Internet of Things is surrounded with a lot of buzz, which is there for a reason. It is one of the most revolutionary technologies and it is the closest we’ve come to predicting our future. Of course, the IoT is not based on spells and witchcraft (it’s way scarier than that), but on machine-to-machine communication, cloud computing and networks of small sensors, which collect and analyze data. In this article we’ll share some of things and processes that will change in the IoT Era.

Home security systems

Today you can monitor home security cameras from your smartphone screen. More advanced home security systems go even further. They come with different types of sensors that control air quality, motion, sound, vibration and temperature. These systems use machine learning to determine the normal activity in your home and they send alerts to your smartphone, when something out of the ordinary occurs. Because of their smart machine learning approach, home security systems that are based on IoT concept drastically reduce the incidence of false alarms.

Bed

Even our beds will become smart. At the moment you can buy several types of sleep trackers from the ones that come in the form of bracelet and measure your heart rate and blood pressure to smart mattresses that can connect to home automation systems, prepare your bed temperature, track your heart and breathing rate and wake you up in the morning. These special mattresses also collect information about your sleep and give you recommendations for improving your bed rest.

Energy use

Recently several companies released Wi-Fi enabled sensors that can connect to the home electrical panel and control and track your energy use. These small sensors recognize all appliances and gadgets by their “power signatures” and can monitor the energy use and brake it down to every single device. They will allow you to have a deep look into your monthly energy use, to recognize and deal with critical points and to save money on utility bills. Same as many other home security and home automation systems, these sensors learn to interpret the activity of your home devices and send warnings when incidents happen.

All home appliances and systems

All-in-one smart home automation systems can control several home appliances at once. People can use them to turn their porch lights on and off when they are on vacation and to preheat their home or their oven before they arrive home from work. These systems also control various conditions in your home and use smart sensors and machine learning to create the perfect comfort. Some home automation systems also come with a Bluetooth speaker and a microphone and they can work as voice assistants.

Self-storage monitoring

Self-storage monitoring protects stored goods from climate changes, theft and other unforeseen incidents. New storage monitoring systems based on the IoT concept control storage lighting, air-conditioning and security. They also use sensors to track variables that are critical for perishable goods like temperature and humidity. You can find these smart storages in many different cities around the world. 

Construction sites

Construction site managers can use IoT solutions to monitor the work of heavy machinery and the movement of construction employees. This basically means that they don’t need to leave their trailer office. Sensors track the movement of supply and dumping trucks through geo-location technology and insure that everything works as scheduled. If there’re any irregularities in the work of heavy machinery, supply trucks or employees, the site manager will be instantly notified by smartphone push-notification.  

Emergency vehicles

In many cities the only connection between emergency vehicles and their headquarters is established through old-fashion radios. This offers a limited control in emergency situations. Advanced telematics already appeared in many emergency vehicles around the world. This technology allows lone drivers to receive updates in real time from the environment they are entering, including: over speeding, harsh events or the incidents of other team members. Employees at the headquarters also receive the information about emergency vehicle’s hours of service, speed, siren state and location. This way, they can easily schedule vehicle’s regular maintenance and minimize its downtime.

Internet of Things is the biggest tech trend that is happening at the moment. It will completely rock our world and bring a lot of positive disruption to every segment of our lives. Soon, we’ll be able to control all of our possessions through one smart app, which will leave us more time to focus on ourselves and our friends and family.

Guest post by Kenie Ho and Charles Huang

You might be riding to work in a driverless car without ever having to look up from your text messages. Or you might rely on weather forecasts derived from micro-weather patterns using the barometric sensor of every iPad inside a local area. These kinds of IoT miracles will use dozens or even thousands of IoT devices. That creates challenges for a company trying to protect its IoT innovations, or for a company trying to avoid infringing someone else’s protected technology.

Businesses typically protect their R&D through patents. A patent allows an inventor to exclude others from making, using, selling, or importing a patented invention.

Companies, like IBM, Intel, and Qualcomm, recognize patents as potent business tools because they can use them to keep competitors out of a market or obtain lucrative licensing royalties by allowing the competitors to practice the inventions. In 2015 alone, these companies each applied for and obtained several thousand new U.S. patents, many on IoT-related inventions. Experts estimate that more than 20,000 patents and patent applications covering IoT technologies exist world-wide.

But are IoT patents truly valuable?

I Want to Sue You, But I Can’t

At its core, IoT is basically a massively distributed network. IoT devices across the world work together to implement creative IoT solutions. Because of that, it can be challenging to obtain patents that are useful against competitors in the IoT space.

To prove infringement in the United States, a patent owner must show a single entity infringes the invention claimed in the patent. In an IoT ecosystem where many devices and actors must interact to implement a use case, it can be difficult to meet that requirement. Often times, no single entity implements or uses the entire claimed invention.

For example, in the autonomous-driving scenario, smart cars might have IoT-enabled sensors reporting on the vehicle’s position, nearby obstacles, speed, and vehicle status. IoT sensors embedded on a smart highway, in a smart city’s traffic-control system, and around a smart parking lot might provide additional information for routing and safety. These devices might communicate with each other to pick up commuters, drive them to work, drop them off, and then park their cars—all without them lifting a finger.

The problem is that different entities own or manufacture each component in this scenario. And if a patent owner has a patent covering this situation, who can it sue for infringement? The smart-car manufacturer? The county maintaining the smart highway? The city with its IoT-enabled traffic system? The owners of the smart parking lot? Depending on how the patent was prepared, the patent owner might be able to sue some, none, or all of them.

Divided Infringement

If you have never read a patent, consider yourself lucky. It is an arcane combination of technical writing and legalese that will put all but the most stalwart patent attorney to sleep. And the most arcane section of the patent—called the “claims”—happens to be the most important because it describes what the inventor is actually claiming as the invention.

Under U.S. law, an entity infringes a patent only if it practices or uses the invention described in the claims. Without getting into all of the legal details and numerous exceptions, if an entity practices or uses only a portion of the invention described in the claims, it is typically not liable for infringement.

In the autonomous-driving case, if a patent claims the combination of using a smart car, a smart highway, a smart traffic-control system, and a smart parking lot, then an entity that practices or uses all of them in combination is liable for infringement. But if there are multiple entities acting in concert, and each practices or uses only a part of the claimed combination, then a “divided infringement” situation exists and the patent owner might not be able to sue any of them for infringement.

For these and other reasons, patent attorneys consider it a best practice to procure patents with claims targeting the actions of individual entities. A well-designed portfolio of patents might include (1) a patent directed to the smart car made by the manufacturer, (2) a patent on the smart highway maintained by the county, (3) a patent on the smart traffic-control system owned by the city, and (4) a patent on the smart parking lot run by the parking company. The patent owner would then have a portfolio of patents to choose from when deciding whom to sue (e.g., the car manufacturer, county government, city, or parking company, respectively)—preferably the entity with the deepest pockets.

But what happens if the novelty in the invention comes from the combination of all the “smart” elements, and the patent office will issue only a patent claiming the combination? Enforcing this kind of patent in a divided-infringement situation is much harder, but still possible.

In 2015, the U.S. Court of Appeals for the Federal Circuit—the highest court overseeing U.S. patent cases besides the U.S. Supreme Court—explained that an entity can still be held liable for patent infringement if it controls or directs multiple entities to jointly use a patented invention. That is, an entity would be liable for divided infringement if the acts of the other entities can be attributed to the first entity.

For example, if a smart-car manufacturer has a contractual relationship obligating other entities to embed and use IoT sensors on the highway, in the traffic-control system, and around the parking lot to implement the autonomous-driving use case, then the smart-car manufacturer could be found liable for infringing a patent claiming the combination. But unless the patent owner can show this type of control or joint enterprise, it will likely not be able to prove infringement for that combination patent.

Territorial Scope

Besides divided infringement, another obstacle facing IoT patents is territorial scope. A U.S. patent grants rights in the United States. Thus, a U.S. patent presumptively does not confer any protection to infringing acts outside of the United States. This poses a problem for IoT patents because many IoT use cases employ devices located outside of the United States.

For instance, in the autonomous-driving scenario, sensor data from a smart car might be routed to a server located in Canada—because it might be cheaper there—for routing and map updates before being sent back to the car. A U.S. patent claiming a “process” for autonomous driving that includes routing and updating maps would generally not be enforceable here because those routing and updating steps take place outside of the United States. But due to patent policy set by the government and U.S. courts, a U.S. patent claiming an autonomous driving “system” might be enforceable if the Canadian server was being controlled in the United States. The differences between the policy reasons for the two are beyond the scope of this article. The point is that territorial scope of a patent matters, particularly for IoT applications.

Good Patents, Big Consequences

A good patent that avoids the above problems and covers a competitor’s IoT products provides a big competitive advantage, especially if the competitor cannot design around the patent. Further, if the competitor had full knowledge of its infringing activities and had no reason to doubt the patent’s validity, but nonetheless continued with its infringing activity, it may be liable for willfully infringing the patent, an act that can triple the amount of actual damages.

The U.S. Supreme Court recently changed the law to make willful infringement easier to prove. Before the change, a patent owner needed to show, by clear and convincing evidence, that the accused infringer was reckless in infringing the patent and knew or should have known its infringing actions were reckless. Now, the patent owner needs to show by a preponderance of evidence—a lower standard—only that the infringement was “egregious” and not just simply a “garden-variety” infringement case.

In the past, if a company became concerned about a patent, it would seek a patent attorney’s opinion on the matter to avoid liability based on willful infringement. That practice went out of favor in the mid-2000s after the courts raised the standard for proving willful infringement. Now, with the lowering of the standard, that practice has enjoyed a revival if only to show that the company took due care in investigating the matter to reduce the likelihood of willful infringement and treble damages.

Strategic Patenting

Despite the divided-infringement and territorial-scope issues, thousands of patents on IoT-related technologies are being issued each year. The key is to make sure to get patents that are well thought out to avoid divided-infringement and territorial-scope issues in the first place.

On average, it takes over 2 years to obtain a patent and most patents have a term of 20 years. It might be 10 or 15 years before the patent owner asserts the patent. How the market uses the invention can change significantly during that time. Thus, a patent applicant must carefully predict and anticipate likely infringement scenarios when protecting its IoT technology.

Authors’ Bio

Kenie Ho has litigated over 50 patents in U.S. courts on electrical and consumer-electronics technology. He is a thought leader on intellectual-property issues for IoT and leads the IoT Legal Group at Finnegan, Henderson, Farabow, Garrett & Dunner, LLP.

Charles Huang prepares patent applications for IoT patents. His practice includes litigation, client-counseling, patent portfolio management, and patent analysis.

Patent Photo Credit to Nick Normal via Flickr.

Well October was definitely a scary month for IoT. In this edition our newsletter revisits the security issues that hacked their way into IoT last month. If you haven't been paying attention, or are looking for different points of view, you'll want to read the pieces below from our members and contributors. Lets hope for a more secure and sane month of November.

Also, a reminder, this Thursday, November 3, 2016, join me, John Myers of Enterprise Management Associates and Dan Graham of Teradata where we look at what people REALLY do with the Internet of Things and Big Data? Registration information is here.

If you're interested in being featured, we always welcome your contributions on all things IoT Infrastructure, IoT Application Development, IoT Data and IoT Security, and more. All members can post on IoT Central. Consider contributing today. Our guidelines are here.

The Internet of Evil Things

Guest post by Joe Barkai 

You may have heard me at a conference or read my response to questions concerning the security of the Internet of Things. When asked, I sometimes “refuse” to answer this question. This is not because I do not think that data security—and the closely-related data privacy—are not important; of course they are.  But I want to highlight the point that data security and privacy are foundational issues that are not unique to IoT devices. Every enterprise must ensure that all data—IoT generated or not—is secured and that data privacy and ownership are handled properly.

Do not stop asking for security in IoT

Posted by Francisco Maroto

Almost three years ago, I wrote in my IoT blog  the posts “Are you prepared to answer M2M/IoT security questions of your customers ?. and “There is no consensus how best to implement security in IoT” given the importance that Security has to fulfil the promise of the Internet of Things (IoT). And during this time I have been sharing my opinion about the key role of IoT Security with other international experts in articles “What is the danger of taking M2M communications to the Internet of Things?, and events (Cycon , IoT Global Innovation Forum 2016).

Hacking a Home Can Be Easier Using IoT - Is Your Smartphone Safe?

Posted by Mike Davidson  

Internet of Things has raised concerns over safety. Nowadays, it is possible to control your home using your Smartphone. In the coming years, mobile devices will work as a remote control to operate all the things in your house.   Some devices display one or several vulnerabilities that can be exploited by the hackers to infiltrate them and the whole network of the connected home.

How insecurity is damaging the IoT industry

Guest post by Ben Dickson

The Internet of Things (IoT) is often hyped as the next industrial revolution—and it’s not an overstatement. Its use cases are still being discovered and it has the potential to change life and business as we know it today. But as much as IoT is disruptive, it can also be destructive, and never has this reality been felt as we’re feeling it today. On Friday, a huge DDoS attack against Dyn DNS servers led to the majority of internet users in the U.S. east coast being shut off from major websites such as Twitter, Amazon, Spotify, Netflix and PayPal.

IOT Security Trends// Is the Online World More Dangerous ??

Posted by Bill McCabe 

Security threats are the biggest concern among the main concerns on the Internet of Things. Due to its very nature, it is a target of interest for those who want to commit either industrial or national espionage. By hacking into these systems and putting them under a denial of service, or other attacks, an entire network of systems can be taken out. This has caused cyber criminals to become very interested in the IoT and the possibilities that surround its misuse.

Report: List of Top 10 Internet of Radios Vulnerabilities

Posted by David Oro

The IoT has a big security problem. We've discussed it here, here and here. Adding to these woes is a new report on the Top 10 Internet of Radios Vulnerabilities. Yes, radios...because IoT so much more than data, networking, software, analytics devices, platforms, etc. When you're not hardwired, radio is the only thing keeping you connected.

5 Steps to Creating a Secure Smart Home

Posted by Ryan Ayers 

First came smartphones, equipped with the ability to set alarms and calendar notifications, reminders, and other convenient apps and services to make our lives easier. Taking that a step further are “smart homes” or automated homes, which allow users to remotely control devices in the home such as lights, televisions, and even toilets and water pumps, using a smartphone or computer. Aside from remote control, however, smart systems in homes can also help make the home more adaptable. For example, Nest is a smart system that learns the home’s inhabitants’ schedules and preferences to heat or cool the house for maximum efficiency and comfort. Sounds great, right? Many people think so, which is why the industry is projected to keep growing quickly from 48 billion in 2012 to an estimated $115 billion by 2019. 

How the IoT industry will self-regulate its security

Guest post by Ben Dickson

Following last week’s DDoS attack against Dyn, which was carried out through a huge IoT botnet, there’s a general sense of worry about IoT security—or rather insecurity—destabilizing the internet or bringing it to a total collapse.

All sorts of apocalyptic and dystopian scenarios are being spinned out by different writers (including myself) about how IoT security is running out of hand and turning into an uncontrollable problem. There are fears that DDoS attacks will continue to rise in number and magnitude; large portions of internet-connected devices will fall within the control of APT and hacker groups, and they will censor what suits them and bring down sites that are against their interests. The internet will lose its fundamental value. We will recede to the dark ages of pre-internet.

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Industry 4.0 or, as it is also known the fourth industrial revolution is the trend that is currently coming into play of automating the manufacturing processes and the use of IoT and other technologies to make industrial processes more readily accomplished. It is working hand in hand with things like the internet of things, cloud computing and cyber-physical computing. 

Using Industry 4.0, we create what are called smart processes and smart computing.

According to Wikipedia, "Within the modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real time, and via the Internet of Services, both internal and cross-organizational services are offered and used by participants of the value chain."

The term Industry 4.0 or fourth industrial revolution began in the German government with a project that they had created that was markedly high tech. It promoted computerized manufacturing and provided the reasons for that manufacturing to take place as well as how industry 4.0 would play out with other areas of manufacturing such as logistics and supply.

Industry 4.0 provides for changes in the way in which we work. It makes our work smarter and faster and in most cases will save a great deal of money for the factories and businesses which embrace it. For those that do not embrace the fourth industrial revolution, they will be hard pressed to keep up to those who have introduced smarter factories. Better manufacturing, better use of space and better safety results are just a few of the things that Industry 4.0 provides.

For those who embrace Industry 4.0 the results can be faster, better, more profitable results from their business. What's not to love about that.

This is the second in a series. To see # 1 in the series please use this link https://www.linkedin.com/today/author/0_1gNYYer-mY9IO8KGV50j_c?trk=prof-sm

Or Check out our website at www.internetofthingsrecruiting.com

Guest blog post by Ajit Jaokar

Background

Dresner advisory services has published  a report on IoT business models. This report covers IoT, Big Data and Analytics. I have been focussing on this subject in my teaching at Oxford University and the Data Science for IoT course . So, it’s nice to see the insights. Forbes has written a good analysis of this report’s findings.  Based on this analysis, I find that the report has some areas I agree but also some surprising omissions. I suspect that the report was based on from survey results – and hence innovation is missed.  For example, the inclusion of Map reduce for IoT is surprising (and I suspect arises from familiarity of survey respondents). For the same reasons, ‘Relational Database support’ is seen to be very important, whereas ‘Real time’ is much less so according to the survey. This is similar to asking a group of Telecom Operators in 2005: ‘Will Skype succeed?’ All would say no .. but the reality does not reflect survey findings. Having said that, there are many other findings and trends that I agree with.  

Focus on the Enterprise is correct – but is only part of the story

Emphasis on the Enterprise

The emphasis on the Enterprise is accurate. For IoT, consumer gets lot of traction – but the value is in the Enterprise.

However, the word ‘Enterprise’ also encompasses many areas – and each of these verticals have their unique intricacies. If IoT analytics (data) is the main value-add for IoT, then the question is:  How will IoT data will be leveraged in an Enterprise considering IoT itself comprises of multiple silos? In a recent article, I advocated an Enterprise AI layer which will incorporate IoT datasets.

Such a layer is likely to be the best way to integrate the currently small and diverse IoT data into the Enterprise and also cope with the very large data volumes in the near future. So, the emphasis on the Enterprise is only part of the story

Data As a Service

The report says: “Sales and strategic planning see IoT as the most valuable today. Strategic planning’s prioritization of IoT is also driven by a long-term focus on how to capitalize on the technology’s inherent strengths in providing greater contextual intelligence, insight, and potential data-as-a-service business models.”

As a service data model for IoT is valid. As per a blog from Gartner - IoT creating Data as a Service DaaS opportunities  : “The moral of the story is that organizations should seize the opportunity to grab all the gold (data) and create the rules (algorithms and analytics) they can, both to benefit businesses and scenarios they are already focused upon but also to potentially create new data brokerage businesses. These new offerings may be adjuncts to their core competencies, enabling them to reap the benefits that the IoT revolution is bringing. There are packaged providers, there are evolving marketplaces, there are crowdsourced collections, and there are many basic building blocks an erstwhile organization can implement to capture, manage, slide, dice and provide data brokerage offerings.”

This advice is valid – but will companies spend money speculatively on getting hold of all the IoT data they can if it does not have immediate business payoff? I doubt it. This again leads to the idea of the Enterprise layer for IoT which has a much more tangible payoff  

Data warehousing

Finally, the report emphasises Data Warehousing – but does not say how and why existing Data warehouses will work with IoT. The report says “Data warehouse optimization is considered critical or very important to 50% of respondents, making this use case the most dominant in the study“ Again, in the Enterprise AI article, I advocated that the Enterprise AI layer could be seen as an intelligent Data Warehouse

To conclude – Enterprise relevant for IoT – but only with AI  

‘Enterprise IoT’ encompasses many areas – each of these verticals have their unique intricacies. If IoT analytics (data) is the main value-add for IoT, then the question is:  How will IoT data will be leveraged in an Enterprise considering IoT itself comprises of multiple silos? IoT is also a complex domain and there are  many differences between traditional Data Science and Data Science for IoT.To actually implement Data Science for IoT at an Enterprise level, you would need to consider Enterprise AI layer which will incorporate IoT datasets.

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Originally posted on Data Science Central

What exactly is “IoT”? Internet of Things, yes; but what does that mean?

Internet of Things is a structural definition; it says there are “Things” such as sensors and devices (on machines or people) connected together in a Network. So what? What does a Network of Sensors & Devices allow us to DO? What is the functional description of IoT?

Being able to connect things together is “table stakes” at the intelligence augmentation game. What you are able to do with this network is the real story.

IoT is an enabler of THREE high-level objectives:

(1)    DO MORE: Whether machines producing more (high throughput) because of less breakdowns or a weekend athlete burning more calories because she is able to keep her heart rate in the fat-burning zone, we are accomplishing more.

(2)    HIGHER QUALITY: By monitoring environmental pollution, cities restrict automobile access into city center for better health outcomes over time.

(3)    BETTER USER EXPERIENCE: In the near future, mass customization will allow me to find eyeglasses with perfect fit at low cost based on video inputs at connected additive manufacturing facilities.

All three objectives can be met because Network of Sensors & Devices exist and will grow. But Networking of Sensors & Devices does NOT capture what has to happen so that we can DO MORE at HIGHER QUALITY & BETTER UX! The information that rattles around the network has to be consumed properly, insights generated and decisions made.

A functional description of IoT is a network that “processes” information in the network towards our three objectives. One can see that “processing” is enabled by a multitude of technologies: IP networking, wireless, chipsets, protocols, security, cloud computing, database technologies, analysis software, visualization and so on. Subsuming this under “IT or Information Technology” and keeping it aside for the moment, the “higher-level” processing involved is Applied Data Science!

Applied Data Science is a tautology. Data Science IS the applied aspects of many pure sciences (see “What exactly is Data Science?” for details). Beyond the network of sensors & devices and base IT technologies partially listed in the last paragraph, what is unique and new in IOT is Data Science applications –Data Science applied with the focus on information extraction, insights generation and prescriptive decisions. There is no identifying name for this *applied* aspect of Data Science but I have been referring to it as “Engineering” Data Science. We use “engineering” in the sense of the applied aspect of any science (Engineering is the applied aspect of Physics, for example).

IoT = (Network of Sensors & Devices) + IT + (Engineering Data Science)

Each component is critically important to IoT; major advances in all three in recent years have made IoT and its promise real.  Engineering Data Science (EDS) is the youngest and the least mature of the three. Immediate next steps in EDS evolution seem clear to me  (more in Next Stage in IoT revolution – “Continuous Learning”).

When IoT is defined as “(Network of Sensors & Devices) + IT + (Engineering Data Science)”, it seems to pervade ALL industries from my vantage point! What do I mean by that?

Let us look at the largest 5 (excluding IT) sectors of S&P 500: Consumer Discretionary, Energy, Financials, Health Care & Industrials.

I have mentioned only a few instances in the right hand column but you can add many more. They all require some data generating mechanism, IT connectivity and decision making software. What this shows me is that IoT has already pervaded and will totally engulf all the businesses! As such, I tend to look at IoT as a technology framework that underpins ALL businesses and industries of the 21^st century.

Now, does the exact same IoT serve all these sectors or are there nuanced variations? As I mentioned, EDS is the youngest and fastest evolving portion of IoT today – let us focus where the changes are most rapid.

I have partitioned applied Data Science into three: Industry, Business & Social Data Science. 

As you can see, each application area calls for refinements and adaptations to its verticals. Specialization for each vertical notwithstanding, the three “types” of Data Science are best seen as a unified whole, which we are calling “Engineering Data Science or EDS”. Rapid progress in Engineering Data Science is required to achieve our three goals of “DO MORE at HIGHER QUALITY & BETTER UX” with IoT.

IoT is not JUST what GE, Siemens, ABB or Hitachi do! It is a technology framework for all business and industrial technologies going forward. IoT is just a metaphor for this powerful, all-encompassing technology framework.

SUMMARY:

• IoT = DO MORE at HIGHER QUALITY & BETTER UX.
• IoT = (Network of Sensors & Devices) + IT + (Engineering Data Science).
• IoT = Technology framework that underpins ALL businesses and industries of the 21^st century.

PG Madhavan, Ph.D. - “Data Science Player+Coach with deep & balanced track record in Machine Learning algorithms, products & business”

http://www.linkedin.com/in/pgmad

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In 2016, many companies are using Industry 4.0 as a buzzword. This doesn’t mean that the old industry has been revolutionized into a new version. On the contrary, this is an extension of what has currently existed, with the dawn of the modern variation arriving about 2010 in Germany.

While the first reference to Industry 4.0 would not occur until 2011, the German Federal Ministry of Education and research began to explore the various trends that were taking place. They wanted to identify things in high level technology that could help to improve the world and boost technology. This would allow those seeking future employment in the industrial sector to have a simplified work experience while allowing us to do more in a fraction of the time.

By 2012, the Germans had collected a great deal of research and they used this information to hold the first presentation. As part of this presentation, they took the smart factory setting and began to showcase some of the potential that was there. This allowed potential customers and industry professionals to gain a deeper understanding of what all was possible. Now machines could almost think and react to real life situations in order to boost effectiveness and help to make the industry more incredible than ever before. The German government was thrilled with the results and they began to boost funding to the research in the hopes it would advance their country and help them to become a frontrunner during the Industrial Revolution.

Once the research was determined and there was an understanding that the internet was far more powerful than originally believed, the incorporation of information relay over the internet helped to further propel the internet of things, which was already gaining significant prominence in other countries at this time. Funding was not at a new high through Germany’s manufacturing industry and the invention of the process was solidifying. It was at this time that the Platform of Industry 4.0 was introduced. But it was still a ways from where we find Industry 4.0 today.

In 2014, companies outside of Germany began to step in. There was more virtulization and input from neighboring countries, so that effective work solutions could be created. Decentralization became a key component for the process, and ensuring that digital manufacturing would ultimately benefit from the new processing the most. This is the point where the internet of things became perfectly aligned with the industrial revolution and a sweet harmonious union was formed.

Further evolution occurred as new things began to appear thanks to the research and development that has taken place during the fourth industrial revolution. This includes advanced medical technology, effective cost saving mechanics for production plants and so much more. This is an exciting time in our world to be alive and witness the incredible changes that are taking place.

This is the 1st in a Series - be on the lookout for additional articles on this topic.

For more information about us check out www.internetofthingsrecruiting.com

In recent years, two of the biggest topics of discussion when it comes to technology have been Big Data and the IOT (Internet of Things).On the off chance that all the hype has passed you by, the IOT is a rapidly expanding network of sensors that are internet connected and attached to a vast range of “things.” The internet connection can be either wireless or wired and the potential measurements that could be taken by the sensors are nearly endless. The “things” involved can be virtually any object, whether living or inanimate, to with a sensor can be attached or embedded. Anyone with a smartphone can, in effect, become a living IOT sensor and enables many routine, daily activities to be tracked and analyzed.

The Internet of Things and Big Data clearly have a very intimate connection because these billions of “things” that are internet connected will generate data in unimaginable amounts. The generation of this data alone however, won’t bring about industrial revolution, alter day to day living, or create earth saving technology. Big Data is characterized by what are known as the “four V’s”. They are: volume, variety, velocity, veracity. Putting it simply, the structured and unstructured data (variety) arrives in vast amounts (volume) at high speeds (velocity) and is of uncertain value (veracity). Data processing systems such as Apache’s Hadoop are helpful, but in some cases the human touch is needed. That is where data scientists enter the picture.

It has been speculated that the introduction of artificial intelligence would mean the end of the relatively new profession of data scientist, but that is far from being the case. Machines are being programmed to learn, but the applications of artificial intelligence are limited for the present. One example of the need for data scientists in the IOT, Big Data equation are autonomous vehicles. These vehicles are loaded with sensors that continuously transmit data that allows the vehicle to respond to its surroundings. Analyzing that data and programming the vehicle’s response to certain conditions requires a human with real driving experience.

With the sheer number of “things” that could potentially join the IOT, there will be much less actionable data involved than you might imagine. With data analysts, data scientists, and processing systems such as Hadoop however, the internet of things and Big Data have the potential to change society as we know it.

For More information check out our new website at www.internetofthingsrecruiting.com - or to schedule at call using our schedule link.. https://app.acuityscheduling.com/schedule.php? owner=11427493&appointmentType=468451

Ever wonder what people REALLY do with the Internet of Things and Big Data? Join us on November 3, 2016 to find out. I'm hosting a webinar with John Myers of Enterprise Management Associates and Dan Graham of Teradata where we look at real world implementations. Registration information is here.

This week's newsletter has new contributor B Jansen looking at IoT Programming languages. I also cover his very useful Interactive Map of IoT Organizations (people in business development this is for you!). Mark Niemann-Ross, also a new contributor, looks at why we're going to need sophisticated device management, Ajit Jaokar guest blogs about the AI layer for the enterprise and the role of IoT, Bill McCabe on the moves of IBM, and Sandeep Raut pens a piece on data science for predictive maintenance. I also include an industry call to action: government intervention is needed for the IoT.

If you're interested in being featured, we always welcome your contributions on all things IoT Infrastructure, IoT Application Development, IoT Data and IoT Security, and more. All members can post on IoT Central. Consider contributing today. Our guidelines are here.

IoT Programming Languages

Posted by B Jansen

I began collecting information on various home automation hubs, industrial IoT Platforms, hardware solutions, software technologies, and variety of different “things”. All of the data I collated into what I am calling my “Thing of Things” (ToT) database. 

I currently have 8,821 data points across 541 organizations, 532 product lines, and 63 countries. A large number of the organizations have formed over the past 6 years. If you are interested in getting into IoT, this could help guide you on which language(s) to learn.

The Internet of Us

Posted by Mark Niemann-Ross

We are going to have devices using low-power, short-range networks to communicate with other devices. This type of communications will require adaptive and flexible methods. This is going to require sophisticated device management.

We Need to Save the Internet from the Internet of Things

Posted by David Oro 

Over on MotherBoard, noted cryptographer, computer security and privacy specialist, and writer, Bruce Schneier pens his thoughts on the recent gaping holes in security for Internet connected devices. When Bruce speaks, people listen. First, if you haven't been following the recent DDoS attacks using IoT devices, read this. In short, IoT devices have been comprised to attack networks. It's so bad that Bruce is calling out the IoT market for failing to secure their devices and machines that connect to the Internet and is asking for government intervention.

The AI layer for the Enterprise and the role of IoT

Guest blog post by Ajit Jaokar

According to Deloitte: by the “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”. Gartner also predicts that 40 percent of the new investment made by enterprises will be in predictive analytics by 2020. AI is moving fast into the Enterprise and AI developments can create value for the Enterprise. This value can be captured/visualized by considering an ‘Enterprise AI layer’. This AI layer is focussed on solving relatively mundane problems which are domain specific.  While this is not as ‘sexy’ as the original vision of AI, it provides tangible benefits to companies.

Interactive Map of IoT Organizations 

Posted by David Oro

Here's a map that shows the location of the headquarters of organizations around IoT including standards bodies, manufacturers of Things, IoT Platform companies, etc. On the map you can click on a category on the left to highlight the organizations in that category. Or zoom in to see the areas where IoT organizations are near you.

Big Blue/ On the way back And Still Crazy about IOT ??

Posted by Bill McCabe 

There have been some interesting developments for Big Blue in the IOT space recently. Last time we reported on them, we were monitoring analysts’ worries about the semiconductor business and other divestures late last year. This year, it seems clear IBM is poised to create even more profitable opportunities in our IOT space. Let’s check in and see where they are.

Using Data Science for Predictive Maintenance

Posted by Sandeep raut

Remember few years ago there were two recall announcements from National Highway Traffic Safety Administration for GM & Tesla – both related to problems that could cause fires. These caused tons of money to resolve. Aerospace, Rail industry, Equipment manufacturers and Auto makers often face this challenge of ensuring maximum availability of critical assembly line systems, keeping those assets in good working order, while simultaneously minimizing the cost of maintenance and time based or count based repairs. Identification of root causes of faults and failures must also happen without the need for a lab or testing. As more vehicles/industrial equipment and assembly robots begin to communicate their current status to a central server, detection of faults becomes more easy and practical.

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Here's a map that shows the location of the headquarters of organizations around IoT including standards bodies, manufacturers of Things, IoT Platform companies, etc.

On the map you can click on a category on the left to highlight the organizations in that category. Or zoom in to see the areas where IoT organizations are near you.

This was found over at the The Pointy Haired Manager and the author says he's tracked 246 organizations, 59% (144) of them are based in the U.S.A. and 26% of them are based in California (63). This graph shows the locations of IoT companies in the U.S.A with the exception of California.

Update: The creator updated his map which can be found here.

Originally posted on Data Science Central

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

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

Some Applications of Printed Sensor Technology

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

Click here to get report.

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

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

Biosensors Lead Printed and Flexible Sensors Market

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

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

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

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

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Guest blog post by Ajit Jaokar

Introduction 

According to Deloitte: by the “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”. Gartner also predicts that 40 percent of the new investment made by enterprises will be in predictive analytics by 2020. AI is moving fast into the Enterprise and AI developments can create value for the Enterprise. This value can be captured/visualized by considering an ‘Enterprise AI layer’. This AI layer is focussed on solving relatively mundane problems which are domain specific.  While this is not as ‘sexy’ as the original vision of AI, it provides tangible benefits to companies.

In this brief article, we proposed a logical concept called the AI layer for the Enterprise.  We could see such a layer as an extension to the Data Warehouse or the ERP system. This has tangible and practical benefits for the Enterprise with a clear business model. The AI layer could also incorporate the IoT datasets and unite the disparate ecosystem. The Enterprise AI layer theme is a key part of the Data Science for Internet of Things course. Only a last few places remain for this course!.

Enterprise AI – an Intelligent Data Warehouse/ERP system?

AI enables computers to do some things better than humans especially when it comes to finding insights from large amounts of Unstructured or semi-structured data. Technologies like Machine learning , Natural language processing (NLP) , Speech recognition, and computer vision drive the AI layer. More specifically, AI applies to an algorithm which is learning on its own.

To understand this, we have to ask ourselves: How do we train a Big Data algorithm?  

There are two ways:

• Start with the Rules and apply them to Data (Top down) OR
• Start with the data and find the rules from the Data (Bottom up)

The Top-down approach involved writing enough rules for all possible circumstances.  But this approach is obviously limited by the number of rules and by its finite rules base. The Bottom-up approach applies for two cases. Firstly, when rules can be derived from instances of positive and negative examples(SPAM /NO SPAN). This is traditional machine learning when the Algorithm can  be trained.  But, the more extreme case is : Where there are no examples to train the algorithm.

What do we mean by ‘no examples’?

a)      There is no schema

b)      Linearity(sequence) and hierarchy is not known

c)      The  output is not known(non-deterministic)

d)     Problem domain is not finite

Hence, this is not an easy problem to solve. However, there is a payoff in the enterprise if AI algorithms can be created to learn and self-train manual, repetitive tasks – especially when the tasks involve both structured and unstructured data.

How can we visualize the AI layer?

One simple way is to think of it as an ‘Intelligent Data warehouse’ i.e. an extension to either the Data warehouse or the ERP system

For instance,  an organization would transcribe call centre agents’ interactions with customers create a more intelligent workflow, bot etc using Deep learning algorithms.

Enterprise AI layer – What it mean to the Enterprise

So, if we imagine such a conceptual AI layer for the enterprise, what does it mean in terms of new services that can be offered?  Here are some examples

• Bots : Bots are a great example of the use of AI to automate repetitive tasks like scheduling meetings. Bots are often the starting point of engagement for AI especially in Retail and Financial services
• Inferring from textual/voice narrative:  Security applications to detect suspicious behaviour, Algorithms that  can draw connections between how patients describe their symptoms etc
• Detecting patterns from vast amounts of data: Using log files to predict future failures, predicting cyberseurity attacks etc
• Creating a knowledge base from large datasets: for example an AI program that can read all of Wikipedia or Github.
• Creating content on scale: Using Robots to replace Writers or even to compose Pop songs
• Predicting future workflows: Using existing patterns to predict future workflows
• Mass personalization:  in advertising
• Video and image analytics: Collision Avoidance for Drones, Autonomous vehicles, Agricultural Crop Health Analysis etc

These  applications provide competitive advantage, Differentiation, Customer loyalty and  mass personalization. They have simple business models (such as deployed as premium features /new products /cost reduction )

The Enterprise AI layer and IoT

So, the final question is: What does the Enterprise layer mean for IoT?

IoT has tremendous potential but faces an inherent problem. Currently, IoT is implemented in verticals/ silos and these silos do not talk to each other. To realize the full potential of IoT, an over-arching layer above individual verticals could ‘connect the dots’. Coming from the Telco industry, these ideas are not new i.e. the winners of the mobile/Telco ecosystem were iPhone and Android – which succeeded in doing exactly that.

Firstly, the AI layer could help in deriving actionable insights from billions of data points which come from IoT devices across verticals. This is the obvious benefit as IoT data from various verticals can act as an input to the AI layer.  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 to 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 and is already happening, 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

We can extend the idea of ‘machines teaching other machines’ more generically within the Enterprise. 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.  We see early examples of this approach in Salesforce.com and Einstein. Longer term, Reinforcement learning is the key technology that drives IoT and AI layer for the Enterprise – but initially any technologies that implement self learning algorithms would help for this task

Conclusion

In this brief article, we proposed a logical concept called the AI layer for the Enterprise.  We could see such a layer as an extension to the Data Warehouse or the ERP system. This has tangible and practical benefits for the Enterprise with a clear business model. The AI layer could also incorporate the IoT datasets and unite the disparate ecosystem.  This will not be easy. But it is worth it because the payoffs for creating such an AI layer around the Enterprise are huge! The Enterprise AI layer theme is a key part of the Data Science for Internet of Things course. Only a last few places remain for this course!.

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Happy last quarter of 2016 and welcome new members! If you haven't been paying attention, IoT is having its moment in security, and it's not good. Andrew Hickey of A10 Networks gets you up to speed on this still developing story. Also in this edition uber-IoT recruiting guru and regular contributor Bill McCabe has a five point plan for hiring in IoT, Ben Dickson is back with a look at greenfield vs. brownfield development in IoT, Phillip Tracy has the top five use cases for the Industrial IoT, and finally Ajit Jaokar gives us a look at GE's much-hyped IIoT platform Predix. If you're interested in being featured, we always welcome your contributions on all things IoT Infrastructure, IoT Application Development, IoT Data and IoT Security, and more. All members can post on IoT Central. Consider contributing today. Our guidelines are here.

IoT Devices Common Thread in Colossal DDoS Attacks

A pair of distributed denial-of-service (DDoS) attacks against high-profile targets last week rank among the largest DDoS attacks on record. And a common thread has emerged: these attacks are leveraging botnets comprising hundreds of thousands of unsecured Internet of Things (IoT) devices.

What is the difference between greenfield and brownfield IoT development?

By Ben Dickson 

The Internet of Things (IoT) is one of the most exciting phenomena of the tech industry these days. But there seems to be a lot of confusion surrounding it as well. Some think about IoT merely as creating new internet-connected devices, while others are more focused on creating value through adding connectivity and smarts to what already exists out there. I would argue that the former is an oversimplification of the IoT concept, though it accounts for the most common approach that startups take toward entering the industry. It’s what we call greenfield development, as opposed to the latter approach, which is called brownfield. Here’s what you need to know about greenfield and brownfield development, their differences, the challenges, and where the right balance stands.

The 5 Point Plan for IOT Recruitment

Guest blog post by Mehul Nayak

Artificial Intelligence has effectively convinced its necessity to the entire world by performing excellently in various industries. Almost all the industries including manufacturing, healthcare, construction, online retail, etc. are adapting to the reality of IoT to leverage its advantages.

Machine learning technology is constantly evolving and the current trends in the field promise that every enterprise will be data driven and will have the capacity of using machine learning in the cloud to incorporate artificial intelligence apps. Yes, that’s right! Companies will be successful in analyzing large complex data and providing meticulous insights without spending a huge amount on installing and maintaining machine learning systems.

The three newest machine learning trends that will make this possible are Data Flywheels, The Algorithm Economy, and Cloud Hosted Intelligence. In the coming years, every application built will be an intelligent app by incorporating open source algorithms and machine learning codes. Let’s see how these trends will reshape the cloud industry, data handling and everything that’s digital.

Data Flywheels- See the future’s ruler!

Data is anticipated to be the ruler of the digital world in the coming years. It is observed that the world’s data doubles every 18 months while the cost of cloud storage decreases at almost the same rate, which suggests that data will be available in abundance after a few years.

This availability of high amount of data will open the doors of better and extensive machine learning experiments as well as deployment. With the use of the improved machine learning services we will be able to get a hold on more refined data. Ultimately the users of these services will increase which will give us more data. This data flywheel will keep on rolling and expanding.

For instance, Tesla’s data flywheel is planning to release a self-driving car by 2018, and for that project they have collected a massive driving data of 780 million miles and are adding a new million within every tenth hour.

This extravagant capacity of data collection at a very low cost than before will enforce people to use cloud technology primarily. Machine learning algorithms will get an economic market to flourish in the coming years.

The Algorithm Economy- Every industry will be a smart industry!

If there is an abundance of data, but there is no way of manipulating it or generating insights from it, then what’s the use, right? With massive data generation using flywheels, there will be an economy created for algorithms, like a marketplace for algorithms. The engineers, data scientists, organizations, etc. will be sharing algorithms for using the data to extract required information set.

Business owners from different sectors will be able to receive insights in seconds by sending their data directly to the algorithm marketplace. You can also buy algorithms you need for your data research and deploy it to manipulate data and get insights. Making every app and each business smart is absolutely possible with this concept.

Cloud Hosted Intelligence- Intelligence on rent!

Imagine the troubles you may have to go through to create an AI for your own business. Can’t even imagine the hard work and intelligence required, can you? There are obvious options such as approaching Machine Learning service providers for your needs, but the future will be rather different. You will be able to use the cloud hosted AI such as deep learning, Google’s machine learning venture. This is the coolest machine learning trend followed currently.

This advancement will be an efficient cost cutting method as the companies will not need to deploy AI for their business. Analytics and data science will be easier than it ever was. Getting accurate results, faster mining and generation of new models will be possible with the help of cloud-based intelligence.

The smarter tomorrow of manufacturing industry using AI

IoT has already delivered business value to the manufacturing industry through various use cases such as Remote Asset Monitoring, Logistics and Supply Chain, Predictive Maintenance, etc. However, the future is far more interesting than the current scenario of AI deployment in manufacturing.

The most promising factor of AI in the manufacturing industry is automotive production using robots. We are pacing towards a highly robotic industry where assembling of products and packaging of shipments will be handled by Artificial Intelligence. Currently, most of the AI technologies need human support and supervision, which is expected to change in the coming future.

The tomorrow of healthcare industry is in safe hands of AI

Healthcare is the most influenced enterprise by artificial intelligence. IT companies have already started developing AI applications that can track the health of employees or monitor senior citizens’ health remotely from quite some time. However, the future of AI in healthcare is unbelievably hopeful.

IBM Watson has been deployed by a number of medical organizations to help doctors provide intense care to their patients. However this is the current scenario of cognitive computing, the future has a bigger picture. The coming age of artificial intelligence will include mining of medical records to provide better and faster health services.

The most promising example of it is Google’s DeepMind Health Project. The AI research branch of Google has developed this project to collect the medical data, normalize it, and trace its lineage. Being in its initial phase, the DeepMind project is helping the Moorfields Eye Hospital to improve eye treatment.

Following IBM’s Watson and Google’s DeepMind, Microsoft, Dell and Hewlett-Packard are setting their mark in the healthcare industry and analysts predict that 30% of the providers will run cognitive analysis on patient data by 2018.

The future of construction business is secure with Artificial Intelligence

Artificial intelligence has changed the world and will continue to do so being an integral part of Industry 4.0. The construction industry will be affected positively by the deployment of automation. AI can help in saving a lot of money if there is a smarter option available in determining the expenditures on materials, choosing the perfect engineering companies and so on.

Autonomous TMA truck is a fascinating development of artificial intelligence in construction. This truck can function efficiently without the presence of a driver which suggests that for his safety, the driver could remove himself from the truck if any dangerous situation comes up. ATMA trick is equipped with the electro-mechanical system and the fully integrated sensor suite that enables choosing the leader or follower truck. This truck is being used in road construction and will change the scenario of the construction industry in the coming years.

The old talk of 3-D and 4-D will soon be or rather is replaced by the next generation 5-D building information modeling. This is a five dimensional representation of functional as well as physical characteristics of a construction project. All the important aspects such as geometry specifications, aesthetics, thermal and acoustic properties are taken into account to generate its cost and schedule.

However, this model has already been adopted by many companies. The future of this use case is its integration with augmented reality technology using wearable devices. The combination of 5-D BIM and augmented-reality devices will transform the entire construction industry. This technology will enable you to see through a holographic display and allow you to pin holograms to physical objects. You will be able to interact with data using gestures and voice commands.

The future of AI in retail will be a reality, not virtual reality!

Virtual reality is one of the most emerging uses of Artificial Intelligence in the retail industry currently. You may be able to see the virtual reality headsets in stores with the help of which you can actually see how the product looks. It helps the shoppers in selecting products more easily.

However, the future of AI in retail is the inclusion of chatbots in the retail industry. E-commerce is the most flourishing retail platform which is soon going to transform into conversational commerce with the introduction of chatbots. Utilization of chatbots in the retail sector will enable business owners to provide a personalized shopping experience to their customers. This will definitely help in building a strong customer base. Development of chatbots has already begun, and it’s time for every online retail business to adopt this technology.

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As the Internet of Things becomes more important for companies of all sizes, Information Technology professionals are beginning to seek out roles related to this growing niche. The Internet of Things is built on many of the technologies that professionals are already familiar with. Internet Protocol (IP) experts, hardware engineers, and even GUI designers could find themselves working on IoT projects in companies ranging from startups, to the technology giants that are driving the industry.

If one were to ask; “what kind of field do I need to be in to land a job in IoT?”, the answer would not be simple. IoT works on many layers. Software plays a key role in usability and functionality. Network layers are key to infrastructure, and hardware layers define the capabilities and development opportunities involved in any IoT system. Perhaps a better way to find out what is required of IoT professionals, would be to take information from some of the opportunities that are available in the job market right now.

Take Amazon as an example. Amazon AWS is the online retail giant’s cloud services arm. Cloud systems like Amazon S3 power some of the most widely adopted cloud computing systems in use today. To be considered for a role on a team working within AWS, the qualifications are no different to most IT development roles. A Bachelor’s Degree in Computer Science, professional experience (4+ years is a must), fundamentals in object design, and programming proficiency in a contemporary programming language will at least ensure a candidate’s resume is looked at.

But this doesn’t paint the full picture. Businesses who engage in IoT technologies are businesses who are invested in the future. This means that they’re seeking forward thinking professionals. Meeting the requirements where it comes to academic achievement is only part of what it takes to make it in IoT.

Last year, Forbes published a number of articles on what it would take to make it in the growing IoT industry. According to Forbes, the necessary qualities go beyond academia, and incorporate more soft skills and innovative thought.

High on the list was associative thinking. Collaborators who can integrate varying strategies and concepts were also tipped to be in demand. Finally, professionals who can communicate complex ideas easily through speech, written word, and abstract methods were considered more likely to be successful in the IoT niche than those who were only proficient in their technical field.

Take a look at the job market on any given day, and you will find dozens of IoT related jobs advertised by high profile tech companies. The second quarter of 2015 has seen positions opening at Dell and IBM (Software Development), Verizon (IoT Product Management), and Accenture (IoT Delivery Consultants), to name just a few.

The reason these companies are hiring in IoT is simple; it is the next big thing. Technology firms like Dell and IBM have a vested interest. Their core products and services are built around delivering and facilitating IoT. With companies like Verizon and Accenture, it is more about preparing for the future. IoT will allow Verizon to better deliver the services that they already have. Customer billing and customer experience can be improved by incorporating IoT into the ways that customers can interact with the company, but there’s also the fact that Verizon is a cellular network leader. Their consumer and business devices (i.e. smartphones) are key to incorporating IoT into daily consumer life. Wireless payments, mobile banking, home automation, and sensor interaction can be achieved through smart devices from Verizon. The talent that these companies recruit will be actively involved in designing, maintaining, and delivering IoT in the immediate future.

Although IoT hasn’t completely changed the face of Information Technology, it has created new opportunities for jobseekers in the market. Existing professionals with transferable skills will find new challenges and progression opportunities within the Iot Job Market, and also in smaller companies that are incorporating IoT concepts into manufacturing, packing, logistics, and even medical.

International Data Corporation has predicted that IoT will be a $7 trillion industry by 2020. With growth as fast as it currently is, IoT job market is the perfect platform from where jobseekers can showcase their skills, and where companies can form relationships with the talented professionals who will take them into the future.

For more information please check out our website at

www.internetofthingsrecruiting.com or contact me directly at 303-337-7871

delivering IoT forward thinking professionals IoT Delivery Consultants IoT jobseekers IoT Product Management IoT professionals IoT related jobs

When considering any new or emerging technology, it can be easy to immediately think of the potential implementation in developed markets. After all, these are the markets where consumers have high purchasing power, and businesses and governments have strong credit lines and funding options. Well, wouldn’t it be a surprise to learn that the developing world will likely be responsible for almost half of all revenue generated by IoT? This is exactly what a 2015 report from the International Telecommunication Union stated, and if you look at trends and innovation around the world, there is evidence that supports the prediction.

Industry Leaders Recognize the Value of IoT in Developing Markets

Take India as an example. Although it is one of the largest countries by area, and the second most populous in the world, it is still considered to be a developing country by leading economists. Even so, there are some areas where India is a leader in IoT. In 2015, IBM selected the Indian city of Vizag as a winner in their Smarter Cities Challenge. This city wants to improve its disaster preparedness and response programs through the use of IoT technologies, and with the help of IBM, the government will work towards implementing a sensor based utility grid, improve citywide electronic communications, and develop an emergency command center that uses historical data and machine sensors to better predict and respond to natural disasters.

This program has the potential to attract foreign investment, create jobs, and save lives.

Markets That are Ideal for IoT Investment

One reason why developing nations are prime for IoT investment is because many of them can make immediate use of IoT technologies for critical applications. In the gridlocked Philippine region of Metro Manila, government agencies are using connected machines to monitor traffic in real time and provide public alerts. The metropolitan area is served by a number of CCTV systems and sensors that can be accessed through APIs, allowing for news stations and privately developed smartphone apps to provide instant updates to the general public.

Safety is also an issue in many developing countries, and again, we can use Metro Manila as an example. The region’s widely utilized MRT rail lines are often overcrowded and sometimes dangerous. With connected technology, members of the public can already access the MRT security CCTV feeds from smartphones and web browsers, allowing them to view real time platform video to help plan their daily commutes.

Perhaps one of the biggest advantages that developing countries have is that they are lacking in some areas of infrastructure. A developing city that now has the funds to invest in widespread water metering will have more incentive to use accurate and efficient machine driven meters. By contrast, a long developed city would have to weigh up the cost savings of an IoT based system, compared to the efficiency of their current metering system.

IoT Infrastructure Can Be Built on Existing Cellular Networks

Despite lack of infrastructure in some areas, LTE penetration is high in a number of developing economies, meaning that there is increased opportunity for bringing IoT services to corporations and the general public. India has LTE penetration throughout more than 50% of the population, which means that there is potential to connect more than half a billion people to the Internet of Things. China, which could be considered still developing in some provinces and cities, boasts LTE coverage across 76% of the mainland. That’s only two points behind the United States, and China has more than four times the population, allowing for massive opportunity in the consumer and public service IoT sectors.

While the developed world is no doubt leading in IoT innovation, developing countries will contribute significantly to revenue, adoption, and investment. With more than $6 trillion in worldwide IoT investment expected by 2020, developers and innovators cannot afford to ignore the world’s developing economies.

For more information please review our new website www.internetofthingsrecruiting.com

iot internet of things

Digital Transformation is here and that means everyone—IT and non-IT alike—must embrace the disruptions. Automation and modernization is a bandwagon and just letting it pass by is missing a great business opportunity.

Digital Transformation means a wave of technology disruptions taking over vertical and horizontal industries. Disruptive technologies are considered to challenge the status quo and beat the conventional all for the sake of better business efficiency, credibility, sustainability and most importantly, higher chances at succeeding in an ever progressing era where information technology has become Midas—everything it touches turns into gold.

Digital Transformation for the most part promises to make data driven business decisions more accurate, predictive, and extremely reliable compared to traditional tools and processes. This phenomenon in the IT landscape pushes business processes to deliver results at an impressive speed and become more efficient and unified. With the right tools and solutions, and with the proper migration, design, and implementation, Digital Transformation can lead an enterprise towards success.

It is no wonder that most organizations, startups, and high-performing enterprises are taking firmer steps in treading the path towards this phenomenon in high technology. And who wouldn’t take this leap? Apart from providing more informed decisions that aim to get valuable and productive outcomes, Digital Transformation also enables sustainability and agility in most business aspects. It positively affects key areas including customer engagement, finance, unified communications and collaboration, networking, and many others.

The disruptions in IT is not an unknown domain to a good number of people. In the recent Accenture Technology Vision 2016 Survey, it was revealed that there are 58% who say that the pace of technology will change in their industry rapidly. This says a lot about proving that the Digital Transformation is not anymore a mere setting for sci-fi or IT fiction films but is the present reality.

Digital Transformation calls for everyone to beef up IT know how

As back office processes gradually but surely begin to become automated, other roles in an organization such as recruiters, finance officers, and human resources managers are highly encouraged (if not compelled) to add in their skillset some IT know-how. Apparently, in this age of automation, setting up and doing some minor software troubleshooting is no longer the sole responsibility of an IT officer. Though it may not be required for a non-IT professional to have some IT skills among their competencies, it surely is a great advantage to be knowledgeable and capable in IT.

A great example is the demand on expanding the role of a chief finance officer. In an article titled Great Expectations: How the CFO’s Role is Growing, authored by the General Manager for Enterprise Resource Planning (ERP) of Oracle ANZ Thomas Fikentscher, it was revealed that there has become a need for the chief financial officer’s (CFO’s) role to expand and this particularly means that they need to gain some IT capabilities. This is due to the uptake of data analytics in making the processes of finance more efficient and reliable by enhancing it with improved forecasting and decision making.

Meanwhile, the emergence of HCM software and tools also proves that there is a demand from non-ITs to gain skills on automated processes and data analytics. In an annual study titled Sierra-Cedar 2014–2015 HR Systems Survey White Paper, 17th Annual Edition, it was revealed that the adoption of cloud-based SaaS Human Capital Management (HCM) is expected to rise to 58%.

IT Demands

As most non-IT members of an enterprise are encouraged to become adept in various areas of IT concerns, IT professionals become even more vital in many key areas in a company and must always sharpen their skillset themselves. These individuals are not only responsible in making sure that IT tools are working and rolled out. Most importantly, IT decision-makers and leaders are expected to spark knowledge on the latest business software advancements and guide the teams in embracing the disruptions in technology.

Accenture Technology Vision 2016 also confirms such trend when it revealed that 37% of the business and IT executives surveyed reported that “the need to train workforce is significantly more important today compared to three years ago.”

The exceptional talent and brains of IT professionals are much sought-after now than ever as their role becomes challenging in this day and age where office mobility, online banking, business process management tools, and the Internet of Things are further becoming everyday essentials. Due to automation and massive connectivity, much focus and attention are placed upon IT security, applications development, servers, and data center housekeeping (virtually or physically).

The reality that must be embraced now, however, is that IT knowledge and skills even to non-IT pros are highly beneficial in a thriving and progressing enterprise. This will be true as long as companies are becoming more open to modernizing their offices and are willing to cope with the impressive disruptions in information technology.

As long as Digital Transformation is dominating in vertical and horizontal industries, non-IT roles in a company will also have to add some IT professional skills in their competencies.

Guest blog post by ajit jaokar

Background and Approach

This blog is based on my talk in London at the Re.work Connected City Summit on Deep Learning Applications for Smart cities. The talk is based on a forthcoming paper created with the help of my students atUPM/citysciences on the same theme. Please email me at  ajit.jaokar at futuretext.com  or follow me  @ajitjaokar  for more details.

Here are some notes on our approach:

• When we speak of Machines – the media dramatizes the issue.  Yet,  city officials and planners plan for ten to twenty years in the future. They will have to consider many of these issues in a pragmatic way.
• Deep Learning / Artificial Intelligence will impact many aspects of Smart cities. We decided to approach the subject in a pragmatic manner and to explore the impact of Deep Learning/AI technology on the lives of future citizens.

How could self-learning machines affect humanity in cities?

Initially, we started off with the usual Smart City approach i.e. domains such as Security – Transport – Health – Governance – Environment etc

Then, we were inspired by a statement “Man becomes the sex organs of the machine world – the bee of the plant world – enabling machines to evolve ever new forms” – Marshall McLuhan

It indicates that disruptive innovations like Deep Learning and AI cannot be viewed in silos. Instead, we decided to reframe the problem in a more disruptive way by asking the questions;

    What can Machines learn from Observations?

    What can Machines learn from Data?

    What impact does it have on new services, culture, citizens ?

    What are the threats?

    How will the lives of future citizens be impacted through self learning machines?

The shortest introduction to Deep learning:

Here is a brief introdcution to Deep Learning.  I have spoken of the Evolution of Deep Learning models and An introduction to Deep Learning and it’s role for future cities

Deep Learning can be seen more as a specific form of Machine Learning that leads to creating Self Learning Machines.  The whole objective of Deep Learning is to solve ‘intuitive’ problems i.e. problems characterized by High dimensionality and no rules.  With Deep learning, Computers can learn from experience but also can understand the world in terms of a hierarchy of concepts – where each concept is defined in terms of simpler concepts. The hierarchy of concepts is built ‘bottom up’ without predefined rules . This is similar to the way a child learns ‘what a dog is’ i.e. by understanding the sub-components of a concept ex  the behavior(barking), shape of the head, the tail, the fur etc and then putting these concepts in one bigger idea i.e. the Dog itself.

More specifically, a form of Deep Learning called Reinforcement Learning is making a huge impact in areas such as AlphaGo. Reinforcement Learning (RL) is based on a system of rewards. RL is a form of unsupervised learning – An RL agent learns by receiving a reward or reinforcement from its environment, without any form of supervision other than its own decision making policy.

In machine learning, the environment is typically formulated as a Markov decision process (MDP) as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Further, there is a focus on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). (adapted from wikipedia)

Analysis

Here are the trends we note from the themes noted above. Link sources from Home of AI info and the web

What are machines learning from Data and Observations?

• New computer program first to recognize sketches more accurately than a human
• Deep Learning Algorithm ‘Paints’ in the Style of Any Artist it Copies
• New big data system developed at MIT is more intuitive than humans
• Artificial intelligence breakthrough as intuition algorithm beats humans in data test

• MIT Develops Device That Can See People Through Walls
• Lie-detecting algorithm spots fibbing faces better than humans
• Machines That Can See Depression on a Person’s Face
• An algorithm aims to be able to replace human intuition
• ‘Psychic Robot’ System Guesses Intentions From Your Movements
• MIT’s intelligent drone can avoid crashes and fly at 30 MPH
• Facebook working on AI that can tell what’s in photos
• Computer Algorithms Could Aid Schizophrenia Diagnose
• Machines That Can See Depression on a Person’s Face
• Robot Radiologists Will Soon Analyze Your X-Rays
• Predicting change in the Alzheimer’s brain
• A new computer program that can diagnose cancer in just two days!

• Machine learning to help predict online gambling addiction
• Predicting people’s daily activities with deep learning
• MIT Scientists Create An AI System That Can Determine How Memorable Your Face Is
• This Algorithm Is Better At Predicting Human Behaviour Than Humans Are
• New Artificial Intelligence: Russia Endows Robots With Collective Mind
• Scientist Develop New Machine Which Can Calculate Pattern Recognition with Near Human speed
• Machine Vision Algorithm Learns to Recognize Hidden Facial Expressions
• Artificial Intelligence: Scientists Developed a Handwriting Algorithm
• Computer With Built-In Algorithm Beats Man In A Turing Test
• Machine learning to differentiate between positive and negative emotions using pupil diameter

Self learning for Robots(from observation)

• Giving robots a more nimble grasp
• Why it is hard to teach robots to choose wisely
• Machine learning plays vital role in the evolution of Man
• Designing Robots That Learn as Effortlessly as Babies
• How Robots Can Quickly Teach Each Other to Grasp New Objects
• Why IBM just bought billions of medical images for Watson to look at
• Read my lips: truly empathic robots will be a long time coming

Learning Culture, Humanity, emotions and ethics

• Smart Programs Read Shakespeare
• Artificial intelligence learns how to put together interactive stories just as good as a human
• How do you teach a machine to be moral?
• ‘Psychic Robot’ System Guesses Intentions From Your Movements
• Lie detection software learns from real court cases
• Why Helping Humanity Should Be Core to Learning
• Could Artificial Morals and Emotions Make Robots Safer?
• AI: In search of the sarcasm algorithm
• Microsoft Teaches Computers To Be Funny
• Microsoft’s Project Oxford Can Now Detect Emotions from Photos
• Robots are learning to disobey humans: Watch as machine says ‘no’ to voice commands
• Robots could be converted to religion someday: Scientists
• Intimacy & Falling In Love With A Robot Could Happen In 50 Years Because Of Artificial …
• Health
• If We Want Humane AI, It Has to Understand All Humans
• Humai Is Working On A Way To Bring Your Loved Ones Back From The Dead
• Mum Robot Goes Darwinian on Her Kids

How does that (self) learning affect services and our lives in future cities

• Artificial intelligence comes to toys
• Beyond the Pill: Data Is the New Drug – Google Life Sciences Rebrands As Verily, Uses Big Data To Figure Out Why We Get Sick
• Nvidia Aims To Power Flying Vehicles with Jetson TX1 Board
• Motorcycle-riding robot may take on world champion racer
• Meet Mercedes-Benz’s Vision Tokyo, a self-driving car for the megacity
• How artificial intelligence could lead to self-healing airplanes
• Trains with brains: how Artificial Intelligence is transforming the railway industry
• A self-driving sailboat to patrol the oceans and monitor the environment
• Malaysia testing ‘artificial intelligence’ for prisons
• Real-Time Seizure Detection Possible with Learning Algorithm
• Facebook Is Helping People With Blindness “See” the Photos on Their Walls
• Mitsubishi Electric uses machine-learning tech to detect distracted drivers
• Tinder matches made easy with new intelligent algorithm
• Deep Learning Algorithm Successfully Identifies Potential Intracranial Haemorrhaging
• An artificial intelligence based third Umpire
• When children talk to toys, some are talking back
• Predicting change in the Alzheimer’s brain
• Robotic Automation Meets Agriculture
• Food delivered by drones, driverless cabs and cyber PAs to organise your party: A revolution in …
• AI will soon be forecasting the weather
• How Artificial Intelligence Can Fight Air Pollution in China
• Starfish-killing robot to protect Great Barrier Reef
• Self-Driving Car Tech Allows Vehicle To ‘See’ Environment In Real Time
• US Company On Plan To Bring People Back From Dead Using Artificial Intelligence
• A trillion tiny robots in the cloud: The future of AI in an algorithm world
• Teforia Is A Tea Brewing Robot That Uses Algorithms To Pour The Perfect Cup
• Japanese artificial intelligence passes university exams (but still can’t quite get into the country’s …
• Facebook AI built to help visually impaired people
• Problem of Climate Change and Global Conflicts Can Be Solved Using Human and Computer …

Risks to humanity and cities

• ‘Only movies build bad robots‘ – famous last words?
• Why human-in-the-loop computing is the future of machine learning
• As Robots Steal Millennials’ Jobs, Young Workers Focus On Skills, Not Careers
• Millions of jobs at risk from artificial intelligence
• Davos report projects 5 million jobs will be lost to new technologies by 2020
• Can Humanity Rein In The Rise Of The Machines?
• Christian leader warns of ‘Frankenstein monsters’ due transhumanism
• The rise of the killer robots — and why we need to stop them
• Producer of Russia’s Armata T-14 plans to create army of AI robots
• Inside the Pentagon’s Effort to Build a Killer Robot
• How Technology Could Prevent Another Paris-Like Attack
• Kaspersky deepens security offering through machine learning
• Robots will declare war on humans within 25 years, claims artificial intelligence expert
• Law firm bosses envision Watson-type computers replacing young lawyers
• Hitachi Hires First ‘Artificial Intelligence’ Boss To Manage Workers

Conclusion and Evolution

We reframed the problem of Deep Learning and Smart cities by asking the Question:

How could self-learning machines affect humanity in cities?

    What can Machines learn from Observations?

    What can Machines learn from Data?

    What impact does it have on new services, culture, citizens

    What are the threats?

Please contact me at ajit.jaokar at futuretext.com to know more updates – especially if you are a city official. We are also planning to explore the implementation of these ideas by working with companies like Nvidia.

I would also like to thank the students who helped me with this project.