PureLiFi imagines bright future, where every light’s an internet access point

pureLifi imagines a bright future, where every light can connect you to the internet

PureLiFi may have the answer to the wireless spectrum overcrowding challenge with its plans to use light to transmit data.

Towns and cities are not only crowded in the physical sense, teeming with people and vehicles and high-rise buildings. The airwaves around us are alive with data – voices fighting to be heard in a narrow radio spectrum. In recent years, this has been alleviated somewhat by the introduction of the 5Ghz band to WiFi standards.

However, with 20 billion IoT devices predicted to be in use by 2020, it’s only a matter of time before our networks outgrow current capabilities.

Edinburgh-based company pureLiFi thinks it has the answer. By using light to transmit data, it can utilize a spectrum 1,000 times greater than that used for radio frequencies – giving us the bandwidth required to transmit vast amounts of data wirelessly over short distances. These sorts of use cases, such as in homes, offices and cafes, are currently responsible for the majority of our wireless data use.

Read more: NextLiFi teams with Monash Uni to shine light on Li-Fi potential

The latest in LiFi

The company’s most recent release is the world’s first certified, complete LiFi system. The Lifi-XC, is a plug-and-play device that shrinks previous iterations of PureLiFi’s technology, vastly improving its usefulness.

“Over the past year, we have been driving adoption of LiFi and deploying real-world applications of LiFi for our customers globally. We have now reached the point in miniaturization where we will see LiFi move beyond the dongle and be integrated,” said Alistair Banham, CEO of pureLiFi.

“The LiFi-XC is a big step towards getting this disruptive technology into every bulb and every mobile device”

How Lifi is lighting the way

We’re not new to using light to transmit data. From signal fires, mirrors and gas lamps, to the fibreoptic cables and IR remotes we use today, we’ve been using it for millennia. LiFi is yet another type of Optical Wireless Communications [OWC]. However, it differs in that the same light energy used for illumination may also be utilised for communication – an appealingly efficient use of energy.

An enabled LED light can modulate the intensity of light (imperceptibly to the human eye), which can then be received by a photo-sensitive devices. This signal is then converted into electronic form. The high intensities of off-the-shelf LEDs make them well suited to high-speed data transfer.

PureLifi is also keen to emphasise the improvements it has made to its products’ ease of use. “The LiFi-XC is not just an accomplishment in reduced form factor, we have also made substantial leaps in delivering a great user experience,” says chief technology officer Mostafa Afgani.

“The LiFi-XC offers plug and play connectivity out of the box and supports an even wider range of off the shelf LEDs. We have not just improved the design with LiFi-XC – we have also delivered a module that can enable smart devices and appliances to be LiFi connected today.”

LiFi explained

How pureLiFi works (credit: pureLiFi)

Read more: OneWeb aims to bridge digital divide with internet satellites

Potential uses of LiFi

I have some concerns over the security of LiFi. One of PureLiFi’s videos suggests closing the blinds to secure your network, which, for all the futuristic allure of the technology, seems archaic and flawed. On the other hand, you can’t similarly prevent WiFi signal from leaking beyond the four walls of a building.

PureLiFi promotes the apparent safety that comes with the ability to define the communication areas of access points – enabling precise partitioning of the office environment. Connection also requires proprietary hardware before anyone can access the system. These claims are reinforced by BT Defence’s use of the technology.

It’s easy to see how this tech could be used in smart cities, vehicle-to-vehicle communication and other smart transport solutions, thanks to the widespread use of LEDs. One particularly interesting user case is in hospital environments. LiFi doesn’t cause electromagnetic interference, nor is it affected by MRI scanners – something petrochemical plants also stand to benefit from.

It doesn’t look like widespread adoption is imminent, but the technology boasts several advantages over WiFi in more specialised environments. Longer term, as our WiFi networks reach saturation point, LiFi’s 1,000-times higher data density will make it an appealing alternative.

Read more: Battery-free Bluetooth tech from Wiliot one step closer to transforming IoT

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Internet of Business

New Policy Brief published on Community Networks and Access to Spectrum

Yesterday we published a new policy brief: Spectrum Approaches for Community Networks

Access to affordable and available spectrum is critical for Community Networks. Policy makers can play a key role in ensuring adequate access to spectrum. The policy brief examines the various ways that Community Networks can gain access to spectrum, including:

  • the use of unlicensed spectrum,
  • sharing licensed spectrum, and
  • innovative licensing.

Network operators also play a key role in helping Community Networks. The policy brief outlines recommendations for operators which include:

  • access to backhaul infrastructure at fair rates,
  • equipment and training partnerships, and
  • the sharing of infrastructure as well as spectrum.

Please read our press release for more information about this new paper.  Also visit our World Telecommunications Development Conference (WTDC) 2017 page for more about what our team is doing there in Buenos Aires this week.

The post New Policy Brief published on Community Networks and Access to Spectrum appeared first on Internet Society.

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Accelerate Access to Data and Analytics With AI

Even if businesses have made sizable investments in data and analytics, every employee doesn’t necessarily understand how to properly use that data. Learning enough to take advantage of those investments requires time and effort for each employee. While deep reservoirs of data may exist in the organization, the flow through individual employees may be more of a trickle than a cascade.

Artificial intelligence-based approaches may be able to help by enabling each employee everywhere to know what the organization overall knows somewhere.

The “last mile” is a common problem in transferring information. In telecommunications, high-bandwidth channels can move data quickly between central locations. The difficulty is that, at the periphery, data transfer over the “last mile” to individual users is a bottleneck. The rate of information flow in the slowest segment, typically the final one where each communication channel services just a single user, governs the overall rate of information flow. All the massive central bandwidth is for naught when the final link to the user is slow. For example, internet content may travel from all over the world to your local provider without a problem, but getting that to your home office can be stymied by all sorts of problems, such as antiquated cables on your nearby telephone pole, frail wiring in your house walls, or outdated components in your computer. No amount of central investment by your provider can correct limitations at your house — and investments within your neighbor’s house don’t help you, either. Improving the final bottleneck requires an idiosyncratic investment by each user.

Unfortunately, organizations that are ratcheting up their use of analytics must overcome similar last-mile issues. While the organization may have considerable data and analytics capacity, each employee must make investments to understand what is available and how it can be used. Pervasive use of data and analytics by an organization requires pervasive understanding of data and analytics. This is a fundamentally difficult and slow organizational learning undertaking.

One of the exciting possibilities is that artificial intelligence may help businesses accelerate organizational learning. Machine learning can enable faster organizational learning as it can help each employee quickly understand what others in the organization understand — artificial intelligence can distribute learning quickly.

Our forthcoming report on AI and business strategy provides an excellent illustration of this potential in the lead example of Airbus. As Airbus began production of its new A350 aircraft, the company wanted to fabricate the aircraft faster than any prior model — but without any compromise in quality.

One specific challenge was that the new aircraft was, by definition, new. No one had any experience in building it. Airbus faced an organizational learning problem as much as a production problem.

Experience is an important component of the manufacturing process. In a project the size of an aircraft assembly, numerous production difficulties and anomalies were bound to occur. Some problems would be big and require manufacturing to stop until they were isolated and corrected. Other problems would be small and could be quickly worked around without affecting product quality. With time, employees would learn how to handle problems and, most important, learn to discern which problems warranted stopping production and which did not.

To meet the goal of faster production, the whole Airbus organization would need to learn more quickly than it ever had before. Airbus already had the data and analytics infrastructure to collect data related to problems quickly. Yet it would still take considerable time for each of the employees involved to garner the experience necessary — this was the potential bottleneck.

Building Blocks for Organizational Learning Through AI

Matthew Evans, vice president of digital transformation, describes that the system Airbus built allowed it “to speed up production and to really shrink the amount of time it takes us to deal with these disruptions — to essentially come down the learning curve faster.” The system “put together a complete view of everything that had happened on the A350 program,” Evans says, and began with a data collection process “when there was an issue on the floor, such as a tool that was placed improperly and damaged a part. The first action that that team, that supervisor, would always take was to document the issue very quickly, with some text and a picture.”

Linking Machine Learning to Organizational Learning

But collecting and analyzing the data wasn’t enough for the organization to learn. Airbus went further to build an AI-based system that put, as Evans describes it, “a recommendation in hand right away that says, for example, that the best course of action is to ask for help from the stress engineering department and eliminate a lot of the previous leaps that we had where there was a lot of guesswork involved.” Employees aren’t limited to what they themselves have experienced and learned. Instead, each employee can quickly build on the experience of others in the organization.

Overcoming the Last Mile Problem

But if Airbus stopped at consolidating experience centrally, the benefits of data and analytics wouldn’t reach back to the shop floor. Evans describes the problem as getting information to employees who can use it to make a difference in their production rate: “Fundamentally, it’s all about having access to that integrated information, and designing a system and a tool that is there to augment the work that the human is doing. It’s not full automation. It’s not a replacement for the work that the supervisor is doing. It’s there to help them. And it’s the combination of the machine having the breadth of knowledge and the insights to find the close matches, supplemented by what the shop floor supervisor knows about exactly what is going on, exactly what their precise situation is. The combination of those two factors is what really made it effective.” The AI system reduces the bottleneck in the last mile by helping the employee put the data and analytics to use exactly at the point they’re needed.

MIT Sloan Management Review

UK citizens want Uber-style app to access driverless cars

Many UK citizens are open-minded about driverless cars

There’s no clear indication yet as to how UK citizens feel about fully driverless vehicles, with many unsure about whether they would use one. However, many of those who say they would have suggested an Uber-style app would be ideal to book a driverless car.

That’s according to a new survey conducted by researchers at Cambridge University’s Engineering Department and the Department of Psychology. The report was commissioned by UK Autodrive, a consortium of technology and automotive businesses, local authorities and academic institutions who are working together on a three-year UK trial of self-driving vehicle and connected car technologies.

The researchers surveyed 2,850 UK citizens, and asked them whether they would use a fully driverless vehicle. Only 10 percent said they definitely would, while 26 percent said they probably would. Fifteen percent said they would definitely not, and under a fifth (18 percent) said they probably wouldn’t, leaving nearly a third who said they weren’t sure.

Citizens remain scepical

Although only a small proportion of respondents completely opposed the idea of driverless vehicles, many expressed some reservations about the ability of the technology to replace the driver completely.

“In response to questions about what levels of control they would like to retain, 85 percent expressed a desire to retain some control over the choice of route, and 74 percent wanted to retain an option to drive manually,” the report revealed.

When asked what the respondents would use a driverless vehicle for, there was again a wide range of views which included shopping (23 percent), commuting (22 percent), visiting friends (21 percent) and drinking (15 percent).

The most popular uses of recovered time, meanwhile, were viewing scenery (55 percent), responding to emails (37 percent), making phone calls (35 percent), eating or drinking (35 percent), socializing (33 percent) and doing nothing (24 percent).

Driving the sharing economy

When questioned about operations and ownership, a slight majority of respondents saw greater potential for self-driving vehicles in the realm of public or shared transport systems than in the traditional private ownership arrangement.

The preferred means of booking access to a driverless car if it was part of a shared transport system was through a smartphone app (45 percent), with home telephone (27 percent) and conventional hailing at a public bus stop (23 percent).

Read more: IBM launches new security services for connected cars and IoT

Navigating problems ahead

The report revealed that UK citizens were open-minded about autonomous vehicles, but Clive Longbottom, analyst at IT advisory company Quocirca believes this will change when the vehicles become more widely used.

“I think some problems will come out eventually,” he said.

“An autonomous vehicle has to be tracked at all times; the amount of personally identifiable data that is being created and stored will be high. When there are millions of such vehicles, there are bound to be failures in the system, and passengers will be injured or killed – who is then responsible? The owner for not having any control or not checkcing the condition of the control systems on a regular basis, or the car manufacturer,” he questioned.

This, Longbottom explained, was just one of the many questions being posed around autonomous cars, as there are so many implications from both a legal and insurance perspective.

“I think that respondents are possibly more in the Jetsons jetpack mentality at the moment: they want an autonomous vehicle because it sounds cool. The reality may be different; even if it is a pretty good experience, once the idea takes off and becomes mainstream, it is no longer ‘cool’ – just commonplace,” he said.

Read more: Driverless cars are coming, despite consumer doubts, says OpenText

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Askey Computer Selects Sequans’ LTE-A Chipset to Design Broadband Wireless Access Devices

Askey Computer Selects Sequans’ LTE-Advanced Chipset to Design Broadband Wireless Access Devices

LTE chipmaker Sequans Communications S.A. announced that Askey Computer Corporation, leading provider of wireless communication devices, has selected its Cassiopeia LTE-Advanced platform to design broadband data devices for the global market.

The first product to be delivered by Askey is an indoor CPE (model number RTL0012W).

“We chose Sequans’ Cassiopeia platform because of its high performance capabilities, including highly flexible carrier aggregation and LTE Cat 4/6 expansion,” said Alan Kao, VP of sales and marketing, Askey. “Cassiopeia gives us the flexibility to design and build CPE with unique features, customized for target markets.”

Askey is using Sequans’ Cassiopeia LTE-Advanced platform, which is compliant with 3GPP Release 10 specifications. Cassiopeia supports highly flexible dual-carrier aggregation that allows the combination of any two carriers of any size up to 20 MHz each, contiguous or non-contiguous, inter-band or intra-band.

Cassiopeia also supports other Release 10 enhancements such as new MIMO schemes, enhanced inter-cell interference coordination (eICIC) schemes for heterogeneous networks (HetNets), and improvements to eMBMS (evolved multimedia broadcast multicast service) or LTE broadcast.

Cassiopeia features Sequans’ advanced receiver technology for improved performance. Cassiopeia can support additional optional features, including envelope tracking and secure boot, at customer request.

Hugues Waldburger, VP of Sequans’ Broadband business unit, said:

“Askey’s new CPE is an elegant, powerful device and we are proud to see Cassiopeia inside. The new Askey CPE is state-of-the-art, providing customers with the latest advances in broadband connectivity, including ultra high throughput.”

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