The Promise Of The Internet Of Things

Back in 1978, the great science fiction writer Isaac Asimov stated, “I do not fear computers. I fear the lack of them.”

It’s an interesting and optimistic perspective coming from the late 1970s—a time when computers were far from common. But Asimov, who passed away in 1992, was a visionary. He was, after all, the person responsible for the enduring I, Robot.

So I doubt he would be completely surprised by our technological progress today. Certainly we’ve made significant advancements since the ‘70s. For instance, our cellphones have more computing power than all of NASA did when it put two men on the moon in 1969. And the little chips inside those annoying cards that play “Happy Birthday” are more powerful than all the computing power the Allied Forces had available during World War II.

Still, I think there is one unique fact today that would surprise him: We’re now entering an era even more impressive than that of ubiquitous computers—an era in which everyday appliances like bike locks, vacuum cleaner, and lights are themselves turning into computers. And these computers are seamlessly talking among themselves, and in turn talking back to us.

This is the era of the Internet of Things (IoT)—and it promises to change our lives completely.

The concept IoT is relatively straightforward: It describes things that are embedded with sensors and connected to the Internet, producing and making sense of vast amounts of data. Think cars, scales, refrigerators, stereos, thermostats, buildings, and medical devices that produce, transfer, and process data in real time. In terms of what’s applicable, no answer is seemingly wrong. Daniel Burrus, writing in Wired, even offers the example of “smart cement.”

However, IoT is still a relatively new concept; only in 2013 did it begin to gain some traction, and its popularity has exploded since then. It is predicted that 24 million IoT devices will be connected to the Internet by 2020—in addition to another 10 billion traditional devices such as smartphones and smartwatches.

The popularity of IoT is due to its applicability, but also because of its promise. In this Shots of Awe episode, Jason Silva offers a nice summary of how it’ll impact us:

You walk into a room, and the room knows how you like the lighting. And the song that you love starts automatically playing. And the curtains automatically rise. And the computer offers you your favorite snack.

Of course, IoT technology will not be limited to just our homes. IoT will impact entire cities. Traffic, pollution, and even crime may become things of the past. Consider something as common as a lamppost, embedded with an air-quality sensor that alerts you if a certain area is particularly polluted.

And think about the potential IoT holds for healthcare: You wake up each morning, look into the mirror, and it provides a real-time update on your health, updating your doctor with any concerns.

These are just a few of the promises of IoT. If we get it right——that is, if we plan carefully and keep security in mind—we can expect greater efficiency and economic growth. In short, we can expect a vast improvement to our everyday lives.

I can’t say what Asimov would have thought of IoT. But based on his depictions in I, Robot of humans and robots working together peacefully, I imagine he would have greeted this development with open arms.

Learn how SAP can help your business Run Live with IoT. 


Internet of Things – Digitalist Magazine

How To Solve IoT’s Big Data Challenge With Machine Learning

Machine learning will come of age this year, moving from the research labs and proof-of-concept implementations to cutting-edge business solutions. Along the way, it will help power innovations such as autonomous vehicles, precision farming, therapeutic drug discovery, and advanced fraud detection for financial institutions.

Machine learning intersects with statistics, computer science, and artificial intelligence, focusing on the development of fast and efficient algorithms to enable real-time data processing. Rather than just follow explicitly programmed instructions, these machine learning algorithms learn from experience, making them a key component of artificial intelligence platforms.

Machine learning helps tackle IoT data flows

Machine learning may also help us with a challenge from one of last year’s most buzzed-about technology developments: the Internet of Things. The first generation of Big Data analytics grew up around the flow of information generated by social media, online shopping, online videos, web surfing, and other user-generated online behaviors, according to Vin Sharma, the director of machine learning solutions in Intel’s Data Center Group.

Analyzing these massive datasets required new technologies, flexible cloud computing, and virtualization software such as Apache Hadoop and Spark. It also needed more powerful, high-performance processors that provided the tools to uncover the insights in Big Data.

And today’s IoT-connected networks dwarf the data volume from this first era of Big Data. As devices and sensors continue proliferating, so will the volume of data they create.

For example, a single autonomous car will generate 4,000 GB of data per day. The new Airbus A380-1000 is equipped with 10,000 sensors in each wing. Legacy Big Data technology won’t be able to handle the data created by connected appliances in smart homes, traffic sensors in smart cities, and robotic systems in smart factories.

New and exciting system requirements

Machine learning is key to analyzing the enormous, repetitious volumes of data flowing from vast, always-on IoT networks. While machine learning may seem like science fiction to many, it is already in use and familiar to users of social media and online shopping (Facebook’s news feed relies on machine learning algorithms, and Amazon’s recommendation engine uses machine learning to suggest what book or movie you should enjoy next).

Machine learning systems recognize the normal flow patterns of data present on IoT networks and focus on the anomalies or patterns outside the norm. So from billions of data points, machine learning can separate the “signal from the noise” in vast data flows, helping organizations focus on what’s meaningful.

However, to be useful and effective for businesses, machine learning algorithms must run computations at enormous scale in a matter of milliseconds — on an ongoing basis. These ever more complex computations put pressure on traditional datacenter processors and computing platforms.

To operate at scale and in real time, machine learning systems require processors with multiple integrated cores, faster memory subsystems, and architectures that can parallelize processing for next generation analytical intelligence. These are platforms with built-in analytical processing engines as well as the capacity to run complex algorithms in-memory for real-time results and immediate application of insights.

Final prediction

Processors built for high-performance computing will be in high demand. Machine learning and artificial intelligence will need a lot more power as they begin to connect the dots between IoT data flows and customer engagement for improved sales and outreach.

These processors were traditionally the province of research laboratories and supercomputing challenges, such as modeling weather patterns and genome sequencing. But machine learning platforms will become more and more necessary as IoT networks become larger and more pervasive — and as businesses increasingly base their success on the insights found in machine-to-machine communication.

These processors deliver the performance required for the most demanding workloads, including machine learning and artificial intelligence algorithms. So they will no longer be confined to the rarified environments of supercomputing in research centers and universities, as they increasingly become a requirement for cutting-edge businesses.

For more on future tech, see 20 Technology Predictions To Keep Your Eye On In 2017.


Internet of Things – Digitalist Magazine

Cashing In On Space Data [VIDEO]

If you want to know what’s happening on Earth, the European Space Agency (ESA) has your back. Every day dozens of ESA satellites generate around ten terabyte of data. Billed as “Europe’s gateway to space,” ESA is the largest provider of Earth observation information in the world, constantly monitoring the planet’s security and environment.

Until recently, that information was held under lock and key, unless you were a scientist with clearance to use it. However, in 2007, the European Union (which works closely with ESA and provides some 20 percent of its funding) changed its policy, allowing the agency to make its data freely available to the public.

This change has opened a new world of opportunity for ESA, the EU and businesses. Nicolaus Hanowski, who heads the ESA Earth Observation Programme, said, “When the EU decided a few years ago that all that observation data was free and open, it triggered new possibilities for ESA and the industrial world.”

Particularly with the maturation of Internet of Things, Big Data, and cloud technologies, the commercial sector now has effective ways to access this data and use it in real time.

Space data helps business and society

Here’s how it works: Satellites, drones, and other airborne “things” can transmit data, which is combined and turned into usable information by Big Data solutions like geospatial, real-time, and predictive analytics. Cloud computing makes it possible for the ESA to deliver specific sets of information to organizations that can use it to solve problems like evaluating agriculture land use, managing gas pipelines, and measuring the effect of climate change.

Hanowski explains ESA already has thematic data repositories including coastal, forestry, urban development, climate, and hydrology.  “Our mission is to make the data consumable. We want to the uptake to be as big as possible — and economically influential. We need to understand what kind of data is interesting to commercial organizations.”

Once they understand key topic areas for businesses, ESA can combine its satellite data with additional types of airborne and ground data to help companies bring new digital business models to life.

With the release of an Earth observation analysis service, organizations can now analyze historic and real-time satellite from ESA, which will help businesses better understand current conditions – and predict future situations.

Through an in-memory computing platform, decision makers can predict future scenarios, their probability, and potential actions to take. Farmers, for instance, will not only know about upcoming storms, but also how to optimize water and fertilizer use on their fields based on satellite information. Even better, the farmer can detect imminent onset of the common crop diseases – and start a preventive treatment immediately.

Munich Re, one of the world’s largest reinsurance companies, is one of the first companies using the analysis service. The increasing frequency of natural disasters like wildfires due to climate change pose a huge challenge for the insurance industry. By analyzing real-time and historic satellite data of wildfires in different regions, Munich Re can more accurately calculate insurance risks and costs. Munich Re can use wildfire data to do predictive analysis that estimates the probability of future wildfires and potential damage to people, homes, and businesses, thus minimizing costs for clients.

Dr. Carsten Linz, head of the SAP Center for Digital Leadership, said, “Like many organizations, ESA is going through a digital transformation, and this technology is helping them pave the way by closing the gap between a traditional Earth observation institution and the digital business world. ESA’s mission is to disseminate space data that is relevant to businesses – and was previously only available to scientists and data specialists. Hence, a major part of our work together is to make the information usable, accessible, and secure, which is why the in-memory computing platform and cloud technologies are so important to ESA.”

While commercial data use is a priority for ESA, Hanowski is hopeful that with analytic services, they will be able to help unite scientific and relief communities on pressing topics like smart cities, food security, and water management.

Eventually businesses will use the data to improve efficiency and offer better products, ESA will gain a revenue stream, and NGOs and the public sector can use it to improve people’s lives. In other words, everyone wins.

For more on the transformative scientific potential of data analytics, see The Promise Of The Internet Of Things.


Internet of Things – Digitalist Magazine

Smarter Collaboration For Smarter Cities

Advancements in analytics and artificial intelligence have allowed cities to become more efficient and autonomous than ever. As a result, the concept of a “smart city” has transformed from simple traffic cameras and motion sensors to a real-time grid capable of generating insights from traffic patterns and foot traffic – elements of a live, connected city.

However, outdated infrastructure can present a significant hurdle on the path to realizing a truly smart city capable of generating insights from real-world challenges. For example, spiking global populations have placed a strain on municipal water and power supply – an issue made worse by aging infrastructure ill-equipped to support this demand.

As a result, city officials and planners face mounting pressure to optimize public services to sustain spiking metropolitan populations (expected to reach $ 5.6 billion globally by 2030). Unfortunately, updating legacy infrastructure is costly, especially for cash-strapped local governments. To meet this challenge, city planners have zeroed in on analytics platforms as a cost-efficient means of improving existing infrastructure and reinvigorating the complex operations that citizens rely on in their daily lives. Enter Future Cities: the next wave of metropolitan management backed by the Internet of Things (IoT).

As a city that experiences heavy fires each summer, Cape Town needed to develop a coordinated incident management system that fit well within their budgetary constraints. The first system of its kind, the Emergency Policing and Incident Command (EPIC) provides Cape Town with a single, integrated public safety solution that facilitates combined operation and data-sharing among all city emergency and policing departments. It even includes support for emergency and non-emergency call centers, dispatch of resources and even field-enabled mobile devices. For Cape Town, the benefits of EPIC mean quick and efficient responses to any emergency, efficient dispatch of resources and insight and control for the heads of the emergency response services – all things that are critical when nature strikes.

IoT improves all aspects of city infrastructure

Every process – from public transportation and energy efficiency to waste management and parking – can benefit from IoT adoption. After all, connective technologies deliver real-time insights for urban development and management, areas that thrive on proactive monitoring and nose dive without it.

However, transforming existing assets and infrastructure that are cost-effective and environmentally friendly requires thousands of individual sensors and processors – each with their unique challenges.

Partnerships drive future cities

City officials, with an understanding of the value of an IoT-backed smart city, will need to leverage collaborative partnerships between companies that supply the pieces to successfully address the complexity of the Future Cities puzzle. A great example is the city of Nanjing, which is using technology to help maximize the area’s traffic efficiency – a hot competitive topic for any metropolitan hub looking to attract visitors. By working in tandem with separate parties that supply specialized technology and expertise, an end-to-end citizen services management system is achievable.

It’s our hope and our vision that more and more cities “get smart” about their investments in IoT and digital technologies. Together, solutions like these help cities improve, transform, and prosper to create a better urban world.

Visit the SAP stand (Hall 3, 3N31) at Mobile World Congress in Barcelona to get a 360-degree view of how SAP is working with its customers and partners to usher in a connected future.


Internet of Things – Digitalist Magazine

Ali Baba’s Magic: ‘Open Sesame!’ And Digital Transformation

Remember the childhood story, “Ali Baba and the Forty Thieves”?

“Open, sesame!” was the magical phrase that the poor woodcutter Ali Baba uttered to open the door of a secret cave in which 40 thieves had hidden bags of gold and treasure. These words, along with the power of his voice, gave him access to that fortune and changed his life forever.

We are on the cusp of an “Open, sesame!” moment that promises to change our lives through digital transformation. It’s a fact that our lives are becoming more digital. We buy, work, store information, and even communicate with other people through media and digital platforms. I never owned a laptop until the age of 35, whereas my daughters have always had laptops in the house, and they learned how to use them much earlier than I did.

Whether we like it or not, digital transformation is creating a new era. It is changing how we do things and how we live, and many of us are already fully immersed into it. It also offers us a great opportunity to be more effective, efficient, fast, and agile.

As consumers, we expect ultra-connected experiences. Whether in-store, on the web, or via a mobile or wearable device, we want all our interactions to be simple, effortless, relevant, and lightning-fast.

The Internet of Things has already started to change our lives. For example, connected cars may know your preferred temperature at home and adjust it accordingly. A mobile app is connected with all smart home devices to alert you of anything suspicious happening while you are away. It can send a list of items you need when you approach a grocery store. Drones enable you to tour properties to help choose the right one.

To reach 50 million users, radio took 38 years. Google took 6 years, and Google+ just 88 days. The Pokémon Go game hit 50 million users in just 19 days!

Our lives have become a collection of mobile moments in which we use mobile devices like magic wands, to get what we need, wherever and whenever we need it. We use our smartphones to do online banking, post family photos, check in with social media, send e-mails and text messages, search for restaurants, and book movies. Oh, and also to make the occasional phone call.

We are alerted of our appointments and meetings even before we’ve had breakfast. A weather app updates us on the day’s forecast. To ease our commute, the GPS in our car alerts us on traffic and suggests alternative routes to get us to work on time.

We have also become more health-conscious, thanks to wearable devices like Apple Watch and ctivity trackers like Fit Bit, Jawbone, Google’s smart contact lenses, etc. Wearables like Oculus Rift VR enable us to enter into the exciting new realm of augmented reality, which enhances what we see, hear, and touch.

Big Data analytics is an ideal entry to digital transformation. It is like turning the lights on in a dark room: Every interaction we have with businesses—including point-of-sale transaction details, loyalty card information, surveys, and social media postings to Facebook, Twitter, Pinterest, and more—provides deep insight into our behavior, attitudes, and opinions, which businesses can leverage to improve relationships via hyper-personalization.

Voila! Life is simplified.

Ali Baba’s “Open sesame!” may have come from a childhood fable, but digital transformation is reality – and it is changing everything.

For more about the power of digital transformation in retail, see Small And Midsize Retailers’ Digital Strategy Is All About The Shopper.

 


Internet of Things – Digitalist Magazine