cloud computing, mobility, low-cost sensors, smart devices, ubiquitous networks and fast-data will help them achieve competitive advantages, market dominance, unyielding brand power and shareholder riches.

Global Enterprises (and big venture capital firms) will spend billions on the race for IoT supremacy. These titans of business are chomping at the bit to develop IoT platforms, machine learning algorithms, AI software applications & advanced predictive analytics. The end-game of these initiatives is to deploy IoT platforms on a large scale for;

  • real-time monitoring, control & tracking (retail, autonomous vehicles, digital health, industrial & manufacturing systems, etc.)
  • assessment of consumers, their emotions & buying sentiment,
  • managing smart systems and operational processes,
  • reducing operating costs & increasing efficiencies,
  • predicting outcomes, and equipment failures, and
  • monetization of consumer & commercial big data, etc.

 

IoT reality is still just a vision

No technology vendor (hardware or software), service provider, consulting firm or self-proclaimed expert can fulfill the IoT vision alone.

Recent history with tech hype-cycles has proven time and again that 'industry experts' are not very accurate predicting the future... in life or in business!

Having said this, it only makes sense that fulfilling the promise of IoT demands close collaboration & communication among many stake-holders.

A tech ecosystem is born

IoT & Industrial IoT comprise a rapidly developing tech ecosystem. Momentum is building quickly and will drive sustainable future demand for;

  • low-cost hardware platforms (sensors, smart devices, etc.),
  • a stable base of suppliers, developers, vendors & distribution,
  • interoperability & security (standards, encryption, API's, etc.),
  • local to global telecom & wireless services,
  • edge to cloud networks & data centers,
  • professional services firms (and self-proclaimed experts),
  • global strategic partnerships,
  • education and STEM initiatives, and
  • broad vertical market development.

I'll close with one final thought; "True IoT leaders and visionaries will first ask why, not how..!"

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Editors Note: Members of IoT Central are encouraged to participate in Ventana Research's study. The author of the blog shares details below.

The emerging Internet of Things (IoT) is an extension of digital connectivity to devices and sensors in homes, businesses, vehicles and potentially almost anywhere. This innovation means that virtually any device can generate and transmit data about its operations – data to which analytics can be applied to facilitate monitoring and a range of automatic functions. To do these tasks IoT requires what Ventana Research calls operational intelligence (OI), a discipline that has evolved from the capture and analysis of instrumentation, networking and machine-to-machine interactions of many types. We define operational intelligence as a set of event-centered information and analytic processes operating across an organization that enable people to use that event information to take effective actions and make optimal decisions. Ventana Research first began covering operational intelligence over a decade ago.

In many industries, organizations can gain competitive advantage if they reduce the elapsed time between an event occurring and actions taken or decisions made in response to it. Existing business intelligence (BI) tools provide useful analysis of and reporting on data drawn from previously recorded transactions, but to improve competitiveness and maximize efficiencies organizations are concluding that employees and processes in IT, business operations and front-line customer sales, service and support also need to be able to detect and respond to events as they happen.

Both business objectives and regulations are driving demand for new operational intelligence technology and practices. By using them many activities can be managed better, among them manufacturing, customer engagement processes, algorithmic trading, dynamic pricing, yield management, risk management, security, fraud detection, surveillance, supply chain and call center optimization, online commerce and gaming. Success in efforts to combat money laundering, terrorism or other criminal behavior also depends on reducing information latency through the application of new techniques.

The evolution of operational intelligence, especially in conjunction with IoT, is encouraging companies to revisit their priorities and spending for information technology and application management. However, sorting out the range of options poses a challenge for both business and IT leaders. Some see potential value in expanding their network infrastructure to support OI. Others are implementing event processing (EP) systems that employ new technology to detect meaningful patterns, anomalies and relationships among events. Increasingly, organizations are using dashboards, visualization and modeling to notify nontechnical people of events and enable them to understand their significance and take appropriate and immediate action.

As with any innovation, using OI for IoT may require substantial changes to organizations. These are among the challenges they face as they consider adopting this evolving operational intelligence:

  • They find it difficult to evaluate the business value of enabling real-time sensing of data and event streams using radio frequency identification (RFID) tags, agents and other systems embedded not only in physical locations like warehouses but also in business processes, networks, mobile devices, data appliances and other technologies.
  • They lack an IT architecture that can support and integrate these systems as the volume, variety and frequency of information increase. In addition, our previous operational intelligence research shows that these data sources are incomplete or inadequate in nearly two out of five organizations.
  • They are uncertain how to set reasonable business and IT expectations, priorities and implementation plans for important technologies that may conflict or overlap. These can include BI, event processing, business process management, rules management, network upgrades, and new or modified applications and databases.
  • They don’t understand how to create a personalized user experience that enables nontechnical employees in different roles to monitor data or event streams, identify significant changes, quickly understand the correlation between events and develop a context adequate to enable determining the right decisions or actions to take.

Today’s fast-paced, 24-by-7 world has forced organizations to reduce the latency between when transactions and other data are recorded and when applications and BI systems are made aware of them and thus can take action. Furthermore, the introduction of low-cost sensors and the instrumentation of devices ranging from appliances and airline engines to crop management and animal feeding systems creates opportunities that have never before existed. Technological developments such as smart utility meters, RFID and embedded computing devices for environmental monitoring, surveillance and other tasks also are creating demand for tools that can provide insights in real time from continuous streams of event data.

As organizations expand business intelligence to serve operational needs by deploying dashboards and other portals, they are recognizing the need to implement technology and develop practices that collect events, correlate them into meaningful patterns and use workflow, rules and analytics to guide how employees and automated processes should react. In financial services, online commerce and other industries, for example, some organizations have built proprietary systems or have gone offshore to employ large teams of technicians at outsourcing providers to monitor transactions and event streams for specific patterns and anomalies. To reduce the cost, complexity and imperfections in these procedures, organizations now are seeking technology that can standardize and automate event processing and notify appropriate personnel of significant events in real time.

Conventional database systems are geared to manage discrete sets of data for standard BI queries, but event streams from sources such as sensing devices typically are continuous, and their analysis requires tools designed to enable users to understand causality, patterns, time relationships and other factors. These requirements have led to innovation in event stream processing, event modeling, visualization and analytics. More recently the advent of open source and Hadoop-related big data technologies such as Flume, Kafka, Spark and Storm are enabling a new foundation for operational intelligence. Innovation in the past few years has occurred in both the open source community and proprietary implementations.

Many of the early adopters of operational intelligence technologies were in financial services and intelligence, online services and security. However, as organizations across a range of other industries seek new competitive advantages from information or require real-time insight for risk management and regulatory compliance, demand is increasing broadly for OI technologies. Organizations are considering how to incorporate event-driven architectures, monitor network activity for significant event patterns and bring event notification and insight to users through both existing and new dashboards and portals.

To help understand how organizations are tackling these changes Ventana Research is conducting benchmark research on The Internet of Things and Operational Intelligence. The research will explore how organizations are aligning themselves to take advantage of trends in operational intelligence and IoT. Such alignment involves not just information and technology, but people andprocesses as well. For instance, IoT can have a major impact on business processes, but only if organizations can realign IT systems to a discover-and-adapt rather than a model-and-apply paradigm. For instance, business processes are often outlined in PDF documents or through business process systems. However, these processes are often carried out in an uneven fashion different from the way the model was conceived. As more process flows are directly instrumented and some processes carried out by machines, the ability to model directly based on the discovery of those event flows and to adapt to them (either through human learning or machine learning) becomes key to successful organizational processes.

By determining how organizations are addressing the challenges of implementing these technologies and aligning them with business priorities, this research will explore a number of key issues, the following among them:

  • What is the nature of the evolving market opportunity? What industries and LOBs are most likely to adopt OI for IoT?
  • What is the current thinking of business and IT management about the potential of improving processes, practices and people resources through implementation of these technologies?
  • How far along are organizations in articulating operational intelligence and IoT objectives and implementing technologies, including event processing?
  • Compared to IT management, what influence do various business functions, including finance and operations management, have on the process of acquiring and deploying these event-centered technologies?
  • What suppliers are organizations evaluating to support operational intelligence and IoT, including for complex event processing, event modeling, visualization, activity monitoring, and workflow, process and rules management?
  • Who are the key decision-makers and influencers within organizations?

Please join us in this research. Fill out the survey to share your organization’s existing and planned investments in the Internet of Things and operational intelligence. Watch this space for a report of the findings when the research is completed.

Regards,

David Menninger

SVP & Research Director

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DIY Home Automation

Home Automation DIY Case Study

The following is from a Mind Commerce interview with residential owner/installer/operator: 

I got into the home automation craze by accident when one of my managers described what he was doing.  After looking at it, the added convenience, security, and cost savings made me a believer.  The overall category of devices that I use are the Internet of Things (IoT).

My setup is as follows:

  • I have an Amazon Echo that allows me to issue voice commands to the majority of my IoT devices.  It also will play music from my Amazon Prime account and allow me to order merchandise (all voice of course).  It additionally allows me to keep a TODO and shopping list that is synchronized to my Alexa app on my iPhone.  As I think of items, I just tell Alexa (the name for the Echo), and she will add the items to the list.  I use this all the time.  You can also set timers and alarms vocally, which is another well-used feature.  There's tons more.  The Echo talks WiFi.
  • I use a Wink Hub to interface the Echo to devices that don't directly talk over WiFi, or that the Echo doesn't directly support.  The Wink Hub talks Z-Wave, Zigbee, WiFi, and Lutron's proprietary communications (dimmers).  The Wink Hub also has a nice APP that lets me control everything directly from my cellphone if I want.
  • I use Luton dimmers that allow me to turn on, turn off, or set the dimming level for my most commonly used lights.  The echo supports this so I can say "Alexa set living room lights to XX%" and it happens.
  • I have a Rain Machine which is a connected sprinkler controller.  I can turn on stations from the Echo, but I don't.  What it allows me to do is to set the watering parameters and then it connects to NOAA and it will modify my preferences based on how much rain has fallen.  Money saver.  It has a great APP and will tell me how much each station actually watered per week.  A real money saver in Florida.
  • The Ecobee 3 thermostat was an expensive but awesome IoT purchase that also saved me a lot of money this past winter.  It is very smart and connects to the Echo directly (WiFi).  I can tell Alexa to raise or lower the temperature by voice.  Setup couldn't be any simpler, and the APP is awesome.  Conventional wisdom in the winter is to lower your temperature at night and then have it increase before you wake to save money.  Wrong!  The Ecobee tracks when your fan and compressor run (view on the website).  I found out that turning the temperature down by 4 degrees overnight was causing my heat strips (expensive) to turn on for a couple of hours around 5AM to bring the temperature back up.  I was much better off just leaving it one degree less all the time.
  • For my garage door controller, I bought an IoT box that allows me to view the status of the garage door and to remotely open or close the door by using the Wink APP.  Really nice when I can't remember if I closed the door, or left it open.  This doesn't work with the Echo by design (having a crook yell into your house "Alexa open the garage door" wouldn't be a good thing).
  • Nest Cam is an awesome security device.  When I'm on travel I can view what's going on in the house and even hear what's going on.  It's got 1080p resolution and night IR capability (see at night with the lights off).  I can even talk to my cat through it.  I pay for the cloud recording service, so when it's on, a month of recording is held on the cloud, which would be useful if the house is ever robbed.  The problem is I don't want it recording while I'm home.  That is solved by...
  • Leviton makes smart bricks that plug into an outlet and let you plug an appliance (anything) into it and control that appliance on/off state through Wink or the Echo.  So when I leave, I can just vocally tell the Nest Cam to turn on, or if I forget, I can just use the Wink APP to turn it on remotely.  I use these to control the Nest Cam, my DirecTV internet device, and my Amazon Fire TV.  Whey have them sucking energy all the time when I use them maybe 2% of the time?

As an advanced user*, he also had this to say:

  • The is a function call IFTT (If This Then That) that works with the Echo, Wink and the IoT devices to allow creation of recipes that handle what to do if something happens.  For example, I set up an IFTT that when I ask the Echo where my cellphone is, the IFTT will call the phone so it rings.  The possibilities are limitless.  Think Geo-fencing or linking input from IoT sensors to automatically cause actions.

*Note: Remember, this is a more advanced, tech user.  However, IoT is increasingly becoming part of the consumer lexicon!!

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The ‘connected’ car, not to be confused with the self-driving, autonomous car, is defined as any vehicle equipped with Internet access that allows data to be sent to and from the vehicle.

Since the automobiles were invented, car makers have been trying to add features which may reduce driver error. Today’s car has the computing power of 20 personal computers, features about 100 million lines of programming code, and processes up to 25 gigabytes of data an hour.

Digital technology is also changing how we use and interact with our cars, and in more ways than you probably realize.

The market for smart vehicles is certainly set for takeoff and many analysts predict they could revolutionize the world of automobiles in much the same way smartphones have changed the face of telecommunications.

Is your car connected to the Internet? Millions of vehicles around the world had embedded Internet access, offering their drivers a multitude of smart options and benefits. These include better engine controls, automatic crash notifications and safety alerts, to name just a few. Owners can also interact with their connected vehicles through apps from any distance.

Vehicle-to-vehicle communications, for example, could help automobiles detect one another's presence and location to avoid accidents. That could be especially useful when it comes to driver-less cars - another advance already very much in development. Similar technology could help ensure that cars and their drivers slow down for school zones or stop at red lights.

Connected vehicle technologies provide the tools to make transformational improvements in safety, to significantly reduce the number of lives lost each year through connected vehicle crash prevention applications.

The Connected Car will be optimized to track and report its own diagnostics, which is part of its appeal for safety conscious drivers.

Connected cars give superior Infotainment services like navigation, traffic, weather, mobile apps, emails and also entertainment.

Auto insurers also have much to gain from the connected car revolution, as personalized, behavior based premiums are already becoming new industry standard.

OEMS and dealers must embrace the  Big Data revolution now, so they’re ready to harness the plethora of data that will become available as more and more connected cars hit the roads.

Cloud computing powers much of the audio streaming capabilities and dashboard app functions that are becoming more commonplace in autos.

In the next 5 years it seems that non-connected cars will become a thing of the past.  Here are some good examples of connected cars:

  • Mercedes-Benz models introduced this year can link directly to Nest, the Internet of Things powered smart home system, to remotely activate a home’s temperature controls prior to arrival.
  • Audi has developed a 12.3 inch, 3d graphics fully digital dashboard in partnership with NVIDIA.
  • Telematics Company OnStar can shut down your stolen car remotely helping police solve the case.
  • ParkMe covers real time dynamic parking information and guide drivers to open parking lots and meters. It if further integrating with mobile payments.

The next wave is driver-less, fully equipped and connected car, where there will be no steering wheels, brakes, gas pedals and other major devices. You just have to sit back, relax and enjoy the ride!!

This article originally appeared here.
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What options do you have for remotely monitoring Water and Fluids with Industrial IoT sensor telemetry?

IIoT or Industrial IoT (Internet of Things) is everywhere. It’s across all industries, from high tech transport, to natural resources and governments. IIoT software and hardware is deployed for numerous, varying applications, and it’s critical to understand just what the customer needs. Especially since the customer can’t always articulate exactly what the remote monitoring and sensor telemetry should do. According to a study performed by Verizon: the worldwide Internet of Things market spend will grow from $591.7 billion in 2014 to $1.3 trillion in 2019. That’s tremendous.

One of the areas that we’ve seen recent growth is water and fluid monitoring. Water comes to us as a life sustaining asset and also as a force of destruction. The utility of water needs to be measured and monitored in order to effectively and efficiently use our greatest natural resource. Similarly, monitoring the destructive force of water can be just as important. Let’s talk about the different ways that you can measure and monitor water!

 

Flow Meters

Flow meters calculate the amount of water that flows through them. Flow meters are everywhere from your house to your office, to anywhere and everywhere water is used. Measuring water flow is a need recognized across industries, from agriculture to commercial, pharmaceuticals, and oil and gas. Flow meters in an IIoT solution provide not only a total flow amount, but allow you to utilize real time data to predict and adjust consumption. Further still, real time analysis allows immediate recognition of catastrophic events such as a burst pipe. The analysis will be drawn out further to establish predictive failure behavior and potentially prevent massive water loss issues like the ones that happened in Los Angeles and Hollywood Hills.

 

Water Detection

Almost certainly this one is all about protecting assets. There are essentially four ways that we have used to detect presence, quantity, volume, and levels of water. Each of these fits quite well for a particular purpose. They also compliment each other nicely!

 

Presence of Water: The Rope Sensor

Rope sensors are great and they come in a variety of lengths. A rope sensor will tell you if you have water present at any point along the sensor. Imagine a large trailer with rope sensors running along the bottom of the trailer. If you have a spill in that trailer, truck, or vehicle and any fluid reaches the rope sensor, then you’ll receive an alert and immediately know there’s a problem.

Rope sensors are also great for flood detection. Because you can purchase these sensors in practically any length, you can lay them across a flood channel. If any portion of that rope sensor gets wet then you know you have water present. However, in terms of flood detection rope sensors will tell you if there is water, but they won’t tell you how much.

 

Presence of Water: Yes or No

If your rope sensor went off on a flood channel you might want to know how much water is flowing through. Depending on the lay of the land there are a number of different applications that we use to provide this information.

 

Ultrasonic, Ultrasound, Pulse, and Radar Sensors

If you have a fixed structure next to or going over a flood channel then a great solution is an ultrasonic sensor. Essentially, once the sensor is fixed in place it will continuously ping the ground. When the reading between the sensor and the ground becomes more compact, you can calculate that distance and in turn determine how much water is flowing through the channel and the flood level. Also note that radar and ultrasonic fluid level sensors are quite useful for remotely monitoring levels and volumes of liquid products in assets like tanks!

 

Pressure Transducers

Another way that we have measured quantity of water is by using a pressure transducer. A sensor with a membrane sits at the bottom of a water well, lake, or a reservoir, or a flood channel. As the water increases above the sensor so does the pressure on the sensor’s membrane. The higher the pressure the more water you have moving through!

 

Making things Digital

Water metering and water detection are now all IIoT solutions. All of these meters / sensors connect to sensor hub connector hardware that sends data out into the internets and into a cloud data analysis solution. Whether you’re monitoring agriculture / viticulture, oil / gas / mining, municipal water treatment facilities or other water plants, nowadays you can obtain a cost-effective, rapidly deployable monitoring solution.

 

 

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The Top 50 IoT People to Follow on Twitter

I recently shared the Top 10 Books to Read Now on IoT. In an attempt to keep everyone smarter and share resources in the most simple way, I created the ever ubiquitous listicle by compiling what I believe are the Top 50 IoT people to follow on Twitter. These are, as far as I can tell, real people and not brands. 

How did I compile this list? No hard data science here, just good old grunt work from researching, reading and talking with people over the last few months. If you have any suggestions or if I missed an important person, please leave a comment. Or better yet, tweet to me @DavidOro.

If you make it to the bottom of this list, I provide an easy way for you to follow them all with just one click.

Without further adieu, the Top 50 in no particular order.

  1. @gigastacey - Stacey Higginbotham. OK, I put Stacey Higginbotham first on purpose cause I like her and for the fact that she’s been reporting on IoT or years and also hosts the popular podcast iotpodcast.com

 

  1. @Kevin_Ashton Credited with coining the term “Internet of Things”

 

  1. @mjcavaretta Michael Cavaretta, Manager, Connected Vehicle Analytics, Ford Motor Co.

 

  1. @techguyadam  Adam IoT, Content Editor for http://www.appcessories.co.uk

 

  1. @chrismatthieu Chris Matthieu, Director IoT Engineering at Citrix

 

  1. @GilPress Gill Press, claims to have launched the #BigData conversation

 

  1. @CB_Telzerow Alex Telzerow, Editor-in-Chief COMPUTER BILD

 

  1. @JonBruner Director of hardware and IoT @OreillyMedia

 

  1. @timoelliott Timo Elliott, Innovation Evangelist at SAP

 

  1. @cgiorgi Cédric Giorgi Head of Startup Relations, IoT Evangelist @sigfox

IoT Central members can see the full list here. Become a member today here

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