Nokia and EDF join forces to test Internet of Things technology for industries

Nokia and EDF join forces to test Internet of Things technology for industries

Nokia and EDF join forces to test Internet of Things technology for industries

Project led by R&D division of EDF, will explore low power, wide area (LPWA) wireless technologies to support safe and secure connections with potentially millions of sensors and other devices. Joint effort incorporating Nokia TestHub services is among the industry’s most comprehensive testing to date using IoT devices for industries. Represents key step in EDF’s move towards the use of IoT; highlights Nokia’s role as a key partner for the deployment of networks for industries.

Nokia has been selected by French power utility EDF’s R&D unit to test the performance of LPWA wireless networking technologies – key emerging standards for Internet of Things (IoT) device connectivity – to support critical operations for industries.

The two companies will engage in a comprehensive testing regime, among the first of its kind in the industry, exploring the capabilities of LPWA technologies to support real-world industrial applications. Nokia is EDF R&D’s exclusive partner for this effort.

EDF R&D will utilize Nokia TestHub Services in Nokia’s Device Testing Lab in France – which gives customers access to state-of-the-art, carrier-grade wireless infrastructure – when testing IoT/M2M objects, chipsets, modules and user devices across all wireless technologies and frequencies. This enables devices to be tested on real network infrastructure rather than a simulated network, which reduces guesswork in testing and analysis and minimizes risks in advance of widespread commercial introduction.

The testing will compare IoT technologies recently standardized by the 3G Partnership Project (3GPP) – including NarrowBand-IoT (NB-IoT) and LTE-Machine (LTE-M) (also known as enhanced Machine-Type Communications or eMTC) – with other emerging, largely unlicensed IoT technologies.

This agreement builds on Nokia’s strong track-record providing mission-critical networks toindustries, and highlights the company’s strong position in the emerging market for IoT connectivity. It also highlights the progress of Nokia’s strategy of expanding its customer base outside of the traditional telecommunications sphere, a key focus of the company’s diversification efforts.

Stéphane Tanguy, head of IT Systems, EDF R&D, said:
“The Internet of Things offers tremendous opportunities for our group. Many use cases can be enabled by IOT technologies in various businesses from power generation to marketing. As the R&D engine of the EDF Group, it is our responsibility to characterize the objects, their connectivity, their integration into IoT platforms and the related end-to end cybersecurity properties. Among the connectivity solutions, it is essential that we understand the performance, the maturity and the adequacy of each technology for our different use cases by an objective and agnostic approach. The cellular IOT technologies (LTE-M and NB-IOT) are two major technologies that we have decided to test with Nokia, which provides us with a very interesting test environment and valuable expertise to carry out these evaluations.”

Matthieu Bourguignon, head of Global Enterprise and Public Sector, Europe, for Nokia, said:

“The use of IoT devices in industrial networks is in its infancy, but given the expected huge numbers of devices that will be deployed in the future, it is critical that our customers can evaluate now the various technologies before making substantial investments. Nokia’s Device Testing Lab, staffed by some of the most experienced wireless networking experts in the industry, will make it much easier for EDF to evaluate the performance of LPWA against other emerging technologies and reduce the risk of future deployments.”

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How AI Will Define New Industries

If you were a brilliant artificial intelligence (AI) expert just graduating from a doctoral program at a prestigious school, would you pursue that startup you’ve been thinking about, join a company that wants to build cutting-edge AI applications, or use your expertise to help scientists in other fields conduct basic research?

Admittedly, this is a bit of a silly question. The opportunities presented by the first two options are outrageous, and growing more outrageous by the day. With more than 2,000 startups absorbing much of the top-tier AI talent — estimated by some to be just 10,000 individuals worldwide — the combination of great scarcity and even greater demand for talent is driving salaries through industry roofs. Some businesses offer seven-figure compensation packages for elite AI talent.

Plus, it’s never been a better time to launch an AI startup. Investment in AI-focused ventures has grown 1,800% in just six years, from $ 282 million in 2011 to more than $ 5 billion in 2016, according to CB Insights.

The sanity behind these numbers comes, in part, from the fact that companies expect AI to allow them to move into new business segments or to maintain their competitive advantage in their industry. Driving this expectation is the idea that AI will enable them to dramatically improve the efficiency or effectiveness of their operations and offerings.

Despite the lucrative financial opportunities in these first two career paths, the alternative choice — to collaborate with scientists in other fields — may be more pivotal to how nations and businesses compete over the long term.

Conventional wisdom, based on no small amount of research, holds that AI-driven automation will create technological unemployment in a variety of sectors in the next five to 10 years. Optimists believe that this won’t be a big deal for the labor sector as a whole because corporate adoption of AI (and other digital trends) will create new industries and new job categories that will replace whatever AI-driven losses occur in labor sectors of the current economy. But there are two open questions about this hypothesis:

  1. How will these new industries be created?
  2. How soon will they come, if they come at all?

The creation of new industries frequently depends on dramatic advances in science and technology that can take decades to move from discovery to commercial application to new industry. The industry around AI is already 60 years in the making, and we’re still not there yet. A cursory look at the history of three technologies — crucial to modern life — shows the lengthy, complex path to new industry creation.

One is the global positioning satellite (GPS) system. Stephen Hawking, in his book A Brief History of Time, contends that GPS would not be possible were it not for Einstein’s 1915 theory of general relativity (GR). GR both explains why satellites in space track time differently from chronometers on Earth and enables satellites to work together to precisely track movements on Earth.

Precision agriculture, autonomous cars, Waze, and Uber are just some of the many applications of GPS. According to a 2015 government estimate, GPS contributes at least 0.4% to the U.S. economy, a number that omits several sectors and indirect benefits.

Another example is the internet, which was first conceived, arguably, by J.C.R. Licklider of MIT in August 1962, in a series of memos that described a “Galactic Network,” a globally interconnected set of computers through which people could quickly access data and programs from any site. It took many inventions (such as packet-switching technology) and investment (by the Defense Advanced Research Projects Agency of the US government) to develop the foundations for commercial applications related to this idea. An industry around the internet emerged only after the creation of the World Wide Web (1989) and commercial browsers (circa 1994). The internet bubble occurred nearly four decades after the internet was first conceived.

A third example is recombinant DNA and other gene-related technologies, which have become tremendous sources of economic value in terms of jobs and business opportunity — the market value of recombinant DNA technology by itself is expected to reach $ 844 billion by 2025. The “genes industry” owes much to the discovery of DNA structure in the early 1950s by James Watson, Francis Crick, and Maurice Wilkins (and Rosalind Franklin). Their discovery of the double-helix structure of DNA revolutionized scientific understanding of genetic code, making possible advances in agriculture, cancer treatment, and personalized health care.

Given how long it takes to develop new industries (and the basic research breakthroughs that enable them), there is a real question about whether — even if new industries based on AI and other digital advances eventually create new job categories — these new jobs will emerge fast enough to maintain sufficiently high employment levels in the economy in the interim. While a recent study by Gartner indicates that AI may create more jobs than it eliminates, it does admit that AI will destroy “millions of middle- and low-level positions.”

Creating these new industries and retraining a labor force to fill them in a timely way is a significant issue for at least two reasons. One is that labor unrest and/or economic downturns become more likely if unemployment stays too high for too long. Even retraining is no panacea if new jobs from new industries have yet to be created. Two, climate change-related economic effects are on the rise, with a disproportionately negative impact on lower- and middle-income populations. If new job categories are not created before climate-related effects wreak havoc on infrastructure and private property, the entire economic system becomes less stable.

Today, AI expertise is focused more on developing commercial applications that optimize efficiencies in existing industries, and focused less on developing scientific applications that could give rise to new industries. These efficiencies are accelerating the sectoral consolidation and convergence, and are less about new industry creation. Platform companies in the sharing economy, such as Uber, Lyft, and Airbnb, are disrupting existing industries, not creating new ones. Amazon’s moves into groceries and pharmaceutical distribution are shaking up incumbents, but not creating new industries. Squeezing ever more efficiency out of business models is usually great for profitability, but rarely a boon for the labor force.

We don’t want to minimize the effect of digital trends on existing labor markets. The web is filled with lists of new job titles and job types; by and large, jobs have yet to be lost as a result of AI. For now, managers remain much more optimistic about what AI can do for them in their jobs.

However, AI’s most potent, long-term economic use may just be to augment the discovery and pursuit of basic scientific advances that could be the foundations of new industry. Few companies have a long-term interest in using AI in this way. It’s simply not in their near-term commercial interests. But promoting this potential is a role that can be played by government. And if the history of the internet and GPS is a guide, the real winners in an economic era dependent on AI technologies will be the countries or regions that align private and public sectors to allocate scarce AI expertise to augment basic scientific discovery (as well as commercial applications).

If AI can supercharge discovery, commercial applications, and the creation of new industries, it is short-sighted from a social perspective to focus scarce AI talent on developing only commercial applications and disrupting current industries. Accelerating the pace of scientific discovery may be the most important societal use of AI. It’s time for business and government to work together to promote that potential.


MIT Sloan Management Review

Road to IIoT: Lessons for Manufacturers from Other Industries

For most manufacturers, the road to the Industrial Internet of Things (IIoT) is an evolutionary journey. And while it may be marked with twists and turns, it doesn’t have to be fraught with risk and uncertainty.

In a recent article I wrote for Manufacturing Business Technology, I explain how manufacturers can learn from other industries that have already headed down the path of IIoT. Here are some highlights from my article:

1. Go for Standards
Legacy automation infrastructures are often proprietary, and upgrades are typically controlled by a single vendor. That’s not only costly, but also limits options for introducing new, modern capabilities like IIoT. It’s similar to the challenge telecommunications faced until some forward-thinking carriers saw the promise of industry-standard solutions and operating systems. That opened the door for delivering enhanced services that gave them a competitive advantage over traditional providers of basic dial tone services. By adopting standards-based technology, manufacturers could open the door to a new wave of business-enhancing innovation.

2. Open the Door to Integration
Operations technology (OT) organizations like to keep their industrial control systems (ICS) walled off from the rest of the enterprise to avoid points of vulnerability. But enterprise-wide connectivity is essential to gain the intelligent automation capabilities of IIoT. Consider how the highly risk-averse financial services industry made the leap. Using the latest network security and continuous availability solutions, they allowed connectivity to business-critical transaction systems. This created a wealth of new business opportunities for financial firms in today’s mobile, digital consumer marketplace.

“Operations technology (OT) organizations like to keep their industrial control systems (ICS) walled off from the rest of the enterprise to avoid points of vulnerability. But enterprise-wide connectivity is essential to gain the intelligent automation capabilities of IIoT. Consider how the highly risk-averse financial services industry made the leap. Using the latest network security and continuous availability solutions, they allowed connectivity to business-critical transaction systems. This created a wealth of new business opportunities for financial firms in today’s mobile, digital consumer marketplace.”

3. Tap into Distributed Intelligence
One of the hallmarks of IIoT is the gathering of data from a wide range of sensors and systems to gain valuable insights. This distributed intelligence is central to improving production efficiency, enabling predictive maintenance, and sparking innovation. Many industries have already seen this. Take oil and gas companies, for example. They use data collected from sensors at remote pipeline compression stations to run analytics that detect early signs of component failure. With advance warning, these companies can shrink maintenance windows and avoid costly unplanned downtime.

4. Protect the Avalanche of Data
One thing is certain: as manufacturers embrace IIoT, the volume—and value—of production data will increase substantially. So manufacturers must ensure availability of both the data and the automation systems generating it. The building automation and security industry, for example, needs to make sure they protect the fountains of data generated by their video monitoring solutions. To mitigate risk of losing valuable video evidence, these companies make end-to-end fault tolerance a priority. You can do the same to prevent data loss or downtime from the production floor to the historians that store ICS data to the analytics engines creating insights from that data.

The road to IIoT might seem lonely at first. But it’s easy to see that you have good company from firms in other industries that are making the journey. While each industry has its own challenges and priorities, they all stand to gain similar benefits by charting a course to next-generation IIoT automation. Unlocking valuable insights and strategies enabled by IIoT is helping all companies across the spectrum compete better and improve their efficiency and profitability in a meaningful way.

Originally published on Stratus.

(c)istockphoto.com/ BlackJack3D | zorazhuang

Stratus Technologies are sponsoring, speaking and exhibiting at the IoT Tech Expo North America on the 29-30 November 2017.

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IoT Tech Expo

Three Industries Poised to Take IoT Mainstream

Three Industries Poised to Take IoT Mainstream

By Manuel Nau, Editorial Manager at IoT Business News.

The internet is lauded for its uncanny efficiency in connecting humans to one another and to large amounts of data. Eventually all of our devices — from fridges to cars to phones to the chips embedded in our skin — will be online and connected to one another. The IoT is poised to make all these connections happen.

Many devices originally created to work with the IoT were designed to become a part of smart homes, which were marketed largely to Gen Xers and baby boomers intending to age-in-place. Home automation is a reality now and it shows no sign of slowing down with new innovations constantly coming out on the market. Beyond smart and age-in-place homes, IoT devices are breaking away from traditional platforms and into the health care, criminal justice, and education sectors. Here are a few examples of what that may look like over the next decade.

Health Care

As technology advances, nurses’ duties change and expand while some of the more monotonous tasks are eliminated. This allows nurses to focus more on their patients’ well-being. The scope of nurses’ responsibilities is likely to transform dramatically as wearable devices like blood sugar monitors and brain-computer interfaces cut down on nurses’ time investment with each individual patient.

At the same time, the digitization of patient data brings an added layer of complication in terms of security. Hospitals are so well-known for their poor security and laissez-faire attitude about software upgrades, that many hackers on the dark web have declared them off-limits. But if the 2016 doping scandal leaks taught us anything, it’s that a hacker’s code of ethics are subjective. Hospitals have been hit hard by hackers in the past, and they will surely be hit again in the future if they do not take proper security precautions. Medical offices will need to hire data analysts and network specialists to ensure that sensitive patient data remains in the right hands and is efficiently streamlined into existing electronic health records.

IoT presents good news for patients, too. Both Google and Apple have been working on smart contact lenses that can measure glucose levels in tears. In the future this could allow diabetics to avoid getting poked with needles for those painful blood tests. Types of IoT technology include remote monitoring of patient, telemedicine via smartphone messaging, online chat or video conferencing, and location tracking of dementia patients and people who fall. IoT can also help patients modify certain behaviors such as exercise, eating habits and addiction management. Even more exciting, a prototype of a sticker skin-sensor has been recently created by a computer science team at the University of Washington in Seattle. A simple wireless sensor could remotely monitor vital signs such as temperature, sweat, cardiac activity, and more.

Criminal Justice

The increasing number of public surveillance cameras is no secret, but data is starting to be used to predict high-risk locations and victimization risk. Predictive policing technology, along with citizen and official city-led police monitoring, should in theory be able to be utilized effectively. Short-term illegal activity can be predicted in certain areas based on the data. As a result, law enforcement will have an increased presence in the area to monitor potential crime.

However, this data needs to be carefully monitored and analyzed by trained professional data analysts and scientists.

Earlier this year, police used a man’s pacemaker data as a key piece of evidence to charge him with burning his house down and trying to commit insurance fraud. Cops looked at his heart rate, pace demand, and cardiac rhythms before, during and after fire. The data didn’t match the story he told about timing of the incident.

However, as criminal investigators seek to use data as evidence, they must have a balancing act between justice and privacy, otherwise lawsuits are waiting to happen.

Education

Education-related apps have joined the fray to become another field poised to embrace IoT technology. Some of the big leaps are happening at the college level.

For example, many college football stadiums use sensors connected to Wi-Fi to monitor things like temperature, leaky faucets, and even noise levels within the stadium. These data-driven observations then allow them to work on things like tracking parking availability and concession/restroom wait times. The final magnificent step is to make all of that useful information available to mobile phone users in the stadium, giving them more time at the game and less waiting in lines.

Another application includes campus washing machines texting students when their clothes are ready to be taken out. In collegiate sports, coaches and trainers can track information of their athletes weight and body fat, as well as air quality to optimize training schedules. The possibilities are endless.

Someday, every device you own and every object you can fathom will be connected to the internet. It could be through your phone, wearables, and everyday household items. IoT will connect us in ways that we don’t even know yet. The industries above will already be ahead of the curve.

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