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Silver Level Contributor

An example of embedded vision technology is Basler's dart BCON for MIPI camera and an adapter board developed to work with the Nvidia Jetson module. (Basler)

Embedded vision technology is hot.

It runs inside everything from smartphones to manufacturing lines where high-resolution images of finished products can be used to detect imperfections and other irregularities in milliseconds.

Today, engineers are also developing applications for smart cities and smart cars.  Tiny cameras can process precision images for objects both near and far, recognizing everything from bar codes to license plates.

Internet of Things applications are also on the rise in factories, medical and retail settings.  Many require image capture that is processed locally and connected to the cloud for further processing, data analytics or storage.  Industrial applications of embedded vision are similar to what happens with a smartphone but customized to a specific application need and also ruggedized and designed with a longer life cycle.

“We see a lot of value in robotics for embedded vision with applications that need to respond quickly,” said Brian Geisel, CEO of Geisel Software. Robotics are often used in remote areas, from space to coal mining.

Advanced Driver Assistance Systems are also incorporating embedded vision. “We’re going to see embedded vision become more mainstream as hardware becomes smaller, faster, cheaper,” Geisel said. Algorithmic improvements will allow developers to undertake tasks with less powerful devices.

“We’ll have a lot more ability to make use of embedded vision as we are able to shrink the necessary compute footprint,” Geisel added. “There are so many places where streaming a mass amount of data isn’t feasible, so we will see an explosion of new applications as we can enable more computer vision at the edge.”

Embedded vision for industry  "is moving from bleeding edge to cutting edge and will become mainstream in the coming decade,” said Tim Coggins, head of sales for embedded imaging modules in the Americas for Basler AG, an industrial camera manufacturer that produces a range of software and hardware products.  “Early adopters have a strong, compelling business case to do it now and not wait.”

Engineering students may know Basler for its web-based Vision Campus tutorials that explain in simple terms camera technology, interfaces and standards, vision systems and components and applications.  Some are presented with text and diagrams, while others are explained in quick videos by expert presenter Thies Moeller.  There are tutorials on everything from, “What’s the best way to compare modern CMOS cameras?” to “At which point does software come into play during image processing?”

One recent video explains five tips for making embedded vision systems to avoid common pitfalls. One tip: Have the system developed by a single source instead of having the development of key components carried out separately. With separate development, components may not interact with each other in a high-performance manner causing costly delays, Moeller explains.

A growing number of IoT applications that involve image capture require non-recurring engineering (NRE) for a one-time cost to research, design, develop and test a new approach. “Many early adopters have high volume requirements or strategic application requirements and the NRE in these cases is not an obstacle,” he said. “They can justify it by cost.”

Without standard plug and play solutions on the market, custom embedded vision can take time to market and require NRE costs that vary depending on the complexity of the application. “The primary challenge for developers is a large variety of hardware and software variables that need to come together to adopt a common connect ability,” Coggins said.

OpenCV for image processing is a good example of standard embedded vision software, noted Adam Taylor, founder of Adiuvo Engineering.  First developed in 2000 by Intel, OpenCV is a library of programming functions for real-time computer vision that is cross platform and open source under an Apache 2 license. Developers use it to process images, capture video and analyze the video for object or facial detection and other purposes.

 “Standards are how you scale and define the maximum benefit of accelerated development and easier engineering development,” Taylor said. “Embedded vision should be just plug and play—and boring to a large extent – allowing companies to focus on value-added activities and not just trying to get an image from yet another sensor/camera with a different interface.”

Basler is driving standards in the embedded vision industry, and a quick look at its website shows just how involved this standardization effort can be.  “The embedded ecosystem is already in place and continues to grow with many talented companies and individuals who can educate and provide help, answer questions or develop systems solutions,” Coggins said.

Basler offers complete design as well mass production, but so do many of Basler’s partners, Coggins said.  Basler’s partners include companies like Nvidia, with its Nvidia Jetson platform. In one example, Basler last June announced an embedded vision development kit, which extends Basler’s support for Jetson products to provide AI at the edge in robotics, logistics, smart retail and smart cities.  Nvidia boasts nearly half a million AI-at-the-edge developers.

The kit comes with a Basler dart BCON for MIPI camera with a 13-megapixel lens and an adapter board developed for the Jetson Nano module.

The chief advantage of embedded vision tech is the ability to offer scalability to the overall computer vision market with low cost, high performance and real time operation bolstered by edge-to-cloud connectivity.

 “Companies moving to embedded vision technology are quite content and early adopters have good reason to be so,” Coggins said. “Most of our clients want the reliability that comes in an industrial ruggedized solution with a long-life cycle. They can’t get this from the consumer market.”  

Grand View Research valued the overall global computer vision market at $10.6 billion in 2019 with 70% coming from hardware such as high-resolution cameras.  Growth of 7% each year is expected through 2026.    That 2019 total does not specifically segment out embedded vision systems, but Grand View says that the industrial vertical made up half of all revenues in 2019.

The researchers count Intel, Omron, Sony and Texas Instruments among the most prominent players in computer vision.

Originally published by
Matt Hamblen | January 20, 2021
Fierce Electronics

Embedded Vision will be the subject of digital keynotes and a panel discussion on January 27 as part of Embedded Innvation Week. Sign up for the free event online.

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Gold Level Contributor

COVID-19 Vaccine: The Role of IoT

Image: Unsplash - Hakan Nural

After nearly a year of fighting the pandemic, the U.S. is starting to roll out COVID-19 vaccines. As daily infections continue to climb and lockdowns take their toll on people’s lives, an effective vaccine rollout becomes all the more critical. Technology — specifically the Internet of Things (IoT) — is helping authorities with that.

The health care industry understood the advantages of IoT devices long before the pandemic. These technologies had proved themselves in businesses across many sectors before COVID-19 tested their effectiveness. From an outside perspective, the role of IoT in vaccine rollout may not be immediately apparent. Here’s how these technologies are ensuring new vaccines work as they should.

More Efficient Production

One of IoT’s leading areas of implementation is in manufacturing, which has helped pandemic responses. Medical manufacturing centers have had their hands full, with some producing millions of tests, treatments, and PPE products each month. Maintaining these production levels amid widespread disruption is no easy task, but IoT technologies help.

IoT devices in a manufacturing plant can gather data that shows how the facility can become more efficient. In a 2019 PWC survey, 81% of industrial manufacturers said IoT had improved their efficiency. These gains help vaccine producers maximize their output, helping more people get vaccinated in less time.

Many facilities are now unable to have their full workforce in the building at once due to social distancing requirements. Automation has filled in the gaps for these plants, and IoT devices improve automated machines. With IoT connectivity, these machines can communicate with one another and respond to real-time changes, further improving efficiency.

Supply Chain Transparency

One of the most significant challenges facing vaccine rollouts is transportation and storage. Both of the approved COVID-19 vaccines in the U.S. break down if they remain in warm temperatures for too long. Pfizer’s vaccine must be kept at -70°C, and Moderna’s at -20°C, meaning maintaining constant low temperatures is crucial.

Thankfully, many organizations already use IoT sensors to monitor temperature and other quality indicators. By adjusting these systems to match the vaccines’ requirements, companies can ensure their safety. They can remotely monitor temperature and other factors inside trucks and storage units to see if they need to change anything.

If temperature readings show the vaccines are warming, logistics companies can reroute trucks to arrive faster. Similarly, this data can inform crews if they need to fix storage equipment before they compromise the vaccines. Without these readings from IoT sensors, it would be far more challenging to ensure efficient vaccine delivery.

Managing Data After Injections

IoT devices can help after patients receive their vaccinations, too. Both the Moderna and Pfizer vaccines require two shots, and patients can use wearables to remind them of this. Many people already use health wearables like smartwatches, so using these devices to track their vaccinations is straightforward.

Hospitals can use IoT devices to improve the efficiency of their record-keeping processes. The more efficient these practices become, the better hospitals will keep track of vaccination records. As a result, the overall vaccine rollout will be more effective.

As hospitals see more COVID-19 patients and more vaccine recipients, they can use their plasma to study antibodies. These antibodies can lead to new vaccines or better treatments, but plasma requires low-temperature storage like the shots. With IoT sensors, organizations have reduced spoilage and waste from plasma, ensuring a healthier future.

Crucial to the Success of COVID-19 Vaccine

The vaccines will likely require a long time to take full effect, and it will take careful planning and implementation to get there. The challenges ahead are daunting, but IoT technology provides a way forward. Thanks to these devices, manufacturers, hospitals, and authorities can ensure effective vaccine rollouts and hopefully end the pandemic.

Originally published by
Jane Marsh | January 15, 2021
iot for all

Jane Marsh - Editor-in-Chief,
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Gold Level Contributor

(Photo by Ihor Saveliev on Unsplash)

A new study has found that 51 percent of consumers have bought a smart home device to help adapt to pandemic living. 

The study, commissioned by Xiaomi, provides some fascinating insights into the ways people have responded to the complete upheaval that COVID-19 has caused to their lives.

70 percent of respondents said they’ve made changes to their living environment due to spending more time at home for both work and leisure. Three in five of the consumers reported that it has become harder to create personal space to relax “or find joy” at home.


On average, consumers bought two IoT devices in response to being home more due to the pandemic. Gen-Z consumers purchased an average of three.

Daniel Desjarlais, Global Product Marketing Manager at Xiaomi, said:

“Smart living has always been about reimagining and optimising physical space to solve problems and adapt to new realities through the use of technology, and we’ve seen this adoption accelerate in 2020.

Connected homes, automated systems, and new technology are helping people create ecosystems within their homes to solve new challenges presented by increased time at home, whether it’s adapting or creating new uses for old spaces, such as office space or classrooms, or just creating a more streamlined home that is easier to manage and control.”

Orders to remain at home where possible and help limit the spread of COVID-19 while vaccines roll out have led people to use technological means for working, learning, and exercising at home.

With remote working arrangements expected to remain widespread after the pandemic, many changes to people’s home spaces will remain permanent. 60 percent of respondents plan to continue using their home for activities typically performed elsewhere, even after a COVID-19 vaccine is widely available. 

The report highlights the potential to help people adapt their homes to changes which look to remain well after this pandemic has passed.

Originally published by
Ryan Daws | January 7, 2021
IOT Tech News


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Gold Level Contributor

UL’s IoT Security Rating creates a security baseline for IoT consumer products through a comprehensive evaluation process that assesses security aspects of a device against known vulnerabilities and common attack methods. (metamorworks/iStock/Getty Images Plus)

Pop quiz: You see two different smart thermostats for sale, one from a well-known brand and the other, cheaper one is from a no-name vendor. You’re trying to make a decision about which product to buy and you might be wondering, “Which one is more likely to be hacked?”

The answer: You have no way of knowing. That’s because today there is effectively no transparency regarding a product’s security level to the end user. UL, however, is aiming to change that, with its new IoT Security Rating, a solution that provides a UL Verified Mark security label to IoT consumer products.

FierceElectronics sat down with IoT Security Solution Leader, Laurens Van Oijens, to gain insight into how the solution works and when consumers can expect to see the security label popping up on IoT consumer products.

FE: Tell me about UL’s IoT Security Rating, which UL introduced in May 2019.

Van OijensUL’s IoT Security Rating creates a security baseline for IoT consumer products through a comprehensive evaluation process that assesses security aspects of a device against known vulnerabilities and common attack methods. It also ensures minimum security capabilities are met, as articulated by ETSI TS 103 645 and other industry standards.

In short, it’s a security verification and labeling solution for IoT products—meaning basically anything connected to a network-- that categorizes products according to an ascending five-level scale: Bronze, Silver, Gold, Platinum and Diamond. Verified products receive a differentiated UL Verified Mark security label—specifying the achieved security level—which is evaluated by UL on an ongoing basis. And I should note it is available worldwide.

The idea of creating a security rating for IoT devices actually came up at UL as far back as 2015, but we did not pursue the idea because we felt industry wasn’t ready at the time. But since then, we’ve been keeping tabs on industry developments, and a little over two years ago a team was formed to turn that idea into a reality. My role as solution leader has been to coordinate the different activities required to design the solution with our technical and business development teams, and then to validate the concept with device makers and retailers.

FE: What is the benefit to device makers in securing the UL rating?   

Van Oijens: I would say the most important benefit is that the UL rating enables device makers to be more transparent to the end user. When evaluating competing products, consumers typically want to be able to tell how they differ in features, functionality, and of course price. But over the past few years, security and privacy are becoming more important to consumers. In fact, they are on their way to becoming one of the top influencing factors for making purchasing decisions, and I think that is because so many hacks and security breaches have made the news. 

End users are demanding to know about the security of the product they intend to buy, which in turn means that manufacturers need a way to convey a product’s security capabilities to the end customer. When applied to a product, UL’s IoT Security Rating demonstrates that it uses industry best practices for IoT cybersecurity and protection of consumer data.

FE: Are there alternative security ratings or markings for consumer IoT products today? 

Van Oijens: The concept of a security rating is at a really early stage today, and it is sort of a fragmented marketplace that isn’t regulated the same way as standards are. There are a number of other solutions out there, but to our knowledge UL is the first to introduce the concept of multiple levels of security ratings as opposed to a singular mark.



Under UL’s IoT multi-level security rating solution, a product receives a verified and  differentiated UL Verified Mark security label specifying the achieved security level. (Source: UL)

FE: How does the UL IoT Security Rating solution work in practice?

Van Oijens: It starts with a technical requirements framework, a document called the UL MCV 1376, which is available on the UL website. The document outlines how the five levels of the rating system are designed and which security requirements are part of that rating level.

I would advise as a first step that a device maker take a look at that framework to gauge how their product would perform. Ultimately, every company should want to get to the highest rating possible, but the diamond level is quite challenging to reach. I think today the choice comes down to how much security assurance a particular devices needs [those directly accessible from the Internet needing the most], and what makes sense from the cost and the return on that investment.

UL would then perform an analysis of that product to ensure it meets the requirements of the specified security level. One of the other things that we do when we issue the actual rating is provide a QR code to the end customer, which takes them to a webpage that in layman’s terms describes the rating system and what each of the levels means. 

FE: If I were to see a smart thermostat made by a “no-name” brand at 30% of the cost of a name brand, could I almost assume security is one of the areas that the manufacturer would have chosen to cut costs?

Van Oijens: I am inclined to say yes, but the truth is we just don’t know especially if neither product carries a security label. Obviously, building in security takes more upfront development. You can also enhance a product’s security by adding third party hardware and software, so technically this could all result in a higher retail price. But you are touching on the exact problem that we are trying to solve: The level of product security today is simply not available at the point of purchase.

FE: UL rolled this out in 2019. What has been the adoption rate so far?

Van Oijens: We announced the solution in May 2019, and this year we started rating products. The first manufacturer that we announced was GE Appliances, which has a gold level for their entire portfolio of smart home appliances. The mark should now be appearing on products in stores.  

The types of products we have rated are primarily consumer IoT—mainly appliance products--and a number of commercial products. We have worked with several dozen manufacturers, providing ratings for literally hundreds of products. Products in the pipeline today range from wearables to lighting devices and robots. From a geographic and company size perspective, the interest is coming from a pretty diverse set of manufacturers.

FE: Recently after reverse-engineering a dozen smart home devices, the global security lab RIscure found security shortcomings in all of them. Why is it that makers of these devices do such a poor job when it comes to security?  

Van Oijens: I don’t believe that any company intentionally sets out to build a product that is not secure. We think that device security is more of a commercial issue than a technical problem. From a design standpoint, there is a wealth of educational resources, IoT standards like NIST IR 8259A, and plenty of service providers that can teach companies how to secure their IoT device.

The issue is that product security today is still seen as a cost instead of a benefit. We believe that device makers are insufficiently incentivized from a commercial standpoint when they make an investment in security, and that it is not viewed as a competitive advantage today. 

Transparency around a specific product attribute can change that dynamic. A great example is the energy labels that starting popping up on products about ten years ago. Basically, these labels made a product’s energy efficiency transparent to the end user. What happened is that when the end users expressed that they valued buying more energy-efficient products, manufacturers in turn invested more to demonstrate the efficiency of their products. Today, the motivation in that space has shifted from trying to stay ahead of the competition to not falling behind. Ultimately, the effect of these ratings is that consumers are more likely to purchase the product.

We’re hoping to instigate a similar trend with our IoT Security Rating and encourage manufacturers to make their products more secure.

In this case, our rating is voluntary not mandatory—it’s a carrot, not a stick. That changes as soon as regulations come into play. Referring back to the energy label, once it started to be adopted by industry, the government decided to enforce it.  I don’t know if we can expect the same thing to happen here. But where we know regulators are working on security legislation, we want to tie our solution to those regulations. Whatever form it ultimately takes, I do believe that in the future greater product security and transparency around that security will happen.

FE: How do you think the new California and Oregon laws, which  hold device makers responsible for the inclusion of reasonable security features will impact design practices and are you aware of any cases that have been brought as a result? Could states mandate your designation on IoT products sold or made there?

Van Oijens: We are happy that these regulations have been introduced, as they represent a hallmark in the history of IoT security. But they do have their shortcomings, and there is room for improvement. The definition of “reasonable security” features is considered vague, but that is not uncommon when it comes to security-related regulations, which today are typically open to multiple ways of interpretation.

At the same time, both state laws call out having unique passwords for a device as a security measure. This is where at least we would bring in additional rigor, such as secure communications with a remote system, or routine updates after purchase when a vulnerability is discovered. I think the benefits of these regulations is that device makers are stimulated to perform continued security due diligence on their products.  Having their products regularly tested and verified will help them to meet those objectives. And of course by working with us, we ensure that connected devices from a company’s portfolio are compliant with the California and Oregon regulations and any other state that might issue enact them.

FE: How are you getting the word out?

Van Oijens: It all starts with awareness, and that is one of the things that we are actively focusing on. We are collaborating with the vendors that we have rated, as well as our exploring partnerships with retailers and other industry organizations that have more direct contact with the end users. 

FE: What is the role of retailers here, and is their willingness to carry a brand and offer things like warranties a de facto endorsement that it meets cybersecurity design standards? Does UL work with specific retailers and if so, how?

Van Oijens: We definitely believe that anyone who owns or controls the marketplace can have a great impact on security requirements in general. We also think that consumer representative organizations should call on retailers to take a more proactive stance on IoT security and become more demanding of their vendors. In fact, we’ve  received feedback from device makers that if a retailer should back security ratings, that would help them decide to get it.

Originally published by
Karen Field | December 29, 2020
Fierce Electronics

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Gold Level Contributor

Amazon's Ring doorbells have been compromised in some swatting attacks - Getty images

Hackers have livestreamed police raids on innocent households after hijacking their victims' smart home devices and making a hoax call to the authorities, the FBI has warned.

It said offenders had even spoken to responding officers via the hacked kit.

It marks the latest escalation of a crime known as "swatting", in which offenders fool armed police or other emergency responders to go to a target's residence.

The FBI said there were "deadly" risks.

A fake call about a hostage situation led to police shooting a man in Kansas three years ago, and there have been non-fatal injuries in other cases.

Shouted insults

The FBI said it believed the latest twist on the "prank" was able to be carried out because the victims had reused passwords from other services when setting up their smart devices.

Lists of hacked credentials are frequently bought and sold via illegal markets.

And offenders often run the details stolen from one service through others to find where passwords have been reused.

There have also been reports of security flaws in some products, including smart doorbells, which have allowed hackers to steal network passwords and gain access to other smart devices sharing the same wi-fi.

The apps and websites used to set up such products often store the user's name and address in their account settings in order to offer location-specific services.

"The [perpetrators] call emergency services to report a crime," the alert issued by the FBI states.

"The offender watches the livestream footage and engages with the responding police through the camera and speakers. In some cases, the offender also livestreams the incident on shared online community platforms."

The notice does not refer to any specific incident, but there have been related press reports in recent weeks.

In November, NBC News highlighted a case in which police went to a Florida home after receiving a fake 911 call from a man saying he had killed his wife and was hoarding explosives.

When they left the building after discovering it to be a hoax, officers reported hearing someone insult them via the property's internet-connected Ring doorbell.

In another incident the same month in Virginia, police reported hearing the hacker shout "help me" after arriving at the home of a person they had told might be about to kill himself.

When they questioned the attacker via the device, he claimed to have compromised four different cameras at the location and to be charging others $5 to watch online.

"After this we'll log out, tell him to change his Yahoo password, his Ring password, and stop using the same passwords for the same [stuff]," the offender was quoted as saying by local news station WHAS11.

A further event was also reported in Georgia in which the attacker shouted racial abuse at his victims after the police stood down, and claimed to have carried out more than a dozen such hacks that day.

Ring has denied its own systems have been compromised. It uses two-step verification, which means device owners can only access their accounts from a new computer if they enter a code emailed or sent to them via text message.

However, if either of those forms of communication are also compromised the user remains vulnerable.

As a consequence, the FBI has advised smart device owners to ensure they provide a different complex passcode to each online service they use.

"Users should also update their passwords on a regular basis," it adds - although the UK's National Cyber Security Centre has suggested this additional step itself poses a risk if it encourages people to opt for weaker codes.

Originally published by
BBC News | December 31, 2020

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Silver Level Contributor

IoT: The Essence of Business Operations

image: Unsplash - Riccardo Annandale

Every business is a mixture of a few hundred different processes, and a few hundred different employees responsible for those processes. Every activity that a business indulges in comes at an expense, making it extremely important for an enterprise to make the most efficient use of its resources.

Very often, businesses make the mistake of going the traditional route and depending entirely or majorly on their human resources to get work done. While it is wrong to look down on the potential of the personnel recruited completely, it is pretty obvious that a great margin of error can be reduced by employing AI and ML into the enterprise’s everyday tasks.

Operations can be performed in a more efficient way when routine tasks are automated. The employees have more time and energy to allot to tasks that require their attention and mental capabilities. Enterprise IoT refers to IoT in improving an organization’s existing systems and processes and enabling organizations to increase operational efficiency or unlock entirely new value.

How IoT Helps in Automating Business Operations

Heightened Security

Every business is at some degree of unprecedented risk at all times. Just like in mutual funds, the greater the risk, the higher the returns. While a business gambles in the market with its innovations, that may or may not appease the customers, they need to invest in reliable security systems to relieve themselves of the burden of a vulnerable structure.

IoT based surveillance systems ensure the highest level of security that is technologically possible. From smart cameras and sensors to protected gateways to unified cloud platforms, they provide the perfect security structures for any enterprise. These types of security systems are especially beneficial for businesses dealing with expensive and volatile commodities. Warehouse security and bank security are currently two of the most common fields of applying this IoT-based security system. At the same time, it continues to grow in other fields as well.

SOP Adherence

There is a plethora of basic day to day activities that can easily be performed via technology, saving time and effort on the enterprise’s part, and let’s not forget, saving it from errors.

In general, humans are very susceptible to making errors; while in some cases, these minor errors may not pose much of a threat to the enterprise’s functioning, it is not the same in other cases. These errors that initially go unnoticed to the human eye can often adversely affect the enterprise in the long run.

Keeping a check on all the activities and performance of every employee isn’t something that can be done manually, especially in a large organization. In such a situation, it can be hard for the enterprise to find the point of error in a process and the personnel responsible for it. IoT-based systems keep a constant check on the organization’s functioning and alert the in-charge in case of any deviation. Entrusting the repetitive and routine tasks to artificial intelligence-based automation systems notify the officers-in-charge.

OPEX Reduction

Successfully operating a business can often be expensive and cumbersome, and so, these enterprises need to find ways to cut down on their operational expenditure. Automating certain routine tasks that do not require human intervention can give faster and more accurate results, thus giving a better return on investment.

Even though it does not sound as appealing as a cashback policy, it is much better than that. Automation of these operations allows employees to invest their time in other processes, resulting in more output at the end of the day. Not only that, but IoT based platforms can analyze your data and provide effectivy and useful insights.

Efficient Functioning

When it comes to the use of artificial intelligence for the day to day working of an enterprise, it comes with a promise of greater returns in error-free functioning and efficient completion of tasks. When most basic tasks are automated, it allows employees to give attention to more critical tasks. The automation of these routine tasks also allows employees to have more time on their hands and less burden on their shoulders. You know how the saying goes; A happy employee is an efficient employee. Maybe it’s not a saying, but it is true nonetheless.

Customer Care

It has not only transformed the way organizations work but also how they provide customer service. Without wasting the time of customers, the organizations can provide immediate help and assistance to them, especially in case of common and recurring issues.

Using artificial intelligence, you can create great customer service that will answer questions directly and provide the team working at the back end with the required information and resources. Automated customer service not only helps in the reduction of response time but also nullifies the constraints of different time zones or public holidays. The use of chatbots today guarantees immediate response to the customers that human support cannot provide.

Originally published by
Pratik Salia, CX Professional, IGZY | December 29, 2020
IoT for all


Read more…
Gold Level Contributor

Image: Unsplash - Charlize Birdsinger

The Internet of Things (IoT) has been booming all around the globe for a decade. The most obvious focus of IoT is the consumer domain. However, Industrial IoT (IIoT) is probably the greatest champion of this new technology.

Industrial IoT is an extension of IoT in the manufacturing industry, commonly referred to as IIoT or Industry 4.0. IIoT connects machines and industrial equipment with a secure network and enables automation in the entire industrial facility. IIoT leverages a combination of M2M communication, Big Data Analytics, SCADA, and other similar industrial equipment.

The industrial IoT (IIoT) market for devices & technologies is expected to grow from USD 77.3 billion to USD 110.6 billion by 2025, at a CAGR of 7.4 percent during the forecast period. To understand how Industrial IoT is benefiting the Manufacturing Industry, let’s check some key points.

Benefits of IIoT

Asset Management

Imagine a factory that is manufacturing some product; the operator needs to regularly verify at what stage the product has reached, change the motor’s control based on production timings, and control the production by looking at entry and exit points. In this scenario, it would be beneficial to reduce human intervention and track the assets intelligently. Smart factory systems with the help of IIoT can track asset locations with smart sensors, monitor demand-supply requirements, and manage the workflow, records, and production accordingly.

Predictive Analytics

By introducing IIoT, manufacturers can better monitor equipment status, estimate the timeline required to reach certain milestones, and monitor the maintenance requirements on a machine-by-machine basis. Predictive analytics tools help engineers identify the root cause of an issue and work on the mitigation plan, thereby warning potential failures much farther in advance. As we move with this ecosystem, the production line will have more data to provide more visibility to deal with complex scenarios leading to a more robust system.

Efficient and Productive Process

Mechanical equipment in the manufacturing industry needs servicing and maintenance at regular intervals. If the equipment suffers unexpected damages, it can affect planned production and waste both time and resources. IoT-assisted production can avoid production downtime by servicing the equipment before it can lead to breakage. Eventually, production lines will see increased productivity and better decision making through enhanced analytics.

Safety Operations

Equipment in the industrial space can be hazardous, especially when it is not serviced regularly. IIoT automation units can help assure employees’ safety by monitoring equipment for potentially dangerous failures and sending floor-wide alerts in the case of a potentially hazardous event.

Many manufacturing companies have started investing their time & money towards developing smart IT-driven manufacturing solutions. The adoption of Industry 4.0 enables industrial users to leverage benefits such as asset management, predictive analytics, efficient and productive processes, and safety operations. And while IIoT has definitely proven its values, it also comes with its own set of challenges in adopting manufacturers and enterprises.

Challenges of Adopting IIoT for Manufacturers and OEMs


The manufacturing processes involved in different sectors vary. There are neither standards nor sets of industrial smart sensors for the industrial sectors available. For example, transferring data between machines from different vendors within an ecosystem may prove a challenge. Standards like Zigbee and Thread have enabled greater interoperability between devices, but similar standards have not been set for Industry 4.0. OEMs and SMEs will need to change/digitize their process to ensure the business unit’s interoperability based on existing processes.


Ensuring smart technology is pointless if it does not have an interconnected system. However, connecting industry-grade machines with a network carries risks. It can lead to vulnerabilities and can cause malfunction of the manufacturing process, potentially putting the safety of employees and machines at stake. It is a must for Industry 4.0 OEM to have the most robust and secure environment possible to protect from cyber threats.

Insufficient Expertise Amongst Industry Makers

As IIoT is still in the early stages, some industry skills are still lacking or hard to find. Things like coordinating hardware, integrating data from various sources, and vendors’ realization can all prove challenging. Small scale companies will find it difficult to get started with Industry 4.0 technology due to lack of knowledge and huge investment requirements.

Lack of Proven Technology

A smart manufacturing unit will need to have a mechanism or sensors that detect and collaborate data from one machine to another. This requires industry-grade sensors and smart devices or equipment. Currently, there isn’t a great range of smart sensors and machines in the market. And developing industry-grade sensors requires extensive training and simulation within the ecosystem, which can prove difficult, especially for cash-strapped companies.


IIoT Architecture

We’ve reviewed some of the benefits and challenges of implementing IIoT–– now let’s go through some of the architecture of an IIoT system.

Industrial IoT Gateway

An industrial gateway interfaced with PLC, DCS, and other data collection sensors via OPC UA or some other industry standards. It will support the cloud, which helps to connect the real things with the digital world. Cloud-based gateway will enable an analysis of production data that will drive core business logic.

Key Features

  • Cloud connectivity via reliable high-speed ethernet, Wi-Fi, or LTE connectivity (for very remote areas)
  • Supports standard industrial protocols (Modbus, OPC Unified Architecture -OPC UA, MTConnect, etc.)
  • Integration with PLC and SCADA
  • Time-sensitive networking (TSN)
  • Sends important data to the cloud for big data analysis and predictive maintenance

Use of Smart Manufacturing Solution

A smart manufacturing solution would help manage manufacturing units in a phase-wise manner and provide failure reports for assets. This solution will also support industrial motor control and speed diagnostics. To track and to control manufacturing remotely, this solution would provide a cloud interface.

Key Features

  • Control the operations and speed of the motors
  • Temperature and pressure measurement and analysis
  • Take appropriate actions on hitting the threshold values of temperature and pressure
  • Manufacturing phase-wise tracking with Rain-RFIDs and location plotting
  • Inventory management and order more stock/component when necessary
  • Cloud connectivity via reliable high-speed ethernet, WiFi or LTE connectivity (for remote areas) and manufacturing statistics

Origially published by
Kunal Kotecha, Senior Embedded Engineer, VOLANSYS Technologies | 

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Gold Level Contributor

Image: Amazon

COVID-19 is disrupting so many facets of our lives, including the way we will celebrate this holiday season. Online shopping, which has consistently grown over the last several years, has surged during the pandemic and promises to peak at over $189 billion in online holiday spend this year. 

Experts have forecast a "shipmageddon" this winter, with extreme delays as already overburdened shipping companies struggle to keep pace with rising demand — and cities may acutely feel the impact. As delivery trucks crisscross main streets, double park on busy streets to offload packages and create mazes of gridlock, traffic could come to a grinding halt.  

Emerging technologies like IoT-enabled devices and artificial intelligence (AI) will play a critical role in helping transportation companies avoid extreme delays, for both holiday gifts and drivers. By harnessing these technologies, packages can get out on time and cities can make certain that congestion does not rise to extreme levels, ensuring that the holiday season remains the most wonderful time of the year.  

Getting packages on their way faster 

For logistics companies, the holiday season is already the equivalent of the Super Bowl. With the projected exponential rise in online shopping this year, the spotlight on these companies will get brighter — and there will be less room for failure. 

As inventory comes and goes at all hours of the night, IoT-enabled pallets will provide managers with a single view of the warehouse and offer increased opportunities for organization and optimization of deliveries. Using AI, spacing can optimize the placement of packages for distribution, removing the need for workers to zig-zag across the warehouse to get packages into trucks. 

When packages are closely monitored and directed with AI on a single platform, all teams involved in shipping have visibility into the same streamlined data, and managers can keep their focus on sending out trucks at a steady and regulated pace. This consistent rhythm keeps distribution normal and ensures that cities aren’t overburdened with too many trucks at the same time. 

By combining the power of IoT and AI, logistics companies can further streamline the distribution of packages. Something that will be critical given the incredible volume of boxes, containers and parcels that will be entrusted to them for safe and timely deliveries.   

Of course, shipping companies can only control so much. Cities have a role to play in combating the "shipmageddon" as well.  

Keeping traffic rolling and curbs clean 

Managing traffic in major metropolitan areas has long been a struggle, and anyone who has driven in a large city can personally attest to the opportunities for improvement. It should be of no surprise, then, that many cities – from Columbus, OH to Austin, TX to Los Angeles – are already turning to technology to address congestion. The impending rise of holiday deliveries will put these systems to the test.  

Many of the devices used to help mitigate traffic jams and keep vehicles flowing through cities leverage remote controlled devices like changeable speed limit signs, radar, stop lights and road signs. All of them will need to work overtime this holiday season to endure wave after wave of delivery trucks. However, if everything is not working in harmony, streets can quickly become mired in gridlock with trucks needing to drop off deliveries on already crowded streets. 

A single location to control all these critical systems and provides a comprehensive street-to-street view is essential for keeping pace with rising traffic problems. 

By incorporating IoT and AI across traffic devices, cities can ensure that changes made to reduce traffic levels are reflected everywhere. If an IoT-connected road sign tells drivers to divert to a particular street due to construction, AI can alert the stop light on that street to change its timing to accommodate for the increased number of cars and allow for more packages to get to their destination on time.  

Cities can also utilize IoT to ensure that these tools are in working order. A stoplight going down in the middle of the day can back up a city for hours – the kind of a backup during peak delivery hours that can envelop an entire neighborhood. 

With information such as location and time of the stoplight failure, in a single platform, city officials can keep track of all devices on the network and use early detection to fix an issue before it devolves into a traffic nightmare and schedule maintenance during off-hours to reduce congestion. With IoT monitoring, device downtime is reduced, and, in some cases, issues are resolved even before drivers are aware of a problem. 

The future of deliveries is now 

Even when all these systems work in harmony, there will still be traffic and delays can happen. IoT-equipped devices can detect backups before they begin, and AI can push out an alert to inform drivers. Cities that utilize IoT technology can quickly share traffic data with delivery companies to alert dispatchers to hot spots and reroute trucks accordingly to avoid layering on to backups. Likewise, delivery companies can alert customers to updated timelines based on traffic data, keeping them informed with the latest. 

Though the surge in online shopping is unavoidable this year, the "shipmageddon" scenario can be prevented. Delivery companies and cities need to leverage technology like IoT and AI to ensure a smooth holiday delivery season as well as a reliable and streamlined process all year round.

By deploying these technologies now, everyone wins. The city of the future will be digitally connected, and the sooner that services like delivery begin tapping into that vision, the sooner we can start to reduce chronic issues like holiday congestion and shipping delays.  

Originally written by
Jonathan Sparks, VP of IoT products at ServiceNow. | December 14, 2020
for Smart Cities Dive

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Gold Level Contributor

Image: Pixabay

The COVID-19 pandemic has caused a seismic shift in the global economic landscape as manufacturers move toward more local production and distribution. As supply chains become shorter and more transparent, the combination of edge computing and Industrial IoT (IIoT) devices will help meet the demands of changing markets by increasing safety, optimization, and situational awareness — all things that were important before the pandemic, but are essential during a crisis. 

Edge Computing Market

Although the edge computing market has existed for decades, it is forecast to have a major growth spurt, rocketing 30 percent a year from $3.2 billion to $44.0 billion by 2030. Meanwhile, the Internet of Things (IoT) Business Index 2020, a survey and report created by the Economist Intelligence Unit (EIU), shows that over 10 percent of manufacturers have doubled their IoT investment over the last three years while 64 percent are between the early and advanced stages of IoT planning or implementation.

These numbers may increase, as, amid the health crisis, enterprises aren’t scaling back their digital transformation projects, but are in fact, accelerating them. The high level of digital intelligence found at the convergence of edge computing and IIoT devices will profoundly impact companies by giving them better insights into supply chains while enabling more efficient local production.

Taking the Edge Off the Remote Industrial Workforce

The health crisis caused global economic activity to grind to a halt as warehouses, factories, and businesses shut down to protect workers. This caused companies to press forward with their automation strategies — in March, 41 percent of bosses across 45 countries said they were investing in automation in preparation for a post-COVID-19 world.

Edge computing will help many companies reach their automation goals. Its decentralized framework doesn’t replace but rather complements cloud computing by enabling data processing at the production site (the “edge”), resulting in lower latency, higher bandwidth, and reduced network overheads. Equipping IIoT devices with edge-enabled data storage and computing capabilities gives manufacturers insight into their operations by allowing even the smallest IIoT sensors, instruments, and other devices to connect to wireless networks via gateways, gatherings and share real-time data that leads to rapid decisions and fast responses.

By taking immediate action, machine performance is optimized, and predictive analytics can identify and prevent equipment failure saving high costs. Smarter predictive maintenance is not the only IoT-derived benefit to find its way onto the production line; insights gathered from these sensors can optimize processes or performance of assets at a time when it’s needed most.

Although edge data centers contain network equipment and servers that power cloud computing services and various video and social media platforms, they also house mission-critical data, applications, and services that support enterprises’ emergency systems. Many of these data centers are placed near the areas they serve, enabling companies to make autonomous decisions without human intervention. The centers have proven particularly important during the current health crisis. Many data center operators continue to limit employee and vendor access to their facilities in favor of remote management. They may also assist in reshoring efforts as global supply chains reconstitute regional production capabilities.

Edge Computing and 5G Help Compute Local Traffic

Shorter supply chains are inherently risky — production lines can be set up to meet increased market demand, but demand can dissolve quickly. Yet, there are also great benefits to regionalizing production, and IIoT systems, devices, and sensors can help the manufacturers position their operations to respond to fluctuating production demands.

To compute local traffic, 5G allows tens of thousands of devices to access individual cells while edge devices perform complex processing tasks. This helps prevent the slightest loss of connectivity or speed, rendering digital services useless, impacting mission-critical systems, or causing dangerous problems for services such as driverless transportation or industrial machinery.

Before the pandemic, networking technologies such as 5G were already preparing for surges in increased network traffic and big data. The recent disruptive global events shone a spotlight on the need for intelligent edge computing technologies to keep networks from overloading while transporting data from the cloud to the edge.

Although 5G is still a developing technology, it’s expected that it will help create more agile networks tailored to different enterprises’ varying needs at both global and localized levels. For example, the Port of Rotterdam, the largest port in Europe, has worked with Huawei,  KPN, ExRobotics, Accenture, Shell, and ABB to test the first industrial 5G applications use sensor data to optimize operational performance and automate the movement of vessels and goods. Whereas previous generations of wireless technology connected people and the internet, 5G connects things to people, to the internet, and, importantly, to other things.

One of the latest developments in 5G is O-RAN (Open Radio Access Network), which expands mobile networks’ performance and efficiency even more broadly. In 2019, Vodafone launched O-RAN trials in the UK after previously launching trials in the Democratic Republic of Congo and Mozambique to lower the cost of network equipment, making wireless networks more democratic in both rural and urban areas, and increasing the potential for 5G.

Offsetting Costs

The global supply chain is at a pivotal stage in its evolution, with all signs pointing to manufacturing becoming more regional. Faster processing at the edge fits perfectly into many enterprises’ digital transformation projects. In fact, it may accelerate them by giving manufacturers better insights into the entire supply chain while supporting data-driven local production.

Originally writtem by
Arm | December 9, 2020
for IoT for all


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A new series of vulnerabilities dubbed Amnesia:33 puts millions of IoT devices at risk of being compromised.

Security researchers from Forescout disclosed the 33 vulnerabilities today. The flaws are found in four open-source TCP/IP libraries used in the firmware of products from over 150 vendors.

According to the researchers’ estimates, millions of consumer and enterprise IoT devices are at risk from Amnesia:33 vulnerabilities.

The affected libraries are uIP, FNET, picoTCP, and Nut/Net. Manufacturers have used these libraries for decades to add TCP/IP support to their products.

Here are the number of vulnerabilities discovered in each library:

  • uIP – 13
  • picoTCP – 10
  • FNET – 5
  • Nut/Nut – 5

uIP, the most vulnerable library, was also found to be used in the highest number of vendors.

Forescout also analysed the following libraries but did not find any vulnerabilities: lwIP, CycloneTCP, and uC/TCP-IP. 

Due to the prevalence of these libraries, just about every type of connected hardware is impacted by Amnesia:33—from SoCs to smart plugs, from IP cameras to servers.

Unlike the previously disclosed Ripple20 vulnerabilities, Amnesia:33 primarily affects the DNS, TCP, and IPv4/IPv6 sub-stacks.

Ripple20 and Amnesia:33 vulnerabilities both predominately consist of Out-of-Bounds Read, followed by Integer Overflow.


IoT devices (46%) represent the highest number of affected device types, according to Forescout’s research. This is followed by OT/BAS and OT/ICS at 19 percent, and then IT at 16 percent.

You can find a copy of Forescout’s full report here.

Originally published by
Ryan Daws | December 8, 2020
IoT News

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Silver Level Contributor

Why Edge Computing Matters in IoT

Image: Unsplash - Living Smarter

Edge computing is critical for many IoT applications, enabling lower latency and decreased bandwidth usage. However, most people miss one of the most important benefits of Edge computing when it comes to IoT.

Before we get to this key, overlooked benefit, let’s define both Edge computing and Cloud computing.

Cloud vs Edge

“Cloud Computing is the on-demand availability of computer system resources, especially data storage (Cloud storage) and computing power, without direct active management by the user.” (Wikipedia)

Edge Computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth” (Wikipedia)

Before the age of Cloud computing, organizations had to buy their own physical servers to get the computational power and storage they needed. This was expensive upfront (to buy all the hardware and set it up) and expensive to manage (to maintenance and update). Cloud computing means that businesses no longer need to purchase and manage the hardware. The business can pay for what it needs, and the Cloud provider takes care of management.

Cloud computing has profoundly impacted, providing scalability, reliability, security, and ease-of-use to businesses. However, Cloud computing isn’t perfect and comes with tradeoffs.

Cloud computing is centralized, which means that no matter where the end-device (e.g., your smartphone) is located, data needs to travel from the end-device, over a network (e.g., a 4G cellular connection), to the data centers of the Cloud provider. And then do that again in reverse to reach the end-device. For applications that require a lot of data to be transferred quickly, this can be both slow and expensive.

This is where Edge computing comes in. To understand the benefits of Edge computing, autonomous vehicles are often cited as an example:

  1. Latency: Autonomous vehicles need to make split-second decisions. If a car swerved in front of you, would you want your vehicle to have to wait to get instructions from the distant Cloud? No! You want your car processing on its local computer to make a decision as fast as possible.
  2. Bandwidth: Autonomous vehicles capture a LOT of data estimated at 4 Terabytes per hour of driving. Compare that to the average of 100 Megabytes per day for your smartphone, and that’s 40,000x the data. Streaming all this data would be both expensive and could lead to network congestion.

For both of these reasons, it makes sense to perform computation at the Edge (in this case, on the vehicle itself) for autonomous vehicles.

The question of Cloud computing and Edge computing isn’t a question of which to use. Both Cloud and Edge have their strengths depending on the context. The question to ask is when to use Cloud computing vs. Edge computing.

A helpful rule of thumb is this: “Cloud computing operates on big data while Edge computing operates on ‘instant data’ that is real-time data generated by sensors or users” (Wikipedia).

What is the “Edge” Exactly?

The Edge basically means “not Cloud” because what constitutes the Edge can differ depending on the application. To explain, let’s look at an example.

In a hospital, you might want to know the location of all medical assets (e.g., IV pumps, EKG machines, etc.) and use a Bluetooth indoor tracking IoT solution. The solution has Bluetooth Tags, which you attach to the assets you want to track (e.g., an IV pump). You also have Bluetooth Hubs, one in each room, that listens for signals from the Tags to determine which room each Tag is in (and therefore what room the asset is in).

In this scenario, both the Tags and the Hubs could be considered the “Edge.” The Tags could perform some simple calculations and only send data to the Hubs if there’s a large sensory data change. The Hubs could aggregate data from the Tags, calculate each Tag’s position, and only send data to the Cloud if a given Tag has moved to a different room in the hospital. Both of the above approaches could be combined. Or neither could be used, and the Tags could send all raw data to the Hubs, and the Hubs could send all raw data to the Cloud.

The Key, Overlooked Benefit of the Edge for IoT

As teased at the beginning of this article, there’s a key benefit that almost everyone overlooks when evaluating Edge computing.

We already covered the benefits to Latency (faster response) and Bandwidth (reducing bandwidth requirements and saving data costs). Still, these benefits are for a particular subset of IoT applications such as autonomous vehicles, smart home, or security cameras.

The Coming of LPWAN IoT

One of the issues with the term ”IoT” is how broadly it’s defined. Autonomous vehicles that cost tens of thousands of dollars collect Terabytes of data and use 4G cellular networks are considered IoT. At the same time, sensors that cost a couple of dollars collect just bytes of data and use Low-Power Wide-Area Networks (LPWANS) are also considered IoT.

The problem is that everyone is focusing on high bandwidth IoT applications like autonomous vehicles, the smart home, and security cameras. That’s because everyone is a consumer, so the people writing about these things have a much bigger audience when writing about the consumer space than when writing about the enterprise. Enterprise IoT is directly relevant to fewer people and can be somewhat boring.

LPWAN IoT is poised for rapid growth and is where the truly transformative nature of IoT will be most felt.

When it comes to LPWAN IoT applications, energy consumption is critical because it’s not for other IoT applications. Autonomous cars will have massive batteries and be recharged regularly. Smart home devices and security cameras are plugged directly into outlets. 

However, if your business is placing GPS trackers on all 20,000 of your vehicles on your automotive auction lot, the batteries in those GPS trackers better last a few years! Replacing 20,000 batteries on any timeframe less than a few years would be a huge operational headache and costly to manage. The benefits you’d get from knowing where your vehicles are in real-time would be heavily outweighed by the sheer costs of just managing the system.

Edge Computing Reduces Energy Consumption

When it comes to battery-powered devices’ energy consumption, do you know what costs the most energy? The wireless radio. Sensors and simple computations usually don’t consume much energy, but sending and receiving wireless messages does. The lower the number and the smaller the messages sent and received, the longer the devices can last on battery (all wireless connectivity represents a tradeoff between power consumption, range, and bandwidth).

Edge computing is therefore highly effective for LPWAN IoT applications if devices perform calculations on the device itself, the number and size of messages, and use logic to reduce messages.

Originally published by
IoT For All | December 4, 2020

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Gold Level Contributor

Image: Unsplash - Umberto

Since its introduction, eSIM technology has been lauded as a new step in the evolution of connected gadgets. Arguably the most interesting implications are in the domain of IoT devices, which are expected to receive improvements in reliability, security, and longevity by using embedded chips instead of cards. However, despite being on the market for more than four years, eSIM technology is used predominantly in phones, laptops, and portable modems.

In this article, we’ll look at why eSIM is expected to be the next step in the evolution of IoT devices and why this evolution is still almost nowhere to be seen.

Advantages that Drive IoT Adoption

eSIM technology offers many benefits for IoT devices. Some are incremental improvements that are in line with SIM cards’ evolution, while others are complete game-changers. Below is an overview of the most noteworthy ones. A word of warning, though – many of these are still in the untested theory domain, with some more speculative than others.


The killer feature of eSIM is switching between several profiles by downloading them onto a chip. While a great quality-of-life improvement, to anyone concerned with cybersecurity, this is a glowing warning sign as it suggests the possibility of tampering. Hypothetically, a malevolent party may try to push a profile onto a target device to access it.

To address this risk, GSMA has proposed a layer of protection called SM-DP+. This solution involves verification through an external server, which prevents profile switching from any other device. It’s still difficult to say how reliable the solution is without widespread adoption. Nevertheless, it shows promise in terms of a secure connection.

Digital threats aside, eSIM has a massive advantage that stems from its embedded nature. Unlike a regular SIM card, which is easy to remove, eSIM is hardwired into the device. This makes the device traceable whenever it is turned on, which may actually discourage theft.

Design Improvements

eSIM follows the route of miniaturization taken by SIM cards. It is much smaller for starters than its predecessor, measuring around 2.5 by 2.5 mm compared to 12.3 by 8.8 mm for the nano-SIM card. This is not really a fair comparison since we’re talking about a chip versus a card with housing and a connector, but an important factor nonetheless. IoT devices vary considerably in size, which is a valuable asset for many applications:

  • Beacons and tracking devices
  • Standalone connectivity solutions
  • Healthcare devices
  • Wearables

Besides its tiny size, the embedded design of eSIM allows getting rid of a slot, which eats up a lot of space inside the gadget, not to mention additional complexity. In this light, the technology removes several restrictions that slow down the growth of the IoT segment.


Aside from being bulky, the SIM card port mentioned above introduces a considerable drawback – it compromises the gadget’s casing’s integrity. This is not an issue for phones, which are gentle devices that need to be handled with care anyway. On the other hand, many IoT gadgets are often exposed to the elements, with some even expected to work in harsh environments. There are a lot of challenges such devices need to withstand:

  • Moisture
  • Extreme temperatures
  • Vibration
  • Physical impacts
  • Dust and other particles

With eSIM, this is not really an issue since the casing can be made as secure as possible, which will extend the device’s lifespan significantly. Simultaneously, the absence of connectors minimizes the chance of failure or malfunction, further increasing the reliability of the gadget.


Finally, it is worth mentioning the strategic-scale considerations. eSIM uses a single standard that is supported by many carriers and hardware manufacturers. This is a major argument in favor of widespread adoption, especially in fields that are slow at accepting innovation. By extension, this means opportunities for scalability, allowing the deployment of eSIM-powered IoT solutions on a large scale.

Current State of eSIM in IoT

Despite all the advantages, the actual adoption of eSIM in the IoT domain is painfully slow. Currently, eSIM-ready devices on the market are quite rare. Major manufacturers are expressing interest in eSIM solutions for IoT devices, but aside from that, the actual products are only starting to emerge. This can be attributed to several factors:

Novelty: The technology is just four years old – enough for designing a gimmicky gadget but too short for rolling out industry-grade solutions.

Technological challenges: Despite having layers of protection, the technology has not yet stood the test of time to prove its trustworthiness.

Regulation: In addition to being technically standardized, the solution should also comply with local regulations, making it less feasible.

Lack of Vision: The shortage of business models and successful implementation examples takes time to be filled.

As can be seen, nothing of the above is insurmountable. In fact, these are routine challenges to any new solution, some of which are already being addressed.

The Future of eSIM

eSIM technology sounds like a perfect match for IoT devices, offering improvements in security, design, and scalability of solutions. To be fair, most of these advantages are yet to be proven, with actual examples of successful use still in short supply. Fortunately, new attempts are emerging on a daily basis, so while some miraculous promises may be proven false, others will undoubtedly benefit the innovation among IoT devices.

Originally wriitten by
Arkadiy Kvashuk - Chief Wordsmith, nectMODEM | December 2, 2020
for IOT For All



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Gold Level Contributor

Image: Bady Abbas - Unsplash

Snøhetta has designed a landmark mixed-use building in the centre of Hong Kong’s former Kai Tak airport with views over the Victoria Harbour and Kai Tak River. It is currently under construction and is expected to be completed in 2022.

The building is situated at a unique and rapidly transforming location in central Hong Kong. The Kai Tak Airport was formerly the international airport of Hong Kong from 1925 until 1998, after which it was closed and replaced by a new and larger one at Chek Lap Kok, 30km to the west.

CBD 2.0 focal point

With the recent developments in the area, the site is transforming into a new central business district (CBD 2.0), a focal point of urban development and commerce.

The 176,000sqm building merges a 200m tower with a base in one continuous form and will serve as the main gateway to the Kai Tak development in Hong Kong, while offering visitors access to public spaces through a series of exterior plazas and rooftop gardens. It is located on top of the Kai Tak metro station and in proximity to other public transport interchanges.

Snøhetta explained that Airside advocates a sustainable green lifestyle supported by unique facilities. These include Hong Kong’s first ever automatic bicycle parking bay to encourage green mobility, use of local materials, sky farming, automated smart waste sorting and storage, natural ventilation, daylight enhancement, solar radiation protection, focus on thermal comfort, water-saving and rainwater management.

“We are proud to be working on an urban project of this scale with such a strong determination to offer an inviting space for the people of Hong Kong”

Commissioned by the Nan Fung Group, it is designed to target the highest sustainability ratings including LEED platinum. Its flexible design allows for tenants to efficiently adapt the building to future needs, aiming to be a pleasant place for people to stay and work in throughout the building’s lifespan.

The building marks Snøhetta’s first built project in Hong Kong. It said a gently curving facade composed of fluted glass is evocative of the textile that anchored Nan Fung Group’s historic industry, and is present throughout the project from the façade to the interior and landscape design.

The textile pattern can also be read as a nod to the development of both the Nan Fung Group and the city of Hong Kong as a whole, as they both have experienced a transformation in focus from textile manufacturing and industry to real estate development, finance and tech.

“As Snøhetta’s first built project in Hong Kong, we hope that this building will serve as a place for both commercial activities and recreation for many years to come”

As one of the most densely populated cities in the world, Hong Kong’s commercial spaces are typically designed to have the urban landscape extend into its buildings. To support the thousands of people that flow through the Kai Tak metro station on a daily basis, the building’s retail space at the base is designed to accommodate this intense pedestrian traffic.

In order to achieve this, the continuous building mass is composed of five volumes that step up from the Kai Tak River and culminates in the tower. The building’s unique shape creates a series of human-scale urban spaces at grade, and rooftop gardens which have views of the Victoria Harbour and the Kai Tak park.

The building further offers visitors access to public spaces and green gardens through a series of exterior plazas and rooftop gardens suited for urban farming, restaurants, events and recreation. At the heart of the building is a retail atrium of almost 66,000sqm filled with natural light. This central atrium space culminates in a rooftop garden, above which a tower tops out at 200m containing more than 110.000sqm of grade A office space, retail spaces, as well as provisions for a hotel.

“We are proud to be working on an urban project of this scale with such a strong determination to offer an inviting space for the people of Hong Kong,” said Snøhetta managing partner, Robert Greenwood. “As Snøhetta’s first built project in Hong Kong, we hope that this building will serve as a place for both commercial activities and recreation for many years to come.”

Originally published by
SmartCitiesWorld news team | November 27, 2020
Smart Cities World

original article with images

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Gold Level Contributor

GSMA Intelligence and Juniper Research argue that 5G and Embedded SIMs (eSIM) will play a significant role in industrial IoT

Despite some initial slowdown in adoption during this year’s healthcare crisis, the number of connected IoT devices keeps growing. The latest figures released by Juniper Research indicate that in just five years, industrial IoT connections will more than double, going from 17.7 billion in 2020 to 36.8 billion in 2025.

This year’s pandemic has sped up the desire to automate more industrial processes further, as factories need to prepare for more restrictions and potential lockdowns. Additionally, many of the current processes requiring a machine operator’s presence could be automated or remotely controlled, allowing some factory workers to work from home or in a more protected environment.

Additionally, two new cellular technologies can further penetrate the industrial IoT market: the fifth generation of cellular networks (5G) and embedded subscriber identity module (eSIM).

Initially adopted for connected cars and wearables, eSIMs are now entering the industrial space, especially for massive IoT deployments. The ability to deploy thousands of IoT devices, especially sensors, perform secure onboarding, and provision cellular credentials over the air, makes eSIMs a key technology for adoption in several industries.


According to GSMA Intelligence, “eSIM adoption in the IoT market is still low relative to its long-term potential. Automotive is an exception and a benchmark for other verticals. Connected cars account for a significant share of eSIM connections today. […] Beyond connected vehicles and wearables, eSIM could become the primary means of cellular network authentication in other use cases such as consumer electronics, utilities, and smart manufacturing, especially if 5G adoption reaches scale in the enterprise market.”

Juniper Research suggests that 5G roll-out has the potential to introduce next-generation functionalities to industrial IoT, including:

  • Video data processing to drive real-time quality testing of output from smart factories and machines.
  • The ability to remotely manage and diagnose connected machinery.
  • The introduction of industrial robots using real-time latency; enabling machines to work safely alongside humans.
  • Data processing in the cloud or at the edge.
  • Deployment of AI within connected networks; allowing for advanced analysis of machine functionality and potential threats.


Network slicing and ultra-low latency are crucial for 5G IoT adoption

Another key technology supported by 5G is Network Slicing. By creating different slices of the spectrum for critical use, specific services can enjoy secure, continuous connectivity and low latency. Some critical applications such as healthcare, autonomous vehicles, and remote machine operation require this reliability level.

Additionally, the ultra-low latency that the 5G New Radio (5G NR) provides will enable industries to perform critical tasks such as remote machine operation, autonomous driving, secure monitoring, and emergency shutdowns.

As the new 5G networks are deployed, the availability of these and other new capabilities are beginning to catch many industries’ attention. It will take several years, however, for the full capabilities of 5G to be widely available.

GSMA Intelligence report says that “Covid-19 reduces car sales, but also drives stronger-than-ever urgency for digital transformation.” Adding that “while cellular networks currently serve 15% of total IoT connections, the explosion of the IoT market provides significant room for growth in the cellular IoT space and, within it, eSIM adoption.”

Juniper Research author Scarlett Woodford noted: “Manufacturers must exercise caution when implementing IoT technology; resisting the temptation to introduce connectivity to all aspects of operations. Instead, manufacturers must focus on the collection of data on the most valuable areas to drive efficiency gains.”

Originally published by
Pablo Valerio | November 24, 2020
IoT Times


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Platinum Level Contributor

Managing IoT Data Security Risks

The use of 5G technology and IoT infrastructure is about to change our lives in ways we coud not of imagined a few years back, with access to new applications and new types of "smart devices" and the ability for devices to communicate between each other, its breath taking and mind blowing thinking about the possibilities, but  there are still issues holding back this capability taking off into the mainstream and that is the concern over personal and data security issues (and rightly so).  The article below "The Need to Secure Data in Modern Computing"  written by  Matthew Rosenquist and published on, hits the nail on the head, on why there needs to be a new approach to security in this environment. The reason for this is that we have compute and connectivity capability built into your toaster, refrigerator, door bell, inodoor/outdoor lights, street lights and traffic lights and we could go on and on. The variety of devices, the lack of management, power and resources in general mean that we can not protect these devices andthe underlying infrastructure the way that traditional computing infrastructure can be protected. So although I don't personally endorse the techniques and the platform that Matthew and the rest of the Eclipz team have suggested and are bringing to market, as I believe there will be other ideas coming forward also, I do applaud them for making the bold step and thinking outside the box and to take head on a difficult problem inorder to move an industry forward.



The Need to Secure Data in Modern Computing



Matthew Rosenquist, Published on on November 24th 2020
Cybersecurity Strategist and CISO specializing in the evolution of threats, opportunities, and risks in pursuit of optimal security



Smart devices are everywhere and being integrated into all facets of our lives, from toothbrushes to automobiles. Entire cities are becoming ‘smart’, as are factories, governments, global retail, freight logistics, and all national critical infrastructure sectors. As individuals, we are becoming hubs for multiple connected devices in our homes and on our persons. Phones, watches, health monitors, medical devices, and clothing manufactures have joined in to develop connected apparel and accessories. Cameras, doorbells, appliances, televisions, thermostats, voice assistants, and light fixtures are just the beginning of the digitalization of our homes. These wonderful tools of the modern world, some no bigger than a coin, provide amazing capabilities and tremendous convenience; they connect and enhance our lives in amazing ways.

Unfortunately, they also introduce equitable risks. The aggregated risks from all the Internet-of-Things (IoT) devices, now approaching 50 billion in number, adds up to a big problem for everyone.

Sadly, the dark secret is that IoT and their close cousins Industrial IoT (IIoT) devices which we typically embrace, are very insecure. These systems are notoriously hackable; the data they create and share is often vulnerable to exposure, and the devices themselves can be leveraged as a platform by attackers to target more important systems in our lives. IoT insecurity represents one of the next great challenges for the technology industry that is struggling to preserve the trust of consumers from cyber threats which are easily finding ways to undermine the security, privacy, and safety of users.

Most IoT devices are miniature and very limited when it comes to the computing resources necessary for secure capabilities. It is difficult to know who owns or possesses them, if they have been hacked, and if they are acting in undesired ways. This makes IoT devices not very trustworthy. To compound the problem, IoT devices tend to share data over insecure networks like wireless and the Internet. This mix is a recipe that cybercriminals and hackers enjoy.

The functional backbone for IoT devices is all about gathering, processing, and sharing data. One of the primary challenges is to protect the data going to and emanating from the devices. Legacy technology largely fails when it comes to secure communications at this scale and difficulty. More comprehensive, effective, and sustainable capabilities are needed to keep pace with evolving threats.

Connecting IoT technologies to share data securely is difficult. Some standards exist for specific use-cases, such a web browsing, but most of the emerging IoT devices and services require a synthetization of architectures, algorithms, and compatibilities that current solutions don’t satisfy. That is why we are seeing a flood of IoT compromises and the future advances of hackers will only increase the victimization unless something extraordinary happens.

Where there is innovation leadership, hope survives.

Protecting digital data is important for everyone. Andy Brown, CEO of Sand Hill East, and I penned a joint article Managing IoT Data Breaches, that was published in the Sept 2020 issue of Cybersecurity Magazine, describing the scale and complexity challenges of IoT data protection. Innovation is needed to safeguard data in the new digital landscape!

After 30 years in the industry, I anticipated the future needs and realized the upswell of insecure devices would put everyone at risk if sensitive data could not be protected. I joined the Eclipz team as an Advisory Board member to help advance and tailor the greatly needed capabilities into the commercial market for everyone’s benefit. The Board of Directors asked that I join a stellar executive team as the CISO to further help empower the best technology to make devices and the global digital ecosystem more trustworthy.

Eclipz is an elegant and robust capability to connect untrusted endpoints across insecure networks in ways that protect data from current and evolving threats. Eclipz is not a product unto itself, but rather an architecture and code integrated into everyday products and services, empowering them to communicate securely. That makes it ultimately scalable. It can be applied to protect a vast array of devices, infrastructures, and experiences across every market, making the technology and services people use more secure by protecting the flows of data. The explosion of IoT devices poses one of the greatest attack surfaces ever known and must be better secured. Eclipz technology can strengthen the foundations of IoT ecosystems for the benefit of the global digital community.

Link to Original Article

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Gold Level Contributor

4 IoT Medical Devices That Are Vulnerable to Hacks

The Internet of Things (IoT) has made it easier for point-of-care centers to track and analyze sensitive medical data for their patients. But with so much confidential data transmitting to and from physicians, it’s crucial that IoT medical devices use safe communication protocols that encrypt their data.

Unfortunately, many IoT medical devices have major security vulnerabilities, which put patient data at too much risk and can make it harder for healthcare professionals to rely on them in the future. What’s more, many IoT devices rely on a limited pool of computing resources, which makes it tough to create solutions that can keep their data encrypted on wireless networks.

To better understand the security vulnerabilities that IoT medical devices face, it’s important to know exactly which products are most at risk of being hacked. In this article, we will cover the four IoT medical devices that are most susceptible to cybersecurity breaches and how to protect them.

1 – Wireless Infusion Pumps

Wireless infusion pumps, as the name may suggest, remove the need for physicians to give their patients vital medical fluids in-person. Instead, these IoT devices can talk with a patient’s electronic health records to speed up fluid infusions and cut down on healthcare costs.

However, the wireless connection protocols that these pumps use can provide low-hanging fruit for cybercriminals to pluck. Wireless infusion pumps, just like a tablet or home computer, need to be hooked up to a network to take in data from a server and send it back out to receiving devices, which makes them vulnerable to malicious software that finds its way onto a wireless network.

Protecting IoT data on the cloud can help point-of-care centers avoid threats on an unencrypted physical network. This is because cloud storage services such as Google Drive or DropBox offer a reduced number of entry points that hackers can use to gain access to a network and compromise IoT devices.

Furthermore, medical organizations can use Google Drive and Dropbox for storing files that contain protected patient information while maintaining HIPAA compliance, so long as a business associate agreement (BAA) is signed with either service.

2 – Implanted Devices

Implanted devices, like the ones that track your body’s cardiovascular functions, wirelessly transfer patient data to expedite the healthcare they receive. However, a faster rate of data transfer doesn’t mean much if it compromises a patient’s confidentiality and puts their health at risk. Hackers who remotely access implanted medical devices can wreak havoc on their functionality and subsequently endanger patients’ lives.

The biggest security issue with implantable devices lies in the way they communicate with each other. Wireless communication systems, like Medtronic’s Conexus protocol, often fail to stop data breaches because they don’t include an incident response plan. Fortunately, in early 2020 Medtronic released patches for security flaws for its devices that had been disclosed in the prior two years.

While this can offer a little assurance, the simple fact remains that these kinds of devices still freely transmit wireless information without authenticating or encrypting it, and they have no Plan B in place in the event that hackers intercept their data. It’s no surprise, then, that implantable devices can be exploited by cyber breaches such as DDoS attacks.

3 – Smartpens

Smartpens are a godsend to physicians who need to quickly access a complete snapshot of their patient’s medical background. These small IoT devices can store and quickly transmit massive amounts of sensitive data to pharmacies and point-of-care centers. It certainly sounds convenient for both patients and doctors, but much of their information is at risk of being compromised.

Smartpens, like implanted devices, expose themselves to cybercriminals with gaping backdoors that can be opened via their network communication protocols. Instead of safely accessing medical records by installing protective software, smart pens often rely on servers directly connected to the internet to store and access sensitive data. Once a hacker exploits these communication protocols, there’s not much left standing in the way between them and a server filled to the brim with confidential patient records.

4 – Vital signs monitors

The IoT makes it possible to remotely monitor a patient’s vital signs using Bluetooth technology and allows doctors to rapidly respond to changes in a patient’s vitals, but it comes at the cost of low-quality encryption methods. This is why as an additional option to relying on the cloud to store patient data, healthcare companies should investigate alternative encryption protocols that target low-power IoT devices.

One solution is for medical companies to make it a policy to always use virtual private networks (VPNs) that come with proven encryption protocols like IKEv2 or L2TP/IPSec when connecting IoT devices to the organization’s network. Using a VPN will hide the IoT devices’ IP addresses and ensure that company and patient data transmitted over the network are kept untraceable.

In any case, encryption protocols need to start compensating for vital signs monitors’ limited pool of computing resources by becoming more sophisticated. Right now, too few encryption protocols for IoT vital monitors sacrifice their quality by being low-power solutions themselves.


It’s crucial for IT teams and cybersecurity personnel working for healthcare companies to know what medical devices powered by IoT are most at risk of hacking and cyber-attacks. A complete understanding of how data assets become vulnerable can help medical organizations figure out how to protect them. This becomes truer than ever as more IoT medical devices are being developed and deployed to hospitals, health clinics, and even patients’ own homes.

Healthcare businesses can give their IT departments a head start in the near future by combining a monitoring view of their active IoT medical devices with the rest of their security initiatives. Right now, the solutions to gain broader visibility into each IoT device that is online are limited. However, creating strategies to discover and detect security threats that integrate with IoT medical devices can safeguard sensitive medical data and protect vulnerable patients.

Originally written by
Ludovic F. Rembert, Head of Research at Privacy Canada | November 11, 2020
IoT Business News

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Silver Level Contributor

MIT researchers have developed a system, called MCUNet, that brings machine learning to microcontrollers. The advance could enhance the function and security of devices connected to the Internet of Things (IoT)

System brings deep learning to “internet of things” devices

Advance could enable artificial intelligence on household appliances while enhancing data security and energy efficiency.

Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the “internet of things” (IoT).

The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.

The research will be presented at next month’s Conference on Neural Information Processing Systems. The lead author is Ji Lin, a PhD student in Song Han’s lab in MIT’s Department of Electrical Engineering and Computer Science. Co-authors include Han and Yujun Lin of MIT, Wei-Ming Chen of MIT and National University Taiwan, and John Cohn and Chuang Gan of the MIT-IBM Watson AI Lab.

The Internet of Things

The IoT was born in the early 1980s. Grad students at Carnegie Mellon University, including Mike Kazar ’78, connected a Cola-Cola machine to the internet. The group’s motivation was simple: laziness. They wanted to use their computers to confirm the machine was stocked before trekking from their office to make a purchase. It was the world’s first internet-connected appliance. “This was pretty much treated as the punchline of a joke,” says Kazar, now a Microsoft engineer. “No one expected billions of devices on the internet.”

Since that Coke machine, everyday objects have become increasingly networked into the growing IoT. That includes everything from wearable heart monitors to smart fridges that tell you when you’re low on milk. IoT devices often run on microcontrollers — simple computer chips with no operating system, minimal processing power, and less than one thousandth of the memory of a typical smartphone. So pattern-recognition tasks like deep learning are difficult to run locally on IoT devices. For complex analysis, IoT-collected data is often sent to the cloud, making it vulnerable to hacking.

“How do we deploy neural nets directly on these tiny devices? It’s a new research area that’s getting very hot,” says Han. “Companies like Google and ARM are all working in this direction.” Han is too.

With MCUNet, Han’s group codesigned two components needed for “tiny deep learning” — the operation of neural networks on microcontrollers. One component is TinyEngine, an inference engine that directs resource management, akin to an operating system. TinyEngine is optimized to run a particular neural network structure, which is selected by MCUNet’s other component: TinyNAS, a neural architecture search algorithm.

System-algorithm codesign

Designing a deep network for microcontrollers isn’t easy. Existing neural architecture search techniques start with a big pool of possible network structures based on a predefined template, then they gradually find the one with high accuracy and low cost. While the method works, it’s not the most efficient. “It can work pretty well for GPUs or smartphones,” says Lin. “But it’s been difficult to directly apply these techniques to tiny microcontrollers, because they are too small.”

So Lin developed TinyNAS, a neural architecture search method that creates custom-sized networks. “We have a lot of microcontrollers that come with different power capacities and different memory sizes,” says Lin. “So we developed the algorithm [TinyNAS] to optimize the search space for different microcontrollers.” The customized nature of TinyNAS means it can generate compact neural networks with the best possible performance for a given microcontroller — with no unnecessary parameters. “Then we deliver the final, efficient model to the microcontroller,” say Lin.

To run that tiny neural network, a microcontroller also needs a lean inference engine. A typical inference engine carries some dead weight — instructions for tasks it may rarely run. The extra code poses no problem for a laptop or smartphone, but it could easily overwhelm a microcontroller. “It doesn’t have off-chip memory, and it doesn’t have a disk,” says Han. “Everything put together is just one megabyte of flash, so we have to really carefully manage such a small resource.” Cue TinyEngine.

The researchers developed their inference engine in conjunction with TinyNAS. TinyEngine generates the essential code necessary to run TinyNAS’ customized neural network. Any deadweight code is discarded, which cuts down on compile-time. “We keep only what we need,” says Han. “And since we designed the neural network, we know exactly what we need. That’s the advantage of system-algorithm codesign.” In the group’s tests of TinyEngine, the size of the compiled binary code was between 1.9 and five times smaller than comparable microcontroller inference engines from Google and ARM. TinyEngine also contains innovations that reduce runtime, including in-place depth-wise convolution, which cuts peak memory usage nearly in half. After codesigning TinyNAS and TinyEngine, Han’s team put MCUNet to the test.

MCUNet’s first challenge was image classification. The researchers used the ImageNet database to train the system with labeled images, then to test its ability to classify novel ones. On a commercial microcontroller they tested, MCUNet successfully classified 70.7 percent of the novel images — the previous state-of-the-art neural network and inference engine combo was just 54 percent accurate. “Even a 1 percent improvement is considered significant,” says Lin. “So this is a giant leap for microcontroller settings.”

The team found similar results in ImageNet tests of three other microcontrollers. And on both speed and accuracy, MCUNet beat the competition for audio and visual “wake-word” tasks, where a user initiates an interaction with a computer using vocal cues (think: “Hey, Siri”) or simply by entering a room. The experiments highlight MCUNet’s adaptability to numerous applications

“Huge potential”

The promising test results give Han hope that it will become the new industry standard for microcontrollers. “It has huge potential,” he says.

The advance “extends the frontier of deep neural network design even farther into the computational domain of small energy-efficient microcontrollers,” says Kurt Keutzer, a computer scientist at the University of California at Berkeley, who was not involved in the work. He adds that MCUNet could “bring intelligent computer-vision capabilities to even the simplest kitchen appliances, or enable more intelligent motion sensors.”

MCUNet could also make IoT devices more secure. “A key advantage is preserving privacy,” says Han. “You don’t need to transmit the data to the cloud.”

Analyzing data locally reduces the risk of personal information being stolen — including personal health data. Han envisions smart watches with MCUNet that don’t just sense users’ heartbeat, blood pressure, and oxygen levels, but also analyze and help them understand that information. MCUNet could also bring deep learning to IoT devices in vehicles and rural areas with limited internet access.

Plus, MCUNet’s slim computing footprint translates into a slim carbon footprint. “Our big dream is for green AI,” says Han, adding that training a large neural network can burn carbon equivalent to the lifetime emissions of five cars. MCUNet on a microcontroller would require a small fraction of that energy. “Our end goal is to enable efficient, tiny AI with less computational resources, less human resources, and less data,” says Han.

Originally publilshed by
  MIT News Office | November 13, 2020

original article

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Gold Level Contributor


Global spending on smart city initiatives is expected to reach $327 billion by 2025, driven by strong growth in projects related to intelligent transportation, data-driven public safety, as well as platform-related and digital twin use cases.

The promise of operational transformation is exciting. If cities architect this correctly, they will be able to harvest unique operational insights from the myriad internet of things (IoT) devices generating massive amounts of data per second. Armed with real-time data analysis, managers can rapidly respond to events requiring immediate investigation, such as a sudden flood or a major traffic accident.

This is great, but let's not celebrate prematurely. Despite the hoopla, smart cities are failing to accomplish most of what's being promised. Not that the idea is flawed — software technology just isn't up to the task.

If cities hope to integrate real-time data sources and devices – such as AI-enabled IP and thermal cameras, IoT sensors, real-time location data or edge sensors – and build applications that will be able to monitor assets, events, people and environments, they need to be both event-driven and distributed. They must be collaborative and scalable. You can't take shortcuts.

Too often, however, we see unwieldy Rube Goldberg-like contraptions built with antiquated tools that are destined to fail. In fairness, it's exceptionally hard to write software that connects everything, not to mention the myriad challenges getting applications to work together in a reliable, scalable manner. But there's no getting around the following:

  • Reliability: A smart city isn't going to be very smart if buggy applications require frequent downtime to install updates and to fix problems. Software needs to be connected 24 x 7 and everything needs to operate at full capacity, no matter what.
  • Response time: Most deployments don’t feature the real-time response and situational awareness that are critical components of any smart city infrastructure.
  • Security: This will continue to be an issue as long as smart cities use applications which rely on databases that routinely capture everything.

Navigating past the shoals

Privacy advocates have already raised qualms about the security of personal information in the era of IoT. Given the massive amount of data ricocheting around a smart city network, they justifiably worry about potential worst-case scenarios.

Clearly, if all this data is going to get captured and stored for processing and analysis, that's going to invite the attention of cyber criminals and others bent on mischief. But modern, event-driven applications don’t require the use of databases. They distribute compute resources and other services out to the edge to guarantee maximum performance, and they filter out huge volumes of extraneous data in order to focus on data related to critical events. When an event is detected, all the required processing and system actions are executed at the edge, immediately.

Two big pluses here: response time is shorter and no personally identifiable information is stored in a database. After a situation has been resolved, the data can be deleted from all of the edge devices.

What's more, most incidents won't require divulging the identity of the individuals involved. Even if the system spots somebody being attacked, you don't have to identify either the attacker or the person being attacked. The system simply identifies the fact that somebody is being attacked and the system then alerts the police in real time.

Originally written by
Marty Sprinzen, CEO and co-founder of Vantiq | November 11, 2020
for Smart Cities Dive


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Silver Level Contributor

Image: Unsplash - Taylor Simpson

San Francisco (CNN Business)Amazon-subsidiary Ring is recalling hundreds of thousands of video doorbells after receiving reports of them catching fire.

The potential fire hazard impacts around 350,000 2nd generation Ring doorbells sold in the United States and roughly 8,700 more sold in Canada, according to a notice posted by the US Consumer Product Safety Commission (CPSC) on Tuesday. The $100 doorbells were sold on Ring's website and on Amazon (AMZN) between June 2020 and October 2020, according to the CPSC.

"The video doorbell's battery can overheat when the incorrect screws are used for installation, posing fire and burn hazards," the notice said.
According to the notice, Ring has thus far received 23 reports of doorbells catching fire and causing property damage, as well as eight reports of minor burns.

Ring did not immediately respond to a request for additional comment.
Customers can check whether their Ring doorbells are impacted by the recall at this link on the company's support website, by entering the model and serial number printed on the back of the device.

Ring, bought by Amazon in 2018, has been caught up in controversy in the past. Last year, it announced partnerships with more than 400 police departments across the United States to give law enforcement easier access to videos recorded on its doorbells. The partnerships allowed police to submit requests for video recordings for certain locations to help with active investigations.

But privacy advocates slammed the move, saying at the time that it threatened to create a 24/7 surveillance program.
More recently, in September, Ring unveiled the Always Home Cam — a $250 drone with an attached camera that can automatically fly around your house and stream video to your smartphone.

Originally published by
By Rishi Iyengar, CNN Business | November 10, 2020

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Silver Level Contributor

Image: Unsplash - Scott Webb

New guidelines from ENISA (European Union Agency for Cyersecurity) recommend that all stages of the IoT device lifecycle need to be considered to help ensure devices are secure.

The supply chain around the Internet of Things (IoT) has become the weak link in cybersecurity, potentially leaving organisations open to cyber attacks via vulnerabilities they're not aware of. But a newly released set of guidelines aims to ensure that security forms part of the entire lifespan of IoT product development.

The Guidelines for Securing the IoT – Secure Supply Chain for IoT report from ENISA sets out recommendations throughout the entire IoT supply chain to help keep organisations protected from vulnerabilities which can arise when building connected things.

One of the key recommendations is that cybersecurity expertise should be further integrated into all layers of organisations, including engineering, management, marketing and others so anyone involved in any part of the supply chain has the ability to identify potential risks – hopefully spotting and addressing them at an early stage of the product development cycle and preventing them from becoming a major issue.

It's also recommended that 'Security by Design' is adopted at every stage of the IoT development process, focusing on careful planning and risk management to ensure that any potential security issues with devices are caught early.

"Early decisions made during the design phase usually have impactful implications on later stages, especially during maintenance," said the report.

Another recommendation that organisations throughout the product development and deployment cycle should forge better relationships in order to address security loopholes which may arise when there's no communication between those involved.

These include errors in design due to lack of visibility in the supply chain of components – something which can happen when there's misunderstandings or lack of coordination between parts manufacturers and the IoT vendor.

However, not all responsibility should rely with IoT manufacturers, the paper also recommends that customers and end-user organisations need to play a role in supply chain implementation and can "benefit greatly from dedicating resources to studying the current landscape and adapting the existing best practices to their particular case".

"Securing the supply chain of ICT products and services should be a prerequisite for their further adoption particularly for critical infrastructure and services. Only then can we reap the benefits associated with their widespread deployment, as it happens with IoT," said Juhan Lepassaar, executive director or ENISA.

Originally published by
Danny Palmer| November 10, 2020

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