One advantage that the IoT brought to design was the ability for a small local device to access the network’s virtually-unlimited computing power.  The Amazon Echo is a classic example: a low-cost local device that provided powerful speech recognition AI and an immense application library by way of its Internet connection. Now, some of that AI is moving into the local device to help minimize bandwidth and latency concerns by employing an efficient form of machine learning (ML) for smaller devices.

An example of what can be accomplished by placing AI in edge devices can be found in the article AI helps turn gas sensor into electronic nose. In this instance the ML that generates the sensor’s algorithms takes place during the design cycle, and the local device simply runs the algorithm. This is a first step in bring AI to the edge, but there are more to come.

To reach its full potential, AI at the edge will need to be self-adaptive. This means that the edge device will have to implement ML locally. How, exactly, this is to be done with the limited compute power edge devices typically have available is currently the subject of considerable research and development. Providing a form for information and idea exchange in local machine learning is the goal of the tinyML Foundation.

pable of learning their tasks without excessive developer effort. Source: TensorFlow

The foundation held its first industry event – the tinyML Summit – in 2019 and generated considerable interest along with participation by more than 90 companies. That event revealed three essential trends:

• Tiny ML-capable hardware is currently becoming “good enough” for many commercial applications with new and even better architectures on the horizon.
• Algorithms, networks, and models have seen significant size reduction, with many sized down to 100 kBytes and below.
• There is growing momentum demonstrated by technical progress and ecosystem development.

This result demonstrated that ML is not only coming to the edge, in some cases it is already there.

COVID-19 prevented a 2020 event, but for 2021 the tinyML Foundation created a free online event that recently concluded but should be available as an archive for registered attendees. In addition, the organization has developed a series of lectures called the tinyML Talks that are available on YouTube and other platforms.

The trend is clearly gaining traction. The organization’s sponsors now span the range from major hardware players such as Arm, Cypress Semiconductor, and Samsung to software start-ups focusing on low-power AI applications. Most are focused on either vision or audio (voice recognition) systems for now, but smart sensors are gaining ground as a viable application as well.

This trend bodes well for IoT developers. Creating compact, low-power devices with reasonable cost that perform complex tasks can be a developers nightmare using conventional programming techniques. Yet depending on connectivity to network-based AI processing for the device’s performance has its own drawbacks. Home networks are already becoming clogged with demands from streaming media and communications; adding a host of network-hogging smart devices can overload the typical home connection. The latency of network communications can also be an issue, as can be the total failure of device operation when the network is down.

Moving the AI to the edge – at least for basic functionality – solves most of these concerns. With ML in the edge device, developers can craft their systems to learn how to meet customer demands without the developers needing to exhaustively analyze use cases in advance. Having AI in the edge device reduces the need for network bandwidth, eliminates network latency issues, and ensures operation in the network’s absence. The efforts to expand tiny ML technology will help speed the movement of AI into IoT devices.

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Apart from its Bull’s Eye Landing, What’s so Unique about Perseverance Rover Mission from NASA?

Last Thursday, NASA’s Perseverance rover grabbed headlines all around the world, with its historic landing on the Martian surface. The rover, which was launched July 30, 2020, from the Cape Canaveral Air Force Station in Florida atop a ULA Atlas 541 rocket, finally made touched down at 3.44pm ET (8.44pm GMT). The mission objective is to search for signs of ancient life and to collect rock and soil samples for possible return to Earth – where it will be scanned for presence of microbial life.

Why Jezero?

The name Perseverance was given by Alex Mather, who won a K-12 public naming contest with 28,000 entries. Named after the human characteristic, the mission follows similar name-scheme of its predecessors: Curiosity, Spirit and Opportunity. The rover made a touch down at an ancient river delta site in Jezero Crater.

Jezero, which means lake in Balkan languages, is believed to be a water filled lake that existed nearly 4 billion years ago. It is reported that this Martian lake was as big and wet as Nevada and California’s Lake Tahoe. The deposits in the crater are rich in clay minerals, which form in the presence of water, meaning life may have once existed there – and such sediments on Earth have been known to store microscopic fossils. The rover will start at base of delta cliffs, before moving across the delta towards what was possibly a shoreline, then climbing up the 610-metre crater rim. It will take about two Earth years (one Mars’ year) to complete half this journey.

But the journey is not smooth ‘sailing’! The Jezero Crater is full of obstacles and dangers to the rover, including boulders, cliffs, sand dunes and depressions, any one of which could end the mission, both in landing and as the rover drives along the surface.

Perseverance vs Curiosity

One of the significant upgrades in Perseverance rover from Curosity is that it has received a gentle tread pattern and larger diameter. The reason behind this upgrade is to prevent the rover from getting stuck in the fine Martian sand, as Curiosity did in 2014, and also protect it from sharp Martian rocks called ventifacts.

Both Curiosity and Perseverance share the same basic body (WEB or Warm Electronics Box) and the same type of power source (radioisotope thermoelectric generators), and both landed using a spectacular overhead crane strategy. According to Matt Wallace, the deputy mission manager at NASA’s Jet Propulsion Lab, Perseverance can drive three times faster than any previous Mars rover. It will have a maximum speed of only 4.4 cm per second, which is one-thirtieth as fast as human walking speed.

The rover is powered by 4.8 kilograms of Plutonium 238, which can supply 110 watts of electrical power continuously. A pair of lithium-ion batteries will work in tandem with the RTG to help handle peak demand.

The last NASA spacecraft to land on Mars was the InSight robotic probe, which touched down on 26 November 2018. While there was no live video, the landing was monitored through telemetry clocking the probe’s velocity. Till now, Nasa has nailed eight of nine landing attempts, making the US the only country to achieve a successful touchdown. Following behind is China, whose Tianwen-1 mission along with UAE’s Hope orbiter made it to Martian orbit few days ago. While Tianwen-1’s rover will attempt to land on Mars in May, Hope will remain in orbit to study the Martian atmosphere.

Even the Soviet Union had successfully made the first impact on Mars on 27 November 1971. Unfortunately, the spacecraft was destroyed. Though Soviet Union did manage a soft landing on Mars a few months later, its spacecraft started to transmit an image back to Earth, before going silent a little over a minute and a half into the transmission.

Interesting Technologies Aboard Perseverance

It is natural to assume that the rover will carry out a number of experiments during its mission. These experiments will leverage a blend of technologies like IoT sensors, robotics, cloud, artificial intelligence and more.

For instance, Perseverance features an automated hazard avoidance system called Terrain Relative Navigation (TRN.) The TRN, or Landing Vision System (LVS), compares real-time images from its camera with an onboard map of the surface in Jezero Crater, Perseverance’s landing site. The map is created from high-definition orbital images of the crater area. If the rover is heading for a hazardous obstacle, it can fire its retrorockets and avoid the hazard.

Perseverance is equipped with a multi-functional instrument called SuperCam. SuperCam contains three separate spectrometers. One of them is called LIBS, or Laser-Induced Breakdown Spectroscopy. The rover also has a drill that uses rotary motion with or without percussion to penetrate the Martian surface to collect the precious samples. The drill is equipped with three different kinds of attachments that allow sample collection and surface analysis. SuperCam also has a microphone. The microphones onboard Perseverance will help scientists record the sounds of its tense “seven minutes of terror” touchdown sequence.

NASA had installed microphone in its Mars Polar Lander spacecraft, and had one built into the Phoenix lander’s descent camera. Sadly, neither mic returned any data. While Mars Polar Lander crashed during its touchdown attempt in December 1999, Phoenix’s descent camera was never turned on due to concerns that its use could complicate the entry, descent and landing (EDL) process. Phoenix which landed in May 2008, had found buried water ice during its successful surface mission.

The rover also has a ground penetrating radar that could detect water up to 10 meters deep. It further, contains multiple science experiments including an electrically powered oxygen generator called MOXIE (Mars Oxygen In-Situ Resource Utilization Experiment), that converts the CO2 from the atmosphere into oxygen. It also has several scientific cameras and a special sensor to protect the robot from incorrectly contacting the surface to reduce the chance of damage.

During the course of its two-year mission, Perseverance will collect up to 43 samples of Martian rock and soil. These samples will be stowed in white tubes on the Martian surface to be returned to Earth on a future planned mission. Official sources report that the sample tubes will be placed in caches on the surface, and the locations of these samples will be catalogued. Orbiter images will identify the sample locations to within one meter (3 ft) and the rover itself will increase that accuracy to within one centimeter. Perseverance also has what are called “witness tubes.” It has five of these tubes, which shall be used to collect molecular and particular contaminants during drilling sessions.

Collecting samples are key part of space-geology and other space sciences. Recently, Japan’s Hayabusa2 spacecraft collected samples from asteroid Ryugu and returned them to Earth. And NASA’s OSIRIS-REx just successfully collected samples from asteroid Bennu. Those samples will be returned to Earth in September 2023.

It robotics arm will also use an artificial intelligence powered device called the Planetary Instrument for X-ray Lithochemistry, or PIXL. PIXL is a lunchbox-size instrument carried at the end of Perseverance’s 7-foot-long robotic arm. Using a coring drill on the end of the arm, the rover will collect core samples from the planet that will be left on the Mars surface for collection by a future mission. A tiny, powerful X-ray beam blast from PIXL can detect over 20 chemical elements by pointing a beam at rocks. The beam produces a telling glow associated with each element present in about 10 seconds.

It also has a partner called Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals or SHERLOC. SHERLOC will seek out organic molecules and minerals, which helps inform science teams of where to collect and cache samples. Its ultraviolet laser will provide a different glow depending on the organic molecules and minerals it detects. And obviously, elementarily, SHERLOC will have his WATSON even on the Martian surface. WATSON, or Wide-Angle Topographic Sensor for Operations and eNgineering, is a camera that can take microscopic images of grains in rock and textures.

Before astronauts step their foot or even enter the atmosphere of Mars, it is crucial to understand the Martian weather and environment conditions they will face. Perseverance’s monitoring system, called Mars Environmental Dynamics Analyzer, called MEDA, is a suite of sensors that will help scientists study weather science, dust and radiation, and how they change over Martian seasons.

Meanwhile, artificial intelligence is used aboard Perseverance for navigation on the planet’s surface. Heather Justice, robotic operations downlink lead at JPL cites that the Perseverance rover will be using the autonomous technology that is used for self-driving cars on Earth. This autonomous technology, officially known as a vision compute element (VCE), will help it do something called “thinking while driving”. It is installed to help Perseverance land itself on Mars and avoid hazards on the Martian surface.

Perseverance also leverages technologies from HPE and Amazon Web Service (AWS). HPE’s specialized, second-generation Spaceborne Computer-2 (SBC-2), will mark the first time that broad AI and edge computing capabilities will be available to researchers on the space station. SBC-2 computer follows the original Spaceborne Computer-1 that was sent to the International Space Station (ISS) in 2017 as part of a validation study to test it in the rigors of space aboard the orbiting laboratory. SBC-1 returned to earth in 2019 after completing its mission. Both Spaceborne Computer-1 and Spaceborne Computer-2 are sponsored by the ISS National Lab.

Also given the massive volume of data that shall be dealt by the Perseverance rover, the Jet Propulsion Lab will store, process, and distribute this high volume of data using cloud features of AWS.

Helicopter Ingenuity

The Perseverance rover will not be alone at Mars. Tagging along is a tiny drone-based helicopter called Ingenuity. Running on LINUX, Ingenuity won’t actually do any science, but it will provide important feedback on flight operations in the thin Martian atmosphere. If successful it will be designated as the first powered flight on any planet other than Earth and to hopefully be the blueprint for future Mars missions.

Designed to last about 30 Martian sols, Ingenuity weighs about 2kg, is 1.2 meters wide, and carries two computers. The drone is more of an experiment than a piece of equipment for scientific discovery; engineers want to test its ability to fly autonomously. It can travel to distances stretching to almost 300 meters, and hover 3 to 4.5 meters from the ground for 90 seconds.

What’s Next?

Mars missions are difficult with a success rate of only 40% so far. However, that has not deterred it from planning its future missions to the Red Planet. For instance, NASA has proposed to send a similar but more capable quadcopter drone to Titan, one of the Moons of the ringed planet Saturn, in 2027. This device is called the Dragonfly mission and it will be nuclear-powered with the ability to fly many kilometers before landing to recharge its battery.

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The Internet of Things (IoT) and machine learning are two of the most disruptive technologies in business today. Separately, both of these innovations can bring remarkable benefits to any company. Together, they can transform your business entirely.

The intersection of IoT devices and machine learning is a natural progression. Machine learning needs large pools of relevant data to work at its best, and the IoT can supply it. As adoption of both soars, companies should start using them in conjunction.

1. Highlighting and Addressing Inefficiencies

Around 25% of businesses today use IoT devices, and this figure will keep climbing. As companies implement more of these sensors, they add places where they can gather data. Machine learning algorithms can then analyze this data to find inefficiencies in the workplace.

Looking at various workplace data, a machine learning program could see where a company spends an unusually high amount of time. It could then suggest a new workflow that would reduce the effort employees expend in that area. Business leaders may not have ever realized this was a problem area without machine learning.

Machine learning programs are skilled at making connections between data points that humans may miss. They can also make predictions 20 times earlier than traditional tools and do so with more accuracy. With IoT devices feeding them more data, they’ll only become faster and more accurate.

2. Business Process Automation

Machine learning and the IoT can also automate routine tasks. Business process automation (BPA) leverages AI to handle a range of administrative tasks, so workers don’t have to. As IoT devices feed more data into these programs, they become even more effective.

Over time, technology like this has contributed to a 40% productivity increase in some industries. Automating and streamlining tasks like scheduling and record-keeping frees employees to focus on other, value-adding work. BPA’s potential doesn’t stop there, either.

BPA can automate more than straightforward data manipulation tasks. It can talk to customers, plan and schedule events, run marketing campaigns and more. With more comprehensive IoT implementation, it would have access to more areas, becoming even more versatile.

3. Supply Chain Visibility

One of the most promising areas for IoT implementation is in the supply chain. IoT sensors in vehicles or shipping containers can provide companies with critical information like real-time location data or product quality. This data alone improves supply chain visibility, but paired with machine learning, it could transform your business.

Machine learning programs can take this real-time data from IoT sensors and put it into action. It could predict possible disruptions and warn workers so they can respond accordingly. These predictive analytics could save companies the all-too-familiar headache of supply chain delays.

UPS’ Orion tool is the gold standard for what machine learning can do for supply chains. The system has saved the shipping giant 10 million gallons of fuel a year by adjusting routes on the fly based on traffic and weather data.

4. Risk Management

If a company can’t understand the vulnerabilities it faces, business leaders can’t make fully informed decisions. IoT devices can provide the data businesses need to get a better understanding of these risks. Machine learning can take it a step further and find points of concern in this data that humans could miss.

IoT devices can gather data about the workplace or customers that machine learning programs then process. For example, Progressive has made more than 1.7 trillion observations about its customers’ driving habits through Snapshot, an IoT tracking device. These analytics help the company adjust clients’ insurance rates based on the dangers their driving presents.

Business risks aren’t the only hazards the Internet of Things and machine learning can predict. IoT air quality sensors could alert businesses when to change HVAC filters to protect employee health. Similarly, machine learning cybersecurity programs could sense when hackers are trying to infiltrate a company’s network.

5. Waste Reduction

Another way the IoT and machine learning could transform your business is by eliminating waste. Data from IoT sensors can reveal where the company could be using more resources than it needs. Machine learning algorithms can then analyze this data to suggest ways to improve.

One of the most common culprits of waste in businesses is energy. Thanks to various inefficiencies, 68% of power in America ends up wasted. IoT sensors can measure where this waste is happening, and with machine learning, adjust to stop it.

Machine learning algorithms in conjunction with IoT devices could restrict energy use, so processes only use what they need. Alternatively, they could suggest new workflows or procedures that would be less wasteful. While many of these steps may seem small, they add up to substantial savings.

It’s Time to Embrace the IoT and Machine Learning

Without the IoT and machine learning, businesses can’t reach their full potential. These technologies enable savings companies couldn’t achieve otherwise. As they advance, they’ll only become more effective.

The Internet of Things and machine learning are reshaping the business world. Those that don’t take advantage of them now could soon fall behind.

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How can embedded machine learning for IoT bring new advancements in the Industry?

The internet of things (IoT) is unprecedently changing the way people live and work. It is booming at a rapid pace and the domains where IoT is evolving are as diverse as its applications, ranging from decision control to the exploration and discovery of new information. It is predicted that there will be around 175 zettabytes of data generated worldwide by 2025. Companies who already invested in IoT will gain a massive competitive advantage and new opportunities through such amounts of data. Applying embedded machine learning for IoT devices can also deliver several new capabilities as it has the potential to lessen data transmission payload and integrate Low-Power Wide-Area Network (LPWAN) technologies, which offer wide range connectivity while providing a long battery life.

Now as businesses are approaching a new era of innovation, various new abilities of embedded machine learning on IoT devices will occur in 2021. Here are the top embedded ML for IoT predictions for 2021.

Smarter Chips Will Enable Intelligence on IoT Sensors

Building an IoT solution using legacy approaches faces numerous challenges and one of them is to connect millions of IoT devices to the cloud across distinct sensors, actuators, operating systems, compute power, and others. The amount of data currently being gathered by IoT is already huge, and it is expected that it will get far bigger and more interesting. The shipment of more powerful IoT AI chips and more domain-specific IoT AI chips will enable smarter intelligence on IoT sensors, according to Hiroshu Doyu, an embedded AI researcher at Ericsson.

Embedded ML to Revolutionize Manufacturing

According to IDC, 20% of leading manufacturers will rely on embedded intelligence by 2021, using AI, IoT, and blockchain applications to automate processes and intensify execution times by up to 25%. Embedded machine learning can identify certain areas that need to be efficiently tested and provide manufacturers with information to better predict the maintenance and equipment failure issues to prevent any uncertainty in the future. Embedded ML solutions can also automate manufacturing processes entirely along with smart manufacturing operations. They can unlock sensors’ data that is currently discarded due to cost, bandwidth, or power constraints.

Enterprise Maturation Through Smarter Chips 

Business decision-makers these days are entirely overwhelmed by the technology which has been already embedded. These embedded technologies are becoming a new trend across businesses owing to the availability and generation of massive digital data daily. However, the problem is connecting to it and absorbing it. The adoption of more intelligence at the device level may address the issues associated with bandwidth and latency. Lucy Lee, a senior associate at Volition Capital who tracks embedded AI/ML on IoT predicts that many more autonomous chips are expected to be made this year.

AI/ML Acceleration

The paradigm shift towards digitization is giving businesses a huge opportunity and enabling them to garner greater benefits and ROI. Advancements in artificial intelligence and the introduction of application-specific custom AI chips are now allowing businesses to glean real-time data about their business processes and their customers. As the AI chips market is gaining visibility across industries worldwide, it is expected to grow at a CAGR of over 42% during 2020-2024. The adoption of AI chips in data centers will expedite its growth.

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The importance of data analytics within everyday online interactions, sports, media and general day-to-day life has become more prevalent. Football until recently, was never included within the data monitoring equation before, yet IoT (Internet of Things involves sensory, connectivity and data analytics) has provided some serious advancements that have made the regulation of sports and football in general much more modern and efficient. Big Data is especially useful to analyse player performances on a more intimate level and allows teams to come to more action related conclusions which are superb for plans of the future.

These metrics are not just used within the world of media, the data evidence-based approaches have also become very relevant to concluding and conjuring future promotional welcome betting offers for online sports gambling. With persevering data collection, gambling platforms can come to conclusions of smart ways to entice and interact with their future traffic, which means the system now is the most efficient and catered, then it has ever been. 

I0T and Big Data within football is composed of the following recognised attributes.

Sensory Devices 

Sensory devices are worn by athletes to gain the quantifiable data that is necessary to monitor fitness levels of the player. Many of the fitness objectives such as muscle composition, activity, running speeds within a distance and overall performance levels are assessed. This is quite imperative, as players and fitness coaches can build goals to work towards for improvements and maintenance for peak physical activity. These sensors will be input within the clothing worn by the players and then transmitted via Wi-Fi networks within the stadium and training grounds.

There have been technological advancements within this sector of IoT. The HUMANoX Soccer has provided a solution to increase a greater capacity for digital performance and monitoring, where football is involved. The HX50 shin pads have been made from smart materials to increase resistance when under high endurance and physical environments; at even greater levels of data intelligence. It can be tracked by mobile phone systems too, which is a huge leap within fitness monitoring.

VAR Technology 

We are sure hard-core football fans will already be aware of this form of technology when it comes to football regulation. VAR is an IoT camera that aids referees on-site to conclude decisions for goals, penalties, red cars etc. Decisions received by this technology are smart and even more accurate than human judgement. Meaning it can greatly improve the efficiency and accuracy of gameplay altogether. We saw this technology first appear within the Russian World Cup 2018. 

Big Data in Football 

Big Data in football is primarily composed of computer analytical programmes that are equipped within monitoring algorithms and Artificial intelligence. The data gained from these insights has allowed much more accurate predictive models that can improve player predictions, especially when defining a plan of action for player performance.  It is evidence-based and is specifically tailored to the statistical figures generated for each player. Football teams can suddenly be more aware of how their players respond and act on the field. This is particularly useful to maximise unique strategic tactics in addition to minimising injury risks.

It is very obvious how IoT and Big Data mould together and harmonise player and team capabilities within football. There is no doubt that the data collected contributes to the evolution and understanding of a football team’s capabilities but also enhances it.

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In September, the House of Representatives passed a bill requiring that all internet of things (IoT) devices purchased by the government meet minimum security requirements.

Of course, with everything being connected to everything, there needs to be a step function improvement in the security of the networks being used to share information. I have seen reports that indicate as much as 98% of traffic from IoT devices are unencrypted today, and clearly that number has to become near zero. Security is not free. It is relatively bulky from a silicon area perspective and burns energy. But it is a prerequisite for the services on which we want to be relying on in the coming years.

H.R. 1668 has the potential to improve the security of the IoT for two high-level reasons. Any activity that places cybersecurity front and center of IoT conversations is a good thing. This bill could and should create demand for higher quality devices, which incentivizes the supply chain to build platforms. This is different from other (market “push”) security initiatives and standards such as Arm’s Platform Security Architecture, in which it is a technology company proposing something. Here it is an end customer stipulating requirement that creates market “pull.”

The bill also outlines key themes that should be addressed rather than getting caught up in specific technologies.

That said, I think some elements of this show where the U.S. government may have some challenges. There are three elements we feel could do with improvement here:

No device can be regarded as 100% secure. Software has to provide earlier recognition that a device has been compromised. We have seen that in the enterprise arena; hacks can remain hidden for months, such as in the Citrix case in which hackers laid dormant for five months. The bill’s section 4, subsection 2, should be a separate section that discusses this set of system capabilities. Maybe it is contemplated and articulated under “patching” or “secure development,” but it is important enough to be called out separately.

The publishing guidelines in another section (Section 4, C I) are set for five years. This is simply too slow. This industry is far more dynamic and will need a cadence far quicker than this.

I believe in applying different (tiered) levels of security based on the device’s use case and the value of the data that could be exposed. The concern here is that there will be some applications that need absolute bulletproof security. There will be other things (simple sensors) for which less security is required—doing a one-size-fits-all approach risks making systems too costly, too power-hungry etc.

Now, given that a lot of consumer devices aren’t things that would be purchased by the U.S. government, one could argue whether this will really help with the security of those devices in any way? Granted, volume is relatively low in the U.S. government, so I’m hopeful that companies that are driven to meet these requirements will also sell those products into other applications. That’s why it is important for tiered levels of security, as this might better help hit price and power points for broader adoption.

This is only the first step in a long list that the U.S. government can look to for improving security. It should consider its role with “carrot” and “stick” initiatives, such as the U.S. taking an active role and driving down power usage for certain devices (screens, laptops and TVs) through the delivery of financial incentives for people that purchased energy-efficient devices. That is the “carrot” initiative; something similar for purchasing secure IoT devices might help.

The “stick” represents how the compliance of the bill is policed. The European government has agreed that IoT security is important and they are empowering governments to track and potentially issue punitive fines if devices are found to be “below the line” in terms of security. The U.S. government can do something similar. I believe tier 1 cloud infrastructure companies and service providers will be highly supportive; these are the companies that will likely appear on the front of a magazine if a specific company using their services is hacked, so it is prudent for them to raise the bar and to continue to raise it, as security is not a static thing.

The post The Good and Not So Good of the IoT Cybersecurity Improvement Act of 2020 appeared first on Artificial Intelligence.

South Africa’s economy is easing back towards levels of activity last seen before the Covid-19 lockdown. Logistics fleets are returning to full capacity, and private travel is now similarly back to previous levels.

The reason we can confidently make this assertion is the vast amounts of data now available to us about movement on South African roads. As an operator in the vehicle recovery and fleet management space, we have access to this data.

This data now informs pivotal strategic decisions, it drives business planning, efficiency enhancements, and life-and-death safety initiatives. The source of this vast ocean of information is that popular current buzzword – the Internet of Things (IoT).

IoT technology has been around for decades – even if its name is more recent. Tracking and monitoring devices have long been in use – employing radio and satellite methodology, and later 3G and LTE platforms.

The move towards the mass, high-speed connectivity promised by 5G mobile broadband promises to take the industry to a new level of insight, providing critical data that vastly improves customer services, safety, and profitability.

No longer are IoT insights a nice-to-have capability, or simply a security concern. They are vital. No business operating in the mobility space can afford to ignore the value of IoT.

IoT finds application in several aspects of modern business. In public transport, for instance, camera technology is now used to monitor driver behaviour as well as social-distancing and safety compliance.

Newer-generation cameras are capable of image recognition, customer counts and even remote infrared body-temperature screening.

Initially, short-range RF technology simply told customers the location of their vehicles. As the technology progressed, we were able to gather more information through GPS trackers, cameras and ever more precise sensors.

Today, companies can assess and modify driving behaviour. This has opened up opportunities for both consumers as well as insurers and vehicle finance companies. They can now offer personalised premiums and encourage efficient, safe driving that benefits drivers, businesses and society at large.

We have seen our offering evolve from tracking to connectivity and data management. Technology also now lets us give clients access to data platforms and dashboards where they can manage and customise their own track-and-trace service.

Clients can also now take ownership of their own security, setting geofencing and alert parameters for their vehicles and family members.

Commercial transport firms can set up automated logbooks, panic buttons and driver alerts.

In the consumer space, the next step of technological evolution is the understanding of the car as a permanently connected information and entertainment hub.

When the connected car can guarantee broadband access, the final piece of the puzzle falls into place. We can now move seamlessly between our home, vehicle and office environments while remaining connected throughout. This is the dawn of the Connected Life.

Thanks to IoT, this state of eternal connectivity generates constant, exponentially growing amounts of data. The challenge, and the true differentiator in an IoT space where equipment is largely commoditised, is how to use and interpret that information.

Data enrichment is the new frontier. A company such as ours has moved from simple vehicle tracking, into the Big Data space. We have built our own Big Data platform, in partnership with Stellenbosch University, which we launched about two years ago. The platform currently processes around 300 million data points per day.

Big Data platforms allow for the provision of data-centric services such as predictive models. For instance, insurance companies can now use drivers’ unique biometric data-sets to identify when performance is impaired due to fatigue or alcohol. In this way, IoT enables the evolution from accident reconstruction to accident prevention, using the power of data.

As far as claims validation goes, data analysis powered by artificial intelligence can make this an almost instantaneous process.

Another trend we are seeing is OEMs entering the data and connectivity space. While this transition has not yet reached its ultimate form, based on our global partnerships with Toyota, we anticipate carmakers taking ownership of engine- and vehicle-centric information and its connectivity.

Supplementary services, such as theft recovery, and jamming detection will likely be provided by local suppliers. With the rise of the connected vehicle, infotainment will become another differentiator, and possibly another income stream for OEMs, or third-party suppliers.

The final stage of this data-centric move to connected mobility may be the establishment of a single, interoperable data platform, shared by cars, infrastructure, service providers and governments. Until such a scenario arrives, we will see connectivity gradually gaining in significance as the key enabler of movement.

It must not be overlooked that these oceans of data that our mobility now generates come with significant security risks. Cybersecurity is now a mobility issue, and we’ll need to keep building technologies and policies to protect our customers and their data.

The key is proactive threat management, using artificial intelligence to proactively anticipate and manage such threats on a dedicated cybersecurity platform.

IoT is the technology that has transformed modern business. That transformation is taking place in several spaces at once – safety, mobility, accessibility, sustainability, cost control, operations, information and analytics.

Each of these areas offers opportunities for organisations able to build proficiencies themselves, or to build partnerships to provide such services. In an emerging platform economy, where data is ubiquitous, the organizations with the platforms to deliver services using that data will dominate.

Technology is no longer a differentiator, but an enabler. It enables the objectives and strategies of organizations, so they can serve their customers and be competitive. As such, it is indispensable. IoT, overlaid with AI, is no longer a grudge purchase. It’s essential.

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New Delhi: SoftBank-owned UK chipmaker Arm has partnered with Microsoft to create Azure-based tools to enable developers transfer data from Arm-based Internet of Things (IoT) devices to Azure Cloud. The partnership will enable developers to easily target a broad range of Arm-based devices like intelligent computer-vision-enabled cameras, connected vehicles, AI gateways and intelligent appliances.

With over 30 billion active IoT devices in the world with 400 per cent growth over the past three years, the industry is moving incredibly fast. “Arm’s leading silicon architecture combined with their broad ecosystem and commitment to optimising developer experiences will enable this partnership to accelerate AI innovation for IoT devices, delivering better solutions and a better future for end-users,” said Moe Tanabian, Vice President, General Manager-Azure Edge Devices.

In one of the biggest tech deals, graphics giant NVIDIA in September announced it is acquiring Arm for $40 billion with an aim to create a premier computing company for the age of artificial intelligence (AI). Microsoft is working closely with the silicon ecosystem to launch an Azure-Ready Silicon programme.

“We are committed to helping foster innovation and diversity within the broader silicon and device ecosystems, and making it easy, fast, and cost-effective for our customers,” Microsoft said in a blog post on Thursday. “Our collaboration with Arm to facilitate an end-to-end AI toolchain to simplify development is an important part of this strategy”.

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Dicker Data has added two new companies to its vendor roster to expand its internet of things (IoT) portfolio.

The two user and device tracking providers, Digital Matter and kontakt.io, produce GPS outdoor tracking and Bluetooth indoor tracking applications and devices.

The Perth-headquartered Digital Matter was founded in Johannesburg South Africa in 2000 by Ken Everett. It produces GPS and IoT devices for the agriculture, asset tracking, fleet management, supply chain management, oil and gas industries.

The solutions are focused on long-range, outdoor tracking applications using 2G, 4G LTE-M, NB-IoT, LoRaWAN and Sigfox connectivity.

Dicker Data said its partners could now co-sell Digital Matter solutions with cellular network access from the distributor via its existing Telstra and NNNCo LoRaWAN distribution agreements.

Stuart German, Digital Matter’s business development director said Dicker Data’s technical expertise aligned with the vendor’s “commitment to innovation”.

“Asset tracking is one of the highest growth application segments for the Internet of Things, making this an exciting time to introduce our range of future-proofed LTE-M / NB-IoT hardware and white-label software as a recurring revenue business model.”

Poland-based Kontakt.io was founded in 2013 and makes short-range indoor tracking devices using Bluetooth Low Energy (BLE) connectivity. The vendor has integrations with Dicker vendors Cisco Meraki and Mist.

“The combination of Dicker Data’s IoT solution focus and Kontakt.io’s global leadership in IoT innovation creates the conditions for a perfect storm, one that can change the landscape of the Australian connected enterprise market,” Kontakt.io exec Rom Eizeberg said.

“Together, we support the mission of simplifying IoT, removing obstacles to adoption and creating a path to value for a wide market spectrum of partners and end-users.”

Dicker Data’s COO Vlad Mitnovetski said the two companies’ offerings “complement our IoT portfolio, boosting Dicker Data’s ability to deliver end-to-end IoT solutions”.

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Medical wearables, kitchen appliances and fitness equipment and other connected devices are regularly connecting to corporate networks, prompting technology leaders to warn that significant action should be taken to protect them from being used to hack into businesses.

That’s according to a new survey on practices for securing IoT (the Internet of Things) commissioned by Palo Alto Networks, the global cybersecurity leader. It polled 1,350 IT business decision makers in 14 countries in Asia including India, Europe, the Middle East and North America.

Overwhelmingly, respondents report a rise in the number of IoT devices connecting to their networks over the last year. Among the connected trash cans, light bulbs and hand sanitizers, one red flag emerged: More than half of those who polled said they either need to make a lot of improvements to the way they approach IoT security (43 percent), or that a complete overhaul is needed (36 percent).

In India, businesses surveyed have complete confidence (92%) that they have visibility of all the IoT devices connecting to their organisation’s network. However, two in five (40%) of the largest businesses surveyed (3,000 employees plus) reported that they have not segmented IoT devices onto separate networks — a fundamental practice for building safe smart networks. It is even more worrisome that only 27 percent reported following best practices of using micro-segmentation to contain IoT devices to their own tightly controlled security zones.

“Adoption of digital technologies is being seen on an unprecedented scale and so is the spike in security concerns. We need to bear in mind that cybercriminals are getting smarter, thanks to technological advancements and innovations. They are constantly exploring new avenues of cyberattacks – including through IoT devices – and therefore, more caution needs to be exercised by the organisations as they take a technological leap,” said Anil Bhasin, regional vice president, India & SAARC, Palo Alto Networks. He further added, “As we plan on a wider implementation of the Internet of Things (IoT), there has to be a solid strategy in place to overcome the security challenges that come along.”

“Traditional networks are ill-equipped to handle the surge in adoption of IoT devices,” said Tanner Johnson, senior cybersecurity analyst at Omdia. “Device behavior baselines need to be established to allow for new recommended policies to help stop malicious activity. For instance, it would raise a flag if a connected thermostat started transmitting gigabytes of data to an unfamiliar site.”

Palo Alto Networks released the survey as part of its ongoing effort to shed light on security threats posed by the surge in deployment of devices connected to the internet. Business Insider Intelligence forecasts there will be more than 41 billion IoT devices by 2027, up from 8 billion last year.

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