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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.”
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.”
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 Medium.com, 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 Medium.com 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.
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
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.
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
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.
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.
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.
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.
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.
Internet of Things (IoT) represents a digital mesh of internet-connected devices. IoT comes in various forms and sizes. They could be in your living room as a smart virtual assistant, a smart home security system, or your car in the garage. In the larger scheme of things, they take the form of smart cities that have traffic signals connected to the internet.
Statistics suggest that every second 127 new IoT devices are connected to the web. By 2021, at least 35 billion IoT devices will be installed globally. Such is the rapid growth of IoT. Unknown to many, there is a silent force that is enabling this rapid ascent of IoT: allied Digital Twins.
A Digital Twin is a virtual replica of a physical device. They are used by IoT developers, researchers, and scientists for running simulations without having a physical device. In a way, digital twins can be given credit for the mushrooming growth of IoT.
How Do Digital Twins Work?
An IoT device takes occupancy like a physical object in the real world. A digital twin is the virtual representation of the physical device in a system. It replicates the physical dimensions, capabilities, and functionalities of the IoT device in a virtual environment.
The sensors attached to the IoT device gather data and send it back to its digital twin. IoT developers and researchers use the data to create new schemas and logic and test it upon the digital twin. Once vetted, the working code is updated into the IoT device through over the air updates.
Digital Twin Use Cases
Digital Twin use cases exist in every industry and space of IoT. From delicate healthcare to mechanical manufacturing, digital twins can act as a pillar of support for IoT initiatives in every industry. We are already aware of AI-based chatbots and its use case in different industries, similarly here, a digital twin can be used.
IoT in healthcare takes the form of patient wearables, fitness trackers, motion trackers, etc. Digital twins enable developers to test out new functionalities, make the device take accurate readings, and also invent new ways to exchange data between the data and the servers. In fact, doctors can also use the digital twin of the patient to monitor their vital stats on a real-time basis.
For example, a doctor can visualize a patient’s vital health signs like heart rate, blood pressure, etc. using a digital twin. The digital twin eliminates the need to transfer to create separate physical records of the patient’s data thus eliminating errors the possibility of errors. Also, with patient wearables that are connected to cloud servers, the data can be transmitted to the doctor’s system without requiring the patient to be physically present for examination.
Oil and gas equipment, factory equipment, assembly lines — these are sophisticated utility equipment. Thanks to IoT, these sprawling surfaces have become data spewing smart devices. Digital twins can enable developers to have an ‘as-designed,’ ‘as-built,’ ‘as-operated’ version of the utilities in a virtual environment. This drastically reduces the possibility of mishaps that can cause downtime.
A classic example of this is managing power grids in an urban environment or even a manufacturing plant for that matter. Digital twins can be used as virtual depictions of the actual power grid that can help monitor the real-time power consumption, asset management, and predicting/repairing power outages all without having to station personnel on the site.
How can a digitally connected city become smart? Digital twins help look at the possibilities from multiple angles and suggest future plans. Developers can also toy with innovative ways to make IoT devices work. For example, in the event of a disaster, motion sensors can be used to identify locations where there are maximum activity and risks involved.
In fact, the student community in the UK and the Northumbrian Water authorities are already working together to create a digital twin of the city. The project led by the post-graduate students from Newcastle University will create a virtual twin of the city.
Chris Kilsby, professor of hydrology and climate change in Newcastle University’s School of Engineering, says, “The digital twin will not only allow the city to react in real-time to such freak weather events but also to test an infinite number of potential future emergencies.”
Digital Twins and IoT
From augmenting the ability to run diverse experiments to giving real-time insights, a digital twin helps IoT in a number of ways. Some of them are detailed below:
What will happen if the workflow is tweaked? Will it get more data, will it result in consuming less energy, will it result in better user experience? These are some insights that a digital twin can give in an IoT environment. All this without having to push updates for the physical device working in a production environment.
Experiments of any kind are difficult, to begin with. They incur expensive resources, and if they do not work out as planned can even cost more than planned. IoT is a relatively new technology, and there is an abundant need for experimentation. The experimentation needs to be carried out with judicious usage of resources. Digital twins provide the virtual infrastructure to conduct countless experiments even when there are not many physical devices available.
IoT’s most popular benefit is that it gives access to a large population of devices at the same time. This, in turn, is also a downside. A minor security flow can give room for hackers and unauthorized personnel to gain access to the IoT network. The risk is magnified when actual physical devices deployed in production are used for experimentation.
Digital twins take away that risk. It makes it possible for developers and researchers to safely toy with multiple scenarios before arriving at a final one that is secure and operationally feasible.
Digital Twins: Dr. Jekyll or Mr. Hyde?
Having the same replica of anything can be slightly troublesome. It gives room for misuse and also is considered dangerous from a security point of view. In other words, it is the classic Mr. Hyde or Dr. Jekyll scenario.
A Digital Twin is assured to be Dr. Jekyll. It helps IoT professionals to conduct diverse experiments without having the need for a physical device. It spares a lot of physical resources and also results in cost savings. Additionally, it also reduces the risk of mishaps that could happen if updates are pushed into live production.
While many around the world are watching and waiting for the U.S. Presential election results, let’s focus on three important ballot initiatives that passed. Two strengthen consumer privacy protections while another expands consumer access to data on vehicles. All are good steps forward for the IoT in their respective states but one could have a major nationwide impact.
Earlier this year, the city enacted a temporary ban on usage of this technology, which has repeatedly shown bias against people of color. The successful vote this week extends the ban for five years and provides at least $1,000 in fines awarded to citizens surveilled through facial recognition.
While using AI or ML to recognize objects or people shown on cameras can be a good thing (home security comes to mind), profiling or surveillance of people using facial recognition doesn’t sit well in most democracies. My hope is that these types of bans on facial recognition used by law enforcement becomes a nationwide standard.
My data is MY data
Crossing the coast to California, voters there passed Proposition 24. This new measure takes the 2018 California Consumer Protection Act further by establishing a state agency to enforce consumer privacy directives while tripling fines to $7,500 for violating child privacy laws. Companies that collect consumer data won’t be able to keep that data longer than necessary, although that timeframe is rather vague. Consumers can also prevent businesses from sharing their data and can correct collected information that’s deemed inaccurate. Lastly, businesses are limited by the amount of data they can collect “sensitive personal information” such as religion, race, or sexual orientation.
If you provide an IoT device or service in California, you’re surely already aware of the 2018 law. But you’ll need to follow the newer, stronger one as well. There is an exemption for companies that collect data from 100,000 or fewer consumers or households, so start-ups and the like aren’t required to comply. I would anyway since it’s just a best practice and trust is becoming a “feature” that can make an IoT device attractive or not.
I own it so I want the data from it
Finally, we’re heading to Massachusetts, where a new “right to repair” law could impact the entire U.S.
Voters overwhelmingly passed this measure which requires automakers that collect and upload telemetry data from cars and trucks to make that data available to the owner of the vehicle.
This allows consumers to see the data collected, where they can perhaps use it for their own purposes, but more importantly, lets them share that data with third-parties such as auto-repair businesses.
This is a huge win for competition as the valuable vehicle data isn’t hoarded by the maker of the vehicle. Using the data, consumers can readily shop around for repairs. I love this aspect but also just having access to telemetry data myself: I use an electronic device to change the software in my own car, boosting performance beyond the stock experience.
Even though this is a new Massachusetts law, it will likely impact the entire country.
Why? Because starting with all 2022 model vehicles, any car or truck sold in the state must comply with the law and provide consumer access to telemetry data through a mobile app. Automakers won’t likely make specific models for this compliance in Massachusetts so there’s a good chance they’ll do so across several states in the northeast, if not nationwide at some point.
I’d call all three of these measures a win for consumers and IoT. We’re still trying to figure out national data privacy challenges but these are a good start; perhaps even a model for a national discussion.
By 2023, One-Third of Companies That Have Implemented IoT Will Also Have Implemented AI in Conjunction With at Least One IoT Project.
Despite the disruptive impacts of COVID-19, 47% of organizations plan to increase their investments in the Internet of Things (IoT), according to a recent survey* from Gartner, Inc.
Following the COVID-19 lockdown, the survey found that 35% of organizations reduced their investments in IoT while a larger number of organizations are planning to invest more in IoT implementations to reduce costs (see Figure 1).
One reason behind the increase is that while companies have a limited history with IoT, IoT implementers produce a predictable ROI within a specified timeframe. “They use key performance indicators (KPIs) to track their business outcomes and for most of them they also specify a time frame for financial payback of their IoT investments, which is on the average three years,” said Benoit Lheureux, research vice president at Gartner.
In addition, as IoT investments are relatively new, most companies have plenty of “low hanging fruit” cost-saving opportunities to pursue, such as predictive-maintenance on commercial and industrial assets like elevators or turbines, and optimization of processes such as increasing manufacturing yield.
Digital Twins and AI Drive IoT Adoption
As a result of COVID-19, 31% of survey respondents said that they use digital twins to improve their employee or customer safety, such as the use of remote asset monitoring to reduce the frequency of in-person monitoring, like hospital patients and mining operations.
The survey showed that 27% of companies plan to use digital twins as autonomous equipment, robots or vehicles.
Mr. Lheureux, said:
“Digital twins can help companies recognize equipment failures before they stall production, allowing repairs to be made early or at less cost. Or a company can use digital twins to automatically schedule the repair of multiple pieces of equipment in a manner that minimizes impact to operations.”
Gartner expects that by 2023, one-third of mid-to-large-size companies that implemented IoT will have implemented at least one digital twin associated with a COVID-19-motivated use case.
The enforcement of safety measures has also fueled the adoption of artificial intelligence (AI) in the enterprise. Surveyed organizations said they have applied AI techniques in a pragmatic manner. Twenty-five percent of organizations are favoring automation (through remote access and zero-touch management), while 23% are choosing procedure compliance (safe automation measures) in order to reduce COVID-19 safety concerns. For example, organizations can monitor work areas using AI-enabled analysis of live video feeds to help enforce safe social distancing compliance in high-traffic areas such as restaurants and manufacturing lines.
Gartner expects that by 2023, one-third of companies that have implemented IoT will also have implemented AI in conjunction with at least one IoT project.
* The survey was conducted online from June through July 2020, with 402 respondents across the U.S., U.K., Germany, Australia, Singapore and India.
5G will radically change the way our world networks. It won’t be long before worldwide society will have to adapt to the new way of technological life – across industries, markets, and regions. This new technology standard promises much more than just further developments of existing mobile communication technologies.
Comprehensive changes in digitalization, society, and the economy will take place in almost all areas of life. So far, the primary aim has been to expand the infrastructural conditions of conventional networks across the board, in order to ensure network availability for almost all mobile devices. In the coming years, in addition to the continued networking within 5G IoT, the focus will be on meeting the growing needs of the networked society even more optimally than before.
Unlimited Connectivity Into the Networked Future
The global data volume is increasing continuously, making 5G indispensable. Due to this enormous growth in data, the result in the medium term is that the existing technologies will no longer meet the requirements of the IoT world. Germany is a good example of the development of data volumes. In 2017, the country reached a data volume of one billion gigabytes–already double what it was in 2015.
Based on findings such as these, experts estimate that in 2020, the number of things connected worldwide will be between 50 and 500 billion. This presents enormous potential for our global economy because it demonstrates the necessity of 5G networks: such a high data volume in combination with the number of IoT devices and the individual needs regarding IoT networks is only possible with the help of 5G.
Why is 5G Predestined for IoT?
Thanks to the so-called “3G” cellular standard, using mobile data with a cell phone were made possible, as 3G was the main driver to produce smartphones at the time. The fourth generation of mobile network standards was created sometime later. Thanks to “LTE”, the data transfer rates have increased massively.
Up until today, LTE is the most popular and most-used network. Speeds of up to 100 megabits per second are no problem for the network and are already a reality in many areas of the economy and society. It is even possible to modify the LTE bandwidths to ultimately reach download speeds of up to 4000 megabits per second.
Looking to the future, however, LTE will not suffice to meet the standards and expectations of new technologies. LTE was primarily designed and optimized for use on smartphones, whereas 5G will be the mobile standard for all connected Things.
5G IoT reaches new dimensions in all aspects. The data throughput in the new network should reach up to 20 gigabits per second and allow shorter response times. As a piquant comparison, the first cell phone with 1G network connectivity is eight million times less than a 5G network.
With 5G, it will also be possible to transmit data in real-time. This means that 100 billion mobile devices around the world would be accessible at the same time. In other words, a connection density of approximately one million devices per square kilometer. At the same time, the new technology brings an increase in the relative movement speed. This means that connection quality will be much more stable up to a speed of 500 kilometers per hour, which will bring enormous benefits, especially for rail travelers.
Regardless of smartphones, increasing amounts of data are inevitable in other areas of application. The numbers don’t just sound huge, they are huge. For these reasons and many others, 5G IoT will become the new key technology of connectivity.
Diverse and Innovative Areas of Application
In addition to the Internet of Things, Industry 4.0, for example, will also benefit massively from 5G technology. The continuous data exchange between machines, systems, robots, and people will become an integral part of industrial production. The number of connected devices and parts will increase enormously. For example, the control units of industrial robots are addressed in real-time–and error probabilities are ultimately reducible to a minimum. For example, the driverless courier service would therefore be able to always pick up the materials on time at the loading and unloading points of the machines.
Nokia highlighted an alarming rise in the number of IoT devices infected by malware in its latest security threat report, as it also flagged a trend of criminals exploiting applications purportedly relating to the Covid-19 (coronavirus) pandemic to steal personal data.
In its latest annual Threat Intelligence Report, the vendor said IoT hardware comprised a third of all infected mobile internet connected devices during H1 2020, compared with 16 per cent in the same period of 2019.
Nokia noted a combination of poor security protections and the use of automated tools by criminals to exploit vulnerabilities had caused infections to rise at an “alarming rate”.
In the report, which uses data aggregated from monitoring traffic to devices running Nokia’s security software, it also pointed to the prevalence of hackers attempting to exploit fears and uncertainty around the pandemic in recent months.
Echoing several other studies conducted this year, it noted among the attack methods were fixed and mobile applications masquerading as tools related to tracking the virus and infection maps. Several of these have turned out to be ransomware, data-stealing malware or applications involved in conducting SMS fraud.
Nokia Software president and chief digital officer Bhaskar Gorti said its findings reinforced the need for consumers, enterprises and IoT device makers to up security protection.
“The sweeping changes that are taking place in the 5G ecosystem, with even more 5G networks being deployed around the world as we move to 2021, open ample opportunities for malicious actors to take advantage of vulnerabilities in IoT devices.”
As the Internet of Things (IoT) evolves, some use cases have fast-tracked their way into the spotlight as a result of the global pandemic.
Devices are connecting humans like never before from remote work and learning to streaming video and gaming content. In addition, in-person visits to the doctor have in some cases been replaced by telemedicine, so much so that Forrester predicts that patients will attend over one billion virtual care visits this year.
Additionally, the sudden surge in online shopping has impacted the supply chain. Today, these connected things are helping humans who use them, but in the future, more and more automated systems and IoT devices will emerge in factories and services such as touchless delivery where robots or machines handle all the work without humans.
But that’s not all that’s changed. As these IoT use cases propel into the mainstream, there are changes impacting how IT architects design the underlying storage that enables them.
What’s Changing in the IoT Data Journey?
Automation and Supply Chain Resilience
Consider the data demands of distribution and fulfillment in the supply chain: they’ve been massively disrupted because of the impact the pandemic has had on shipping conditions and distribution centers. Before COVID-19, typical requests would go to a central data center where the request would be disseminated to the biggest hub closest to the consumer with most of the supplies in stock. The result: a two-day delivery that set the standard for the industry so long ago.
But even that has gotten significantly more complex with the enormous surge in demand for shelter-in-place supplies, groceries, electronics, and so on. Massive distribution is a challenge because of the sheer volume of requests. However, it’s not the technology that’s led to longer delivery times; it’s the human factor that has not been able to keep pace. Systems in the supply chain rely heavily on humans to fulfill orders, especially in the “last mile.”
Automation can help move parts faster amid these accelerated demands. Automated IoT devices such as robots or autonomous vehicles can assist from the factory to your doorstep. As these devices both generate and rely on increasing volumes of data, storage is essential at every step in the data journey.
Connectivity: The Need for Speed
Connectivity speeds, reliability and large bandwidth for multiple people with multiple IoT devices are increasingly important in today’s new world. The ability to access data when you need it and quickly get insights is critical. Data infrastructure must be set up to ensure data can be transmitted, received, stored and analyzed when and where it’s needed. The closer it is to the source, the less latency there is, which translates into faster time to insights and value.
The aim of companies working with a vast array of IoT devices is to place specific storage solutions where they are most needed to ensure that data is handled appropriately across its entire IoT data journey. Edge computing is more important than ever before as it helps deliver a positive user experience for use cases like HD videoconferencing, distance-learning, or telehealth.
Acceleration of 5G
The pandemic may be a catalyst that will accelerate the demand for 5G. The newly dispersed workforce still requires quality virtual connections, which will continue to drive demand for high-speed, low-latency connectivity everywhere, even on the go.
5G is also helping industrial IoT move forward by enabling more reliable autonomous manufacturing processes with new standards for ultra-low latency in factories. The processing power required for 5G is tremendous, and along with that comes the requirements for data storage. IoT devices such as robots and cameras are being used to track assets throughout the supply chain and collect data such as temperature and vibration to track shipping container openings. Using IoT-enabled devices on transportation routes can help optimize route planning by collecting in-transit, supply chain data.
AR and VR Expand Beyond Gaming
Since the pandemic, AR/VR is being increasingly used in more use cases, connecting people with connected devices such as cameras, tablets, and phones.
For companies developing new technologies or running a global business, the required expertise won’t always be in the same location of a problem that needs to be solved.
Instead of flying an engineer halfway around the world, teams can turn to AR or VR to meet virtually in the same lab, looking at the same thing, on a common whiteboard while working in augmented reality.
In the post-pandemic world, AR and VR could make a new generation of remote viewership possible. Audiences might use AR and VR to immerse themselves in their favorite events, games, movies, or shows. With advancements in edge computing, a suite of technologies will enable the next generation of remote viewership, opening up new revenue possibilities for sports and performance artists, and reaching broader audiences.
Distance learning might utilize AR and VR to create immersive learning experiences. Online learning could become a standard extension for classroom-based education. Schools might partner with IT departments to create distance learning “tech kits” for their students, including take-home laptops, networking equipment, and desktop data storage solutions.
General-Purpose Architectures No Longer Cut It
Many businesses still use general-purpose architecture to manage their IoT data. But most often, general-purpose compute architectures do not fully meet the needs of IoT workloads. This method falls short of the accessibility, capacity, reliability, and scalability requirements necessary for IoT applications because a general-purpose, commercial architecture does not take into account the various elements an IoT system can face.
Purpose-built architecture uses devices, platforms, systems, and solutions that maximize the value of data for real-time IoT use cases. Your storage strategy has to be designed specifically for IoT. Consider a cell tower, an underground mine, or a windmill where IoT devices may be both remote and able to withstand harsh environmental conditions such as temperature or humidity. Storage cannot be an afterthought; instead, systems architects need to work with the storage experts early on to design an architecture that addresses the system’s unique needs.
IoT in the New Normal
The importance of storage is undeniable as it plays a role in all of these data scenarios, at home and in business settings and across the supply chain. It must be considered as part of an organization’s business strategy. Not only does storage support human and machine-to-machine communications, but when combined with AI, IoT and 5G, storage enables companies to access data quickly to gain insights. Access to such data, at the right time and at the right place, will be important as new post-pandemic business models develop in the new normal.
It's incredibly difficult to look for a silver lining in the midst of a pandemic when all you can see is the carnage it's caused in its wake. Millions of people have lost their lives. Millions have lost their jobs. We've all lost any semblance of normalcy, and it looks like this will be our reality for the foreseeable future.
But believe it or not, there is a silver lining in all of this.
COVID-19 is poised to bring about the same widespread change as other pandemics have by accelerating the adoption of smart city technology across the world. Klaus R. Kunzmann, the former head of the Institute of Spatial Planning at the Technical University of Germany, describes the coronavirus outbreak as being "a lubricant for the smart city."
Let's unpack this.
Familiarity incites higher levels of trust
Technology is so enmeshed in our world that it's hard to imagine life without it. Yet, many people are only comfortable with technology as far as smartphones and computers go; anything else feels too dystopian or invasive.
Smart cities have been around since the 1990s and gained traction following the financial collapse of 2008, but their adoption has been slow-going since then. Consolidating smart technology into a city's existing infrastructure comes with a hefty price tag, but it's also met with skepticism and unease by many people across the world.
The pandemic has now exposed people to conditions that make smart city tech easier to swallow. Its impact on the economy, the community and the healthcare sector have local governments and citizens clamoring for change. Citizens are now more open to smart city solutions than ever before, which has opened the door to rapid expansion.
While modern technology will eventually influence everything about a city's infrastructure, there are a few areas where digital transformation has become the most urgent.
Intelligent traffic management
Smart traffic management systems are replacing the outdated, manual processes that cities have used for so long. For the first time, technology allows cities to respond to changing environments in real-time.
Even a simple shift to smart traffic lights could reduce street congestion by upwards of 25%. Waiting for a red light to change if there's no traffic coming from the other direction will eventually become a thing of the past. Instead of following a predetermined time setting, smart traffic lights will respond to what's happening in the moment.
Digital grids not only make it possible for traffic lights to communicate with one another to enhance traffic flow and decrease congestion, but they also give city managers the ability to implement better traffic policies, like prioritizing pedestrians in school zones during the most active times of the day.
The automated, contactless elevated temperature detection solution is designed to help identify potential Covid-19 infections in facilities with thousands of people.
Nokia is launching an automated, zero-touch elevated temperature detection solution designed to help identify potential Covid-19 infections in facilities with thousands of people.
The Nokia Automated Analytics Solution for access control also confirms mask compliance in large environments with multiple accesses.
How it works
The solution uses a thermal camera to capture video footage and takes individual temperature readings (accurate to +/- 0.3 degrees Celsius) for every person that enters the screening site.
An analytics engine quickly processes the video clip to determine whether the individuals require additional screening, or are not complying with mask-wearing rules.
If an irregularity is detected, a centralised, organisation-wide view is presented and a real-time SMS or email alert is automatically sent to personnel in the field to initiate track-and-trace or post-detection actions.
Nokia claims the entire process takes place in near-real time and the human-less operation enables scaling to very large environments with thousands of people and multiple access points.
Organisations can also expand the solution to support other ongoing use cases to protect employees and building assets, including predictive surveillance, machine maintenance and security threats.
“Whether in factories, ports, offices, airports, schools, or outdoor screening centres, mission-critical networks and digital automation solutions play a leading role in ensuring supply resilience, business continuity, and workers’ safety in real-time,” said Amit Shah, head of analytics and IoT for Nokia.
Individual temperature readings are taken for every person entering the site
The technology uses an open architecture and has a suite of analytics with a flexible set of automation workflows and rules to adapt the solution to each organisation’s needs. The company has deployed the solution at multiple locations, including its own Chennai factory to monitor employee safety and plans to deploy the solution for enterprise customers across multiple industry segments.
“As the factory reopened to production and over 1,000 employees returned to work, we abided by local regulations for monitoring temperatures and mask usage for every single person entering and leaving the site,” said Sudarshan Pitty, head of the Nokia Chennai Factory. “The Nokia Automated Analytics Solution has enabled us to ensure regulatory compliance in an automated way, round the clock with zero misses in real-time.”
Pitty continued: “This solution has enabled us to boost employee availability by reducing the waiting time in queues and removing the need to assign additional staff to carry out manual checks.”
Leo Gergs, research analyst, ABI Research, reckoned the solution is an example of the important role that network infrastructure vendors can play in fighting the spread of a global pandemic, such as Covid-19.
He added: “Furthermore, the modular architecture allows easy repurposing of the solution to use cases such as port or smart city traffic monitoring and therefore to address the ever-increasing demand for automated data analytics capabilities, which will continue to rise in line with further enterprise digitalisation.”
Nokia suggested that once concerns about Covid-19 subside, organisations can repurpose the solution to support other use cases to protect employees, site visitors and facilities, including predictive surveillance and machine maintenance, security threats and anomaly detection, and customised industry-specific analytics.
Originally published by SmartCitiesWorld News Team | October 16, 2020 SmartCitiesWorld
A report from Technavio said the decreasing price of connected devices is expected to fuel the market and highlights the smart governance and education segments as key areas for growth. The decline in hardware and installation costs is also helping to fuel smart city growth
The smart city market size is poised to grow by more than $2118bn during 2020-2024 with the decreasing price of connected devices expected to fuel market growth, a new study finds.
The report from global technology research and advisory company Technavio forecasts growth to progress at a compound annual growth rate of 23 per cent throughout the period. It highlights the smart governance and education segment as key areas of growth.
Connected network ecosystem
IoT systems have revolutionised the connected network ecosystem over the last few years. Smart city infrastructure is based on an efficient and connected network system and the reduction in costs of IoT sensors and associated systems, and in the cost of broadband services, has led to the implementation of smart cities across the world.
Furthermore, Technavio states the decline in hardware costs, installation costs, and tariff rates of network operators have triggered a surge in M2M security systems adoption in applications such as smart homes, connected cars, connected health, and precision agriculture.
As the price for connected devices continue to decrease in the coming years, the smart city market will witness significant growth, notes Technavio.
The proliferation of smart city projects in emerging economies, one of the key smart cities market trends, will also influence market growth, the report finds. While developed economies have been working on creating smart cities for a decade, emerging economies are still in the planning phase and are launching several pilot projects.
For instance, the government of India has initiated smart city projects for 100 cities. The installation of smart devices for these upcoming smart cities in emerging economies is expected to generate huge amounts of data. The analysis of this data would be required to improve business quality and innovate for a better future with faster connectivity by facilitating prompt suggestion-based services. As a result of these factors, the market will grow during the forecast period.
Other report highlights include:
The major smart city market growth came from the smart governance and education segment. These technologies are used extensively in e-governance, homeland security, fire and emergency, and traffic management applications. It helps to analyse the risks and plan and implement preventive measures
Europe had the largest smart cities market share in 2019, and the region will offer several growth opportunities to market vendors during the forecast period. The availability of high-speed wireless networks and increased connected devices such as smartphones and IoT penetration will significantly influence smart city market growth in this region.
The report sets out to offer up-to-date analysis regarding the current market scenario, latest trends, and drivers, as well as the overall market environment. The report also provides the market impact and new opportunities created due to the Covid-19 pandemic.
Originally published by SmartCitiesWorld news team | October 14, 2020 SmartCItiesWorld
With a novel layer to help the metallic components of the sensor bond, an international team of researchers printed sensors directly on human skin. IMAGE: LING ZHANG, PENN STATE/CHENG LAB AND HARBIN INSTITUTE OF TECHNOLOGY
UNIVERSITY PARK, Pa. — Wearable sensors are evolving from watches and electrodes to bendable devices that provide far more precise biometric measurements and comfort for users. Now, an international team of researchers has taken the evolution one step further by printing sensors directly on human skin without the use of heat.
Led by Huanyu “Larry” Cheng, Dorothy Quiggle Career Development Professor in the Penn State Department of Engineering Science and Mechanics, the team published their results in ACS Applied Materials & Interfaces.
“In this article, we report a simple yet universally applicable fabrication technique with the use of a novel sintering aid layer to enable direct printing for on-body sensors,” said first author Ling Zhang, a researcher in the Harbin Institute of Technology in China and in Cheng’s laboratory.
Cheng and his colleagues previously developed flexible printed circuit boards for use in wearable sensors, but printing directly on skin has been hindered by the bonding process for the metallic components in the sensor. Called sintering, this process typically requires temperatures of around 572 degrees Fahrenheit (300 degrees Celsius) to bond the sensor’s silver nanoparticles together.
“The skin surface cannot withstand such a high temperature, obviously,” Cheng said. “To get around this limitation, we proposed a sintering aid layer — something that would not hurt the skin and could help the material sinter together at a lower temperature.”
By adding a nanoparticle to the mix, the silver particles sinter at a lower temperature of about 212 F (100 C).
“That can be used to print sensors on clothing and paper, which is useful, but it’s still higher than we can stand at skin temperature,” Cheng said, who noted that about 104 F (40 C) could still burn skin tissue. “We changed the formula of the aid layer, changed the printing material and found that we could sinter at room temperature.”
The room temperature sintering aid layer consists of polyvinyl alcohol paste — the main ingredient in peelable face masks — and calcium carbonate — which comprises eggshells. The layer reduces printing surface roughness and allows for an ultrathin layer of metal patterns that can bend and fold while maintaining electromechanical capabilities. When the sensor is printed, the researchers use an air blower, such as a hair dryer set on cool, to remove the water that is used as a solvent in the ink.
“The outcome is profound,” Cheng said. “We don’t need to rely on heat to sinter.”
The sensors are capable of precisely and continuously capturing temperature, humidity, blood oxygen levels and heart performance signals, according to Cheng. The researchers also linked the on-body sensors into a network with wireless transmission capabilities to monitor the combination of signals as they progress.
The process is also environmentally friendly, Cheng said. The sensor remains robust in tepid water for a few days, but a hot shower will easily remove it.
“It could be recycled, since removal doesn’t damage the device,” Cheng said. “And, importantly, removal doesn’t damage the skin, either. That’s especially important for people with sensitive skin, like the elderly and babies. The device can be useful without being an extra burden to the person using it or to the environment.”
Next, the researchers plan to alter the technology to target specific applications as needed, such as a precise on-body sensor network placed to monitor the particular symptoms associated with COVID-19.
Other contributors include Hongjun Ji, Senpei Xie, Yaoyin Li, Ziheng Ye, Tiesong Lin, Xiangli Liu, Xuesong Leng, Mingyu Li, Pengdong Feng, Jiaheng Zhang and Xing Ma, all of whom are affiliated with the Harbin Institute of Technology; Houbing Huang and Xiaoming Shi, both with the Beijing Institute of Technology; and Ning Yi, with the Penn State Department of Materials Science and Engineering.
This work was supported by Penn State, the National Science Foundation, the American Chemical Society Petroleum Research Fund, the Shenzhen Science and Technology Program, the Bureau of Industry and Information Technology of Shenzhen and the National Science Foundation of China.
Originally published by Ashley J. Wenners Herron | October 9, 2020 Penn State News
(metamorworks / iStock / Getty Image Plus) Before the next-generation smart home can be fully realized, the devices we already own, including virtual assistants, security systems and network-connected appliances, need to operate at a higher standard.
Amid widespread social distancing, many people are spending more time in their homes than ever before and with that, they’re taking a critical look at their surroundings. Scrutinizing my own home, I like to dream up improvements beyond new paint colors and framed photos. As a long time tech enthusiast and a market strategist for an edge inference company, my mind goes to smart home innovations.
Imagine a microwave that recognizes you as you walk up with a plate of bacon and, without constant monitoring, knows how long to blast it so it's cooked just the way you like it. Or an autonomous vacuum that can locate your favorite pair of shoes, so you don't have to search for them in a frenzy.
As futuristic as these scenarios may seem, from a technical standpoint they’re not so far off. With advances in artificial intelligence and machine learning, and new edge inference chips bringing unprecedented computing power on-device, innovations long-kept for sci-fi books and movies could hit the market very soon.
While we’re close to taking big steps in smart home technology, there is one hurdle we must surpass before old and new devices can lay a proper foundation for the next-generation smart home. The devices we already have—gadgets like virtual assistants and smart speakers, smart home security systems and network-connected appliances—need to operate at a higher standard. Until then, we risk delaying smart home advancement.
Smart home devices must be accurate
First and foremost, in order to be useful a device has to be successful in doing what it was designed to do. Put simply, it must be accurate. This means that your voice assistant understands the intent of your command the first time, not the second or third; your face recognition lock recognizes you even when you’re wearing glasses (or better yet, a mask); and your smart home camera doesn’t constantly trigger false alarms.
As obvious as these examples may seem, accuracy is an area where many of today’s smart devices fall short. The reason for this, I think, is in part because the standard neural network (NN) benchmarks technologists use to test a gadget’s functionality are often not reflective of real life.
As a result, it’s not uncommon that a device does well in testing but performs poorly in the real world. For example, a video motion detector that’s trained to successfully recognize a change in pixels could trigger an alarm in response to nothing more than a moving shadow. Built with the purpose of protecting someone’s home, that motion detector isn’t “smart,” it’s just seeing pixels change.
Companies are constantly refining smart home innovations in hopes that they can work flawlessly right out of the box. Until then, when today’s smart devices fall short on accuracy, users end up frustrated, and rightfully so. While fleeting, these moments of friction stand in the way of what smart home living should be: a seamless integration of technology in our daily lives.
They should never put consumers at risk
It’s no secret that smart home devices have struggled to remain secure—even devices designed to make homes safer have left consumers vulnerable to attack. It’s common knowledge, for example, that Ring, who makes some of the most popular home security products on the market, was plagued with countless security breaches last year.
Voice assistants have also had difficulties. Recently, researchers found that people can unintentionally trigger an assistant with more than 1,000 words and phrases, prompting it to record what you say and then send that recording to the cloud for wake word verification. This raises obvious privacy concerns. Any recording or transfer of data off of a personal device should be initiated by its owner, not triggered accidentally and without their knowledge.
Across devices, security and privacy are clear issues. To reduce risk, I believe we must bring more horsepower to the gadgets themselves and limit the need to expose data by sending it to the cloud. Until network-connected devices prove that they are secure, consumers will be left to question whether the home enhancements they promise are worth a threat to their privacy and security.
You have to be able to trust them
Having experienced accuracy blunders and aware of security concerns, many consumers are skeptical of smart devices at home. In fact, a recent PwC report pointed to lack of trust as one of three factors inhibiting voice technology experimentation. The report quotes a respondent expressing hesitation to trust a virtual assistant with sensitive information, saying: “The assistant can’t answer my questions half the time, but I’m supposed to trust it to help me with something involving money?”
These frustrations are only heightened in quarantine. As the title of a recent article comically made clear, “We’ve been isolated for months, and now we hate our home assistants.” Only after experiencing consistent accuracy, privacy and security will consumers learn to trust their connected devices and embrace the modern smart home.
Recent years have brought unprecedented advancement to smart home technologies, but there’s significant room for improvement. To pave the way for continued innovation, technology leaders should invest in refining the devices we’ve already welcomed into our lives. Connected devices that consumers can trust to be both accurate and secure will provide a solid foundation for the next-generation smart home. With that and sooner than you think, your microwave could know how you like your bacon.
IoT devices aim to make our lives easier, more enjoyable and give us unprecedented control over many functions and aspects of our home right in the palm of our hands. Virtually any electronic device can be connected from TVs, security cameras, smart locks, smart thermostats, and the list goes on. The problem is that the majority of end-users aren’t aware of the trust and security issues that revolve around IoT devices.
Gadgets that are always on and listening, like Amazon’s Alexa, or connected smart-bulbs that have no security protocols, provide no guarantees as to who is listening on the other side. The possibility of whether someone will be able to hack your home network through smart devices is an increasing concern for consumers.
An excellent example of how unsecured IoT devices allowed hackers to make off with valuable data, happened two years ago at a North American casino. Hackers infiltrated the casino’s network through a lobby fish tanks’ IoT device designed to automatically adjust temperature and salinity and stole over 10 gigabytes worth of high-roller data. The IoT device in question was no different than, say, a smart-thermostat you would use in your home, and attacks like this demonstrate that these devices are seriously lacking when it comes to security. How safe are they in your home?
The Internet Society France Chapter was set up to create awareness about the security risks that IoT devices pose. The society has said not enough is being done to strengthen the security and privacy of connected devices. In the meantime, the number of IoT devices is increasing and is expected to reach 20.4 billion globally by next year.
Most devices are still poorly secured and vulnerable to cyberattacks. The security of the connected devices in your home is only as good as the weakest link.
Many unsecured IoT devices like smart bulbs, CCTV cameras, alarm systems, or even baby monitors, connect through a smart app on a mobile device. Even though they aren’t directly connected to other devices you may be using in your home, the fact that they’re on the same network, when it comes to a hacker gaining a foothold on your network, these unsecured IoT devices are an easy entry point.
Easily hackable devices expose owners to a range of security nightmares; chief among them is access to your wireless network. Some devices connected to your network will store Wi-Fi passwords insecurely, allowing a hacker to gain access to that smart device to get your password and potentially see everything you are doing online. It’s important to class your Wi-Fi router like the front door to your smart home. Make sure it’s using the latest software, has the strongest security protocols (like WPA2) and you’ve used a unique and complex password to protect it.
Another solution is to have your IoT devices on one network (connected to a separate router) while your computers and mobile devices are connected to a separate network. This way, any less than secure IoT devices are not connected to the same network. Finally, use a VPN. Many higher-end routers will have this feature and we strongly recommend using it. Otherwise, software VPN like ExpressVPN or NordVPN is a good solution to mask your IP address and encrypt all your network data.
Knowledge is Power
As aforementioned, security is of the utmost importance when it comes to IoT devices, and with these connected devices becoming omnipresent in our homes, educating yourself on the security implications can go a long way towards protecting your home. Online courses on IoT security and privacy are plentiful.
The courses listed by IoT Tech Trends include dynamic courses from providers like Coursera, IoT Security Foundation and Udemy. The courses vary from basic to more advanced and for the most part, are provided online. Udemy’s IT security courses come from experts around the world with specialists from the U.S. and Germany providing their expertise. So, if you’re using IoT devices in your home, it will definitely help to learn their inner workings and how to better protect your home.