MIT develops low-power high-speed chips for IoT security

MIT chip performs hardwired encryption faster and using less power.

MIT researchers have hardwired public-key encryption into a new chip for IoT devices. It uses 1/400 of the power of software execution, one tenth of the memory, and executes 500 times faster. 

From data breaches to weaponised devices, the Internet of Things (IoT) has been plagued with security issues. In part, this is down to hardware manufacturers implementing security as an afterthought, along with a lack of standardisation.

But it’s also true that building a low-power network of connected devices will remain challenging while encryption is so energy intensive.

Sensitive data transactions are usually protected by public-key cryptography. This type of encryption allows computers to transfer information securely without needing to establish a secret encryption key.

However, the software responsible for executing these protocols is both memory and energy intensive. The battery life trade-off required for embedded sensors and smart devices to run has long been a burden on development.

Read more: Virtuosys launches Edge Application Platform

Energy-efficient encryption for the IoT

But that could be about to change. Researchers from MIT have developed a chip that’s hardwired to execute public-key encryption.

It uses a tiny fraction of power (1/400) compared with software execution of the same protocols, and just ten percent of the memory. Better still, MIT’s new chip executes the encryption process 500 times faster.

The new chip relies on a technique called elliptic-curve encryption. The process uses mathematical functions to secure transactions. Previously, chips have been hardwired to handle specific elliptic curves or families of curves. MIT’s latest chip has been developed to work with any elliptic curve.

“Cryptographers are coming up with curves with different properties, and they use different primes,” said Utsav Banerjee, an MIT graduate student in electrical engineering and computer science, and lead author on the paper.

“There is a lot of debate regarding which curve is secure and which curve to use, and there are multiple governments with different standards coming up that talk about different curves. With this chip, we can support all of them, and hopefully, when new curves come along in the future, we can support them as well.”

The researchers will present a paper on the new chip at this week’s International Solid-State Circuits Conference.

Internet of Business says

This is merely the latest innovation from MIT to focus on reducing the energy consumption of intelligent systems, while increasing their power and speed. Our separate report today looks at its work with neural networks. Energy use, cost, and speed are the critical elements in developing sustainable IoT devices and services that can really deliver on their promise.

Read more: NEWSBYTE: ARM launches scalable chips for IoT machine learning

Read more: Dell Technologies unveils new IoT strategy in New York

Read more: MIT’s NanoMap helps drones to navigate safely at high speed

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Digital Biotech: TetraScience develops IoT platform for R&D

IoT platform for R&D

At a company called TetraScience, an IoT platform for R&D has been developed to connect lab equipment, take research to the cloud, and drive the development of what the company calls ‘Digital Biotech’.

Research labs are geared towards expanding the frontiers of their industry. They are in many ways at the forefront of human ingenuity and scientific endeavour. It’s somewhat surprising, therefore, that research and development facilities, particularly in the world of biotech, haven’t been quicker to integrate the IoT into their processes.

For years, researchers have leant on little-altered tools for recording and sharing their findings. Now, an IoT platform for R&D, created by TetraScience, aims to change this by connecting lab equipment to the cloud, for more efficient and accurate research.

The startup was co-founded by Massachusetts Institute of Technology (MIT) graduate Siping Wang, alongside former Harvard University post-docs Salvatore Savo and Alok Tayi. Wang was recently named in Forbes 30 Under 30 list of innovators and it is easy to see why.

Read more: Australian researchers partner with Huawei for smart healthcare

TetraScience’s IoT platform for R&D

The company’s data integration platform can connect disparate types of lab equipment, and their associated software, and aggregate that information on the cloud. Researchers can then monitor, mange and automate their experiments remotely. This streamlines processes that have historically taken up valuable time and resources, and eases data collection and collaboration.

“Software and hardware systems [in labs] cannot communicate with each other in a consistent way,” said Wang, in a MIT report. “Data flows through systems in a very fragmented manner and there are a lot of siloed data sets [created] in the life sciences. Humans must manually copy and paste information or write it down on paper, [which] is a lengthy manual process that’s error prone.”

Over 60 major pharmaceutical and biotech firms have adopted the platform (including more than half of the world’s top 20 such companies), as well as labs at MIT and Harvard, becoming what TetraScience refers to a Digital Biotech companies. These corporations are data-driven, agile, and collaborate externally.

“Our technology is establishing a ‘data highway’ system between different entities, software and hardware, within life sciences labs. We make facilitating data seamless, faster, more accurate, and more efficient,” said Wang.

Read more: Clinical mobility set to transform hospital stays, survey finds

Moving beyond the lab

During his research as an undergraduate (working in the Cornell Semiconducting RF Lab on high-energy physics research), Wang was hampered by the time and effort required to manually record data, so he developed a system that connected and controlled multiple instruments.

At the scale of a pharmaceutical or biotechnology company, where instruments and their different software can number in the hundreds, the benefits of an IoT platform are even more stark. Not only does TetraScience eliminate much of the busywork of recording data in multiple locations, it also cuts out the risk of human error that comes with this process.

This goes beyond data recording too. Notable Labs uses TetraScience Monitoring throughout its lab, where they carry out research on live patient tumor samples, in the search for effective drug combinations.

When an outside lab technician tripped over a power cord and failed to notice they had unplugged an incubator, bioengineer Transon Nyugen was immediately notified by the IoT platform (via email and text) that there was a critical change in the incubator’s environmental conditions.

“If we hadn’t caught that the incubator had been unplugged, our entire screen would have failed due to the sample being compromised,” emphasized Transon. “This could have directly compromised our research. Live patient samples are precious and we couldn’t just go back to the oncologist and ask for another one.”

The platform also has potential uses beyond the pharmaceutical and biotechnology industries. Sectors that similarly rely on tightly controlled and monitored processes, such as oil and gas, brewing and chemistry, would also benefit from the control and efficiency gains offered by TetraScience.

Read more: New digital healthcare advances come to US hospitals

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Private messaging iPhone app Confide develops screenshot-defeating ‘ScreenShield’

Article Image

Secure messaging app Confide has come up with a way to prevent messages appearing in its iOS app from being captured in a screenshot, with the firm also offering its ‘ScreenShield’ technology in a development kit to help keep messages and other content protected in other apps.
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MetaX Develops Ads.txt Plus, Powered by adChain and adToken

This week, MMW learned that MetaX, a blockchain technology company driving development and adoption of open platforms for digital advertising and the creator of the adChain protocol and adToken (ADT), has brought the existing IAB Tech Lab ads.txt utility to the Ethereum blockchain, resulting in Ads.txt Plus.

Ads.txt Plus is hailed as being “one of the first open source decentralized applications (DApps) for digital advertising. It is now available in beta on the Ethereum Rinkeby testnet.”

Ads.txt is an IAB Tech Lab project aimed towards transparency within the inventory supply chain of the programmatic advertising industry.

Since ads.txt is a public webserver file, the company says it makes it easier for buyers to identify authentic publisher inventory.

“Ads.txt was a great leap forward for the digital advertising community,” said MetaX Chief Revenue Officer, Alanna Gombert. “As members of the IAB Tech Lab Blockchain Working Group, we decided to give back to the community and build Ads.txt Plus, an open source version built on Ethereum. This is part of our greater mission to encourage honesty, integrity and communication within the advertising industry.”

The post MetaX Develops Ads.txt Plus, Powered by adChain and adToken appeared first on Mobile Marketing Watch.

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Semtech develops disposable LoRa IoT nano-tag

Semtech develops disposable LoRa IoT nano-tag

Semiconductor specialist Semtech extends its product and services reach further into IoT networking technologies with a new disposable LoRa-enabled Nano-tag for IoT.

Already known to Internet of Business for its work at the Port of Cork in Ireland, Semtech has now developed a new breed of high performance analog and mixed-signal semiconductors with ‘disposable’ characteristics.

LoRaWAN (standing for long range, wide area networking) is a protocol specification that uses unlicensed radio spectrum in the industrial, scientific and medical (ISM) bands, in order to enable communication between remote sensors and gateways connected to a network.

Semtech’s latest nano-tag reference design is a disposable, ultra-thin and low-cost device that can be integrated into disposable systems or attached to assets to communicate a specific event trigger.

As defined here, nano-tags are octagonal pieces of microscopic nickel – 6 to 10 microns thin and ranging from 0.3mm to 0.5mm wide, which feature a micro-image of a personalized brand, created to order. They are extremely robust, chemically resistant, able to withstand temperatures of over 1,000 degrees Celsius without oxidising and with a melting point of 1,453 degrees Celsius.

Read more: Ireland set to benefit from Semtech’s LoRa IoT Network

Smart decision-making

According to the company, Semtech’s LoRa-based nano-tag is suited to deployment across numerous IoT verticals that utilize event data for ‘smart’ decision-making.

The nano-tag is equipped with an ultra-thin printed battery and is designed to be integrated into products or systems that send messages to cloud datacenters when a ‘simple’ event is detected. The LoRa-enabled reference design is said to be capable of working with existing LoRaWAN networks.

Semtech has grand designs (or at least big ambitions) for this technology; the company claims that this could enable the proliferation of completely new types of IoT applications. These would be new apps that require real-time feedback, in logistics and shipping, healthcare and pharmaceutical, asset tracking and general-purpose compliance applications, for example.

MachineQ, a Comcast Industrial IoT service, is the first company to pilot the LoRa-enabled nano-tag with interested third parties on its IoT network in Philadelphia.

“By offering lower cost, disposable LoRa-enabled tags, we can expand the current landscape of use cases for Semtech’s LoRa devices and wireless radio frequency technology and allow companies to integrate the technology to drive many more diverse IoT use cases,” said Marc Pegulu, vice president and general manager for Semtech’s Wireless and Sensing Products Group.

“We believe the number of use cases should expand rapidly as our connectivity and cloud partners start to leverage the disruptive nature of the LoRa-enabled tag,” he added.

These disposable LoRa-enabled tags will be commercially available in both flexible tape and paper substrate formats in 2018 and are currently being trialled by a number of LoRa Alliance members.

Read more: Actility launches LoRaWAN networks in Saudi Arabia and Tunisia

Disposable computing

Disposable computing is indeed now ‘a thing’ then, both in terms of hardware and software. RFID-enabled passes, name badges and other forms of identification have been around for most of the current decade if not longer.

And it is now reasonable to think in terms of short-term software functions being released as ‘disposable apps’ (an app for a special event or conference for example), especially now that it’s possible to install and delete these pieces of software so rapidly and ubiquitously on our smartphones.

Disposability in terms of both hardware and software could be a key trend for the IoT in 2018. There’s a throwaway statement for you if ever there was one.

Read more: Thin film batteries set for solid (state) growth

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Asia Optical develops 5x optical zoom lenses for smartphones

Asia Optical and Coreophotonics are developing a 5x optical zoom lens for use in smartphones. The certification process will start by the end of this year while shipments to manufacturers are expected to begin in Q3 2018. The lens will have aspherical glass lens piece and four plastic ones, said Robert Lai, Asia Optical chairman. The company has the capacity to build 12 million pieces per month due to 220 aspherical glass injection molding machines – 160 based in Taiwan and another 60 in Myanmar. Asia Optical currently ships between 500-550K glass modules for the automotive… – Latest articles

BYU develops sports helmet foam for real-time concussion detection

Cranial collisions are haunting the sports world more and more. A recent survey of 111 former football player's brains found that 110 showed signs of chronic traumatic encephalopathy (CTE), a degenerative disease caused by repeated blows like those d…
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Google Develops Incredible Watermark-Removing Software

Researchers at Google have figured out a method for cleanly and automatically removing watermarks on stock photos. The team debuted the method at the 2017 Computer Vision and Pattern Recognition Conference in July.

As most would-be stock photo thieves know, there’s really no easy way to get rid of large, obnoxious watermarks on photos. One could pay for the photo’s rights, or spend an inordinate amount of time in Photoshop painstakingly erasing the watermark — often with unsatisfactory results. Even the latest version of the Adobe software lacks effective tools for the application.

That’s where Google’s new software comes in. It works by scanning a large batch of images with the same overlaid graphics until it detects a pattern and tracks the watermark being used. Once it identifies the watermark, it’s able to automatically remove it — leaving a photo that’s basically indistinguishable from a non-watermarked copy. The drawback is that the software only works on large batches of photos with the same watermark — like, say, from a professional and high-profile stock photo provider.

Of course, Google isn’t exactly in the business of publishing software for intellectual property thieves. In fact, the researchers used this method to develop a way to foil the software they had created.

The team found that randomly deforming or warping the watermark differently on each image renders its software basically ineffective. Even the smallest or most minute geometric variation between watermarks makes it impossible for software to remove the watermark without leaving traces of it behind. Presumably, it’ll make it hard for other non-Google software to remove watermarks, too.

Google’s research allows for a modified approach that will render watermarks much more effective for professional photographers and stock photo companies alike. Which is a good thing all around, because stealing someone else’s work without credit or payment is never cool.

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DeepMind Develops a Neural Network That Can Make Sense of Objects Around It

The Relationship of Things

One of the many abilities of the human mind that we tend to take for granted is making sense of objects by relating them to one another. The mind can tell where an object is relative to what’s around it. This inference the mind applies to reality is called relational reasoning. Now, it looks like an artificial intelligence (AI) might just be able to pull it off as well.

White House AI Report: Everything You Need to Know [INFOGRAPHIC]
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It’s no small feat for an AI to learn this 3-dimensional sense. Researchers from DeepMind, the AI arm of Google’s parent company, Alphabet, were able to develop a relational reasoning module for neural networks. The effort is part of developing AI systems capable of cognition with the same flexibility and efficiency that the human mind possesses.

This relation network (RN) module, which could be plugged into other neural networks, could allow AI to analyze pairs of objects and deduce relationships between them. Details of their work were published online in two separate studies.

From Abstractions to Realizations

The researchers trained their RN using images of 3D shapes in various colors and sizes. After it analyzed the objects, the researchers asked the neural network questions like, “What size is the cylinder that is left of the brown metal thing that is left of the big sphere?” using a visual question answering task called CLEVR. The results were impressive.

A sample image from CLEVR. Image credit: DeepMind
A sample image from CLEVR. Image Credit: DeepMind

“State-of-the-art results on CLEVR using standard visual question answering architectures are 68.5 percent, compared to 92.5 percent for humans,” according to a post by DeepMind. “But using our RN-augmented network, we were able to show super-human performance of 95.5 percent.”

Such a system could greatly improve visual learning algorithms as well as the AI in virtual assistants. “You can imagine an application that automatically describes what is happening in a particular image, or even video for a visually impaired person,” DeepMind researcher Adam Santoro told New Scientist in an interview.

As clever as this system could be, DeepMind researchers believe there’s still a long way to go before it could be used in our daily lives. “There is a lot of work needed to solve richer real-world data sets,” Santoro added in the interview.

The post DeepMind Develops a Neural Network That Can Make Sense of Objects Around It appeared first on Futurism.