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What is Going on with Residential IoT

Cyber Security?

For sure you have heard about the recent DDoS attacks that occurred last October 21st on Dyn’s DNS service. The news broke out reporting that many well-known Internet services were not available. According to Hacker News Twitter, Etsy, Spotify and other sites were affected. Up to this point, there’s nothing new, just another DDoS attack. Large company outage means big news, but there is still a point that is key in this equation and that has not been addressed. 

  • Was Residential or Consumer IoT affected?

According to Dyn’s report, “the attack come from 100,000 malicious endpoints”. 

On the second last paragraph they quote: “Not only has it highlighted vulnerabilities in the security of “Internet of Things” (IOT) devices that need to be addressed, but it has also sparked further dialogue in the internet infrastructure community about the future of the internet.

Put both quotes together: 100,000 IoT devices have been Hacked. This is astonishing and outstanding!

There has been no news about how the 100,000 IoT device customers have been affected or supported:

  • Do they still have the Bot inside their device? 
  • Do the devices work correctly? 
  • Do they know they have been hacked? 
  • Do they know they are at risk? 
  • Will the Bots change and do other things? 
  • Will the Bots leave backdoors in their home networks?
  • How long will it take for another Bot to hack their IoT device?
  • What are Consumer Protection Agencies doing about this?
  • What are Governments doing?

This is no joke, we are talking about 100,000 devices (IoT Customers), and therefore, has to be addressed very seriously.

Dyn and the Internet community will address the issue. That’s fine! But how and when will they solve the Residential IoT vulnerability problem. Residential IoT needs to be Secured, Monitored and its software Updated. Enterprise IoT already contemplates this, but Residential IoT does not. Individual devices are sold with no security, and in the best case, if they are well developed and secured they still need to be monitored because software always has vulnerabilities, no matter how well and secure it has been developed.

All the questions, above cannot be solved using secure policies inside IoT or in the Internet itself. More has to be done! This is a Game Changer; Home Networks have to be monitored and secured to prevent Malware and Attacks. If not, the Internet will soon be like Hell.

The Residential IoT Avalanche

Gartner estimates that by 2020 there will be 25 billion IoT devices, of these, 13 billion will be Residential Home Devices, more than 50% of the total. Imagine if only 1% of these devices are vulnerable, there will be 13 million devices to hack.

  • Are the Internet Home Users aware of the risk they are taking?
  • Are their Home Networks and GateWays (GW/Router) secure?
  • Will the Internet itself be reliable and secure?

How to Secure Home Networks

Twenty years ago, Home Networks only had PCs, with well-developed software, for examples Windows, but many vulnerabilities were used to Hack Residential and Enterprise PCs. This problem brought up many Anti Malware (AM) Software Companies to safeguard Windows PCs. The same is happening right now with Residential IoT.

IoT devices don’t have the possibility or suppliers are not interested in incorporating AM software to their IoT. They are generally too small and only have specific dedicated software, i.e.: they cannot be easily protected with AM Software embedded in their devices:

  • This is a big problem. How can it be solved?
  • Where and how can AM software safeguard Home Networks, GWs and IoT?

Every Home Network connects to the Internet through the GW, which is the main door into our Home. As with Houses, shouldn’t an armored door be used to prevent thieves from coming in? The GW is the door to the Internet and it is also another device with CPU and Memory, a processing unit that can do the job. Why not use it to block hackers before they even get in? Thanks to FTTH and IoT itself, Gateways have become more powerful. If a GW does not have the power to cope with AM Security, then a security appliance should be connected to it. Using a secure GW, the entire Home Network will be protected from Malware and Attacks.

Many Security Providers and new startups have already foreseen the Secure GW solution.

Current Residential IoT/GW Security Innovation Trends

As described before, the most effective scenario to protect your Home IoT is to Safeguard the Home Network using the GW, this is currently being done with two innovative solutions:

Solution #1.              Attach a physical AM Security Appliance to the Home GW.

Solution #2.              Embedding AM Security software directly into the Home GW.

Solution #1 Is an interesting and effective approach, another device with more CPU and Memory means more processing power, but it adds another gadget to the end-user and it has to be physically connect to the Home GW’s 1Gbit Port.

The Pros: The Appliance adds an extra device to manage security, leaving the GW as it is. The customers will manage alerts and/or security configurations through a simple app on their smartphones. 

The Cons: All the traffic will bypass the appliance through a 1Gbit port, which needs a cable connected to the GW. Customers want to reduce physical gadgets, they already have many, such as the GW itself, IPTV DVB Decoder, the ONT, Game Station, Printers, cables, etc. Another device is not a bad solution but the current trend is to reduce home devices and cables, this solution will work but in a few years Solution #2 will make Solution #1 obsolete.

Solution #2. The Security Software will come within the GW device or it will remotely be installed.

The Pros: The customer will only manage alerts and/or security configurations, with a simple mobile app, that’s all. Simple, no physical appliance, no wires. 

The Cons: Many of the current GW hardware devices don’t have sufficient physical CPU and/or Memory capacity to manage security software, but with the FTTH and the IoT boom, Gateways are becoming more and more powerful and in a few years, most of them, if not all, will have the power to manage AM software.

Make it Simple, Intelligent and Economically Viable for Retail

Both solutions have their pros and cons, and both should, at least, address basic security surveillance. There are many threats that can be addressed using Cloud Intelligent Processing, analyzing Home Network Metadata (GW CPU will be liberated from many security tasks). But, most important of all is the combined Residential Cloud Intelligence, for example; if a new threat is detected and blocked on a provider’s vulnerable IoT device, the solution will automatically be propagated to all of the security providers’ customers, avoiding mass propagation and hacking damage. 

Residential Device “Internet Use Patterns” will be supervised and any mismatch will be reported to the customer or automatically be blocked if a malicious attacker is detected.

Customers don’t or cannot give proper maintenance to their Home IoT. The solution should or will control possible problems like vulnerable firmware, recommend changing easy or default passwords, block dangerous port access, grant or deny access, etc. Most of these simple actions will be prompted on the users’ smartphone, and the problem will easily be solved using a simple one click menu.

And finally, and probably most important, customers don’t want and can’t pay for a highly sophisticated solution. A next generation firewall type solution is way out of scope and expensive, the solution has to be smart and economically viable or sales will draw back.

There is no need to drill down into what can be done and what cannot, both solutions are effective. Solution #1 is good but #2 is in the core of the Home Network, the GW, and simpler for the end user, but it may take some time before all the GWs have sufficient power and capacity. 

Conclusions

  • There are millions of Residential IoT Devices being hacked, but most users are unaware and the press doesn’t really talk about it.
  • Residential IoT is in general insecure and with the predicted IoT Avalanche, hackers will take advantage of the situation to make the Internet be like Hell.
  • Residential IoT must be Secured, Monitored and its software Updated using the Home GW Router.
  • Make it Simple, Intelligent and Economically Viable for Retail.
  • IoT Residential Customers must be 100% aware of the Security risks, this must be strongly driven by Consumer Agencies, Governments, The Press, IoT Suppliers and Security Vendors.

If the security actions described in this publication are not addressed correctly, the Internet and all of us will have to learn the hard way. 

Juan Mora Zamorano

Independent Security Contractor

https://es.linkedin.com/in/morajuan

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Securing the Internet of Everything

The introduction of connected devices is complicating an already incredibly complex security environment for infosec professionals. In just two decades, the enterprise has gone from a controlled scenario of one device per user to a situation in which users may have five or more devices connected to sensitive systems and applications. As the IoT becomes more popular it will soon be impossible to quantify just how many internet-enabled, vulnerable points exist within an organization. So what can companies do to secure the IoT?
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IoT Central Digest, January 17, 2017

Happy New Year to all our members old, new, near and far!

If you want to look ahead into 2017, make sure to read the 50 predictions we put together. With input from vendors large and small, including AT&T, Autodesk, Hitachi, Intel, Salesforce and many others, these predictions cover a lot of ground including security, buildings, big data, ecosystems, and standards. Be sure to check them out.

This issue also looks at cars and IoT, security and stopping IoT attacks, health and big data analytics, and the ongoing IoT standards discussion. Enjoy!

Reminder: All members are free to post on IoT Central. We feature the best content and share across our social networks and other channels. Consider contributing today. Our guidelines are here.

50 Predictions for the Internet of Things in 2017
Posted by David Oro


For 2017, we asked our members, industry contacts and friends for their predictions around the Internet of Things. What new technologies will appear? Which companies will succeed or fail? What platforms will take off? What security challenges will the industry face? Will enterprises finally realize the benefits of IoT?

12 Steps to Stop the Next IoT Attack in its Tracks
Posted by Rob Tiffany

The IoT attack against Domain Name Servers from a botnet of thousands of devices means it’s way past time to take IoT security seriously. The bad actors around the world who previously used PCs, servers and smartphones to carry out attacks have now set their sights on the growing tidal wave of IoT devices. It’s time for consumers and enterprises to protect themselves and others by locking down their devices, gateways and platforms.

IoT Future – 34 Billion new Devices in 4 Years?
Posted by Joao Lopes


The most recent studies indicate that in 2020 more than 34 billion devices will be connected to the internet, in many sectors (Industrial, Agriculture, Transportation, Wearable Devices, Smart Cities, Smart Houses, etc). Of these 34 billion, the IoT will be responsible for 23 billion devices, the others 11 billion will be represented by the regular devices, such as, smartphones, tablets, smartwatches, etc.

Internet of Health: Is medicine ready to Big Data Analytics?
Guest blog post by Olga Kolesnichenko

What is Big Data: data, process of analysis or concept? There are many definitions that describe Big Data as big amount of data or as some methods of analytics of big amount of data. But more applicable is the approach that Big Data is the concept that includes: data with specific characteristics (V3 - volume, velocity, variety, or V5 - plus value and veracity), methods of analytics (the number of different software is growing), and devices, infrastructure, and most important - the ideas how to configure all into needed solution.

This is how Analytics is changing the game of Sports!!
Posted by Sandeep raut

Today, every major professional sports team either has an analytics department or an analytics expert on staff.  From coaches and players to front offices and businesses, analytics can make a difference in scoring touchdowns, signing contracts or preventing injuries. This article highlights the use of devices and analytics for sports.

Cars and Car Transportation in The Internet of Things Era
Posted by Nate Vickery

Whenever we read about, or hear about, people confidently presenting their vision of any kind of future trends, we, of course, need to take it with a grain of salt. However, as a business technology expert, I know where to look for indications on the direction technology is taking, and for the fun of it, I will try to make some educated guesses.
Let’s see what some of the trends that could change the way we travel are.

How to secure your smarthome gadgets
By Ben Dickson

The holiday season is a big time for consumer electronics and smart home gadget sales. With so many advances and innovations that we saw in the Internet of Things in 2016, there’s a likely chance that one of those connected devices has found its way into your home, or that of one of your loved ones, this Christmas.

But while IoT devices make our homes more efficient, drive energy saving and reduce costs, you should also take note that IoT devices are a source of security headaches. A huge number of smarthome gadgets are developed without sound development practices and end up being used for evil purposes.

NB-IoT is Dead. Long Live NB-IoT.
Guest post by Nick Hunn

As the old adage goes, “while the cat’s away, the mice will play”. In the case of NB-IOT, “when the spec’s delayed, LPWAN will play”, which is exactly what’s happening in the Internet of Things market today. The problem is that 3GPP (the 3rd Generation Partnership Project), the standards body which has been responsible for the 3G, 4G and 5G mobile standards, dropped the ball as far as the Internet of Things is concerned. Seduced by the slabs of black glass which suck up both our attention and the mobile networks’ spectrum, the 3GPP engineers totally forgot to design something to replace the old 2G workhorse of GPRS, which is responsible for most of today’s machine to machine communications. Instead, they spent all of their time designing high power, high speed, expensive variants of 4G to support an ongoing dynasty of iPhones, Galaxys and Pixels, none of which were any use for the Internet of Things.


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Guest blog post by Olga Kolesnichenko

What is Big Data: data, process of analysis or concept? There are many definitions that describe Big Data as big amount of data or as some methods of analytics of big amount of data. But more applicable is the approach that Big Data is the concept that includes: data with specific characteristics (V3 - volume, velocity, variety, or V5 - plus value and veracity), methods of analytics (the number of different software is growing), and devices, infrastructure, and most important - the ideas how to configurate all into needed solution.

Another concept is the Internet of Things that based on Big Data Analytics. There are some established configurations of IoT: Smart Home, Smart Health, Smart Manufacturing, Smart City, Smart Mobility, Smart Energy, Smart Farming, Smart Earth & Ocean, Smart Circular Economy.

Smart Health or Internet of Health (or any IoT configuration) has the human in core of concept. I should point out that more easy to accept the medical approaches for different configurations of IoT than accept IoT approaches for Health Care. Why I can insist on this statement? My statement leans on long-term period of complexity of accepting biorhythmology and gravitational biology in medicine.

But biorhythmology and gravitational biology have the direct application for Internet of Health or IoT. The person controls own different medical data day by day during everyday life. And this new situation should be viewed as medical data collecting under gravitational forces and natural biorhythms influences to person.

Three sections of multifactorial regulation of human body should be mentioned: environmental, behavioral and homeostatic. Environmental section includes circannual rhythm (annual) and circadian rhythm (daily). Behavioral section includes body orientation towards gravitational forces (lying down, standing, sitting); active movement (walking, jogging, exercises); passive movement (lift and transport) with influence of acceleration forces; as well as sleep, emotional reactions, eating. Homeostatic section includes the processes of neurohumoral regulation of the body. This section consists of functional systems of the body, described by Russian scientist K.V. Sudakov and his following.

Thus creating Internet of Health configuration and implementing Big Data Analytics the medical data should be considered in terms of three sections of multifactorial regulation of human body. 

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This is how Analytics is changing the game of Sports!!

Analytics and Big Data have disrupted many industries, and now they are on the edge of scoring major points in sports. Over the past few years, the world of sports has experienced an explosion in the use of analytics
Till few years back experience, gut feelings, and superstition have traditionally shaped the decision making process in sports.
It is first started with Oakland Athletics' General Manager, Billy Beane, who applied analytics for selecting right players. This was the first known use of statistics and data to make decisions in professional sports.
Today, every major professional sports team either has an analytics department or an analytics expert on staff.  From coaches and players to front offices and businesses, analytics can make a difference in scoring touchdowns, signing contracts or preventing injuries.
Big name organizations such as the Chicago Cubs, and Golden State Warriors are realizing that this is the future of sports and it is in their best interest to ride the wave while everyone else is trying to learn how to surf.
Golden State Warriors, have similarly used big data sets to help owners and coaches recruit players and execute game plans.
SportVu has six cameras installed in the NBA arenas to track the movements of every player on the court and the basketball 25 times per second. The data collected provides a plethora of innovative statistics based on speed, distance, player separation and ball possession to improve next games.
Adidas miCoach app works by having players attach a wearable device to their jerseys. Data from the device shows the coach who the top performers are and who needs rest. It also provides real-time stats on each player, such as speed, heart rate and acceleration.
Patriots developed a mobile app called Patriots Game Day Live, available to anyone attending a game at Gillette Stadium. With this app, they are trying to predict the wants and needs of fans, special content to be delivered, in-seat concession ordering and bathroom wait times.
FiveThirtyEight.com, provides details into more than just baseball coverage. It has over 20 journalists crunching numbers for fans to gain a better understanding of an upcoming game, series or season.
Motus’ new sleeves for tracking a pitcher’s throwing motion, measuring arm stress, speed and shoulder rotation. The advanced data generated from this increases a player’s health, performance and career. Experts can now predict with greater confidence if and when a pitcher with a certain throwing style will get injured.

In the recent Cricket world cup, every team had its own team of Data Analysts. They used various technologies like Cloud Platform and visualizations to predict scores, player performance, player profiles and more. Around 40 years’ worth of Cricket World Cup data is being mined to produce insights that enhances the viewer's experience. 
Analytics can advance the sports fans' experience as teams and ticket vendors compete with the at-home experience -- the better they know their fans, the better they can cater to them.
This collection of data is also used for internet ads, which can help with the expansion and growth of your organization through social media platforms or websites. 
  • What would be the most profitable food served at the concession stand?
  • What would be the best prices to sell game day tickets?
  • Determine which player on the team is the most productive?
  • Which players in the draft will become all-stars, and which ones will be considered role players?
  • Understand the fans behavior at the stadium via their app and push relevant information accordingly.
In this Digital age, Analytics are the present and future of professional sports. Any team that does not apply them to the fullest is at a competitive disadvantage.
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The Untapped Potential of Data Analytics

The potential of big data just keeps growing. For taking full advantage, companies need to incorporate analytics into their strategic objectives.

A research report from McKinsey Global Institute (MGI), suggests that the opportunity and applications continue to expand in the data-driven world.

With rapid technological transformation, the question for businesses arises on how to position themselves uniquely in the world leveraging analytics. Over 2.5 quintillion bytes of data is generated every day. As information pours in via various digital platforms, VR application, and mobile phones the need for data storage capacity has increased.

The transformational potential

The recent progress shows the potential of big data and analytics in more than five distinct domains. However, transforming to a data-driven decision-making organisation is not always simple.

The first challenge is to incorporate data and analytics along with business objectives into a core strategic vision. Secondly, the lack of talent in the adoption of analytics. New reports denote that despite training programs, the talent is not enough to match the demand. The next step is to develop the right business process and framework which includes data infrastructure.

Simply combining technology systems along with the existing business operations isn't enough. For ensuring a successful transformation, all aspects of business activity need to be evaluated and combined to realize the full potential of data analytics.

Incorporating data analytics

The next generation of analytic tools will unleash even bigger opportunities. With new machine-learning, deep-learning and artificial-intelligence capabilities, an enormous variety of applications can be enabled which provide customer service, manage logistics and analyze data.

Technology and productivity gains seem an advantage, but also carry the risk of people losing jobs. A case of automation is the AI software developed by Bridgewater Associates, the world's largest hedge fund to improve efficiency.

With Data and analytics shaking up every industry, the effects will only become more noticeable as adoption reaches the masses.

As machines gain unprecedented capabilities to solve complex problems, organizations can harness these capabilities to create their unique value proposition and solve problems.

 

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How to secure your smarthome gadgets

By Ben Dickson. This article originally appeared here.

The holiday season is a big time for consumer electronics and smarthome gadget sales. With so many advances and innovations that we saw in the Internet of Things in 2016, there’s a likely chance that one of those connected devices has found its way into your home, or that of one of your loved ones, this Christmas.

But while IoT devices make our homes more efficient, drive energy saving and reduce costs, you should also take note that IoT devices are a source of security headaches. A huge number of smarthome gadgets are developed without sound development practices and end up being used for evil purposes.

So if you don’t want your smarthome gadgets to be used to spy on you, hurt you in some other way, or be used in the next massive IoT DDoS attack, take a minute to read these guidelines. They will help you get the most out of what your IoT devices have to offer without suffering the privacy and security repercussions.

Install the latest updates

Seldom you see a software or hardware released without glitches or bugs. Many of these loopholes leave your devices open to attacks and exploits. That’s why developers and manufacturers regularly roll out updates and security fixes.

First of all, before installing your new device, do a little internet research for known vulnerabilities, and make sure that the manufacturer has released a patch for the bug (patches are announced and delivered on the manufacturer’s website).

Make sure that the manufacturer has a policy and good track record of delivering updates. If a manufacturer doesn’t deliver security patches, I would recommend returning the gadget back to where you bought it from.

In some cases, there are workarounds that can help you plug a security gap by disabling some of the features or changing settings, but do it with caution.

Last word on updates: Since smarthome gadgets are usually installed and forgotten, register your device for update notifications in case the manufacturer does have such an option. This way, you can make sure that you don’t miss any important updates.

Protect your network from IoT hacks

Per se, connected devices such as light bulbs and coffeemakers might not contain sensitive information or functionality, but their vulnerabilities can provide attackers with potential footholds into your home network, giving them a beachhead to conduct more critical attacks against your laptop or workstation.

The first thing you should do is to change factory default settings (e.g. administrative passwords) on your devices after installing them. This is critical as many attacks are conducted by scanning the web for devices for unchanged factory settings.

Also make sure you don’t reuse a password you’ve set on a critical email or social media account, unless you want a breach to propagate to unwanted domains.

If your device offers several different connection channels, disable the ones you’re not using, and always prefer wired connections over WiFi and other wireless mediums. This will minimize the attack surface. If the device is associated with a mobile app, review the privileges it requires (microphone, camera, GPS access, etc.) and only grant permissions if it is absolutely necessary.

If you’re going away for a long time (vacation, business trip, etc.), make sure to turn off unneeded devices or at least disconnect them from the internet.

Last word on network protection: If your home router has a guest network option, you can use it to isolate your IoT devices from your local network. This will prevent breached gadgets from giving attackers network access to your laptop and other devices containing personal and sensitive information.

Protect your IoT devices from hackers

In the previous step, we discussed how to prevent IoT vulnerabilities from harming your network. But you should also protect your smarthome gadgets themselves. Some devices such as smart thermostats can deal real damage if hacked, while nearly all compromised IoT devices can be used to raise botnets and stage widespread DDoS attacks.

Unfortunately, a considerable percentage of IoT devices lack proper defense measures (and will continue to miss them for some time to come), therefore the first order of business should be to set up a firewall.

Most home routers have firewall rules and settings that can be easily set up to block access through unused ports. This can help prevent access to devices that don’t let you turn off unwanted remote access features.

To add an extra measure of defense, use a Virtual Private Network (VPN) to encrypt your outgoing and incoming traffic. The advantages of using VPNs is twofold. First, it’ll make up for lack of encryption in IoT devices. And second, it can make it more challenging for eavesdroppers to deduce life patterns from analyzing network traffic metadata.

Last word on device protection: You might want to consider investing in a smarthome intrusion detector, a breed of devices that analyze your home network’s traffic and look for patterns of malicious activities.

Protect your privacy

Most home IoT devices silently collect data about your daily routines and habits and often send them over to the cloud. While this helps devices and their manufacturers to analyze patterns and deliver better services, it can also become the source of privacy controversies.

First of all, you should clearly know how your data is used and processed before you connect any new device to the internet. Review the vendor’s data collection and sharing policies and make sure it explicitly states whether your data will be shared with third parties or not. There should also be an opt-out option for users who don’t want to have their data collected.

Also, if your device has a microphone or camera component and you’re not using it, disable it outright, because they can lead to some of the worst kind of privacy troubles. If there’s no switch or feature to turn off the camera, cover it or turn it to face the wall.

Last word on privacy: If you decide to sell your device or give it away to someone else, reset it to factory default settings and wipe out any user data you might have stored on it.

Over to you

IoT is the future. But it shouldn’t cost you your privacy and security. Hopefully, with these tips, you’ll be better positioned to make good and safe use of your smarthome gadgets while avoiding the pitfalls and unwelcomed tradeoffs.

How do you vet and secure your devices? Share with us in the comments section.

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What are Microservices in Digital Transformation?

Today’s organizations are feeling the fear of becoming dinosaur every day. Newdisrupters are coming into your industry and turning everything upside down.
Customers are more demanding than ever and will abandon the service that is too slow to respond.  Everything is needed yesterday to make your customers happy.
Now, there is no time for organizations to implement huge enterprise applications which takes months and years. 
What they need is, more agile, smaller, hyper focused teams working together to innovate and provide customer value.
This is where Microservices have gain momentum and are becoming fast go-to solution for enterprises. They takes SOA a step further by breaking every component into effectively single-purpose applications.
Microservices, show a strategy for decomposing a large project, based on the functions, into smaller, more manageable pieces. While a monolithic app is One Big Program with many responsibilities, Microservice based apps are composed of several small programs, each with a single responsibility
Microservices are independently developed & deployable, small, modular services. Each component is developed separately, and the application is then simply the sum of its constituent components. Each service runs as a unique process and communicates with other components via a very lightweight methods like HTTP/Rest with Jason.
Unlike old single huge enterprise application which requires heavy maintenance, Microservices are easy to manage.
Here are few characteristics and advantages of Microservices:
  • Very small, targeted in scope and functionality
  • Gives developers the freedom to independently develop and deploy services
  • Loosely coupled & can communicate with other services on industry wide standards like HTTP and JSON
  • API based connectivity
  • Every service can be coded in different programming language
  • Easily deployable and disposable makes releases possible even multiple times a day
  • New Digital technology can be easily adopted for a service
  • Allows to change services as required by business, without a massive cost
  • Testing and releases easier for individual components
  • Better fault tolerance and scale up
There are some challenges as well, while using Microservices:
  • Incur a cost of the testing at system integration level
  • Need to configure monitoring and alerting and similar services for each microservice
  • Service calls to one another, so tracing the path and debugging can be difficult
  • Each service communicates through API/remote calls, which have more overhead
  • Each service generates a log, so there is no central log monitoring.
Netflix has great Microservice architecture that receives more than one billion calls every day, from more than 800 different types of devices, to its streaming-video API.
Nike, the athlete clothing and shoe giant & now digital brand is using Microservices in its apps to deliver extra ordinary customer experience.
Amazon, eBay are other great examples of Microservices architecture.
GE’s Predix - the industrial Internet platform is based on Microservices architecture.
So, if your IT organization is implementing a microservices architecture, here are some examples of an operating system (Linux, Ubuntu, CoreOS), container technology(Docker), a scheduler(Swarm, Kubernetes), and a monitoring tool(Prometheus).
The technical demands of digital transformation, all front/back-office systems that seamlessly coordinate customer experiences in a digital world is achieved by Microservices as the preferred architecture.
Microservices help close the gap between business and IT & are fundamental shift in how IT approaches software development and are absolutely essential in Digital Transformation.
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IoT Future – 34 Billion new Devices in 4 Years?

Many industry experts and consumers are pointing the Internet of Things (IoT) as an upcoming Industrial Revolution or an upcoming Internet.

Why this? Simple, because IoT will consist of the future form of interaction of businesses, governments and consumers with the physical world.

The most recent studies indicate that in 2020 more than 34 billion devices will be connected to the internet, in many sectors (Industrial, Agriculture, Transportation, Wearable Devices, Smart Cities, Smart Houses, etc).

Of these 34 billion, the IoT will be responsible for 23 billion devices, the others 11 billion will be represented by the regular devices, such as, smartphones, tablets, smartwatches, etc.

BI - IoT - Evolution Graph - IoT FutureSource: BI Intelligence

The business sector will be responsible for the biggest use part of this devices, since the IoT can reduce the Operational Costs, Increase the Production, expand the business for new market niches.

Government will take the second biggest part of the devices connected, in smart cities, fasting up the public process, increasing the quality life of the citizens.

At last but not less important, the home user, will have a lot of IoT Devices, Smart Houses, Wearable Devices.

So the future we can really specify in some words: "The future is Data".

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50 Predictions for the Internet of Things in 2017

Last year we put together our first ever predictions list looking at what would come to the Industrial Internet of Things in 2016. No one predicted at the time the biggest IoT story of 2016: massive DDoS attacks via the Internet of Things. To their credit, there were many security predictions for 2016, but no one nailed #DDoS.

For 2017, we asked our members, industry contacts and friends for their predictions around the Internet of Things. What new technologies will appear? Which companies will succeed or fail? What platforms will take off? What security challenges will the industry face? Will enterprises finally realize the benefits of IoT?

The 50 predictions in this list focus on the Industrial side of the Internet of Things. The list is global in perspective and includes predictions from researchers, professors, independents, start-ups and major brand names like Autodesk, AT&T, Hewlett Packard Enterprise, Hitachi, Intel, Salesforce, and many others.

We received hundreds of inquiries and obviously couldn’t include them all, so if you submitted a prediction and it’s not listed here, know that we read it and appreciate your contribution. Thank you!

Without further adieu, here are the 50 Predictions for the Internet of Things in 2017.

Sanjay Sarma, head of MIT’s open and digital learning efforts and lead instructor “Internet of Things: Roadmap to a Connected World

2017 will be the year we start to bring order to the chaos of IoT. Right now, every connected device talks to each other in a different way. Devices remain siloed in their independent systems, creating a complex communication environment. What’s more, this patchwork of systems is difficult to maintain, upgrade and improve. This is a serious problem that will need to be addressed as the IoT expands, otherwise there is a real chance that the power of the IoT could be compromised.  

In the months to come, we will likely see focus shift to establishing paradigms for effective implementations and use of IoT. There will also be a push for government to get involved. For example, we might see initiatives or agencies that can be used to incubate academic institutions, labs and companies testing and working on best practices for the IoT.

What’s clear is that we need a unified approach that creates genuine ‘interconnectness.’ In my view, this is what will allow us to realize the true potential of the IoT

Pieter van Schalkwyk, CEO, XMPro

IIoT Ecosystem will start to formalize

Industrial IoT requires an ecosystem of partners to provide advisory, technology and services that collectively create effective solutions for customers. In 2017 we will see that some of these relationships start to formalize in different industries and for different use cases as these partners become experienced in working together to create real IoT solutions.

Leadership in IIoT platforms for major vendors

Industrial B2B customers are historically more conservative than the consumer market and these customers will look to support platform offerings from their existing enterprise solution providers such as SAP and GE, rather than the hundreds of independent small consumer centric IoT platforms. The industrial IOT ecosystems that I mentioned earlier, will form around a few of these leading industrial IoT platforms.

ROI case studies for IIoT will start to emerge

In 2015 industrial companies were talking about IoT, in 2016 they started planning how to leverage IoT, and in 2017 we will see real examples and case studies emerge. It will highlight the areas where IoT can provide a significant ROI contribution, but also expose the hype areas where there is no real value other than the “coolness” factor. The combination of the ecosystems and the platforms will lead to real solutions that start to solve real problems.

Loudon Blair, Senior Director, Corporate Strategy, Ciena

IoT will open new opportunities for carriers. This year we’ll see carriers face up to the challenges of digital disruption: avoiding service commoditization and building compelling digital services to compete with Over-The-Top players. Their efforts will focus on the opportunities on offer in the emerging IoT market, and as they launch IoT services and platforms, carriers will benefit from two core differentiators. First, they already own the network: the key piece of infrastructure needed to realize consumer IoT services. Second, carriers ‘own’ the customers as subscribers to their networks.

Chris Penrose, Senior Vice President of IoT, AT&T

AT&T has identified five major IoT trends and predictions for 2017:

1) Global IoT will be impossible without a network scaled to match, and a robust, multi-layer integrated network approach will open new global IoT opportunities.

2) Companies both large and small will look to enterprise-level leaders to respond to their needs for a variety of connectivity solutions. Large-scale, optimized IoT solutions will enable companies to more efficiently manage assets and improve operations.

3) IoT will continue to blur between B2B and B2C solutions so that technologies advance both enterprise and consumer interests. Enterprises and innovators will join forces to manage and even evolve customer expectations, allowing them to do more than they ever thought possible.

4) Through a proliferation of sensors virtually everywhere, millions of new data points and thousands of use cases will be developed. As a result, the rate of data collection will increase algorithmically, not linearly, making more information available than ever before.

5) IoT security will remain a top concern in the upcoming year. Items like network-connected wearables or smart coffee pots will become of increasing interest to hackers due to the often limited attention paid to security in their development cycles.

Vincent Granville, Pioneering Data Scientist, Data Science Central

The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance. The frontier between AI, IoT, data science, machine learning, deep learning and operations research will become more fuzzy. Editor's Note: For a full list of Vincent's predictions go to our sister site Data Science Central here.

Dr. Chase Cunningham, Director of Cyber Security Operations, A10 Networks

IoT continues to pose a major threat. In late 2016, all eyes were on IoT-borne attacks. Threat actors were using Internet of Things devices to build botnets to launch massive distrubted denial of service (DDoS) attacks. In two instances, these botnets collected unsecured “smart” cameras. As IoT devices proliferate, and everything has a Web connection — refrigerators, medical devices, cameras, cars, tires, you name it — this problem will continue to grow unless proper precautions like two-factor authentication, strong password protection and others are taken.  Device manufacturers must also change behavior. They must scrap default passwords and either assign unique credentials to each device or apply modern password configuration techniques for the end user during setup. Editor's Note: For a full list of Chase's predictions go here.

Rod Schultz, VP of Product, Rubicon Labs

Business models will be the new ‘it’ thing to innovate (think Uber for anything and everything), as the value of IoT is realized. The transition from Capex to Opex-based business models, built directly on the power of IoT, will drive innovation and unlock new business models and subscription-driven services for industries that have been static for years.

Robert Vamosi, CISSP, Security Strategist, Synopsys, Inc.

Embedded Security will finally get serious.  Devices, once thought to be too small to include their own security, will undergo a more thorough analysis beginning with firmware testing. The software inside the chip is just as important as the application controlling it. Both need to be tested for security and quality. Some of the early IoT botnets have leveraged vulnerabilities and features within the device itself

Bryan Kester, Head of IoT, Autodesk

Editor’s Note: We interviewed Bryan earlier this year. Read about it here.

My predictions for 2017 in IoT:

1) In 2017, the focus on IoT will shift noticeably away from consumer use cases, like smart toasters and toothbrushes, to industrial and B2B use cases, like connected factories, warehouses, and robots. Edge platforms that focused on “makers” and IoT enthusiasts will pivot to the enterprise, following opportunities to monetize their offerings. However, a number of these IoT platforms will likely go out of business following unsuccessful pivots.

2) There will be another IoT security breach in the headlines. Odds are, someone will be careless with security and the media is on the lookout for these types of stories.

3) There will be an increased focus on case studies and real examples of IoT ROI as the 2013-2015 pioneer projects start generating compelling metrics. A handful of these examples may be in the emerging field of Machine Learning, and how it can fuse together with IoT for predictive and autonomous operations.

IoT Central members can see all the predictions here. Become a member today here. It's free!

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Do you know what is powerful real-time analytics?

In the Digital age today, world has become smaller and faster. 
Global audio & video calls which were available only in corporate offices, are now available to common man on the smartphone.
Consumers have more information of the products and comparison than the manufactures at any time, any place, and any device.
Gone are the days, when organizations used to load data in their data warehouse overnight and take decision based on BI, next day. Today organizations need actionable insights faster than ever before to stay competitive, reduce risks, meet customer expectations, and capitalize on time-sensitive opportunities – Real-time, near real-time.
Real-time is often defined in microseconds, milliseconds, or seconds, while near real-time in seconds, minutes.
With real-time analytics, the main goal is to solve problems quickly as they happen, or even better, before they happen. Real-time recommendations create a hyper-personal shopping experience for each and every customer.
The Internet of Things (IoT) is revolutionizing real-time analytics. Now, with sensor devices and the data streams they generate, companies have more insight into their assets than ever before.
Several industries are using this streaming data & putting real-time analytics. 
·        Churn prediction in Telecom
·        Intelligent traffic management in smart cities
·        Real-time surveillance analytics to reduce crime
·        Impact of weather and other external factors on stock markets to take trading decisions
·        Real-time staff optimization in Hospitals based on patients 
·        Energy generation and distribution based on smart grids
·        Credit scoring and fraud detection in financial & medical sector
Here are some real world examples of real-time analytics:
·        City of Chicago collects data from 911 calls, bus & train locations, 311 complaint calls & tweets to create a real-time geospatial map to cut crimes and respond to emergencies
·        The New York Times pays attention to their reader behavior using real-time analytics so they know what’s being read at any time. This helps them decide which position a story is placed and for how long it’s placed there
·        Telefonica the largest telecommunications company in Spain can now make split-second recommendations to television viewers and can create audience segments for new campaigns in real-time
·        Invoca, the call intelligence company, is embedding IBM Watson cognitive computing technology into its Voice Marketing Cloud to help marketers analyze and act on voice data in real-time.
·        Verizon now enables artificial intelligence and machine learning, predicting the customer intent by mining unstructured data and correlations
·        Ferrari, Honda & Red Bull use data generated by over 100 sensors in their Formula 
One cars and apply real-time analytics, giving drivers and their crews the information they need to make better decisions about pit stops, tire pressures, speed adjustments and fuel efficiency.
Real-Time analytics helps getting the right products in front of the people looking for them, or offering the right promotions to the people most likely to buy. For gaming companies, it helps in understanding which types of individuals are playing which game, and crafting an individualized approach to reach them.
As the pace of data generation and the value of analytics accelerate, real-time analytics is the top most choice to ride on this tsunami of information.
More and more tools such as Cloudera Impala, AWS, Spark, Storm, offer the possibility of real-time processing of Big Data and provide analytics,

Now is the time to move beyond just collecting, storing & managing the data to take rapid actions on the continuous streaming data – Real-Time!! 

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Securing IoT Consumer Devices

As consumer electronics manufacturers release new gadgets for the holidays, security is likely to be the last thing on people's minds. Devices like Apple’s HomeKit turn your iPhone or iPad into a remote control for lights, locks, the thermostat, window shades and even your doorbell, making typical iOS functions like Siri voice-based extensions of controlling a smart home.

Yet even if most electronics on a home network employ top security standards, all it takes is a faulty webcam for an attack to happen.

We just saw this with internet infrastructure company Dyn in late October. Mirai malware took advantage of default, easy-to-guess passwords on the webcams of unsuspecting consumers, leading to a massive Distributed Denial of Service (DDoS) attack temporarily shutting down popular sites like Twitter and PayPal.

Along with Apple’s Authentication Coprocessor, HomeKit’s end-to-end encryption helps mitigate the risk of hacking. The coprocessor only sends a certificate that allows an iOS device to unlock an accessory (like your home’s light dimmers, thermostat and power meter) after the accessory completes a challenge sent by the iOS device. Any Internet of Things device that connects to this network, however, may not have the same robustness rules in place.

According to the IoT graphic from Arxan below, the number of devices connected to the internet reached 6.4 billion in 2016. Thus, in-home communication network security is only half the battle for consumers, as the cars they drive are increasingly becoming connected as well. Car manufacturers have different OEMs when it comes to displays and in-vehicle digital storage, meaning that all devices in a connected car may not use end-to-end encryption. Code scanners can interrupt critical functions and if you look further into automotive IoT security you’ll find that many parts of a vehicle that have been around for years--like the OBD2 port for engine diagnostics and on-board computers--could potentially be decrypted and injected with malware.

 

 

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12 Steps to Stop the Next IoT Attack in its Tracks

The recent distributed denial-of-service (DDoS) IoT attack against DNS is a wake up call to how fragile the Internet can be.

The IoT attack against Domain Name Servers from a botnet of thousands of devices means it’s way past time to take IoT security seriously. The bad actors around the world who previously used PCs, servers and smartphones to carry out attacks have now set their sights on the growing tidal wave of IoT devices. It’s time for consumers and enterprises to protect themselves and others by locking down their devices, gateways and platforms. While staying secure is a never-ending journey, here’s a list of twelve actions you can take to get started:

  1. Change the default usernames and passwords on your IoT devices and edge gateways to something strong.
  2. Device telemetry connections must be outbound-only. Never listen for incoming commands or you’ll get hacked.
  3. Devices should support secure boot with cryptographically signed code by the manufacturer to ensure firmware is unaltered.
  4. Devices must have enough compute power and RAM to create a transport layer security (TLS) tunnel to secure data in transit.
  5. Use devices and edge gateways that include a Trusted Platform Module (TPM) chip to securely store keys, connection strings and passwords in hardware.
  6. IoT platforms must maintain a list of authorized devices, edge gateways, associated keys and expiration dates/times to authenticate each device.
  7. The telemetry ingestion component of IoT platforms must limit IP address ranges to just those used by managed devices and edge gateways.
  8. Since embedded IoT devices and edge gateways are only secure at a single point in time, IoT platforms must be able to remotely update their firmware to keep them secure.
  9. When telemetry arrives in an IoT platform, the queue, bus or storage where data comes to rest must be encrypted.
  10. Devices and edge gateways managed by an IoT platform must update/rotate their security access tokens prior to expiration.
  11. Field gateways in the fog layer must authenticate connected IoT devices, encrypt their data at rest and then authenticate with upstream IoT platforms.
  12. IoT platforms must authenticate each device sending telemetry and blacklist compromised devices to prevent attacks.

Keeping the various components that make up the IoT value chain secure requires constant vigilance. In addition to doing your part, it’s important to hold the vendors of the IoT devices, gateways and platforms accountable for delivering technology that’s secure today and in the future.

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Guest blog by Ajit Jaokar. Ajit”s work spans research, entrepreneurship and academia relating to IoT, predictive analytics and Mobility. His current research focus is on applying data science algorithms to IoT applications. This includes Time series, sensor fusion and deep learning (mostly in R/Apache Spark). This research underpins his teaching at Oxford University (Data Science for IoT) and ‘City sciences’ program at UPM (Madrid). Ajit is also the Director of the newly founded AI/Deep Learning labs for Future cities at UPM (University of Madrid.) His book is included as a course book at Stanford University for Data Science for IoT. In 2015, Ajit was included in top 16 influencers (Data Science Central), Top 100 blogs( KDnuggets), Top 50 (IoT central), No 19 among top 50 twitter IOT influencers (IoT institute.) 

Introduction

In the last two decades, more than six billion devices have come online. All those connected "things" (collectively called - The Internet of Things) generate more than 2.5 quintillion bytes of data daily. That's enough to fill 57.5 billion 32 GB iPads per day (source Gartner). All this data is bound to significantly impact many business processes over the next few years. Thus, the concept of IoT Analytics (Data Science for IoT) is expected to drive the business models for IoT. According to Forbes, strong analytics skills are likely to lead to 3x more success with Internet of Things. We cover many of these ideas in the Data Science for IoT course.

Data Science for IoT has similarities but also some significant differences. Here are 10 differences between Data Science for IoT and traditional Data Science.

  1. Working with the Hardware and the radio layers
  2. Edge processing
  3. Specific analytics models used in IoT verticals
  4. Deep learning for IoT
  5. Pre-processing for IoT
  6. The role of Sensor fusion in IoT
  7. Real Time processing and IoT
  8. Privacy, Insurance, and Blockchain for IoT
  9. AI: Machines teaching each other(cloud robotics)
  10. IoT and AI layer for the Enterprise

1. Working with the Hardware and the radio layers

This may sound obvious, but it's easy to underestimate. IoT involves working with a range of devices and also a variety of radio technologies. It is a rapidly shifting ecosystem with new technologies like LoRa, LTE-M, Sigfox etc. The deployment of 5G will make a big difference because we would have both Local area and Wide area connectivity. Each of the verticals (we track Smart homes, Retail, Healthcare, Smart cities, Energy, Transportation, Manufacturing and Wearables) also have a specific set of IoT devices and radio technologies. For example, for wearables you see Bluetooth 4.0 in use but for Industrial IoT, you are likely to see cellular technologies which guarantee Quality of Service such as the GE Predix alliance with Verizon

2. Edge processing

In traditional Data Science, Big data usually resides in the Cloud. Not so for IoT!. Many vendors like Cisco and Intel call this as Edge Computing. I have covered the impact of Edge analytics and IoT in detail in a previous post: The evolution of IoT Edge analytics.

3. Specific analytics models used in IoT verticals

IoT needs an emphasis on different models and also these models depend on IoT verticals. In traditional Data Science, we use a variety of algorithms (Top Algorithms Used by Data Scientists). For IoT, time series models are often used. This means : ARIMA, Holt Winters, moving average. The difference is the volume of data but also more sophisticated real time implementations of the same models ex (pdf) : ARIIMA: Real IoT implementation of a machine learning architecture for reducing energy consumption. The use of models vary across IoT verticals. For example in Manufacturing : predictive maintenance, anomaly detection, forecasting and missing event interpolation are common. In Telecoms, traditional models like churn modelling, cross sell, upsell model , customer life time value could include IoT as an input.

4. Deep learning for IoT

If you consider Cameras as sensors, there are many applications of Deep Learning algorithms such as CNNs for security applications, eg from hertasecurity. Reinforcement learning also has applications for IoT as I discussed in a post by Brandon Rohrer for Reinforcement Learning and Internet of Things

5. Pre-processing for IoT

IoT datasets need a different form of Pre-processing. Sibanjan Das and I referred to it in Deep learning - IoT and H2O.

Deep learning algorithms play an important role in IoT analytics. Data from machines is sparse and/or has a temporal element in it. Even when we trust data from a specific device, devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Monitoring sensor data continuously is also cumbersome and expensive. Deep learning algorithms can help to mitigate these risks. Deep Learning algorithms learn on their own allowing the developer to concentrate on better things without worrying about training them.

6. The role of Sensor fusion in IoT

Sensor fusion involves combining of data from disparate sensors and sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. (adapted from Wikipedia). The term 'uncertainty reduction' in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view based on the combined information. Sensor fusion has always played a key role in applications like Aerospace:

In aerospace applications, accelerometers and gyroscopes are often coupled into an Inertial Measurement Unit (IMU), which measures orientation based on a number of sensor inputs, known as Degrees of Freedom (DOF). Inertial Navigation Systems (INS) for spacecraft and aircraft can cost thousands of dollars due to strict accuracy and drift tolerances as well as high reliability.

But increasingly, we are seeing sensor fusion in self driving cars and Droneswhere inputs from multiple sensors can be combined to infer more about an event.

7. Real Time processing and IoT

IoT involves both fast and big data. Hence, Real Time applications provide a natural synergy with IoT. Many IoT applications like Fleet management, Smart grid, Twitter stream processing etc have unique analytics requirements based on both fast and large data streaming. These include:

  • Real time tagging: As unstructured data flows from various sources, the only way to extract signal from noise is to classify the data as it comes. This could involve working with Schema on the fly concepts.
  • Real time aggregation: Any time you aggregate and compute data along a sliding time window you are doing real time aggregation: Find a user behaviour logging pattern in the last 5 seconds and compare it to the last 5 years to detect deviation
  • Real time temporal correlation: Ex: Identifying emerging events based on location and time, real-time event association from largescale streaming social media data (above adapted from logtrust)

8. Privacy, Insurance and Blockchain for IoT

I once sat through a meeting in the EU where the idea of 'silence of the chips' was proposed. The evocative title is modelled on the movie 'silence of the lambs'. The idea is: when you enter a new environment, you have the right to know every sensor which is monitoring you and to also selectively switch it on or off. This may sound extreme - but it does show the Boolean (On or Off) thinking that dominates much of the Privacy discussion today. However, future IoT discussions for privacy are likely to be much more nuanced - especially so, when Privacy, Insurance and Blockchain are considered together.

IoT is already seen to be a significant opportunity by Insurers according to AT Kearney . Organizations like Lloyds of London are also looking to handle large scale systemic risk by the introduction of new technology driven by sensors in urban areas ex Drones. Introduction of Blockchain to IoT creates other possibilities - like IBM says:

Applying the blockchain concept to the world of [Internet of Things] offers fascinating possibilities. Right from the time a product completes final assembly, it can be registered by the manufacturer into a universal blockchain representing its beginning of life. Once sold, a dealer or end customer can register it to a regional blockchain (a community, city or state).

9. AI: Machines teaching each other (cloud robotics)

We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms learn on their own. This concept of machines learning on their own can be extended to machines teaching other machines. This idea is not so far-fetched.

Consider Fanuc, the world's largest maker of industrial robots. A Fanuc robot teaches itself to perform a task overnight by observation and through reinforcement learning. Fanuc's robot uses reinforcement learning to train itself. After eight hours or so it gets to 90 percent accuracy or above, which is almost the same as if an expert were to program it. The process can be accelerated if several robots work in parallel and then share what they have learned. This form of distributed learning is called cloud robotics

10. IoT and AI layer for the Enterprise

We can extend the idea of 'machines teaching other machines' more generically within the Enterprise. Enterprises are getting an 'AI layer'. Any entity in an enterprise can train other 'peer' entities in the Enterprise. That could be buildings learning from other buildings - or planes or oil rigs or even Printers! Training could be dynamic and on-going (for example one building learns about energy consumption and 'teaches' the next building). IoT is a key source of Data for the Enterprise IoT system. Currently, the best example of this approach is Salesforce.com and Einstein. Reinforcement learning is the key technology that drives IoT and AI layer for the Enterprise.

"By end of 2016 more than 80 of the world's 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products." (source Deloitte).

Far from the AI Winter, we seem to be suddenly in the midst of an AI spring. And IoT suddenly has a clear business model in this AI Spring.

Conclusion

From the above discussion, we see many similarities but also significant differences when it comes to Data Science for IoT. There are obvious differences (for example in the use of Hardware and Radio networks). But for me, the most exciting development is the fact that IoT powers exciting new greenfield domains such as Drones, Self driving cars, Enterprise AI, Cloud robotics and many more. We cover many of these ideas in the Data Science for IoT course.

Only a final few places remain with this batch.

Re-posted with permission from the author. Originally posted here.  

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Originally posted by Vincent Granville

It's time again to share your predictions for 2017. I did my homework and came with these 10 predictions. I invite you to post your predictions in the comment section, or write a blog about it. Ramon Chen's predictions are posted here, while you can read Tableau's prediction here. Top programming languages for 2017 can be found here. Gil Press' top 10 hot data science technologies is also worth reading. For those interested, here were the predictions for 2016. Finally, MariaDB discusses the future of analytics and data warehousing in their Dec 20 webinar.

My Predictions

  1. Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government.
  2. Some, with no familiarity with data science, will want to create a legal framework about how data can be analyzed, how the algorithms should behave, and to force public disclosure of algorithm secrets. I believe that they will fail, though Obamacare is an example where predictive algorithms were required to ignore metrics such as gender or age, to compute premiums, resulting in more expensive premiums for everyone.
  3. The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance.
  4. With the rise of IoT, more processes will be automated (piloting, medical diagnosis and treatment) using machine-to-machine or device-to-device communications powered by algorithms relying on artificial intelligence (AI), deep learning, and automated data science. I am currently writing an article that describes the differences between machine learning, IoT, AI, deep learning and data science. You can sign-up on DSC to make sure that you won't miss it. 
  5. The frontier between AI, IoT, data science, machine learning, deep learning and operations research will become more fuzzy. Statistical engineering will be present in more and more applications, be it machine learning, AI or data science. 
  6. Many systems will continue to not work properly. The solution will have to be found not in algorithms, but in people. Read my article Why so many Machine Learning Implementations Fail. An example is Google analytics, which fails to catch huge amounts of robotic traffic that is so rudimentary and so obvious, you don't need any statistical or data science knowledge to filter it or block it. People publish elementary solutions to address these issues, yet it continues unabated. Fake reviews, fake news, undetected hate speech on Twitter, undetected plagiarism by Google search, are in the same category. Eventually it leaves room for new players to jump in and build a system that will actually work. 
  7. Reliance on public data and public news will come with bigger scrutiny. Some say that the failure to predict the elections is a data science failure. In my opinion, it is a different type of failure: it is the failure to recognize that the media are biased (they publish whatever predictions that fit with their agenda) and maybe even those doing the surveys are biased or incompetent (there are lies, damn lies, and statistics as the saying goes). It is also a failure to recognize the very high volatility in these elections, and the fact that day-to-day variations were huge. Anyone able to compute sound confidence intervals that incorporates historical data,  would have said that the results were not reliably predictable. Finally, I always thought that the winner would be the one best able at manipulation and playing tricks, be it hacking or paying the media.
  8. More and more data cleaning, pre-processing, and exploratory data analysis will be automated. We will also face more unstructured data, with powerful ways to structure them.  Multiple algorithms and models will be more and more blended together to provide the best pattern recognition and predictive systems, and boost accuracy. 
  9. Data science education will evolve, with perhaps a come back of strong university curricula run by leading practitioners, and fewer people finding a job through data science camps only, as many of these camps do not train you to become a data scientist, but instead a Python / R / SQL coder with classic, elementary, even outdated and dangerous statistical knowledge. Or data camps will have to evolve, or otherwise risk becoming another kind of Phoenix university.
  10. Attacks against data-dependent infrastructure will switch from stealing or erasing data, to modifying data. Some will be launched from IoT devices if security holes are not fixed.

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Fail fast approach to Digital Transformation

Digital Transformation is changing the way customers think & demand new products or services.
Today Bank accounts are opened online, Insurance claims are filed online, patient’s health is monitored online while buying things online is the thing of past. Everything is here and now in real time.
Till few years back any failure of decision making in business was scary & not acceptable. It had cost companies to go out of fortune 100 list. Blockbuster, Nokia, Kodak, Blackberry are well known examples of not trying new experiments quickly.
But with the digital era, failure is accepted & it is seen as part and parcel of a successful digital business. Failure must be fast, and the lessons of failure learned, should be even faster. It allows businesses to take a shotgun approach to digital transformation.
Fail fast is all about deploying quick pilots and check the outcome. If it does not work then drop the concept/idea and move on to new one. Be prepared to change the pace or direction as necessary.
No business will undergo digital transformation without making any mistakes. Even if an organization has the best possible culture & strategy in place, there will be stumbling blocks on the road to success. With the digital technologies like Cloud, Big Data, Analytics, MobilityInternet of Things, at the disposal, organizations can test the innovative ideas quickly before even reaching out to customer for feedback.
Speed is of the essence here. Testing all the ideas without making huge investments, then delivering the applications in weeks and not months or years to remain competitive. This change has helped organizations to reduce the time-to-market of enhancement on customer experience.
Apple is an example of a company which failed but didn’t give up. It moved on, refined its approach, improved its R&D and eventually launched the product its customers deserved.
Domino's bounced back from customers comments like “your pizza tastes like a cardboard”. With the reboot of menu in 2009 & digital technology they experimented online ordering, created a tracker, which allowed customers to follow their pizza from the oven to their doorstep.
Air New Zeland gone from posting the largest corporate loss in its country’s history to being one of the world’s most consistently profitable airlines by using Big Data Analytics to enhance customer experience in many ways including biometric baggage check-in, an electronic “air band” for unaccompanied minors.
There are several individual examples of failures and success over time:
·        Steve Jobs was fired from the Apple but came back as CEO & made history
·        Thomas Edison failed over 10000 times before success of light bulb
·        J K Rowling of Harry Potter had lots of failures
·        Michael Jordan succeeded after his constant failure to win
But organizations don’t have this time at their hand. They can learn a lot from these individuals failures but quickly move on and achieve success in Digital Transformation.
In Digital Transformation, fail fast is not an option but it is a requirement!!
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Want to know how to choose Machine Learning algorithm?

Machine Learning is the foundation for today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms.
Some of the most common examples of machine learning are Netflix’s algorithms to give movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend products based on other customers bought before.
Typical algorithm model selection can be decided broadly on following questions:
·        How much data do you have & is it continuous?
·        Is it classification or regression problem?
·        Predefined variables (Labeled), unlabeled or mix?
·        Data class skewed?
·        What is the goal? – predict or rank?
·        Result interpretation easy or hard?
Here are the most used algorithms for various business problems:
 
Decision Trees: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Fastest way to identify most significant variables and relation between two or more variables. Decision Trees are excellent tools for helping you to choose between several courses of action. Most popular decision trees are CART, CHAID, and C4.5 etc.
In general, decision trees can be used in real-world applications such as:
·        Investment decisions
·        Customer churn
·        Banks loan defaulters
·        Build vs Buy decisions
·        Company mergers decisions
·        Sales lead qualifications
 
Logistic Regression: Logistic regression is a powerful statistical way of modeling a binomial outcome with one or more explanatory variables. It measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution.
In general, regressions can be used in real-world applications such as:
·        Predicting the Customer Churn
·        Credit Scoring & Fraud Detection
·        Measuring the effectiveness of marketing campaigns
 
Support Vector Machines: Support Vector Machine (SVM) is a supervised machine learning technique that is widely used in pattern recognition and classification problems - when your data has exactly two classes.
In general, SVM can be used in real-world applications such as:
·        detecting persons with common diseases such as diabetes
·        hand-written character recognition
·        text categorization – news articles by topics
·        stock market price prediction
 
Naive Bayes: It is a classification technique based on Bayes’ theorem and very easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods. Naive Bayes is also a good choice when CPU and memory resources are a limiting factor
In general, Naive Bayes can be used in real-world applications such as:
·        Sentiment analysis and text classification
·        Recommendation systems like Netflix, Amazon
·        To mark an email as spam or not spam
·        Facebook like face recognition
 
Apriori: This algorithm generates association rules from a given data set. Association rule implies that if an item A occurs, then item B also occurs with a certain probability.
In general, Apriori can be used in real-world applications such as:
·        Market basket analysis like amazon - products purchased together
·        Auto complete functionality like Google to provide words which come together
·        Identify Drugs and their effects on patients
 
Random Forest: is an ensemble of decision trees. It can solve both regression and classification problems with large data sets. It also helps identify most significant variables from thousands of input variables.
In general, Random Forest can be used in real-world applications such as:
·        Predict patients for high risks
·        Predict parts failures in manufacturing
·        Predict loan defaulters
The most powerful form of machine learning being used today, is called “Deep Learning”.
In today’s Digital Transformation age, most businesses will tap into machine learning algorithms for their operational and customer-facing functions
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NB-IoT is Dead. Long Live NB-IoT.

Guest post by Nick Hunn. This article originally appeared here.

As the old adage goes, “while the cat’s away, the mice will play”. In the case of NB-IOT, “when the spec’s delayed, LPWAN will play”, which is exactly what’s happening in the Internet of Things market today. The problem is that 3GPP (the 3rd Generation Partnership Project), the standards body which has been responsible for the 3G, 4G and 5G mobile standards, dropped the ball as far as the Internet of Things is concerned. Seduced by the slabs of black glass which suck up both our attention and the mobile networks’ spectrum, the 3GPP engineers totally forgot to design something to replace the old 2G workhorse of GPRS, which is responsible for most of today’s machine to machine communications. Instead, they spent all of their time designing high power, high speed, expensive variants of 4G to support an ongoing dynasty of iPhones, Galaxys and Pixels, none of which were any use for the Internet of Things.

Noticing this hole, a number of companies who had been developing proprietary, low cost, low speed, low power communication options saw an opportunity and created the Low Power WAN market. Whilst many perceived them as a group of Emperors with no clothes, the network operators were so desperate to have something to offer for upcoming IoT applications that they started engaging with them, rolling out LPWAN infrastructure. Whether they believed the LPWAN story, or just hoped it would fill a hole is difficult to ascertain, but no-one can deny that LPWAN is now firmly on the map, in the form of Sigfox, LoRa, Ingenu and a raft of others. To address that challenge to their hegemony, the GSM Association (GSMA) directed the 3GPP to assemble their own suit of imperial clothing which would be called the Narrow Band Internet of Things, or NB-IoT.

This is the story of why NB-IoT was too late, why it will fail in the short term, why it will win in the long term, and why the industry will struggle to make any money from it.

Download this article as a pdf.

One of the most surprising aspects of this story is how long it took 3GPP and the network operators to realise that they had a problem. It’s not as if they didn’t see the problem coming. Back in 2010, Ericsson set the bar for much of the subsequent hype around the Internet of Things by making a very public prediction that by 2020 there would be 50 billion internet connected devices. They’ve subsequently downgraded that, but very few in the industry noticed – for them, it’s very difficult to discard the prospect of “tens of billions” once it’s made its way into their business plans. Numbers that big get attention in boardrooms, whether or not they mean anything – they just sound so good that they are assumed to be true.

What happened is that the industry became fixated with the concept of revenue today, rather than revenue tomorrow. As users embraced smartphones, their demand for data soared. When competing smartphone vendors made smartphone screens larger, mobile video took off, putting further pressure on the network’s capacity. Everyone’s attention became focused on how to build enough capacity into their network to retain their users. Instead of calling for new standards for M2M and IoT, operators started concentrating on how they could use their existing spectrum more efficiently. There was an easy answer to this – turn off their old 2G networks and use them for 4G, which supported around 40 times as many users. It was only as they started to do this that they belatedly realised that they were euthanising the only technology they had which would support the Internet of Things. At which point the LPWAN industry stepped into the frame and started cutting deals. The GSMA panicked, and directed 3GPP to embark on the path to NB-IoT.

At this stage it’s worth pointing two things out. The first is the normal timeline for developing a new radio standard, and the second is the requirements for the majority of the projected 50 billion IoT devices.

Developing a wireless standard is a slow business. Back in 2010 I tried to estimate the time and cost involved and came to the conclusion that it costs around a billion dollars and takes 8 – 10 years before the standard is robust and getting traction in the market. That was for personal area networks like Bluetooth, Wi-Fi and ZigBee. Cellular networks are more complex, so cost more and take longer. Despite the evidence, the GSMA announced that their new NB-IoT standard would be complete and released in six months. Six months later, they announced that it was going well and that they would release it in six months’ time. And six months after that they put out a press release saying that the specification was complete. We’ll come back to that in a minute.

The second thing we need to look at is what a standard for wireless IoT connectivity needs to do? Most IoT devices will be quite taciturn. They will measure data and events and send that data a few times each day. They’re not going to be streaming video or having lengthy conversations because they’re battery powered. If they’re going to run for several years on a small battery or some energy harvesting power supply, all they can manage is a few messages each day. Sigfox understand this and make it evident in their data plans. They’re not talking about hundreds of Megabytes like the cellular industry, but as little as 14 messages of 12 bytes each day. That’s about the same as a single SMS message. To put it another way, most IoT applications make text messaging look bloated. 

It’s not at all clear that the GSMA understand this. In a recent Mobile Broadband Forummeeting, the GSMA and other operators kept on implying that IoT devices need data rates of tens or hundreds of kilobits per second. That is definitely what network operators want to sell, but it’s not what IoT devices need. If we’re going to get to billions of device, connectivity and silicon needs to be cheap. Cheaper and simpler than GPRS was. The cellular industry has never taken on board that fact that the reason we don’t already have billions of IoT devices is that even GPRS is too expensive. Trying to make NB-IoT more complex than GPRS is not going to kickstart the IoT era. What we need is a standard which will let companies make a chip that costs around a dollar in high volume.

That’s not where the cellular chip industry has been going. In the early days of 2G, networks operated at two different frequencies, with relatively simple radio modulation. That meant that chips were moderately simple. Over time, the GPRS modules which are used in most current IoT devices have fallen in price to around $7. However, as the desire for more bandwidth has grown, 3G and 4G chips have become much more complex. Moore’s law has helped to prevent them becoming exorbitant, but each new release of the standards has to support a growing number of frequency bands (we’re up from 2 to over 70), as well as all of the different protocols in the previous standards which have gone before it. Developing these is prohibitively expensive. As a result, 3G modules cost around $20 and 4G modules $35. The growing complexity, which requires immensely complex protocol stacks to complement the chips, has benefitted a very few silicon suppliers, who have largely destroyed the competition. Qualcomm dominates, with Mediatek taking most of the rest of the market. The business model for both is to sell billions of chips to a small number of high volume manufacturers who have deep technical competence to integrate these into their products. That is very different to the model needed to support tens of thousands of IoT manufacturers who need $1 comms chips which they can just drop into their products.

You can see this contradiction in the NB-IoT standard which has recently been released. There were two industry groupings with radically different approaches. The traditional one, led by Nokia and Ericsson, proposed what is essentially a cut down, lower power variant of 4G. The key feature of this is that it is capable of working with other 4G devices in the same spectrum, so it can easily be slotted into existing networks. However, to do that it needed to retain a fair degree of radio complexity to be aware of other 4G traffic. That has two consequences. It meant the chip was much more complex because it had to be able to identify what was going on around it, hence it’s still expensive. It also made it more difficult to make it very low power.

The alternative approach, led by Huawei and Vodafone was for a “clean sheet“ approach. This was a solution which did not have the intelligence to coexist with 4G networks, but required operators to set aside a small amount of spectrum for it, (which could be a guard band), specifically reserved for IoT traffic. As the chips didn’t need to be aware of any other 4G traffic, they could be much simpler and hence much cheaper. It’s a cleaner approach, but one which goes against the traditional network approach of making complex hardware which can work on any band around the world. Network operators typically prefer the complex hardware approach, as it passes the problem of global interoperability onto the chip and protocol stack companies. Whatever the operators do with their networks, regardless of the frequency bands they own, things just work. But it raises the cost of hardware.

This “clean sheet” approach grew out of the Weightless standard. Neul – a Cambridge start-up helped developed Weightless as a new radio and protocol for use in TV Whitespace. That failed to get traction, but the company was acquired by Huawei and the technology repurposed to work in the licensed spectrum that’s used for LTE. Because it does not have the baggage of backward compatibility, there’s a fair chance that the silicon could get down to the $1 mark.

These two approaches are essentially incompatible, and it was interesting to speculate how 3GPP would resolve the difference between them. Hence I was intrigued to see the resulting specification when it was published. When you start to read it, you can see how they managed to get it out so quickly. Instead of trying to find a compromise, it includes both the Huawei / Vodafone and Ericsson / Nokia / Intel options, so it is entirely up the chip vendor and network operator to decide which they support. That means that a user or manufacturer has absolutely no idea of whether an NB-IoT product they make will work on any particular NB-IoT network.  It’s as if the acronym should really be Nobody Believes the Internet of Things. 

It’s a fudge, where the specification group has produced some pieces of paper to meet a deadline and then passed everything over to a PR department which is taking the post-truth approach to promoting the technology. It would be nice to think that the specification group had realised that this first release was just a PR exercise and were working on harmonising the two conflicting proposals, but it seems they’re ignoring that and looking at adding location features instead, presumably because LoRa is offering that, and they don’t want to be left behind again. In other words, bells and whistles are more important to them than making NB-IoT work.

Making it work appears to be left to market forces. Vodafone is trumpeting the first commercial NB-IoT network. At the same time, Sonera, in Finland is announcing the first commerical NB-IoT trial. Although that may seem confusing, there is no contradiction here. Both are telling the truth, as Vodafone is using Huawei’s NB-IoT, which is totally different for the Nokia NB-IoT which Sonera is using. Nobody knows which variant will win. The key player in this could end up being Huawei. They have a captive silicon supplier in Hisilicon, which should help them get to the $1 chip price point. If they could persuade the Chinese Government to deploy hundreds of millions of devices in the country, this could make it the de facto standard. Nokia, Ericsson and Intel are unlikely to concede without a struggle, but with a higher cost and the lack of scale that a Government backed deployment in China could provide, they may struggle to gain momentum.

Unfortunately, this type of commercial battle generally doesn’t help the market. Without global compatibility, manufacturers will be loath to adopt the technology, as they have no idea whether it will work in any target market. That reduces volumes, which keeps chip costs high. It also delays all of the important things like developing test equipment and compliance programs which are vital to develop a robust network, which further undermines confidence. To survive, NB-IoT needs to be a single low cost, globally interoperable standard. In its current form, NB-IoT is dead.

While it goes through its death throes, the LPWAN suppliers will make mischief. 

Sigfox is being aggressive in pricing, both for modules and data contracts. They recently announced that modules will be available for just $3 in 2017 and already have data plans with charges as low as $1.50 per year. They also desperately need to get the number of connections up, so will probably offer even lower costs in the near future. The company has raised over $300 million in funding and is aiming for an IPO in 2018. However, they feel that they need to get above 100 million active devices to persuade the market to support a decent valuation. So their investors will be putting pressure on them to get more connections made as soon as possible, potentially commoditising the IoT connectivity market in an attempt to buy market share from their rivals.

LoRa is a more distributed community, with multiple vendors providing parts of the ecosystem. However, LoRa has a significant difference from other LPWAN offerings, which could be important. It is the fact that anyone can buy a gateway and set up their own network. A crowdfunded initiative – the Things Network, has designed modules and gateways and persuaded the electronics distributor Farnell / Element14 to sell them in the same way they sell Raspberry Pis. For those who don’t know it, the Raspberry Pi is a highly effective embedded computing board. Originally designed to help teach coding in schools, it has been adopted by the maker community as the basis for thousands of projects and products. Farnell have recently announced that they have shipped their ten millionth Raspberry Pi.

The Things Network / Farnell initiative is relevant, as they will be selling LoRa gateways for €250. In other words, for €250, anyone can become an Internet of Things network operator covering a radius of around 5km. The Things Network - a development community attempting to build a global LoRa network, is providing compatibility layers behind that which will stitch many of these gateways together. Costs will probably be slightly higher than Sigfox, but this will appeal to an open source community, with the innovation benefits that brings to an emerging technology.

There are issues about scaling. Tech hotspots like Cambridge, Amsterdam and Berlin could each have over a thousand LoRa gateways by Christmas 2017, which could make or break the technology. It will be an interesting experiment. It may also give Ingenu an opportunity, as they’ve been in the game longer and appear to have a more robust technology in terms of scalability. But they’ve not achieved the same traction in the minds of IoT developers yet.

This brings us to the important part, which is what this means for network operators? Other than Vodafone, who have firmly nailed their colours onto the NB-IoT mast, most operators are hedging their bets by flirting with at least one proprietary LPWAN option. However, in order to get critical mass, contract prices are racing to the bottom. SK telecom is down to $0.30 per month and Sigfox’s pricing will probably push that down to below $2 a year in the near future. That’s a long way away from the $50- $200 that operators get from their current M2M contracts.

At $2 a year, 20 billion devices will contribute around 4% of current global mobile subscription revenues. That is probably less than network operators currently make from their GPRS subscriptions, yet it will replace much of that revenue. In other words, by supporting 20 billion IoT devices, the network operators will probably be making less money.  Let me emphasise that point. The IoT opportunity of tens of billions of connected devices could reduce mobile operator revenue, not increase it.

Many mobile operators seem to think that they will make money from other parts of the IoT value chain, like cloud services or data analytics, but there is little indication that they’re well positioned for that. Amazon, Google and a host of others are already there. In the next few years, the volume in deployments will probably be using the LPWAN standards of Sigfox and LoRa. The developers who choose them will naturally turn to Amazon and Google, giving them the opportunity to further refine their IoT offerings. I’ll cover this in more detail in a future article.

Despite the present debacle over NB-IoT, the developers at 3GPP are bright – they will eventually get a specification out which meets the industry’s requirements, whether that’s driven by market forces winning out or technical decisions. However, my guess is that it may not be before 2023, as that’s how long wireless standards take. Which gives the different LPWAN standards plenty of time to play, and time for the cloud and analytics providers to shake out, settle down and start some serious customer acquisition.

The great thing about 3GPP standards is that they’re dead easy to roll out. In most cases they’re simply a software upgrade for the base stations. So it won’t take long to go from a final standard to global availability. At which point most IoT manufacturers will probably migrate to it, signalling the end of the short-lived LPWAN era. Of course, most of the LPWAN players and their investors are looking for shorter term returns, so they may already have disappeared. Even five years is a long time in a venture funded world.

What will be missing in the future NB-IoT world will be the hoped-for revenue. The years of LPWAN competition will have driven any profits out of NB-IoT, leaving the operators as pipes. It will also have established other players higher up in the value chain who can cream off what profit there is to be made. A future variant of NB-IoT will come to life and dominate as the connectivity standard for IoT, not least because as volumes grow, the licensed spectrum that operators own will offer a Quality of Service that is missing from the LPWAN offerings. It will also provide the certainty that manufacturers are desperate for, which is that the network will be a stable solution which is available for fifteen to twenty years. NB-IoT will wipe out any remaining alternatives, but it will not be the IoT pot of gold that many in the industry believe.

There is a final sting in the tail of this story, which is that for years we have been striving to develop low power, wide area connectivity which will enable a sensor battery life of ten years or more. The irony is that we now have a set of different LPWAN options which look as if they do support a ten year battery life, but it’s unlikely that any of them will still be operating in ten years’ time. In other words, battery life now exceeds network life. 

One wonders how we got to this point? There is little good news for an equipment manufacturer, who is faced with the prospect that whatever connectivity solution they choose today, it will probably disappear within the next ten years. In other words, their product obsolescence is in the hands of their choice of network operator. But that’s the problem when you forget your King is dying and everyone spends their time running around backing pretenders to the throne. Be careful what you wish for. NB-IoT is dead. Long live NB-IoT.

Read more NB-IoT and LPWAN articles at my Creative Connectivity blog.

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The Emerging IoT Nightmare: Smart Dust

By Mike Krygeris, Sr. Field Engineer at Plixer International

Internet connected thermostats, refrigerators, pet feeders, cameras, DVRs, etc. are all part of the Internet of Things (IoT).  Numerous articles have been written detailing how these devices are being hacked and used for nefarious purposes like hosting illegal web sites to sell contraband, exfiltrating data from other devices and even participating in DDoS attacks.  This information is all true and concerning, however there is something on the horizon that is potentially far more menacing called Smart Dust.

Gartner forecasting that the “connected things” market will grow from 6.4 billion devices in 2016 to 20.8 billion by 2020, this will be the driver pushing DDoS to a double digit growth in 2017.

Smart Dust

Smart Dust is the term used to describe very small chips containing a system of tiny microelectromechanical systems (MEMS) such as sensors, robots, or other devices that can, for example, transmit temperature, vibration, GPS coordinates and more.  Imagine attaching a small sticker of Smart Dust to every package shipped by UPS, FEDEX and US mail. These devices allow the consumer or the shipping company to track everywhere the package goes, measure the temperature, see if it is opened or dropped on the floor. Just add the Smart Dust chip to the shipping label, scan the hardware ID (I.e. IPv6 address) with a mobile application and track it on-line.  

Bridges and buildings could contain sensors to help more accurately monitor wear and tear or even double in functionality to provide weather details to an entire industry of meteorologists. If a company has a problem with staplers disappearing from employee desks, just attach a piece of Smart Dust and start tracking them… “I believe you have my stapler.” Take a look at this article and it might change your idea of what IOT will be in 5-10 years.

Smart Dust Internet Connectivity

IOT vendors will have very specific machine to machine (M2M) communication scenarios.  Unlike our mobile phones, customers won’t be providing the internet access for a lot of these devices. It will just be there. This type of communication is already in place in a few cities. The first being Amsterdam.

SIGFOX is one type of low bandwidth IoT communication technology.  Other low bandwidth IoT technologies include LORA and 6LoWPAN, and they   T all operate at layer2 to communicate directly with the internet. Although each MEMS can only communicate at speeds comparable to a modem, and as an aggregate, there is strength in numbers.

Powering Smart Dust

Today, Low Power Wide Area Network (LORA) radios can be powered for a few years with  just a CR2032 battery but, what about when science develops a way to “harvest” ambient energy to power electronics?  At that point, Smart Dust (MEMS) will never power down leaving the potential for a massive number of micro-computing devices remaining on-line indefinitely.  

Internet of Zombies

To date, public discourse on Smart Dust has not included details around the identity, ownership and security of these devices. These are important topics that will need to be considered.

How do you deal with this type of IoT device if it were to become compromised by a hacker? Would UPS or FEDEX be responsible for millions of infected MEMS participating in DDoS attacks while they sit in landfills all over the world?  Without a definitive end-of-life after their use, these objects could stay connected to the Internet forever!  Without ownership and responsibility, some Smart Dust won’t be decommissioned properly and could end up as the Internet of Zombies, essentially becoming the trash on the side of the information superhighway. 

Embedding security and defining end-of-life processes would add cost into the creation of MEMS, which is the reason it will likely not happen on its own.   For current examples, you need only look to the IoT devices currently being compromised by the Mirai Botnet. There is simply little incentive for manufacturers to create strong security and identity management on IoT devices because it slows time-to-market and increases production cost.

The Future of Smart Dust

Today’s IoT still plays by the rules of perimeter security, ownership and a infrastructure management. The IoT of tomorrow will be much more like the meatspace  of today and we need to plan for it accordingly. Smart Dust technology already exists and is likely being implemented without careful consideration to security.  

A parallel internet meant just for IoT and Space Dust, and bound by a different set of rules, may be the safest way forward. This internet’s control plane might leverage a software defined network (SDN) approach with an open and decentralized traffic-forwarding paradigm similar to BGP. LISP for example, comes to mind as it can provide a standards based location while offering an independent network fully gated from the regular internet.  MEMS manufacturers could consider defining a shelf life, similar to that of a gallon of milk.  After a given time frame, the MEMS will simply stop working.

Monitoring systems will need to be put in place, such as those that consume NetFlow and IPFIX, to help service providers keep an eye on the traffic generated by these devices.  These monitoring systems will measure the volume and traffic types generated by MEMS and will provide forensic data for the investigation of malicious and unwanted activity.    

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