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Apps and Tools (154)

New IoT App Makes Drivers Safer

Transportation has become one of the most frequently highlighted areas where the internet of things can improve our lives. Specifically, a lot of people are excited about the IoT's potential to further the progress toward entire networks of self-driving cars. We hear a lot about the tech companies that are involved in building self-driving cars, but it's the IoT that will actually allow these vehicles to operate. In fact, CNET quoted one IoT expert just last year as saying that because of the expanding IoT, self-driving cars will rule the roads by 2030.

On a much smaller scale, there are also some niche applications of the IoT that are designed to fix specific problems on the road. For instance, many companies have looked to combat distracted driving by teenagers through IoT-related tools. As noted by PC World, one device called the Smartwheel monitors teens' driving activity by sensing when they're keeping both hands on the wheel. The device sounds an alert when a hand comes off the wheel and communicates to a companion app that compiles reports on driver performance. This is a subtle way in which the IoT helps young drivers develop better habits.

In a way, these examples cover both extremes of the effect the IoT is having on drivers. One is a futuristic idea that's being slowly implemented to alter the very nature of road transportation. The other is an application for individuals meant to make drivers safer one by one. But there are also some IoT-related tools that fall somewhere in the middle of the spectrum. One is an exciting new app that seeks to make the roads safer for the thousands of shipping fleet drivers operating on a daily basis.

At first this might sound like a niche category. However, the reality is that the innumerable companies and agencies relying shipping and transportation fleets have a ton of drivers to take care of. That means supervising vehicle performance, safety, and more for each and every one of them. That process comprises a significant portion of road activity, particularly in cities and on highways. These operations are able to be simplified and streamlined through Networkfleet Driver, which Verizon describes as a tool to help employees manage routes, maintenance, communication, and driving habits all in one place.

The app can communicate up-to-date routing changes or required stops, inform drivers of necessary vehicle repairs or upkeep, and handle communication from management. It can also make note of dangerous habits (like a tendency to speed or make frequent sudden stops), helping the driver to identify bad habits and helping managers to recommend safer performance. All of this is accomplished through various IoT sensors on vehicles interacting automatically with the app, and with systems that can be monitored by management.

The positive effect, while difficult to quantify, is substantial. Fleet drivers make up a significant portion of road activity, and through the use of the IoT we can make sure that the roads are safer for everyone.

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Internet of Things has raised concerns over safety. Nowadays, it is possible to control your home using your Smartphone. In the coming years, mobile devices will work as a remote control to operate all the things in your house. 

Some devices display one or several vulnerabilities that can be exploited by the hackers to infiltrate them and the whole network of the connected home. For instance:

1.      During configuration, data – including the device ID and MAC address - is sometimes transmitted in plain text.

2.      The communication between the device and the app passes unencrypted through the manufacturer’s servers.

3.      The hotspot is poorly secured with a weak username and password and sometimes remains active after configuration.

4.      The device comes pre-installed with a Telnet client carrying default credentials.

With rising cases of identity theft and vishing, it has become absolutely necessary to install any of these 5 free tools in your smartphone in order to keep your data safe from hackers. 

1- LastPass - It lets you store passwords in a secure vault that is easy to use, searchable and organized the way you like. It is perhaps the safest vault available online today that lets you store password data for unlimited websites. 

2- Lookout - This tool offers security for the today's mobile generation. It is a free app that protects your iOS or Android device around the clock from mobile threats such as unsecure WiFi networks, malicious apps, fraudulent links, etc. It has a worldwide network of 100 million mobile sensors, world's largest mobile data set and a smarter machine intelligence to keep your smartphone secure from all kinds of threats. 

3- Authy - This app generates secure 2 step verification tokens on your device and protects your account from hackers and hijackers by adding an additional layer of security. Moreover, it offers secure cloud backup, multi device synchronization and multi factor authentication. 2 step authentication is the best kind of security available today that ensures your accounts don't get hacked. 

4- BullGuard - It protects your smartphone from all forms of viruses and malware. With an inbuilt, rigorous anti-theft functionality, BullGuard enables you to lock, locate and wipe device remotely in case it gets lost or stolen. It allows automatic scans so that the security remains updated. Moreover, it doesn't drains down your battery. 

5- Prey - It is a lightweight theft protection software that lets you keep an eye over your mobile devices in case you have more than one and you are leaving one in your home. Prey lets you recover the phone in case it gets stolen. After installing the software on your laptop, tablet or phone, Prey will sleep silently in the background awaiting your command. Once remotely triggered from your Prey account, your device will gather and deliver detailed evidence back to you, including a picture of who's using it – often the crucial piece of data that police officers need to take action. 

 

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As if the Internet of Things (IoT) was not complicated enough, the Marketing team at Cisco introduced its Fog Computing vision in January 2014, also known as Edge Computing  for other more purist vendors.

Given Cisco´s frantic activity in their Internet of Everything (IoE) marketing campaigns, it is not surprising that many bloggers have abused of shocking headlines around this subject taking advantage of the Hype of the IoT.

I hope this post help you better understand what is  the role of Fog Computing  in the IoT Reference Model and how companies are using IoT Intelligent gateways in the Fog to connect the "Things" to the Cloud through some applications areas and examples of Fog Computing.

The problem with the cloud

As the Internet of Things proliferates, businesses face a growing need to analyze data from sources at the edge of a network, whether mobile phones, gateways, or IoT sensors. Cloud computing has a disadvantage: It can’t process data quickly enough for modern business applications.

The IoT owes its explosive growth to the connection of physical things and operation technologies (OT) to analytics and machine learning applications, which can help glean insights from device-generated data and enable devices to make “smart” decisions without human intervention. Currently, such resources are mostly being provided by cloud service providers, where the computation and storage capacity exists.

However, despite its power, the cloud model is not applicable to environments where operations are time-critical or internet connectivity is poor. This is especially true in scenarios such as telemedicine and patient care, where milliseconds can have fatal consequences. The same can be said about vehicle to vehicle communications, where the prevention of collisions and accidents can’t afford the latency caused by the roundtrip to the cloud server.

“The cloud paradigm is like having your brain command your limbs from miles away — it won’t help you where you need quick reflexes.”

Moreover, having every device connected to the cloud and sending raw data over the internet can have privacy, security and legal implications, especially when dealing with sensitive data that is subject to separate regulations in different countries.

IoT nodes are closer to the action, but for the moment, they do not have the computing and storage resources to perform analytics and machine learning tasks. Cloud servers, on the other hand, have the horsepower, but are too far away to process data and respond in time.

The fog layer is the perfect junction where there are enough compute, storage and networking resources to mimic cloud capabilities at the edge and support the local ingestion of data and the quick turnaround of results.

The variety of IoT systems and the need for flexible solutions that respond to real-time events quickly make Fog Computing a compelling option.

The Fog Computing, Oh my good another layer in IoT!

A study by IDC estimates that by 2020, 10 percent of the world’s data will be produced by edge devices. This will further drive the need for more efficient fog computing solutions that provide low latency and holistic intelligence simultaneously.

“Computing at the edge of the network is, of course, not new -- we've been doing it for years to solve the same issue with other kinds of computing.”

The Fog Computing or Edge Computing  is a paradigm championed by some of the biggest IoT technology players, including Cisco, IBM, and Dell and represents a shift in architecture in which intelligence is pushed from the cloud to the edge, localizing certain kinds of analysis and decision-making.

Fog Computing enables quicker response times, unencumbered by network latency, as well as reduced traffic, selectively relaying the appropriate data to the cloud.

The concept of Fog Computing attempts to transcend some of these physical limitations. With Fog Computing processing happens on nodes physically closer to where the data is originally collected instead of sending vast amounts of IoT data to the cloud.

Photo Source: http://electronicdesign.com/site-files/electronicdesign.com/files/uploads/2014/06/113191_fig4sm-cisco-fog-computing.jpg

The OpenFog Consortium

The OpenFog Consortium, was founded on the premise based on open architectures and standards that are essential for the success of a ubiquitous Fog Computing ecosystem.

The collaboration among tech giants such as ARM, Cisco, Dell, GE, Intel, Microsoft and Schneider Electric defining an Open, Interoperable Fog Computing Architecture is without any doubt good news for a vibrant supplier ecosystem.

The OpenFog Reference Architecture is an architectural evolution from traditional closed systems and the burgeoning cloud-only models to an approach that emphasizes computation nearest the edge of the network when dictated by business concerns or critical application the functional requirements of the system.

The OpenFog Reference Architecture consists of putting micro data centers or even small, purpose-built high-performance data analytics machines in remote offices and locations in order to gain real-time insights from the data collected, or to promote data thinning at the edge, by dramatically reducing the amount of data that needs to be transmitted to a central data center. Without having to move unnecessary data to a central data center, analytics at the edge can simplify and drastically speed analysis while also cutting costs.

Benefits of Fog Computing

  • ·         Frees up network capacity - Fog computing uses much less bandwidth, which means it doesn't cause bottlenecks and other similar occupancies. Less data movement on the network frees up network capacity, which then can be used for other things.
  • ·         It is truly real-time - Fog computing has much higher expedience than any other cloud computing architecture we know today. Since all data analysis are being done at the spot it represents a true real time concept, which means it is a perfect match for the needs of Internet of Things concept.
  • ·         Boosts data security - Collected data is more secure when it doesn't travel. Also makes data storing much simpler, because it stays in its country of origin. Sending data abroad might violate certain laws.
  • ·         Analytics is done locally- Fog computing concept enables developers to access most important IoT data from other locations, but it still keeps piles of less important information in local storages;
  • ·         Some companies don't like their data being out of their premises- with Fog Computing lots of data is stored on the devices themselves (which are often located outside of company offices), this is perceived as a risk by part of developers' community.
  • ·         Whole system sounds a little bit confusing- Concept that includes huge number of devices that store, analyze and send their own data, located all around the world sounds utterly confusing.

Disadvantages of Fog Computing

Read more: http://bigdata.sys-con.com/node/3809885

Examples of Fog Computing

The applications of fog computing are many, and it is powering crucial parts of IoT ecosystems, especially in industrial environments. See below some use cases and examples.

  • Thanks to the power of fog computing, New York-based renewable energy company Envision has been able to obtain a 15 percent productivity improvement from the vast network of wind turbines it operates. The company is processing as much as 20 terabytes of data at a time, generated by 3 million sensors installed on the 20,000 turbines it manages. Moving computation to the edge has enabled Envision to cut down data analysis time from 10 minutes to mere seconds, providing them with actionable insights and significant business benefits.
  • Plat One is another firm using fog computing to improve data processing for the more than 1 million sensors it manages. The company uses the Cisco-ParStream platform to publish real-time sensor measurements for hundreds of thousands of devices, including smart lighting and parking, port and transportation management and a network of 50,000 coffee machines.
  • In Palo Alto, California, a $3 million project will enable traffic lights to integrate with connected vehicles, hopefully creating a future in which people won’t be waiting in their cars at empty intersections for no reason.
  • In transportation, it’s helping semi-autonomous cars assist drivers in avoiding distraction and veering off the road by providing real-time analytics and decisions on driving patterns.
  • It also can help reduce the transfer of gigantic volumes of audio and video recordings generated by police dashboard and video cameras. Cameras equipped with edge computing capabilities could analyze video feeds in real time and only send relevant data to the cloud when necessary.

See more at: Why Edge Computing Is Here to Stay: Five Use Cases By Patrick McGarry  

What is the future of fog computing?

The current trend shows that fog computing will continue to grow in usage and importance as the Internet of Things expands and conquers new grounds. With inexpensive, low-power processing and storage becoming more available, we can expect computation to move even closer to the edge and become ingrained in the same devices that are generating the data, creating even greater possibilities for inter-device intelligence and interactions. Sensors that only log data might one day become a thing of the past.

Janakiram MSV  wondered if Fog Computing  will be the Next Big Thing In Internet of Things? . It seems obvious that while cloud is a perfect match for the Internet of Things, we have other scenarios and IoT solutions that demand low-latency ingestion and immediate processing of data where Fog Computing is the answer.

Does the fog eliminate the cloud?

Fog computing improves efficiency and reduces the amount of data that needs to be sent to the cloud for processing. But it’s here to complement the cloud, not replace it.

The cloud will continue to have a pertinent role in the IoT cycle. In fact, with fog computing shouldering the burden of short-term analytics at the edge, cloud resources will be freed to take on the heavier tasks, especially where the analysis of historical data and large datasets is concerned. Insights obtained by the cloud can help update and tweak policies and functionality at the fog layer.

And there are still many cases where the centralized, highly efficient computing infrastructure of the cloud will outperform decentralized systems in performance, scalability and costs. This includes environments where data needs to be analyzed from largely dispersed sources.

“It is the combination of fog and cloud computing that will accelerate the adoption of IoT, especially for the enterprise.”

In essence, Fog Computing allows for big data to be processed locally, or at least in closer proximity to the systems that rely on it. Newer machines could incorporate more powerful microprocessors, and interact more fluidly with other machines on the edge of the network. While fog isn’t a replacement for cloud architecture, it is a necessary step forward that will facilitate the advancement of IoT, as more industries and businesses adopt emerging technologies.

'The Cloud' is not Over

Fog computing is far from a panacea. One of the immediate costs associated with this method pertains to equipping end devices with the necessary hardware to perform calculations remotely and independent of centralized data centers. Some vendors, however, are in the process of perfecting technologies for that purpose. The tradeoff is that by investing in such solutions immediately, organizations will avoid frequently updating their infrastructure and networks to deal with ever increasing data amounts as the IoT expands.

There are certain data types and use cases that actually benefit from centralized models. Data that carries the utmost security concerns, for example, will require the secure advantages of a centralized approach or one that continues to rely solely on physical infrastructure.

Though the benefits of Fog Computing are undeniable, the Cloud has a secure future in IoT for most companies with less time-sensitive computing needs and for analysing all the data gathered by IoT sensors.

 

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Originally posted on Data Science Central

 Printed electronics are being vouched as the next best thing in Internet of Things (IoT), the technology that is rightly regarded as a boon of advancing technology. Silicon-based sensors are the first that have been associated with IoT technology. These sensors have numerous applications, such as track data from airplane, wind turbines, engines, and medical devices, amongst other internet connected devices.

However, these silicon-based are not suitable for several other applications. Bendable packaging and premium items are some of the application where embedded sensors do not work. For such applications, printed electronics befit the need. Using sensor technology, information is transferred on smart labels that can be attached to packages to be tracked in real time.

Some Applications of Printed Sensor Technology

Grocery Industry: While bar code is the standard technology used in the grocery sector, the technology has limitations pertaining to the data it can store. Also, for some products, product packaging can run up to 30-40% of the cost, for which printed sensor are best-suited to save packaging costs. For such needs, a printed sensor is the most apt solution for real-time information about a product’s temperature, moisture, location, movement, and much more. Companies can check these parameters to validate the freshness and prevent substantial spoilage. Smart labels are also used to validate the authenticity of products.

Click here to get report.

Healthcare: The use of smart labels enables manufacturers and logistics firms to track the usage and disposal of pharmaceuticals and to control inventory. The use of smart labels on patients’ clothing enables to check their body temperature, dampness of adult diapers, or bandages for assisted living scenarios.

Logistics: Radio frequency identification (RFID) was the standard tag used by logistics companies until recently to identify shipping crates that carried perishable products. RFID is increasingly being replaced by smart labels that enable tracking of individual items. This facilitates companies to track products at the item level rather than at the container shipping level.

Biosensors Lead Printed and Flexible Sensors Market

As per the research study, the global market for printed and flexible sensors is estimated to grow at a fast pace, due to which several investors are interested in pouring funds into the market. This is expected to create potential opportunities for commercialization and product innovation. In addition, several new players are also projected to participate in order to gain a competitive advantage in the market. In 2013, the global printed and flexible sensors market stood at US$6.28 bn and is projected to be worth US$7.51 bn by the end of 2020. The market is expected to register a healthy 2.50% CAGR between 2012 and 2020, as per the study.

The rapid growth in individual application segments and several benefits over the conventional sensors are some of the key factors driving the global market for printed and flexible sensors. In addition, the developing global market for Internet of Things is further anticipated to fuel the growth of the market in the next few years. On the flip side, several challenges in conductive ink printing are estimated to hamper the growth of the market for printed and flexible sensors in the near future.

Biosensors are most extensively used with the largest market share in the global market for printed and flexible sensors. Glucose strips incorporated with a biosensor are one of the most sought after ways to track and monitor glucose levels among diabetics. Thus, it accounts as a multi-billion dollar segment in the global market for printed and flexible sensors. To evaluate and monitor working of the heart, kidney diseases, and cancer are the other emerging applications where printed biosensors technology is being utilized.

The expanding automobile industry holds promise for piezoelectric type printed flexible sensors for performance testing during production. Due to these varied applications of printed and flexible sensors, the global market for printed and flexible sensors will expand at a slow but steady 2.5% CAGR in the next six years starting from 2012.

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Soft Pasture

By Ben Dickson. This article originally appeared here.

The Internet of Things (IoT) is one of the most exciting phenomena of the tech industry these days. But there seems to be a lot of confusion surrounding it as well. Some think about IoT merely as creating new internet-connected devices, while others are more focused on creating value through adding connectivity and smarts to what already exists out there.

I would argue that the former is an oversimplification of the IoT concept, though it accounts for the most common approach that startups take toward entering the industry. It’s what we call greenfield development, as opposed to the latter approach, which is called brownfield.

Here’s what you need to know about greenfield and brownfield development, their differences, the challenges, and where the right balance stands.

Greenfield IoT development

In software development, greenfield refers to software that is created from scratch in a totally new environment. No constraints are imposed by legacy code, no requirements to integrate with other systems. The development process is straightforward, but the risks are high as well because you’re moving into uncharted territory.

In IoT, greenfield development refers to all these shiny new gadgets and devices that come with internet connectivity. Connected washing machines, smart locks, TVs, thermostats, light bulbs, toasters, coffee machines and whatnot that you see in tech publications and consumer electronic expos are clear examples of greenfield IoT projects.

Greenfield IoT development is adopted by some well-established brands as well as a lineup of startups that are rushing to climb the IoT bandwagon and grab a foothold in one of the fastest growing industries. It is much easier for startups to enter greenfield development because they have a clean sheet and no strings attached to past development.

But it also causes some unwanted effects. First of all, when things are created independent of each other and their predecessors, they tend to pull the industry in separate ways. That is why we see the IoT landscape growing in many different directions at the same time, effectively becoming a fragmented hodgepodge of incompatible and non-interoperable standards and protocols. Meanwhile, the true future of IoT is an ecosystem of connected devices that can autonomously inter-communicate (M2M) without human intervention and create value for the community. And that’s not where these isolated efforts are leading us.

Also, many of these companies are blindly rushing into IoT development without regard to the many challenges they will eventually face. Many of the ideas we see are plain stupidand make the internet of things look like the internet of gadgets. Nice-to-haves start to screen out must-haves, and the IoT’s real potential for disruption and change will become obscured by the image of a luxury industry.

As is the case with most nascent industries, a lot of startups will sprout and many will wither and die before they can muster the strength to withstand the tidal waves that will wash over the landscape. And in their wake, they will leave thousands and millions of consumers with unsupported devices running buggy—and potentially vulnerable—software.

On the consumer side, greenfield products will impose the requirement to throw away appliances that should’ve worked for many more years. And who’s going to flush down hundreds and thousands of hard-earned dollars down the drain to buy something that won’t necessarily solve a critical problem?

On the industrial side, the strain is going to be even more amplified. The costs of replacing entire infrastructures are going to be stellar, and in some cases the feat will be impossible.

This all doesn’t mean that greenfield development is bad. It just means that it shouldn’t be regarded as the only path to developing IoT solutions.

Brownfield IoT development

Again, to take the cue from software development, brownfield development refers to any form of software that created on top of legacy systems or with the aim of coexisting with other software that are already in use. This will impose some constraints and requirements that will limit design and implementation decisions to the developers. The development process can become challenging and arduous and require meticulous analysis, design and testing, things that many upstart developers don’t have the patience for.

The same thing applies to IoT, but the challenges become even more accentuated. In brownfield IoT development, developers inherit hardware, embedded software and design decisions. They can’t deliberate on where they want to direct their efforts and will have to live and work within a constrained context. Throwing away all the legacy stuff will be costly. Some of it has decades of history, testing and implementation behind it, and manufacturers aren’t ready to repeat that cycle all over again for the sake of connectivity.

Brownfield is especially important in industrial IoT (IIoT), such as smart buildings, bridges, roads, railways and all infrastructure that have been around for decades and will continue to be around for decades more. Connecting these to the cloud (and the fog), collecting data and obtaining actionable insights might be even more pertinent than having a light bulb that can be turned on and off with your smartphone. IIoT is what will make our cities smarter, more efficient, and create the basis to support the technology of the future, shared economies, fully autonomous vehicles and things that we can’t imagine right now.

But as its software development counterpart, brownfield IoT development is very challenging, and that’s why manufacturers and developers are reluctant and loathe to engage in it. And thus, we’re missing out on a lot of the opportunities that IoT can provide.

So which is the better?

There’s no preference. There should be balance and coordination between greenfield and brownfield IoT development. We should see more efforts that bridge the gap between so many dispersed efforts in IoT development, a collective effort toward creating establishing standards that will ensure present and future IoT devices can seamlessly connect and combine their functionality and power. I’ve addressed some of these issues in a piece I wrote for TechCrunch a while back, and I think there’s a lot we can learn from the software industry. I’ll be writing about it again, because I think a lot needs to be done to have IoT development head in the right direction.

The point is, we don’t need to reinvent the wheel. We just have to use it correctly.

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Guest blog post by Bill Vorhies

Summary:  Deep learning and Big Data are being adopted in law enforcement and criminal justice at an unprecedented rate.  Does this scare you or make you feel safe?

 

When you read the title, whether your mind immediately went for the upstairs “H” or the downstairs “H” probably says something about whether the new applications of Big Data in law enforcement let you sleep like a baby or keep you up at night. 

You might have thought your choice of “H” related to whether you’ve been on the receiving end of Big Data in law enforcement but the fact is that practically all of us have, and for those who haven’t it won’t take much longer to reach you.

There is an absolute explosion in the use of Big Data and predictive analytics in our legal system today driven by the latest innovations in data science and by some obvious applications.

It hasn’t always been so.  In the middle 90s I was part of the first wave trying to convince law enforcement to adopt what was then cutting edge data science.  At the time that was mostly GIS analysis combined with predictive analytics to create what we called predictive policing.  That is predicting where and at what time of day crime of each type was most likely to occur so that manpower could be effectively allocated.  Seems so quaint now.  It was actually quite successful but the public sector had never been quick to adapt to new technology so there weren’t many takers.

That trend about slow adoption has changed.  So while accelerating the usage of advanced analytics to keep the peace may keep some civil libertarians up at night, it’s coming faster than ever, and it’s our most advanced techniques in deep learning that are driving it.

By now you’ve probably figured out the deep learning is best used for three things: image recognition, speech recognition, and text processing.  Here are two stories illustrating how this is impacting law enforcement.

 

Police Ramp Up Scrutiny Over On Line Threats

The article by this title appeared in the July 20 WSJ.  Given what’s been happening recently both internationally and at home most of us probably applaud the use of text analytics to monitor for early warning signs of home grown miscreants.  The article states “In the past two weeks at least eight people have been arrested by state and federal authorities for threats against police posted on social media”.  It remains to be seen if these will turn into criminal prosecutions and how this will play out against 1st Amendment rights but as a society we seem to be OK for trading a little of one for more of the other.

It’s always in the back of our minds whether this is Facebook, Twitter, Apple, Google and the others actively cooperating in undisclosed programs to aid the police, but this article specifically calls out the fact that the police were the ones doing the monitoring.  Whether they’ve built these capabilities in-house or are using contractors isn’t clear.  What is clear is that advanced text analytics and deep learning were the data science elements behind it.

 

Taser – the Data Science Company

The second example comes from an article in Business Week’s July 18 issue, “Will a Camera on Every Cop Help Save Lives or Just Make a Tech Company Richer”.  

Taser – a tech company?  When I think about Taser, the maker of the ubiquitous electric stun gun, I am much more likely to associate them with Smith & Wesson than with Silicon Valley and apparently I couldn’t be more wrong.

In short the story goes like this.  In the 90s Taser dominated the market for non-lethal police weapons to provide better alternatives for a wide variety of incidents where bullets should not be the answer.  By the 2000s Taser had successfully saturated that market and its next big opportunity came from the unfortunate Ferguson Mo. unrest. 

That opportunity turned out to be wearable cameras.  Although the wearable police cameras date back to about 2008 there really hadn’t been much demand until the public outcry for transparency in policing became overwhelming.

Taser now also dominates the wearable camera market.  Like its namesake stun gun however, sales of Tasers or wearable cameras are basically a one-and-done market.  Once saturated, it offers only replacement sales, not a robust model for corporate expansion.  So far this sounds more like a story about a hardware company than a data science tech company and here’s the transition.

The cameras are producing huge volumes of video images that need to be preserved at the highest levels of chain-of-evidence security for use in criminal justice proceedings.  Taser bought a startup in the photo sharing space and adapted it to their new flagship product Evidence.com, a subscription based software platform now positioned as a ‘secure cloud-based solution’.

According the BW article, “4.6 Petabytes of video have been uploaded to the platform, an amount comparable to Netflix’s entire streaming catalogue”.  Taser is a major customer of MS Azure. And for police departments that have adopted, video is now reported to be presented as evidence in 20% to 25% of cases.

But this story is not just about storing recorded video.  It is about how police and prosecutors have become overwhelmed with the sheer volume of ‘video data’ and the need to simplify and speed access.  The answer is image recognition driven by deep learning.  Taser now earns more than ¾ ths of its revenue from its Evidence.com platform and is rapidly transforming from hardware to app to data science company to answer the need for easier, faster, more accurate identification of relevant images.

 

The Direction Forward

You already know about real-time license plate scanners mounted on patrol cars that are able to automatically photograph license plates without operator involvement, transmit the scan to a central database, and return information in real time about wants and warrants associated with that vehicle.

What Taser and law enforcement say is quite close is a similar application using full time video from police-wearable cameras combined with facial recognition.  Once again those civil liberties questions will have to be answered but there’s no question that this application of data science will make policing more effective.

About those huge volumes of videos and the need to recognize faces and images.  There are plenty of startups that will benefit from this and many with products already in commercial introduction.  Here’s a sampling.

Take a look at Nervve Technologies whose byline is “Visual search insanely fast”.  Using their visual search technology originally developed for government spy agencies they are analyzing hours of sporting event tape in a few seconds to identify the number of times a sponsor’s logo (on uniforms or on billboards) actually appears in order to value the exposure for advertising.

And beyond simple facial recognition is an emerging field called facial or emotional analytics.  That’s right, from video these companies have developed deep learning models that predict how you are feeling or reacting. 

Stoneware incorporates image processing and emotional analytics in its classroom management products to judge the attentiveness of each student in a classroom.

Emotient and Affectiva have similar products in use by major CPG companies to evaluate audience response to advertisements, and to study how NBA spectators respond to activities such as a dance cam.

Real time facial-emotional scanning of crowds to find individuals most likely to commit misdeeds can’t be far away.

For audio, Beyond Verbal has a database of 1.5 million voices used to analyze vocal intonations to identify more than 300 mood variants in more than 40 languages with a claim of 80% accuracy.

All of these are deep learning based data science being put rapidly to work in our law enforcement and criminal justice systems.

 

 

About the author:  Bill Vorhies is Editorial Director for Data Science Central and has practiced as a data scientist and commercial predictive modeler since 2001.  He can be reached at:

[email protected]

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What you need to Know about Bluetooth 5

What You Need to Know About Bluetooth 5

The rate of development in IoT is staggering. Not only are there millions of new devices entering the market every year, but there are numerous emerging technologies for wireless communications, especially those that focus on low power. Keeping track with all of the latest technologies can be difficult, even for industry professionals.

One upcoming technology that is going to be essential to IoT is Bluetooth 5. Promising more range and higher bandwidth, familiarizing yourself with this technology can offer a glimpse into one of the protocols that will power much of our wireless world in the near future.

Bluetooth 5 Requires new Hardware

In 2016, it should not be a complete surprise that a new wireless spec will also require new hardware. While newer Bluetooth 5 enabled devices will be backwards compatible, older devices will not comply with the new specification. This means that current generation devices won’t be able to take advantage of services that use the new specification exclusively.

The level of compatibility between new and old spec devices will depend entirely on developers. With literally billions of older Bluetooth devices around the world, it should be expected that developers will allow basic compatibility between new and old spec machines.

Even Better Battery Life

For some time now, Bluetooth has been well known as a low power network. For consumer devices like smartphones, headsets, and wireless input devices, this has allowed for tiny batteries and small form factor designs. While the Bluetooth Special Interests Group hasn’t specifically stated the power savings of the new spec, they have confirmed that it will use less than the previous generation’s low power mode. For isolated IoT sensors and machines, lower power draw is something that developers will appreciate.

Increased Speed

The current bluetooth spec is incapable of maintaining high bandwidth connections. With 5G fast approaching, and Wi-Fi that exists almost everywhere in developed areas, Bluetooth needed to bring an increased bandwidth solution. The new spec won’t disappoint, and will double the maximum bandwidth of version 4.2. For streaming media, the increase will be significant. Bluetooth should now be able to stream (in ideal conditions) HD video without interruption. Device to device data rates will be increased, and connectivity with Bluetooth beacons and kiosks could benefit from the improved capability.

Four Times the Range

Bluetooth has traditionally been used for machine to close machine communications and local area networks. While some IoT observers may have wished for Bluetooth to become more of a LPWAN solution, this will not be the case with version 5. However, range will be increased by up to 400%, which means better connectivity between devices, and fewer dropped packets in urban transmission. Anyone who has experienced stuttering with Bluetooth audio over distance will appreciate the new developments and (presumably) the new devices that will take advantage of them.

What does all this mean for the Internet of Things? Essentially, Bluetooth 5 promises to bring stronger connections with more bandwidth over longer distances. This could make private Bluetooth networks more viable for home automation, and may even replace Wi-Fi for network access in certain situations.

Ultimately, it will be up to developers to make the most of the new specification, which makes it an exciting addition to the options that innovators have for their future IoT services and devices.

For More Info and our most recent Blog check out www.internetofthingsrecruiting.com 

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Start Building an IOT Solution

By Ashish Modi. This article originally appeared here

To build an IOT application we required following things.

  1. A problem where we required IOT solution. 
  2. Identify and design IOT based solution (Hardware + software + connection).

A problem where we required IOT solution

Nowadays everything is connected to the internet.  We need to move our existing system into IOT based solution.

Identify and design IOT based solution 

Use following mention tool and technology to create our first IOT solution. 

Hardware 

  • Arduino 

Arduino is an open-source prototyping platform based on easy-to-use hardware and software. You need to learn Arduino programming language to pass the set of instruction to a microcontroller on the Arduino board. 

  • Raspberry Pi

The Raspberry Pi hardware has evolved through several versions that feature variations in memory capacity and peripheral device support.

  • Sensors

According to the problem, we are select sensors, here we are sharing some sensors details. 

  1. Pressure Sensor
  2. Temperature sensor
  3. Humidity sensor
  4. Touch sensor
  5. PIR Movement Sensor

Software

To store data collected from different - different device, we required one system, for this, we are used cloud-based storage. AWS, SalesForce or Microsoft Assure etc provide public and private storage location in different - different regions. 

Connection

To Connect our hardware device to software based system we required some connectivity protocol. 

  • CoAP - Constrained Application Protocol
  • MQTT - Machine-to-machine protocol

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By Rick Blaisdell. This article originally appeared here.

In the next five years Internet of Things communications will see unprecedented growth, and cellular connectivity will become even more valuable. Wireless cellular technologies have found enormous potential as key enablers for IoT, and the continuously increasing technology enhancements and innovations in cellular technologies are promising to be the major primary access methodologies to enable a great number of IoT applications.

Cellular technologies are already being used for IoT today in several use cases and are expected to be used even more in the future as these use cases require excellent mobility, strong networks, robust security, economic scale and communications independent of third party access. At the same time, the Internet of Things requires low complexity, low cost devices with long battery life times as well as good coverage for long communication range and penetration to reach the most challenging locations.

The challenge for the cellular industry now is to unlock the value of this interconnected web of devices in a secure, flexible and manageable manner. The goal is to identify a framework of promising solutions and cover a set of innovative approaches and technologies to meet these challenges.

The cellular IoT alphabet

MTC, Cat-0, Cat-1, LTE-M … Some might get confused with all the acronyms related to cellular IoT, so let’s go through it and explain where the different terms come from and what they mean.

As you probably know, 3GPP (3rd Generation Partnership Project) uses the concept of “Releases” to refer to a stable set of specifications which can be used for implementation of features at a given point of time. User Equipment (UE) Category is one important term here. Categories are used to define general UE performance characteristics – for example, maximum supported data rate in uplink and downlink data channels, and to what extent different multi-antenna capabilities and modulation schemes are supported.

The latest stable Release is Release 12, where the categories range from Category 0 up to Category 13. Release 13, which is being finalized at the moment, will include further UE Categories including at least the so-called “Cat-M1” intended for IoT devices.

Cat-1 – Category 1 – was included in the LTE specifications already in the beginning, Release 8. With a Cat-1 UE, it is possible to achieve 10 Mbps downlink and 5 Mbps uplink channel data rates. Cat-1 has not been a relevant UE category for LTE-based mobile broadband services, as its performance is below the best 3G performance. Now it has become an attractive, early alternative for IoT applications over LTE, because it is already standardized.

Cat-0 – Category 0 – is one of the newest standardized categories from Release 12. Cat-0 UEs are intended for IoT use cases, and provide 1 Mbps data rates for both up- and downlink. Cat-0 UEs have reduced complexity by up to 50% compared to Cat-1; requirements include only one receiver antenna and support of half-duplex operation, providing ways for the manufacturers to significantly reduce the modem cost compared to more advanced UE categories.

LTE-Advanced technology, the chief vehicle of 4G cellular connectivity, started to and will continue evolving to provide new features that support a range of high and low performance and cost-optimized IoT device categories. So far, the focus has been on meeting the huge demand for mobile data with highly capable devices that utilize new spectrum.

However, the arrival of LTE-M signifies an important step in addressing MTC (Machine-Type Communications) capabilities over LTE. LTE-M brings new power-saving functionality suitable for serving a variety of IoT applications; Power Saving Mode and eDRX extend battery life for LTE-M to 10 years or more. LTE-M traffic is multiplexed over a full LTE carrier, and it is therefore able to tap into the full capacity of LTE. Additionally, new functionality for substantially reduced device cost and extended coverage for LTE-M are also specified within 3GPP.

The Internet of Things is set to ascend, and operators have a unique opportunity to offer affordable connectivity on a global scale. At the same time, for IoT applications, existing cellular networks offer distinct advantages over alternative WAN technologies, such as unlicensed LPWA.

Read more…

Guest post by Preston Tesvich. This article originally appeared here.

Let’s say you’re in the planning phase of an IoT project. You have a lot of decisions to make, and maybe you're not sure where to start:

 

In this article, we focus on a framework of how you can think about this problem of standards, protocols, and radios. 

The framework of course depends on if your deployment is going to be internal, such as in a factory, or external, such as a consumer product. In this conversation we’ll focus on products that are launching externally to a wider audience of customers, and for that we have a lot to consider.

Let’s look at the state of the IoT right now— bottom line, there’s not a standard that’s so prolific or significant that you’re making a mistake by not using it. What we want to do, then, is pick the thing that solves the problem that we have as closely as possible and has acceptable costs to implement and scale, and not worry too much about fortune telling the future popularity of that standard.  

So, it first comes down to technical constraints:
    - What are the range and bandwidth requirements? 
    - How many nodes are going to be supported in the network?
    - What is the cost for the radio? 


That radio choice has big impacts—not only is it a hefty line item on your BOM on its own, it’s also going to determine the resources that the device needs as well. For example, if you have a WiFi radio at the end, there’s considerable CPU and memory expectations, whereas if we have BLE or some mesh network, it’ll need a lot less. There’s infrastructure scaling costs to consider as well. If we go WiFi: is there WiFi infrastructure already in place where this is being deployed? How reliable is it? If we’re starting from scratch, what’s the plan for covering a large area? That can become very costly, especially if you’re using industrial grade access points, so it’s important to consider these effects that are downstream of your decision.

Zooming in on specific standards

In our opinion, the biggest misconception we find: “Isn’t there going to be one standard to rule them all?” There’s no future of that, and it’s not just because we’re never going to all agree on stuff as an industry, it’s because in many cases different standards aren’t solving the same problems differently, they are solving different problems. So understanding that, we can now look at what each protocol attempts to solve and where they live on the OSI model, or "the stack."

 

MQTT

Some would suggest that it is a full protocol to do communication from a device to a server, but it’s not quite that. MQTT is used as a data format to communicate to something, and that payload can be sent over any transport, be it WiFi, mesh, or some socket protocol. What it tries to solve is to define a way to manipulate attributes of some thing. It centers around reading and writing properties, which lends itself very well to an IoT problem. It certainly saves development time in some regards, but depending on how strictly you’re trying to implement it, it may cost you more development time. As soon as you one-off any part of it, you have to document it really well, and at some point you approach a time and cost factor where implementing your own payload scheme may be a better option.

Is it prolific enough to where you should absolutely use it? No, it hasn't reached that level, and it won’t likely reach that level. What it is right now is a convenient standard for device-direct-to-cloud where we don’t control both ends because it gives some measure of a common language that we can agree on; however, the thing to keep in mind is that most of the time it does in fact need additional documentation—what properties are being read/written and what the exact implementation looks like—ultimately, you’re not getting out of a lot of work using MQTT.

Zigbee and Z-wave

Also starting at the network layer, Zigbee and Z-wave are the big incumbents everyone likes for mesh networking. They attempt to solve two problems: provide a reasonable specification to move packets from one place to another on a mesh network, and actually suggest how those packets should be structured; so, they both reach up higher in the stack. And that's the part hinders their futures. For example, Zigbee uses a system called profiles, which are collections of capabilities, such as the smart energy profile or the home automation profile. When a protocol gets so specific as to say ‘this is what a light bulb does’ it’s pretty difficult to implement devices that aren’t included in the profile. While there are provisions for custom data, you’re not really using a cross-compatible spec at that point—you’re basically off the standard as soon as you’re working with a device not defined in the profile.  

The other consideration with these two is that they are both routed mesh networks. We use one node to communicate with another node using intervening nodes. In other words, we can send a message from A to B to C to D, but in practice we’ve sent a message from A to D. As routed meshes, each node understands the path the message needs to take, and that has an in-memory cost associated with it. While Z-wave and Zigbee have a theoretical limit of 65,535 nodes on a network (the address space is a 16-bit integer), the practical limit is closer to few hundred nodes, because these devices are usually low power, low memory devices. The routing also has a time cost, so a large mesh network may manifest unacceptable latency for your use case. Another consideration, especially if you’re launching a cloud controlled consumer product, is that these mesh networks can’t directly connect to the internet—they require an intervening bridge (a.k.a gateway, hub, edge server) to communicate to the cloud.   

A final caveat is that Z-wave is a single source supplier—the radios are made and sold by Zensys, so you have to buy it from them. Zigbee has a certification process, and there are multiple suppliers of the radio, from Atmel to TI.

Bluetooth

You really just can’t compete with the amount of silicon being shipped based on Bluetooth. 10,000 unique SKUs were launched in Bluetooth in 2014. Other than WiFi, there’s nothing that compares in terms of adoption. Bluetooth was originally designed for  ‘personal area networks,’  with the original standard supporting 7 concurrent devices. And now we have Bluetooth low energy (BLE) which has a theoretically infinite limit. BLE did a ton to optimize around IoT challenges. They looked heavily at the amount of energy required to support a communication. They considered every facet of "low energy," not just the radio-- they looked at data format, packet size, how long the radio needed to be on to transmit those packets, how much memory was required to support it, what the power cost was for that memory, and what the protocol expects of the CPU, all while keeping overall BOM costs in mind. For example, they figured out that the radio should only be on for 1.5ms at a time. That’s a sweet spot—if you transmit for longer, the components heat up and thus require more power. They also figured out that button cell batteries are better at delivering power in short bursts as opposed to continuously. Further, they optimized it to be really durable against WiFi interference because the protocols share the same radio space (2.4GHz).

And then CSR came along and implemented a mesh standard over Bluetooth. Take all the advantages afforded with BLE, and then get all the benefits of a mesh network. The Bluetooth Mesh is flood mesh, meaning instead of specific routing to nodes, a message is sent indiscriminately across all nodes. This scales better than routed mesh because there’s no memory constraints. It’s a good solution for many problems in the IoT and at scale is probably going to be the lowest cost to implement. 

Thread

An up and coming standard that’s built on top of the same silicon that powers the Zigbee radio. It solves the problem of mesh nodes not being able to communicate directly to the cloud by adding IPv6 support, meaning that nodes on the network can make fully qualified internet requests. There’s a lot of weight behind this standard. Google seems to think it’s interesting enough to make their own protocol (known as “Weave”) on top of it. And then there’s Nest Weave which is some other version of Google Weave. As it stands, it takes a long time for a standard to really take hold-- you can immediately see how the story with Thread is a little muddier, which will not help its adoption. It’s also solving a problem that it just doesn’t seem that many devices have. Let’s take sensors as an example. Do these low power, lightweight, low cost, low memory, low processing, fairly dumb devices NEED to make internet requests directly? With Thread, each node now knows a lot more about the world—where your servers are for example, and maybe they shouldn’t be concerned with those things, because not only do the requirements of the device increase, but now the probability and frequency of having to update them in the field goes way up. When it comes to the actual sensors and other endpoints, philosophically you want minimize those responsibilities, except in special cases where offline durability, local processing and decision making is required (this is called fog computing).

When Thread announced their product certification last year, only 30 products submitted. Another thing to note about Thread's adoption is that the mesh-IPv6 problem has been solved before-- there’s actually a spec in Bluetooth 4.2 that adds IPv6 routing to Bluetooth, but very few people are using it. Although Nordic Semiconductor thought it was going to be a big deal and went ahead and implemented it first, it just hasn’t come up much in the industry—that happened Q4 2014 and no one’s talking about it.

One thing Thread does have going for it is that it steps out of defining how devices talk to each other, and how devices format their data—doing this makes it more future proof. This is where Weave comes in, because it does suppose how the data should be structured. So basically a way to look at it is that Weave + Thread = direct Zigbee/Z-wave competitor. We haven’t seen anyone outside of Google really take an initiative on Weave, other than Nest who have put a good marketing effort into making it look like they are getting traction with it.

AllJoyn

Other protocols live higher in the stack and remain agnostic at the network layer. The most well known of these is probably Qualcomm’s Alljoyn effort. They have the Allseen Alliance, although their branding is a bit murky—Allplay, AllShare, etc. We’ve seen some traction with it, but not a ton-- the biggest concern that it’s fighting is that it’s a really open ended protocol, loosely defined enough that you’re really not going to build something totally interoperable with everything else. That’s a big risk for product teams. If there aren’t enough devices in the world that speak that language, then why do I need to speak it? That said, LIFX implemented it, and it worked really well for them, especially since Windows implemented it as well. Now it’s part of Windows 10—there’s a layer specifically for AllJoyn stuff and it seems to do well. There's evidence with AllJoyn that you can bring devices to the table that don’t know anything about each other and get some kind of durable interoperability. However, at a glance, it seems complicated—the way authorization is dealt with and the way devices need to negotiate with each other. There really isn't runaway adoption

IEEE’s WiFi

They’ve ruled the roost with their 802.11 series. B then G then A, and now we have AC. 802.11 has been really good at being simple to set up and being high bandwidth. It doesn’t care about power consumption, it’s more concerned with performance because it’s meant to be a replacement for wires. Almost 2 years ago, they announced 802.11 AH which they’ve branded as HaLow, which attempts to address power, range, and pairing concerns of classic WiFi. Most WiFi devices are not headless ("headless" - no display or other input), they have a rich user interface—meaning we can login and configure them to connect to WiFi. Pairing headless devices has been a very tedious process. With HaLow, they’re solving two problems—how do we get things on easier, and how do we decrease the expectations (particularly power) of the device running the radio. It’s too early to know what type of traction this will get, but IEEE has a great track record at standards adoption.

LoRa and SIGFOX

More like: LoRa vs. SIGFOX. With these protocols we’re looking at how to connect things over fairly long distances, such as in smart city applications. LoRaWAN is an open protocol that's following a bottoms-up adoption strategy. SIGFOX is building out the infrastructure from the top down, and handing APIs to their customers. In that way, SIGFOX is more like a service. It'll be interesting to see the dance-off between these two as the IoT is adopted in these more public-type applications. 

That’s the body of standards that need to be addressed. There’s a ton more, but we don’t see them as exciting for the IoT today.

- P

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internet of things

By Ben Dickson. This article originally appeared here

The Internet of Things is the connection of things beyond your computer and laptop – physical things – to the internet. It has enormous potential for both customers and manufacturers. It’s today’s buzzword. And it’s everywhere. It will soon invade our lives in ways that were unimaginable before, and there’s no stopping it. If you’re a consumer, IoT might have become part of your life without you knowing it. And if you’re a manufacturer, you should start thinking about making your products “smart,” lest you lose the competitive edge against your rivals.

That’s the basic mindset that drives manufacturers in virtually every industry toward integrating internet connectivity into their newest products without thinking about the requirements, implications, challenges and pitfalls. And that’s where they stop: connectivity.

I would call it “barely scratching the surface,” but I think even that would be an overstatement. In reality, it’s worse than that. A recent Forrester research commissioned by Xively showed that 62 percent of companies are just looking to differentiate their brand through adding connectivity to their products. But with more and more companies creating connected devices, connectivity per se is no longer a unique differentiator.

No wonder we’re seeing vulgar references being made to the IoT since a lot of new IoT devices end up creating more trouble and headaches than utility and efficiency. And this is the phenomenon that is supposed to trigger the next digital revolution.

Creating a successful IoT project is much more than just linking your next product to the internet. Here is what you should know before getting engaged in the manufacturing of your next smart appliance.

Security and privacy

One of the main failings of IoT manufacturers is to take security and privacy issues into account before developing and shipping their products. The result is fridges that leak Gmail credentialslight bulbs that leak Wi-Fi networkstoys that spy on kidsTVs that spy on viewers, and the list goes on.

As long as security comes as an afterthought and not as a main area of focus, we’ll be seeing IoT being referred to as one of the most insecure sectors of the tech industry.

Aside from security, privacy is another serious topic of content in IoT. With so much personal data being collected by IoT devices, manufacturers must – and unfortunately don’t – consider the privacy implications before shipping products. Much of this data is subject to regulations such as HIPAA.

So sensitive data must be encrypted whether it’s on the device or in the cloud or while it’s being transferred. Sensitive data shouldn’t be stored at all. Data that is being shared with third parties must be vetted and anonymized.

Users should be able to opt out of data collection programs and should be fully informed about the type of data that is being collected.

Long story short, there are a lot of security and privacy complexities that you need to consider and plan for before diving into the project.

User experience and compatibility

What kind of technologies will this device of yours be using? Is it compatible with other appliances or gadgets that potential consumers will have installed in their home? Do they need to purchase and install a new router just because of your product? Is it really necessary that they install a new mobile app for your device only?

What are the possible scenarios where users would want to connect their devices through platforms such as IFTTT? Does your IoT platform support that?

These are all important questions that you need to answer in regard to your IoT product.

It is imperative that your product seamlessly blend into the connected life of your clients without adding complexities, frustration and extra steps. Also, it is important that your technology be able to work in a legacy environment, so it should be able to continue functioning disconnected. It would be very embarrassing if your customers wouldn’t be able to turn on the lights because they’ve lost internet connectivity (I’ve discussed some potential solutions to this problem here and here).

The point is, if your device ends up being a disconnect island in the IoT ecosystem of your consumers that has to be managed separately, there’s a likely chance that the consumers will abandon it and take their chances with some other brand.

So you should think out of the box and in the broader scope when designing your IoT product. Also plan for the future, and if you’ll be manufacturing other IoT products in the same line in the future, consider how these devices will correlate and how you can standardize your IoT product line to improve compatibility.

Data management

The true potential of the IoT lies in its ability to gather data, glean insights and make smart decisions which lead to improved user experience, better efficiency, costs savings, etc. But unfortunately, most companies stop at the gathering phase, piling up reams of data in their cloud servers and making minimal use of it. According to the Xively report, only about one third of firms are leveraging captured connected device data to provide insight to internal stakeholders and partners, personalize interactions with customers, or profile and segment customers.

This is a missed opportunity for leveraging customer data, as most companies focus their time on just connecting products rather than creating actionable insights from the captured data. Companies should leverage third-party analytics and machine learning services to do a host of activities such as integrating data gathered from IoT devices with previous data they have about their customers. This can enable them to better segment their customers and categorize them based on their preferences and device usage.

Also, data gathered from devices can provide the best feedback to improve existing products. By examining how devices are being used, manufacturers can find the strengths and failings of their products and make software and hardware design decisions to improve their current and future products. Naturally, your first IoT device won’t contain all the relevant features and characteristics that end users will expect form a smart appliance. Device data can help you correct your development path in the future.

There’s much more

These are just some of the considerations that can help you get your feet wet with IoT design and development challenges. The full list can be much more comprehensive. For instance, I didn’t even touch upon the issue of support and management, which deals with updating mechanisms and customer support.

What challenges do you face when designing your IoT products? How do you deal with them? Please share with us in the comments section.

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The power of big data, analytics and machine learning have created unique opportunities in the e-commerce industry. Thanks to data-driven enhancements to ads, upselling and cross-selling, online shoppers are able to get “what they want, when they want it.”

This transformation has had a direct and positive impact on business efficiency, driving more sales and improving customer satisfaction. But it has also had the adverse effect of widening the gap between online and brick-and- mortar businesses, and has faced the retail industry with higher shopper expectations and unprecedented challenges.

However, the advent and development of the Internet of Things (IoT) and the widespread use of mobile devices and mobile apps can help overcome these challenges. Thanks to microprocessors and ubiquitous internet connectivity, smart devices can be deployed everywhere and on everything, from point of sales systems to dressing rooms.

Thanks to microprocessors and ubiquitous internet connectivity, smart devices can be deployed everywhere and on everything, from point of sales systems to dressing rooms.

This enables retailers to gather and analyze data like never before, and to interact with each shopper in a unique and personalized way. Here’s how every aspect of a retail business can benefit from IoT technology and mobile apps, effectively improving sales, cutting costs and drawing customers back to the store.

Supply chain and inventory management

Inventory management problems account for some of the biggest expenditures and losses in retail stores. According to a report by McKinsey, inventory distortion, including overstock, stockouts, and shrinkage, cost retailers a yearly $1.1 trillion worldwide. In the U.S., shrinkage alone is hitting retailers with $42 billion in losses every year, 1.5 percent of total retail sales.

Thanks to IoT, retailers will be able to not only improve inventory control within the store but also expand it to the supply chain. Tracking of goods no longer starts at the store’s receiving dock – it begins at the point of manufacturing.

Better handling of the supply chain

With RFID tags placed on goods and environmental sensors in transportation vehicles, retailers will be able to trace the goods they purchase and their treatment and conditions throughout the supply chain. Information gathered from devices will be analyzed in the cloud and rule-based notifications and alerts can be sent to desktop and mobile apps in order to inform employees and staff members of events that must be acted upon.

The enhanced control will enable suppliers to reduce product damage throughout the journey to retail outlets. This will prove especially useful for the shipping of perishable and temperature sensitive inventory.

Retailers can also leverage IoT technologies such as RFID to track products through the extended supply chain, i.e. after the product has been sold. Having data and improved visibility will streamline otherwise-difficult tasks such as critical product recalls.

Improving in-store inventory tracking

One of the perennial problems retailers are faced with is the lack of accurate inventory tracking. Store shelves aren’t replenished on time; items are misplaced in shelves; sales associates aren’t able to locate items customers are looking for; order management is abysmal, leading to excessive purchase orders to avoid stock-outs. The results are higher inventory costs, lost worker productivity, mishandled stocking, potentially empty shelves and missed sales opportunities.

IoT technology can tackle these problems by bringing more visibility into the location of inventory items and offering more control. By deploying an inventory management system that is based on RFID chips, sensors and beacons, physical assets can be directly synced with database servers. Additional technologies such as store shelf sensors, digital price tags, smart displays and high-resolution cameras combined with image analysis capabilities can further help enhance the control of retailers on goods located at store shelves and in the back storage.

Subsequently retailers can better ensure inventory is adequately stocked, and when stock levels become low, reorder quantities can be suggested based on analytics made from POS data. According to the McKinsey report, reducing stock-outs and overstocks can help lower inventory costs by as much as 10 percent.

The use of IoT can also reduce missed sales opportunities attributed to poorly stocked shelves. When customers are unable to find what they’re looking for, they’ll take their business elsewhere. This can happen while the desired item is actually available in the backroom or displaced to some other shelf. Sales associates can quickly track items by their RFIDs using their mobile devices and beacons installed across the store. They can also receive timely alerts for misplaced items and emptied shelves in order to minimize customer mishaps. Improved on-shelf availability can improve sales by as much as 11 percent, the McKinsey report states.

Improved on-shelf availability can improve sales by as much as 11 percent

Reducing shrinkage and fraud

Shrinkage and fraud is an ever-present challenge in retail stores, whether from customers or employees. IoT can help curb the theft of items by adding a layer of visibility and traceability to inventory items. RFIDs, smart-shelves and camera feeds combined with sophisticated machine learning technology can paint a clearer picture of what takes place in-store, detect suspicious movement and determine whether items have been obtained through legal means.

Also, knowing that items are being tracked will discourage patrons and employees from resorting to the pilfering of goods. This is a huge improvement from traditional systems which rely on human monitoring, point-of- sale data and receipts to validate the sale of goods.

Customer experience

One of the benefits of online shopping is being able to push products and offers to customers instead of waiting for them to find them on their own. This helps to catch the attention of customers at the right moment and improve sales dramatically.

IoT will help enhance the brick-and- mortar experience to this level by helping gather data, perform analysis and make the best decisions for retail stores.

Optimizing product placement

Trying to figure out how customers navigate store isles is valuable information. Retailers always try to lay out their stores in order to maximize exposure to customers and improve sales. In the pre-IoT days, this has been done through human observation, educated guesses, random experimentation and manual sales correlation.

But now, thanks to data gathered from RFID chips, IoT motion detection sensors, beacons and video analytics, retailers can gather precise data from customer movement patterns and identify premium traffic areas. IoT makes is possible to learn how customers interact with specific items and discover which items are abandoned. Changes to store layouts can be automatically correlated to customer behavior changes and sales figures in order to perform precise A/B testing on tweaks and modifications.

Optimized use of in-store staff

Being able to identify customers that need help, and tending to their needs in time is an important factor in closing sales and improving conversion rates. But in-store staff can only watch so many customers at once, and in many cases the presence of a salesperson can be misinterpreted and considered offensive by customers.

IoT helps deal with this problem without disrupting the customer experience. Motion detection sensors, cameras and facial expression recognition algorithms can help identify customer who have been standing too long in one location and are manifesting confusion and ambivalence. The IoT ecosystem can then notify a nearby sales associate through a mobile or smart watch app. This way, shoppers get a better experience because they aren’t kept waiting, and retailers optimize their in-store staff.

Personalized offers and promotions

Banner ads and product suggestions that are customized based on browsing and purchasing history are one of the features that give online shopping channels the edge over brick-and- mortar retail. Cross-selling and upselling have become an important source of revenue for online sellers.

IoT can help retailers collect data and make offers to customers that will put them on par with their online counterparts. RFID chips, sensors and beacons can gather data about customer interactions with store items. The data can be analyzed by machine learning solutions and used to push extra information, customer reviews, recommendations and special offers on smart displays that are installed in stores.

Mobile apps can help move the experience to the next level. While customers interact with in-store items, the IoT ecosystem can merge the collected insights with their online product browsing history in order to provide useful information, offer loyalty programs and offer smarter suggestions for upsells.

Mobile apps in retail

IoT devices and sensors help collect data and glean insights from virtually every physical object and event that takes place in retail stores. But it is with mobile apps that IoT becomes a hands-on experience, especially in retail where most of the tasks are performed in field rather than behind a desk.

it is with mobile apps that IoT becomes a hands-on experience, especially in retail where most of the tasks are performed in field rather than behind a desk.

With a fully featured mobile app (or a suite of app for mobile devices and wearables) retailers can make sure that everyone within the retail chain has access to the data they need anytime, anywhere, in order to become more efficient at their jobs. This includes salespersons, inventory managers, suppliers and everyone else.

Mobile apps will also improve the customer experience as it will drive loyalty and enable customers to engage in a more personalized experience with retail stores and the smart gadgets that are installed in them.

Conclusion

With actionable insights offered by IoT-powered solutions, retailers will be able to offer customers what they actually want through a digital, connected and personalized experience. The gamut of data-driven and cloud-powered technology that is available for the retail sector to take advantage of can help merge the benefits of online and brick-and- mortar shopping experience. Eventually IoT will become the de facto standard and reinvent retail as we know it today.

Read about how Mokriya develops solutions for IoT problems

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Does IoT Need Wireless?

By Wade Sarver. This article originally appeared here

Hell yeah! Don’t get me wrong, you could use CAT 5 to connect most of this stuff, but the idea is to have the equipment everywhere and talking all the time, or at least when we need to. They need to be wireless controlled for it to work properly and to be autonomous. What fun would a drone be if you needed to have a copper line connected to it. The FCC laid out their plan to sunset copper lines. I did a lot of work on them but I won’t miss them because wireless is so cool! If you like copper so much, then put that smartphone down and use a landline, if you can find one.

So, back to IOT, (Internet of Things), they rely on wireless connections for more than convenience. This is how the machine to machine, M2M, really take off. Whether it’s to control valves for a water company or to read your electric meter or to control natural gas flow, you need to have connectivity everywhere. We just need to define what that connectivity will be. It could be the standard carrier networks, LTE really. That is going to be key for so much of this. But most of these systems will need much less bandwidth.

Small data networks, that sounds crazy, right? NOT! You see the new networks are built for larger packets, so they are so inefficient, and too expensive, for a simple command to open or close a valve. LTE and Wi-Fi seem like overkill for these applications, although they are everywhere and the most convenient to work with, especially Wi-Fi, it’s in your house and would be a great way for your smart home full of IOT devices to talk to your smartphone and the real world.

That is why the LTE format may not be the best for IOT, although it would be everywhere so by default it may be the technology of choice.

So how will wireless IOT work?

They need something for outdoor communication like LoRa, the low-bandwidth system. There is a LoRa Alliance, if you want to read more about what they are up to. Another good article on LoRa is here where they go into detail about how it works. What they explain is that they are planning to use the spectrum that is left behind, with smaller bandwidth. They way the Semtech chip works is that they utilize spectrum that is sub giga-hertz, like 109MHz, 433MHz, 866MHz, and 915MHz where they have smaller amounts of spectrum. They need to stay away from the license free spectrum because it might interfere.

There is another format called SigFox for outdoor communication. Again, made for very small packets of data. I found information at here if you want more information but here is what I got out of it. They are using the 915MHz spectrum (ISM band license free), using 2 types of Phase Shift Keying, PSK. This supposedly will help get the data through the noise. I am not sure what the coverage would be for something like this but I would bet its very limited. This is a low power, wide area, (LPWA) network. A good article on SigFox is here if you want to learn how they plan to deploy. I am told that they already have several deployments in the USA, although I don’t know of any personally.

Now, for the smart home, inside a building, or the smart office, you could use Wi-Fi, ZigBee, Z-Wave, Bluetooth, or something proprietary. We all know Wi-Fi and Bluetooth, right? It’s on your smartphones and in your homes. What we don’t know if ZigBee and Z-Wave.

What is ZigBee for IOT? Well, according to the ZigBee Alliance it is a wireless language that is used to connect devices, which is such a generic explanation that I could use for any wireless protocol. Come on!

So I went into Wikipedia at https://en.wikipedia.org/wiki/ZigBee where they give a much better explanation. It is line of site, LOS, and very short-range. It works in the ISM band, just like Wi-Fi, (2.4GHz in most countries but also in 915MHz in USA and Australia, 784MHz in China, 868MHz in Europe). The data rate is very small, remember I said smaller packets are all you need? This is made for very small and efficient bursts of data. They also support mesh networking. Mesh means that the devices not only connect to the hub but they can repeat the signal to each other forming a mesh. This is a great way to extend coverage if you don’t need massive bandwidth.

What is Z-Wave for IOT? Z-Wave takes ZigBee and makes some enhancements. It specifically works in the 908.42GHz range in the USA and 868.42MHz band in Europe. For a great explanation go here but its made for very small networks in the home. Find more at http://www.z-wave.com/ but I haven’t heard much more on this except that they have a version that will work with the Apple iWatch.

As you can see there are many technologies to roll out the IOT format. I don’t really know if there is a clear winner but I think it depends on the need. The wireless backhaul will come down to a chip they add to the device based on need, coverage, and cost. I could see someone using all of the technologies in a device to get the coverage they need, like maybe utility meters. That would make sense because it would be a one-time up front cost. However, for the in home stuff, cheap is what they need. I seriously don’t see people putting in a new network in their homes if they don’t have to but many companies will say you need a “hub” which will be the special format switch that their devices will, in theory, talk to the Wi-Fi in their homes. I already see it but it looks like they want to sell more devices in the home. So maybe high-end stuff will need the hub. I could see the hub as another line of defense in security, where if someone hacks your Wi-Fi and/or cable router then they would need to get by another device to get to your thermostat or light switches.

However, for an outdoor network I could see a dedicated network taking off for several reasons, cost reliability, and security. It costs money to pay the carrier a fee every month when you have a small low data device on it when you could put one of the cheaper hotspots in a space to connect your devices. Again, it really comes down to cost and reliability. Many will say they want security, but how secure can they really be?

A few more articles that may interest you:

http://pages.silabs.com/rs/silabs/images/Wireless-Connectivity-for-IoT.pdf?mkt_tok=3RkMMJWWfF9wsRoguKjNZKXonjHpfsX86%2B4rWKK3lMI%2F0ER3fOvrPUfGjI4DSsJkI%2BSLDwEYGJlv6SgFTLPBMbNsz7gOXBg%3D

http://postscapes.com/internet-of-things-protocols/

https://en.wikipedia.org/wiki/LPWAN

http://www.semtech.com/wireless-rf/internet-of-things/

https://www.micrium.com/iot/devices/

http://www.networkcomputing.com/internet-things/10-leaders-internet-things-infrastructure/1612927605

https://www.thethingsnetwork.org/

So let me know what you think, email [email protected] when you think of something to say!

Photo Credit here.

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For IoT and M2M device security assurance, it's critical to introduce automated software development tools into the development lifecycle. Although software tools' roles in quality assurance is important, it becomes even more so when security becomes part of a new or existing product's requirements.

Automated Software Development Tools

There are three broad categories of automated software development tools that are important for improving quality and security in embedded IoT products:

  • Application lifecycle management (ALM): Although not specific to security, these tools cover requirements analysis, design, coding, testing and integration, configuration management, and many other aspects of software development. However, with a security-first embedded development approach, these tools can help automate security engineering as well. For example, requirements analysis tools (in conjunction with vulnerability management tools) can ensure that security requirements and known vulnerabilities are tracked throughout the lifecycle.  Design automation tools can incorporate secure design patterns and then generate code that avoids known security flaws (e.g. avoiding buffer overflows or checking input data for errors). Configuration management tools can insist on code inspection or static analysis reports before checking in code. Test automation tools can be used to test for "abuse" cases against the system. In general, there is a role for ALM tools in the secure development just as there is for the entire project.
  • Dynamic Application Security Testing (DAST): Dynamic testing tools all require program execution in order to generate useful results. Examples include unit testing tools, test coverage, memory analyzers, and penetration test tools. Test automation tools are important for reducing the testing load on the development team and, more importantly, detecting vulnerabilities that manual testing may miss.
  • Static Application Security Testing (SAST): Static analysis tools work by analyzing source code, bytecode (e,g, compiled Java), and binary executable code. No code is executed in static analysis, but rather the analysis is done by reasoning about the potential behavior of the code. Static analysis is relatively efficient at analyzing a codebase compared to dynamic tools. Static analysis tools also analyze code paths that are untested by other methods and can trace execution and data paths through the code. Static analysis can be incorporated early during the development phase for analyzing existing, legacy, and third-party source and binaries before incorporating them into your product. As new source is added, incremental analysis can be used in conjunction with configuration management to ensure quality and security throughout. 

Figure 1: The application of various tool classes in the context of the software development lifecycle.

Although adopting any class of tools helps productivity, security, and quality, using a combination of these is recommended. No single class of tools is the silver bullet[1]. The best approach is one that automates the use of a combination of tools from all categories, and that is based on a risk-based rationale for achieving high security within budget.

The role of static analysis tools in a security-first approach

Static analysis tools provide critical support in the coding and integration phases of development. Ensuring continuous code quality, both in the development and maintenance phases, greatly reduces the costs and risks of security and quality issues in software. In particular, it provides some of the following benefits:

  • Continuous source code quality and security assurance: Static analysis is often applied initially to a large codebase as part of its initial integration as discussed below. However, where it really shines is after an initial code quality and security baseline is established. As each new code block is written (file or function), it can be scanned by the static analysis tools, and developers can deal with the errors and warnings quickly and efficiently before checking code into the build system. Detecting errors and vulnerabilities (and maintaining secure coding standards, discussed below) in the source at the source (developers themselves) yields the biggest impact from the tools.
  • Tainted data detection and analysis: Analysis of the data flows from sources (i.e. interfaces) to sinks (where data gets used in a program) is critical in detecting potential vulnerabilities from tainted data. Any input, whether from a user interface or network connection, if used unchecked, is a potential security vulnerability.  Many attacks are mounted by feeding specially-crafted data into inputs, designed to subvert the behavior of the target system. Unless data is verified to be acceptable both in length and content, it can be used to trigger error conditions or worse. Code injection and data leakage are possible outcomes of these attacks, which can have serious consequences.
  • Third-party code assessment: Most projects are not greenfield development and require the use of existing code within a company or from a third party. Performing testing and dynamic analysis on a large existing codebase is hugely time consuming and may exceed the limits on the budget and schedule. Static analysis is particularly suited to analyzing large code bases and providing meaningful errors and warnings that indicate both security and quality issues. GrammaTech CodeSonar binary analysis can analyze binary-only libraries and provide similar reports as source analysis when source is not available. In addition, CodeSonar binary analysis can work in a mixed source and binary mode to detect errors in the usage of external binary libraries from the source code. 
  • Secure coding standard enforcement: Static analysis tools analyze source syntax and can be used to enforce coding standards. Various code security guidelines are available such as SEI CERT C [2] and Microsoft's Secure Coding Guidelines [3]. Coding standards are good practice because they prevent risky code from becoming future vulnerabilities. As mentioned above, integrating these checks into the build and configuration management system improves the quality and security of code in the product.

As part of a complete tools suite, static analysis provides key capabilities that other tools cannot. The payback for adopting static analysis is the early detection of errors and vulnerabilities that traditional testing tools may miss. This helps ensure a high level of quality and security on an on-going basis.

Conclusion

Machine to machine and IoT device manufacturers incorporating a security-first design philosophy with formal threat assessments, leveraging automated tools, produce devices better secured against the accelerating threats on the Internet. Modifying an existing successful software development process that includes security at the early stages of product development is key. Smart use of automated tools to develop new code and analyze existing and third party code allows development teams to meet strict budget and schedule constraints. Static analysis of both source and binaries plays a key role in a security-first development toolset. 

References

  1. No Silver Bullet – Essence and Accident in Software Engineering, Fred Brooks, 1986
  2. SEI CERT C Coding Standard,
  3. Outsource Code Development Driving Automated Test Tool Market, VDC Research, IoT & Embedded Blog, October 22, 2013

 

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Originally Posted by:  

With the announcement of the Cisco Solution for LoRAWAN™, Service Providers have an integrated solution that enables them to extend their network reach to where they’ve never gone before – i.e., offering IoT services for devices and sensors that are battery powered, have low data rates and long distance communications requirements. The solution opens new markets and new revenue streams for Service Providers, and can be deployed in a wide range of use cases in Industrial IoT and Smart City applications such as:

  • Asset Tracking and Management
  • Logistics
  • Smart Cities (e.g., smart parking, street lighting, waste management, etc.)
  • Intelligent buildings
  • Utilities (e.g., water and gas metering)
  • Agriculture (e.g., soil, irrigation management)

AU43170

Our Cisco Mobile Visual Networking Index estimates that while LoRa is in its early stages now, these types of Low Power Wide Area connectivity means will quickly gain traction and that by 2020, there will be more than 860 million devices using it to connect.  One of the reasons for such forecasted aggressive adoption, especially in North America and Western Europe, is that LoRa® works over readily available unlicensed spectrum. Cisco is a founding Board member of the LoRa® Allianceformed in January, 2015, with a goal to standardize LPWA Networks in order to stimulate the growth of Internet of Things (IoT) applications.

Cisco has been working with a number of Mobile Operators who are trialing and deploying LoRa® networks to target new low-power consumption IoT services such as metering, location tracking and monitoring services. Many Mobile Operators are looking at LoRa® as complementary to NarrowBand IOT (NB-IOT), an upgrade to current mobile networks that drops the transmit power and data rates of the LTE standard to increase battery life. As NB-IOT networks, devices, and ecosystems will not be commercialized until 2017, LoRa® gives Operators (and all SPs, in fact) a way to gain a head-start on offering new IoT services based on various new low cost business models.

Cisco’s approach to IoT is to deliver integrated solutions that enable SPs to support different class of services aligned with specific pricing models across unlicensed (Wi-Fi, LoRa) and licensed (2G/3G/LTE, and soon, NB-IoT) radio spectrum as demanded by the IoT application. Our multi-access network strategy for IoT is complemented by the Cisco Ultra Services Platform (USP) – our comprehensive, virtualized services core, which includes mobile packet core, policy and services functions. Cisco USP delivers the scalability and flexibility that Operators focusing on IoT need as more and varied “things” get connected to their networks.

Cisco continues to integrate and evolve solutions such as LoraWAN™ to help Service Providers of all types capitalize on new IoT opportunities and transform into next-generation IoT Service Providers.

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Originally Posted and Written by: Michelle Canaan, John Lucker, & Bram Spector

Connectivity is changing the way people engage with their cars, homes, and bodies—and insurers are looking to keep pace. Even at an early stage, IoT technology may reshape the way insurance companies assess, price, and limit risks, with a wide range of potential implications for the industry.

Insurers’ path to growth: Embrace the future

In 1997, Progressive Insurance pioneered the use of the Internet to purchase auto insurance online, in real time.1 In a conservative industry, Progressive’s innovative approach broke several long-established trade-offs, shaking up traditional distribution channels and empowering consumers with price transparency.

This experiment in distribution ended up transforming the industry as a whole. Online sales quickly forced insurers to evolve their customer segmentation capabilities and, eventually, to refine pricing. These modifications propelled growth by allowing insurers to serve previously uninsurable market segments. And as segmentation became table stakes for carriers, a new cottage industry of tools, such as online rate comparison capabilities, emerged to capture customer attention. Insurers fought to maintain their competitive edge through innovation, but widespread transparency in product pricing over time created greater price competition and ultimately led to product commoditization. The tools and techniques that put the insurer in the driver’s seat slowly tipped the balance of power to the customer.

This case study of insurance innovation and its unintended consequences may be a precursor to the next generation of digital connectivity in the industry. Today, the availability of unlimited new sources of data that can be exploited in real time is radically altering how consumers and businesses interact. And the suite of technologies known as the Internet of Things (IoT) is accelerating the experimentation of Progressive and other financial services companies. With the IoT’s exponential growth, the ways in which citizens engage with their cars, homes, and bodies are getting smarter each day, and they expect the businesses they patronize to keep up with this evolution. Insurance, an industry generally recognized for its conservatism, is no exception.

IoT technology may still be in its infancy, but its potential to reshape the way insurers assess, price, and limit risks is already quite promising. Nevertheless, since innovation inevitably generates unintended possibilities and consequences, insurers will need to examine strategies from all angles in the earliest planning stages.

To better understand potential IoT applications in insurance, the Deloitte Center for Financial Services (DCFS), in conjunction with Wikistrat, performed a crowdsourcing simulation to explore the technology’s implications for the future of the financial services industry. Researchers probed participants (13 doctorate holders, 24 cyber and tech experts, 20 finance experts, and 6 entrepreneurs) from 20 countries and asked them to imagine how IoT technology might be applied in a financial services context. The results (figure 1) are not an exhaustive compilation of scenarios already in play or forthcoming but, rather, an illustration of several examples of how these analysts believe the IoT may reshape the industry.2

ER_2824_Fig.1

CONNECTIVITY AND OPPORTUNITY

Even this small sample of possible IoT applications shows how increased connectivity can generate tremendous new opportunities for insurers, beyond personalizing premium rates. Indeed, if harnessed effectively, IoT technology could potentially boost the industry’s traditionally low organic growth rates by creating new types of coverage opportunities. It offers carriers a chance to break free from the product commoditization trend that has left many personal and commercial lines to compete primarily on price rather than coverage differentiation or customer service.

For example, an insurer might use IoT technology to directly augment profitability by transforming the income statement’s loss component. IoT-based data, carefully gathered and analyzed, might help insurers evolve from a defensive posture—spreading risk among policyholders and compensating them for losses—to an offensive posture: helping policyholders prevent losses and insurers avoid claims in the first place. And by avoiding claims, insurers could not only reap the rewards of increased profitability, but also reduce premiums and aim to improve customer retention rates. Several examples, both speculative and real-life, include:

  • Sensors embedded in commercial infrastructure can monitor safety breaches such as smoke, mold, or toxic fumes, allowing for adjustments to the environment to head off or at least mitigate a potentially hazardous event.
  • Wearable sensors could monitor employee movements in high-risk areas and transmit data to employers in real time to warn the wearer of potential danger as well as decrease fraud related to workplace accidents.
  • Smart home sensors could detect moisture in a wall from pipe leakage and alert a homeowner to the issue prior to the pipe bursting. This might save the insurer from a large claim and the homeowner from both considerable inconvenience and losing irreplaceable valuables. The same can be said for placing IoT sensors in business properties and commercial machinery, mitigating property damage and injuries to workers and customers, as well as business interruption losses.
  • Socks and shoes that can alert diabetics early on to potential foot ulcers, odd joint angles, excessive pressure, and how well blood is pumping through capillaries are now entering the market, helping to avoid costly medical and disability claims as well as potentially life-altering amputations.3

Beyond minimizing losses, IoT applications could also potentially help insurers resolve the dilemma with which many have long wrestled: how to improve the customer experience, and therefore loyalty and retention, while still satisfying the unrelenting market demand for lower pricing. Until now, insurers have generally struggled to cultivate strong client relationships, both personal and commercial, given the infrequency of interactions throughout the insurance life cycle from policy sale to renewal—and the fact that most of those interactions entail unpleasant circumstances: either deductible payments or, worse, claims. This dynamic is even more pronounced in the independent agency model, in which the intermediary, not the carrier, usually dominates the relationship with the client.

The emerging technology intrinsic to the IoT that can potentially monitor and measure each insured’s behavioral and property footprint across an array of activities could turn out to be an insurer’s holy grail, as IoT applications can offer tangible benefits for value-conscious consumers while allowing carriers to remain connected to their policyholders’ everyday lives. While currently, people likely want as few associations with their insurers as possible, the IoT can potentially make insurers a desirable point of contact. The IoT’s true staying power will be manifested in the technology’s ability to create value for both the insurer and the policyholder, thereby strengthening their bond. And while the frequency of engagement shifts to the carrier, the independent agency channel will still likely remain relevant through the traditional client touchpoints.

By harnessing continuously streaming “quantified self” data, using advanced sensor connectivity devices, insurers could theoretically capture a vast variety of personal data and use it to analyze a policyholder’s movement, environment, location, health, and psychological and physical state. This could provide innovative opportunities for insurers to better understand, serve, and connect with policyholders—as well as insulate companies against client attrition to lower-priced competitors. Indeed, if an insurer can demonstrate how repurposing data collected for insurance considerations might help a carrier offer valuable ancillary non-insurance services, customers may be more likely to opt in to share further data, more closely binding insurer and customer.

Leveraging IoT technologies may also have the peripheral advantage of resuscitating the industry’s brand, making insurance more enticing to the relatively small pool of skilled professionals needed to put these strategies in play. And such a shift would be welcome, considering that Deloitte’s Talent in Insurance Survey revealed that the tech-savvy Millennial generation generally considers a career in the insurance industry “boring.”4 Such a reputational challenge clearly creates a daunting obstacle for insurance executives and HR professionals, particularly given the dearth of employees with necessary skill sets to successfully enable and systematize IoT strategies, set against a backdrop of intense competition from many other industries. Implementing cutting-edge IoT strategies could boost the “hip factor” that the industry currently lacks.

With change comes challenges

While most stakeholders might see attractive possibilities in the opportunity for behavior monitoring across the insurance ecosystem, inevitable hurdles stand in the way of wholesale adoption. How insurers surmount each potential barrier is central to successful evolution.

For instance, the industry’s historically conservative approach to innovation may impede the speed and flexibility required for carriers to implement enhanced consumer strategies based on IoT technology. Execution may require more nimble data management and data warehousing than currently in place, as engineers will need to design ways to quickly aggregate, analyze, and act upon disparate data streams. To achieve this speed, executives may need to spearhead adjustments to corporate culture grounded in more centralized location of data control. Capabilities to discern which data are truly predictive versus just noise in the system are also critical. Therefore, along with standardized formats for IoT technology,5 insurers may see an increasing need for data scientists to mine, organize, and make sense of mountains of raw information.

Perhaps most importantly, insurers would need to overcome the privacy concerns that could hinder consumers’ willingness to make available the data on which the IoT runs. Further, increased volume, velocity, and variety of data propagate a heightened need for appropriate security oversight and controls.

For insurers, efforts to capitalize on IoT technology may also require patience and long-term investments. Indeed, while bolstering market share, such efforts could put a short-term squeeze on revenues and profitability. To convince wary customers to opt in to monitoring programs, insurers may need to offer discounted pricing, at least at the start, on top of investments to finance infrastructure and staff supporting the new strategic initiative. This has essentially been the entry strategy for auto carriers in the usage-based insurance market, with discounts provided to convince drivers to allow their performance behind the wheel to be monitored, whether by a device installed in their vehicles or an application on their mobile device.

Results from the Wikistrat crowdsourcing simulation reveal several other IoT-related challenges that respondents put forward. (See figure 2.)6

ER_2824_Fig.2a

Each scenario implies some measure of material impact to the insurance industry. In fact, together they suggest that the same technology that could potentially help improve loss ratios and strengthen policyholder bonds over the long haul may also make some of the most traditionally lucrative insurance lines obsolete.

For example, if embedding sensors in cars and homes to prevent hazardous incidents increasingly becomes the norm, and these sensors are perfected to the point where accidents are drastically reduced, this development may minimize or eliminate the need for personal auto and home liability coverage, given the lower frequency and severity of losses that result from such monitoring. Insurers need to stay ahead of this, perhaps even eventually shifting books of business from personal to product liability as claims evolve from human error to product failure.

Examining the IoT through an insurance lens

Analyzing the intrinsic value of adopting an IoT strategy is fundamental in the development of a business plan, as executives must carefully consider each of the various dimensions to assess the potential value and imminent challenges associated with every stage of operationalization. Using Deloitte’s Information Value Loop can help capture the stages (create, communicate, aggregate, analyze, act) through which information passes in order to create value.7

The value loop framework is designed to evaluate the components of IoT implementation as well as potential bottlenecks in the process, by capturing the series and sequence of activities by which organizations create value from information (figure 3).

ER_2824_Fig.3

To complete the loop and create value, information passes through the value loop’s stages, each enabled by specific technologies. An act is monitored by a sensor that creates information. That information passes through a network so that it can be communicated, and standards—be they technical, legal, regulatory, or social—allow that information to be aggregated across time and space. Augmented intelligence is a generic term meant to capture all manner of analytical support, collectively used to analyze information. The loop is completed via augmented behavior technologies that either enable automated, autonomous action or shape human decisions in a manner leading to improved action.8

For a look at the value loop through an insurance lens, we will examine an IoT capability already at play in the industry: automobile telematics. By circumnavigating the stages of the framework, we can scrutinize the efficacy of how monitoring driving behavior is poised to eventually transform the auto insurance market with a vast infusion of value to both consumers and insurers.

Auto insurance and the value loop

Telematic sensors in the vehicle monitor an individual’s driving to create personalized data collection. The connected car, via in-vehicle telecommunication sensors, has been available in some form for over a decade.9 The key value for insurers is that sensors can closely monitor individual driving behavior, which directly corresponds to risk, for more accuracy in underwriting and pricing.

Originally, sensor manufacturers made devices available to install on vehicles; today, some carmakers are already integrating sensors into showroom models, available to drivers—and, potentially, their insurers—via smartphone apps. The sensors collect data (figure 4) which, if properly analyzed, might more accurately predict the unique level of risk associated with a specific individual’s driving and behavior. Once the data is created, an IoT-based system could quantify and transform it into “personalized” pricing.

ER_2824_Fig.4

Sensors’ increasing availability, affordability, and ease of use break what could potentially be a bottleneck at this stage of the Information Value Loop for other IoT capabilities in their early stages.

IoT technology aggregatesand communicatesinformation to the carrier to be evaluated. To identify potential correlations and create predictive models that produce reliable underwriting and pricing decisions, auto insurers need massive volumes of statistically and actuarially credible telematics data.

In the hierarchy of auto telematics monitoring, large insurers currently lead the pack when it comes to usage-based insurance market share, given the amount of data they have already accumulated or might potentially amass through their substantial client bases. In contrast, small and midsized insurers—with less comprehensive proprietary sources—will likely need more time to collect sufficient data on their own.

To break this bottleneck, smaller players could pool their telematics data with peers either independently or through a third-party vendor to create and share the broad insights necessary to allow a more level playing field throughout the industry.

Insurers analyze data and use it to encourage drivers to act by improving driver behavior/loss costs. By analyzing the collected data, insurers can now replace or augment proxy variables (age, car type, driving violations, education, gender, and credit score) correlated with the likelihood of having a loss with those factors directly contributing to the probability of loss for an individual driver (braking, acceleration, cornering, and average speed, as figure 4 shows). This is an inherently more equitable method to structure premiums: Rather than paying for something that might be true about a risk, a customer pays for what is true based on his own driving performance.

But even armed with all the data necessary to improve underwriting for “personalized” pricing, insurers need a way to convince millions of reluctant customers to opt in. To date, insurers have used the incentive of potential premium discounts to engage consumers in auto telematics monitoring.10 However, this model is not necessarily attractive enough to convince the majority of drivers to relinquish a measure of privacy and agree to usage-based insurance. It is also unsustainable for insurers that will eventually have to charge rates actually based on risk assessment rather than marketing initiatives.

Substantiating the point about consumer adoption is a recent survey by the Deloitte Center for Financial Services of 2,193 respondents representing a wide variety of demographic groups, aiming to understand consumer interest in mobile technology in financial services delivery, including the use of auto telematics monitoring. The survey identified three distinct groups among respondents when asked whether they would agree to allow an insurer to track their driving experience, if it meant they would be eligible for premium discounts based on their performance (figure 5).11 While one-quarter of respondents were amenable to being monitored, just as many said they would require a substantial discount to make it worth their while (figure 5), and nearly half would not consent.

ER_2824_Fig.5

While the Deloitte survey was prospective (asking how many respondents would be willing to have their driving monitored telematically), actual recruits have been proven to be difficult to bring on board. Indeed, a 2015 Lexis-Nexis study on the consumer market for telematics showed that usage-based insurance enrollment has remained at only 5 percent of households from 2014 to 2015 (figure 6).12

ER_2824_Fig.6

Both of these survey results suggest that premium discounts alone have not and likely will not induce many consumers to opt in to telematics monitoring going forward, and would likely be an unsustainable model for insurers to pursue. The good news: Research suggests that, while protective of their personal information, most consumers are willing to trade access to that data for valuable services from a reputable brand.13 Therefore, insurers will likely have to differentiate their telematics-based product offerings beyond any initial early-adopter premium savings by offering value-added services to encourage uptake, as well as to protect market share from other players moving into the telematics space.

In other words, insurers—by offering mutually beneficial, ongoing value-added services—can use IoT-based data to become an integral daily influence for connected policyholders. Companies can incentivize consumers to opt in by offering real-time, behavior-related services, such as individualized marketing and advertising, travel recommendations based on location, alerts about potentially hazardous road conditions or traffic, and even diagnostics and alerts about a vehicle’s potential issues (figure 7).14 More broadly, insurers could aim to serve as trusted advisers to help drivers realize the benefits of tomorrow’s connected car.15

Many IoT applications offer real value to both insurers and policyholders: Consider GPS-enabled geo-fencing, which can monitor and send alerts about driving behavior of teens or elderly parents. For example, Ford’s MyKey technology includes tools such as letting parents limit top speeds, mute the radio until seat belts are buckled, and keep the radio at a certain volume while the vehicle is moving.16 Other customers may be attracted to “green” monitoring, in which they receive feedback on how environmentally friendly their driving behavior is.

Insurers can also look to offer IoT-related services exclusive of risk transfer—for example, co-marketing location-based services with other providers, such as roadside assistance, auto repairs, and car washes may strengthen loyalty to a carrier. They can also include various nonvehicle-related service options such as alerts about nearby restaurants and shopping, perhaps in conjunction with points earned by good driving behavior in loyalty programs or through gamification, which could be redeemed at participating vendors. Indeed, consumers may be reluctant to switch carriers based solely on pricing, knowing they would be abandoning accumulated loyalty points as well as a host of personalized apps and settings.

For all types of insurance—not just auto—the objective is for insurers to identify the expectations that different types of policyholders may have, and then adapt those insights into practical applications through customized telematic monitoring to elevate the customer experience.

Telematics monitoring has demonstrated benefits even beyond better customer experience for policyholders. Insurers can use telematics tools to expose an individual’s risky driving behavior and encourage adjustments. Indeed, people being monitored by behavior sensors will likely improve their driving habits and reduce crash rates—a result to everyone’s benefit. This “nudge effect” indicates that the motivation to change driving behavior is likely linked to the actual surveillance facilitated by IoT technology.

The power of peer pressure is another galvanizing influence that can provoke beneficial consumer behavior. Take fitness wearables, which incentivize individuals to do as much or more exercise than the peers with whom they compete.17 In fact, research done in several industries points to an individual’s tendency to be influenced by peer behavior above most other factors. For example, researchers asked four separate groups of utility consumers to cut energy consumption: one for the good of the planet, a second for the well-being of future generations, a third for financial savings, and a fourth because their neighbors were doing it. The only group that elicited any drop in consumption (at 10 percent) was the fourth—the peer comparison group.18

Insurers equipped with not only specific policyholder information but aggregated data that puts a user’s experience in a community context have a real opportunity to influence customer behavior. Since people generally resist violating social norms, if a trusted adviser offers data that compares customer behavior to “the ideal driver”—or, better, to a group of friends, family, colleagues, or peers—they will, one hopes, adapt to safer habits.

ER_2824_Fig.7a

The future ain’t what it used to be—what should insurers do?

After decades of adherence to traditional business models, the insurance industry, pushed and guided by connected technology, is taking a road less traveled. Analysts expect some 38.5 billion IoT devices to be deployed globally by 2020, nearly three times as many as today,19 and insurers will no doubt install their fair share of sensors, data banks, and apps. In an otherwise static operating environment, IoT applications present insurers with an opportunity to benefit from technology that aims to improve profits, enable growth, strengthen the consumer experience, build new market relevance, and avoid disruption from more forward-looking traditional and nontraditional competitors.

Incorporating IoT technology into insurer business models will entail transformation to elicit the benefits offered by each strategy.

  • Carriers must confront the barriers associated with conflicting standards—data must be harvested and harnessed in a way that makes the information valid and able to generate valuable insights. This could include making in-house legacy systems more modernized and flexible, building or buying new systems, or collaborating with third-party sources to develop more standardized technology for harmonious connectivity.
  • Corporate culture will need a facelift—or, likely, something more dramatic—to overcome longstanding conventions on how information is managed and consumed across the organization. In line with industry practices around broader data management initiatives,20 successfully implementing IoT technology will require supportive “tone at the top,” change management initiatives, and enterprisewide training.
  • With premium savings already proving insufficient to entice most customers to allow insurers access to their personal usage data, companies will need to strategize how to convince or incentivize customers to opt in—after all, without that data, IoT applications are of limited use. To promote IoT-aided connectivity, insurers should look to market value-added services, loyalty points, and rewards for reducing risk. Insurers need to design these services in conjunction with their insurance offerings, to ensure that both make best use of the data being collected.
  • Insurers will need to carefully consider how an interconnected world might shift products from focusing on cleaning up after disruptions to forestalling those disruptions before they happen. IoT technology will likely upend certain lines of businesses, potentially even making some obsolete. Therefore, companies must consider how to heighten flexibility in their models, systems, and culture to counterbalance changing insurance needs related to greater connectivity.
  • IoT connectivity may also potentially level the playing field among insurers. Since a number of the broad capabilities that technology is introducing do not necessarily require large data sets to participate (such as measuring whether containers in a refrigerated truck are at optimal temperatures to prevent spoilage21 or whether soil has the right mix of nutrients for a particular crop22), small to midsized players or even new entrants may be able to seize competitive advantages from currently dominant players.
  • And finally, to test the efficacy of each IoT-related strategy prior to implementation, a framework such as the Information Value Loop may become an invaluable tool, helping forge a path forward and identify potential bottlenecks or barriers that may need to be resolved to get the greatest value out of investments in connectivity.

The bottom line: IoT is here to stay, and insurers need look beyond business as usual to remain competitive.

The IoT is here to stay, the rate of change is unlikely to slow anytime soon, and the conservative insurance industry is hardly impervious to connectivity-fueled disruption—both positive and negative. The bottom line: Insurers need to look beyond business as usual. In the long term, no company can afford to engage in premium price wars over commoditized products. A business model informed by IoT applications might emphasize differentiating offerings, strengthening customer bonds, energizing the industry brand, and curtailing risk either at or prior to its initiation.

IoT-related disruptors should also be considered through a long-term lens, and responses will likely need to be forward-looking and flexible to incorporate the increasingly connected, constantly evolving environment. With global connectivity reaching a fever pitch amid increasing rates of consumer uptake, embedding these neoteric schemes into the insurance industry’s DNA is no longer a matter of if but, rather, of when and how.

You can view the original post in its entirety Here

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Guest blog post by Bernard Marr

What does big data know about you?

Quite a lot.

Every time we use a computer, access our phones, or open an app on a tablet, we’re leaving a digital trail. Most people are vaguely aware that Google knows what they’ve searched for, or that Facebook knows who their friends are, but it goes much, much deeper than that.

I’ve compiled a list of 21 things Big Data knows about almost every one of us — right now:

  1. Of course, Google knows what you’ve searched for. So do Bing, Yahoo!, and every other search engine. And your ISP knows every website you’ve ever visited. Ever (even in private browsing).
  2. Google also knows your age and gender — even if you never told them. They make a pretty comprehensive ads profile of you, including a list of your interests (which you can edit) to decide what kinds of ads to show you.
  3. Facebook knows when your relationship is going south. Based on activities and status updates on Facebook, the company can predict (with scary accuracy) whether or not your relationship is going to last.
  4. Google knows where you’ve travelled, especially if you have an Android phone.
  5. And the police know where you’re driving right now — at least in the U.K., where closed circuit televisions (CCTV) are ubiquitous. Police have access to data from thousands of networked cameras across the country, which scan license plates and take photographs of each car and their driver. In the U.S., many cities have traffic cameras that can be used similarly.
  6. Your phone also knows how fast you were going when you were traveling. (Be glad they don’t share that information with the police!)
  7. Your phone has also probably deduced where you live and work.
  8. The Internet knows where your cat lives. Using the hidden meta-data about the geographic location of where the photo was taken which we share when we publish photos of our cats on sites like Instagram and other social media networks.
  9. Your credit card company knows what you buy. Of course your credit card company knows what you buy and where, but this has raised concerns that what you buy and where you shop might impact your credit score. They can use your purchasing data to decide if you’re a credit risk.
  10. Your grocery store knows what brands you like. For every point a grocery store or pharmacy doles out, they’re collecting mountains of data about your purchasing habits and preferences. The chains are using the data to serve up personalized experiences when you visit their websites, personalized coupon offers, and more.
  11. HR knows when you’re going to quit your job. An HR software company called Workday WDAY -1.00% is testing out an algorithm that analyzes text in documents and can predict from that information, which employees are likely to leave the company.
  12. Target knows if you’re pregnant. (Sometimes even before your family does.)
  13. YouTube knows what videos you’ve been watching. And even what you’ve searched for on YouTube.
  14. Amazon knows what you like to read, Netflix NFLX -0.85% knows what you like to watch. Even your public library knows what kinds of media you like to consume.
  15. Apple and Google know what you ask Siri and Cortana.
  16. Your child’s Barbie doll is also telling Mattel what she and your child talk about.
  17. Police departments in some major cities, including Chicago and Kansas City, know you’re going to commit a crime — before you do it.  
  18. Your auto insurance company knows when and where you drive — and they may penalize you for it, even if you’ve never filed a claim.
  19. Data brokers can help unscrupulous companies identify vulnerable consumers. For example, they may identify a population as a “credit-crunched city family” and then direct advertisements at you for payday loans.
  20. Facebook knows how intelligent you are, how satisfied you are with your life, and whether you are emotionally stable or not – simply based on a big data analysis of the ‘likes’ you have clicked.
  21. Your apps may have access to a lot of your personal data. Angry Birds gets access to your contact list in your phone and your physical location. Bejeweled wants to know your phone number. Some apps even access your microphone to record what’s going on around you while you use them.

This is actually just the tip of the iceberg. As we dive deeper into the benefits big data can provide to us, we’ll also be happily coughing up more and more data. The iPhone Health app, for instance, can collect data about all kinds of intimately personal things about your health.

It’s up to us, as consumers, to be aware of what we’re giving away, when, and to whom. I would love to hear your concerns and comments on this topic.

Bernard Marr is a best-selling author & keynote speaker. His new book: 'Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results'

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