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The world is full of normal people like you and me, but I love to think that superheroes live between us and I dream that maybe someday I could become one of them and make a better world with my super powers.

In the universe of superheroes fit gods, mutants, humans with special skills, but also the special agents. I found fun to find similarities between this fantastic world and the world of IoT platforms.  Compare and find a reasonable resemblance between IoT Platforms and Superheroes or Super villains is the goal of this article. Opinions as always are personal and subject to all kinds of comments and appreciations. Enjoy, the article.

About IoT Platforms

Many of my regular readers remember my article “It is an IoT Platform, stupid !.”. At that time, per Research and Markets, there were more than 260 IoT platforms, today some sources speak about 700 IoT platforms. I confess, I have not been able to follow the birth, evolution and in some cases death of all IoT platforms out there. I think that many enthusiasts like me also have given up keeping an updated list.

I cannot predict which IoT platforms will survive beyond 2020, or which will be the lucky start-ups that will be bought by big companies or will receive the investors' mana to become a Unicorn, but I like to speculate, and of course, I have my favourite winners and unlucky losers.

About my Own Methodology

Some reputed analysts have adapted their classification methodologies of IT solutions to put some order and consistency into the chaotic and confusing Internet of Things (IoT) platforms market. But given the moment of business excitement around the IoT, have appeared new analyst firms focused on IoT who also wanted to contribute their bit and at the same time make cash while this unsustainable situation lasts.

After reading numerous reports from various sources on this topic, talking to many IoT platform vendors and seeing endless product demos, I have decided to create my own methodology that includes a questionnaire of near 100 questions around different areas: technical, functional, business, strategy, and a scoring mechanism based on my knowledge and experience to make justified recommendations to my clients.

About Super Powers Methodology

But I also had defined an alternative Methodology based on Super Powers.

Super Heroes and Super Villains usually gain their abilities through several different sources, however these sources can be divided into four categories. The Super Powers methodology is based on these four categories of Power Sources.

  • Mind Powers – Powers with notable mental abilities. Companies like IBM Watson IoI or GE Predix are notable examples.
  • Body Powers – Powers that are gained from genetic mutation. Companies like Microsoft or Amazon mutate to IaaS / PaaS IoT platforms.
  • Spirit Powers  Powers gained over time through extensive investment, and are easily obtainable by companies without the risk of horrible mutation or disfigurement. PTC Thingworx, Software AG/Cumulocity or Cisco-Jasper are examples.
  • Artefact Powers   Powers gained abilities through ancient objects such as networks, or hardware. Incumbent Telcos M2M Platforms, Telco vendors like Huawei, Nokia or Ericsson, and Hardware vendors like Intel IoT platform, ARM Beetle or Samsung Artik are examples.

For each Power Source category, Super Powers are divided into different levels of power that depend on how strong, or unique, their abilities are.

  • Level 0 -  with useless, or minimal abilities.
  • Level 1 -  they are still particularly weak compared to the higher levels.
  • Level 2 -  have developed their powers to a certain point. About 75% of the platforms belong to this class,
  • Level 3 - Mostly are most commonly amateur heroes or sly villains.
  • Level 4 - Some of the most unique with a wider variety of powers.
  • Level 5 - these fellows are seasoned veterans of their abilities, capable of using them without even needing to concentrate.
  • Level 6 - Only a few beings are classified under this level, and their powers are that of being able to control multiple aspects of IoT reality.

Whatever the source of power was, I add Sandy Carter´s recommendation: If you want to become an Extreme Innovator you also need Super Intelligence, Super Speed and Super Synergy.  

About Super Heroes and Super Villains

Previously in “Internet of Things: Angels & Demons” and “Internet of Things – Kings and Servants” , I identified some IoT Platform companies as potential superheroes. What was certain is that we knew who the supervillains were. Governments, organizations and business giants that try to control us, manipulate us and frighten us with their economic, political and military powers.

Deciding which superhero can help you more or what superpower is more important for your business is an extremely important milestone in your IoT Strategy.

I've defined the six types/categories of superheroes / IoT Platforms:

a)The superhero whose power is a birthright like Amazon AWS IoT (Superman) or GE Predix (Magneto/Professor Xavier).

b)The superhero whose power is the result of power acquisitions like PTC Thingworx (The Flash) or Cisco Jasper-Parstream (Spiderman) or Autodesk Fusion Connect (FireStorm).

c)The superhero whose power is made possible by technology like Oracle IoT (Iron Man) or SAP Leonardo(Green Lantern). 

d)There is the superhero who doesn't have any superpowers but who is a superhero by extremely intensive training like Batman (Ayla Networks) or Black Widow (Exosite) or LogMeIn-Xively (Hawkeye)

e)The superhero who obtains his/her powers due to some supernatural event like Satya Nadella named new CEO for MSFT IoT Azure (Thor) or Telit DeviceWise (Dr. Manhattan) or Google acquisition of Nest (Hulk)

f)Finally, there is the superhero, usually a sentient android, who was created by a human like IBM Watson IoT (Vision) or a normal human playing with magic like Salesforce IoT Cloud Einstein (Dr Strange) or leader of a young team like Hitachi Data System(Most Excellent Superbat)

“Do you agree with my classification system for superheroes and superpowers?”

Although the number of superheroes and supervillains is enormous (more that the IoT Platforms Universe), it would take me a long time to assign each one of the IoT platform a single superhero or supervillain. Since I do not think many companies are willing to pay to know who represents them better, at least I have done a partial and fun exercise.

The Bottom Line 

If you are an IoT Platform vendor, you could be doing yourself some questions right now:

-          If I could be a Superhero what would it be?".

-          Worth to acquire a Super Power or reach an upper level to convince customers I am their Superhero?

And remember …

“With power comes responsibility; with great power comes great responsibility”

Although the number of superheroes and supervillains is enormous (more than the IoT Platforms Universe), it would take me a long time to assign each one of the IoT platform a single superhero or supervillain. Since I do not think many companies are willing to pay to know who represents them better, at least I have done a partial and fun exercise.

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From “smart” hairbrushes to lawn mowers, the Internet of Things (IoT) has created a slew of slick products, but there have also been many failures: too many connected devices have gone by the wayside, and in most cases, this is preventable. This scenario, recognized as the Abandonment of Things, is the land where connected devices go to die. Several factors drive the Abandonment of Things: a lack of a proper monetization strategy for connected services; failure to create a community around a smart device; even security issues and clunky backend processes. If a company wants to avoid the digital scrap pile, and subsequent loss of potential revenue and customer gains, it must have all the right parts working in sync.

Keeping an Eye on Profitability

Companies are always looking to expand their user bases, but as that base grows, so do infrastructure costs. Think increased server capacity and the people needed to manage the technology aspect of a subscription service. If the right infrastructure is not in place, profitability takes a hit.   

It comes down to data collection and value. What data do companies collect and how do they use it? Where is the value? Take some of the popular connected health bands for example: their freemium models allow consumers to track food and exercise and then compare and compete with friends. On the other hand, their premium models cost around $100 annually and give consumers the option to compare themselves with strangers who are just like them.

Does the data revealed by the premium model — data about strangers rather than friends — provide enough value to convert 3-5 percent of the consumer base into paying customers? That’s the question the marketer must ask and answer.

Getting in the Consumer’s Head

With traditional, non-connected devices, the relationship between brands and consumers ends with the purchase. But with a smart device, the purchase is the beginning of that relationship. IoT is actually not about the thing — it’s about the service as well as the value provided by the service. A company must therefore consider more than the transaction, or the value of the product, or even the initial needs of the consumer. Companies must understand the value a consumer — or better yet, a member — gets from the service. An integral part of that value is the community consumers join when they subscribe to a service.

The new subscription generation requires companies to think of their consumer base as a membership base, which requires very different communication strategies. Transparency is key, both in terms of the solution offered and the financial aspect of the solution. Companies start to achieve success when they build out these relationships and consumers begin to take in new information, not just as marketing, but as an added value.

Awareness of Regulations

Security is paramount in the era of IoT. Striking the balance between value for consumers and protection of their data will be an ongoing challenge for marketers. One example that showcases the delicacy required in this new order is TVs that watch us — noting not just what you’re viewing, but who is in the room when the device is on. Even for those consumers who see great value in, for example, targeted commercials and programming, real questions revolve around how that data is collected and what companies do with it.

Consumer rights also change with every border crossing. Uber transactions are seamless in the U.S., but are more complicated in India. It’s not just about securing data, but also securing the complex payment processes inherent in a subscription-based, hyper-connected global economy.

How companies adapt to the regulatory environment is key to their success. One important thing to understand is that government is not necessarily proactive about regulating IoT — regulations will most likely come after some company is caught misusing data. One bad apple can affect an entire industry, so companies need to be transparent and meticulous about data collection and how data is used to create value.

Avoiding the Abandonment of Things

The new world ushered in by IoT is just dawning, and already the path forward is littered with abandoned things. A subscription-based economy demands flexibility, convenience and value. But those aren’t the only challenges your company faces when forging ahead. The right balance between monetization, transparent communications and security can create the environment your product needs to thrive.

Photo Credit: Becky

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Beyond SMAC – Digital twister of disruption!!

Have your seen the 1996 movie Twister, based on tornadoes disrupting the neighborhoods? A group of people were shown trying to perfect the devices called Dorothy which has hundreds of sensors to be released in the center of twister so proper data can be collected to create a more advanced warning system and save people.
Today if we apply the same analogy – digital is disrupting every business, if you stand still and don’t adapt you will becomedigital dinosaur. Everyone wants to get that advance warning of what is coming ahead.
Even if your business is doing strong right now, you will never know who will disrupt you tomorrow.
We have seen these disruption waves and innovations in technologies – mainframe era, mini computers era, personal computers & client-server era and internet era. Then came the 5thwave of SMAC era comprising Social, 
Mobile, Analytics and Cloud technologies.
Gone are the days when we used to wait for vacations to meet our families and friends by travelling to native place or abroad. Today all of us are interacting with each other on social media rather than in person on Facebook, Whastapp, Instagram, Snapchat and so on.
Mobile enablement has helped us anytime, anywhere, any device interaction with consumers. We stare at smarphone screen more than 200 times a day.
Analytics came in to power the hyper-personalization in each interaction and send relevant offers, communications to customers. The descriptive analytics gave the power to know what is happening to the business right now, while predictive analytics gave the insight of what may happen. Going further prescriptive analytics gave the foresight of what actions to be taken to make things happens.
Cloud gave organizations the ability to quickly scale up at lower cost as the computing requirements grow with secure private clouds.
Today we are in the 6thwave of disruption beyond SMAC era - into Digital Transformation, bringing Big Data, Internet of things, APIs, Microservices, Robotics, 3d printing, augmented reality/virtual reality, wearables, drones, beacons and blockchain.
Big Data allows to store all the tons of data generated in the universe to be used further for competitive edge.
Internet of Things allows machines, computers, smart devices communicate with each other and help us carry out various tasks remotely.
APIs are getting lot of attention as they are easy, lightweight, can be plugged into virtually any system and highly customizable to ensure data flows between disparate systems.
Microservices are independently developed & deployable, small, modular services. 
Robotics is bringing the wave of intelligent automation with help of cognitive computing.
3D printing or additive manufacturing is taking the several industries like medical, military, engineering & manufacturing by storm.
Augmented reality / virtual reality is changing the travel, real estate and education.
Wearables such as smart watches, health trackers, Google Glass can help real time updates,  ensure better health & enable hands-free process optimization in areas like item picking in a warehouse.
Drones have come out of military zone and available for common use. Amazon, Dominos are using it for delivery while Insurance & Agriculture are using it for aerial surveys.
Beacons are revolutionizing the customer experience with in-store analytics, proximity marketing, indoor navigation and contact less payments.
The new kid on the block is blockchain where finance industry is all set to take advantage of this technology.
As products and services are getting more digitized, traditional business processes, business models and even business are getting disrupted.
The only way to survive this twister is to get closer to your customers by offering a radically different way of doing business that’s faster, simpler and cheaper.
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IoT Developer Trends 2017 Edition

Guest post by Ian Skerrett

For the last 3 years we have been tracking the trends of the IoT developer community through the IoT Developer Survey [2015] [2016]. Today, we released the third edition of the IoT Developer Survey 2017. As in previous years, the report provides some interesting insights into what IoT developers are thinking and using to build IoT solutions. Below are some of the key trends we identified in the results.

The survey is the results of a collaboration between the Eclipse IoT Working GroupIEEEAgile-IoT EU and the IoT Council. Each partner promoted the survey to their respective communities. A total of 713 individuals participated in the survey. The complete report is available for everyone and we also make available the detailed data [xlsodf].

As with any survey of this type, I always caution people to see these results as one data point that should be compared to other industry reports. All of these surveys have inherent biases so identifying trends that span surveys is important.

Key Trends from 2017 Survey

 1. Expanding Industry Adoption of IoT

The 2017 survey participants appear to be involved in a more diverse set of industries. IoT Platform and Home Automation industries continue to lead but industries such as Industrial Automation, Smart Cities, Energy Management experience significant growth between 2016 to 2017.

industries

2. Security is the key concern but….

Security continues to be the main concern IoT developers with 46.7% respondents indicating it was a concern. Interoperability (24.4%) and Connectivity (21.4%) are the next most popular concerns mentioned. It would appear that Interoperability is on a downward trend for 2015 (30.7%) and 2016 (29.4%) potentially indicating the work on standards and IoT middleware are lessening this concern.

concerns2017

This year we asked what security-related technologies were being used for IoT solutions. The top two security technologies selected were the existing software technologies, ie. Communication Security (TLS, DTLS) (48.3%) and Data Encryption (43.2%). Hardware oriented security solutions were less popular, ex. Trusted Platform Modules (10%) and Hardware Security Modules (10.6%). Even Over the Air Update was only being used by 18.5% of the respondents. Security may be a key concern but it certainly seems like the adoption of security technology is lagging.

security

3. Top IoT Programming Language Depends…

Java and C are the primary IoT programming languages, along with significant usage of C++, Python and JavaScript. New this year we asked in the survey, language usage by IoT categories: Constrained Devices, IoT Gateway and IoT Cloud Platform. Broken down by these categories it is apparent that language usage depends on the target destination for the developed software:

  • On constrained devices, C (56.4%) and C++ (38.3%) and the dominant languages being used. Java (21.2%) and Python (20.8%) have some usage but JavaScript (10.3%) is minimal.
  • On IoT Gateways, the language of choice is more diverse, Java (40.8%), C (30.4%), Python (29.9%) and C++ (28.1%) are all being used. JavaScript and Node.js have some use.
  • On IoT Cloud Platforms, Java (46.3%) emerges as the dominant language. JavaScript (33.6%), Node.js (26.3%) and Python (26.2%) have some usage. Not surprisingly, C (7.3%) and C++ (11.6%) usage drops off significantly.

Overall, it is clear IoT solution development requires a diverse set of language programming skills. The specific language of choice really depends on the target destination.

4. Linux is key OS; Raspbian and Ubuntu top IoT Linux distros

Linux continues to be the main operating system for IoT. This year we asked to identify OS by the categories: Constrained Device and IoT Gateway. On Constrained Devices, Linux (44.1%) is the most popular OS but the second most popular is No OS/ Bar Metal (27.6%). On IoT Gateway, Linux (66.9%) becomes even more popular and Windows (20.5%) becomes the second choice.

The survey also asked which Linux distro is being used. Raspbian (45.5%) and Ubuntu (44.%) are the two top distros for IoT.

linuxdistros

If Linux is the dominant operating system for IoT, how are the alternative IoT operating systems doing? In 2017, Windows definitely experienced a big jump from previous years. It also seems like FreeRTOS and Contiki are experiencing growth in their usage.

 5. Amazon, MS and Google Top IoT Cloud Platforms

Amazon (42.7%) continues to be the leading IoT Cloud Platform followed by MS Azure (26.7%) and Google Cloud Platform (20.4%). A significant change this year has been the drop of Private / On-premise cloud usage, from 34.9% in 2016 to 18.4% in 2017. This might be an indication that IoT Cloud Platforms are now more mature and developers are ready to embrace them.

cloud

6. Bluetooth, LPWAN protocols and 6LowPAN trending up; Thread sees little adoption

For the last 3 years we have asked what connectivity protocols developers use for IoT solutions. The main response has been TCP/IP and Wi-Fi. However, there are a number of connectivity standards and technologies that are being developed for IoT so it has been interesting to track their adoption within the IoT developer community. Based on the 2017 data, it would appear Bluetooth/Bluetooth Smart (48.2%), LPWAN technologies (ex LoRa, Sigfox, LTE-M) (22.4%) and 6LoWPAN (21.4%) are being adopted by the IoT developer community. However, it would appear Thread (6.4%) is still having limited success with developer adoption.

connectivity2017

Summary

Overall, the survey results are showing some common patterns for IoT developers. The report also looks at common IoT hardware architecture, IDE usage, perceptions of IoT Consortiums, adoption of IoT standards, open source participation in IoT and lots more. I hope the report provides useful information source to the wider IoT industry.

Next week we will be doing a webinar to go through the details of the results. Please join us on April 26 at 10:30amET/16:30pmCET.

2017 IoT Survey - webinar 2

Thank you to everyone who participated in the survey, the individual input is what makes these surveys useful. Also, thank you to our co-sponsors Eclipse IoT Working GroupIEEEAgile IoT and the IoT Council. It is great to be able to collaborate with other successful IoT communities.

We will plan to do another survey next year. Feel free to leave any comments or thoughts on how we can improve it.

This post originally appeared here.

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Smart IoT - Generate Greatest Value

Digital Transformation

We have now entered an era with a new virtual revolution, particularly, the Internet of things (IoT). The virtual revolution marks the starting of information age. We use the Internet almost every day. The net has turned out to be one of established ways for us to work together, to share our lives with others, to shop, to teach, to research, and to learn. However  the next wave of the Internet isn't about people. it's far about things, honestly?

All about IoT

IoT is defined as the network of physical objects that can be accessed through the Internet. These objects contain embedded various technology to interact with internal states or the external environment.

IoT is characterized as "the figuring frameworks of sensors and actuators associated by systems, where the processing frameworks can screen or deal with the status and actions of connected objects and machines, and the connected sensors can likewise screen the characteristic world, individuals, and creatures." The center of IoT is not just about interfacing things to the Internet. It is about how to generate and use the big data from the things to make new values for individuals, and about how we empower new trades of significant worth between them. In other words, when objects can sense and communicate, IoT has its knowledge to change how and where choices are made, and who makes them, and to pick up a superior esteem, solution or service.

Smart IoT

Fundamental to the estimation of IoT is in actuality the Internet of smart things (smart IoT). Supported by intelligent optimization, smart IoT can increase productivity of work and enhance quality of lives for people. Let us take “cities” — the engines of global economic growth — as an example. Smart cities have the potential to dramatically improve the lives of everyone. In intelligent transportation systems (ITS), smart IoT can not only monitor the status of the transportation, but also optimize traffic signal controls to solve traffic congestion and provide the travelers with better routes and appropriate transportation information, etc. Combining IoT and machine learning (ML) can also make our roads safer. Profits by smart IoT have been shown also in health-care, logistics, environment, smart home, in the aspects of better quality, energy conservation, efficiency increase, and so on.

Smart IoT remains in its infancy now in terms of the technology  development and the effect on our global economy system and our daily lives. Maximum IoT statistics aren't used presently within the era of big data. Maximum IoT has no intelligence inside the generation of artificial intelligence (AI).  IoT which might be used these days are on the whole for anomaly detection and control, as opposed to optimization and prediction. Given the brilliant anticipated increase of the Internet over the following 10 years, it is considered one of vital challenges and possibilities for us to invent and practice in real-global programs on a way to make the IoT smarter to generate the greatest value.

 

 

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Guest Contribution by Kenie Ho and Forrest Jones, Finnegan, Henderson, Farabow, Garrett & Dunner, LLP

Everyone at this point, even the most technology averse, has heard of the Internet of Things (IoT) or seen some of its products.  From “learning thermostats” like Nest to “wearable technologies” like the FitBit, the average consumer sees devices moving toward more integration in unexpected places.  And, like the proverbial iceberg, these consumer-facing technologies make up only the tip of a much larger wave of innovation, which is already reshaping how business, the economy, and society operates.  That rising wave is greatest in the Industrial IoT.  

The Tip

At its core, IoT is a simple concept.  It’s about inter-networking not just traditional computing devices, such as personal computers, laptops, or media centers, but also a host of other objects, such as thermostats, coffee makers, cars, lights, security systems, employee badges, watches, and toothbrushes—to name just a few that have been folded into the IoT world. 

The advantage of having each of these devices talking to each other is the same as it was for inter-networking computers: efficiency, communication, and automation.  Say you live in an IoT-connected house and wake up on a cold, snowy morning.  Your smartwatch detects when you wake up through your movement and heartbeat.  It sends a signal for the coffee maker to start and for the thermostat to warm up the house.  Once you’ve enjoyed your coffee, your smart car uses that as a cue to turn on its engine so that you can hop in an already-warmed car and ride to work. 

Before you leave though, the washing machine and dishwasher set themselves to do a load of laundry and dishes. The appliances and water heater talk to each other taking turns cleaning throughout the day, but with maximum efficiency because they know you won’t be home until dark.  With each of these devices connected together, they get the benefit of the information all the other devices collect, and so can do more with minimal additional expense.

The Industrial IoT

The Industrial IoT (IIoT) applies the same concept as the above consumer-facing example, only on a much larger scale and for more specific purposes.  Instead of a coffee maker in your home, the same concept is applied to a fleet of coffee makers in office buildings across a whole city.  Each one reports back to a central server on its inventory, allowing a beverage-supply company to efficiently plan its resupply route.  Or as another example, the component parts in a fleet of construction vehicles each regularly report on their respective wear and tear, allowing the manufacturer to automatically bill and send out replacement parts just before they are needed, saving construction firms time and money in downtime from worn-out equipment. 

IIoT systems tend to focus more on industry needs, with a specific efficiency in mind.  So we see a wide variety of specific, and often purpose-tailored IIoT systems in particular industries.  Compare this with the commercial IoT you see at home.  Home consumers seek integration across a large swath of household objects.  Without knowing precisely what functions they might like over the life of their IoT devices, consumers want to be able to enable whatever future feature they desire without needing to rebuy an entire system.

As IoT technology matures, disparate IIoT systems are merging towards each other similar to the consumer IoT world, leading to many potential conflicts.  For example, GE Predix and IBM Bluemix are already in a collision course as each expands into the analytics space of the IIoT, and the permeating presence of the lighting industry seems destined to spark a wave of litigations as each player vies to upgrade the existing lighting solutions to IIoT-compatible LED systems.

Standards-Essential Patents

In the past, industries have recognized the problems that multiple competing systems can cause.  This has been particularly acute in areas like telecommunications, where multiple devices made by different manufacturers must talk to one another.  Telecommunication companies have typically relied on a standard-setting organization (SSO) to set interoperability standards to ensure compatibility.  Standards will be just as important to IIoT because interoperability is its lifeblood.

Companies who help set a standard often have invested great time and money developing the technology on which the standard may rely, including securing intellectual property (IP) rights like patents to protect that investment.  To address this issue, SSOs typically require that SSO members license any patents essential to practice the standard on “fair, reasonable, and non-discriminatory” terms (referred to as “FRAND” terms).

Sophisticated companies in each industry sector are keeping an eye on IIoT standard-setting efforts.  While it may be frustrating to be limited to FRAND terms when licensing a patent to a competitor, having competitors adopt your technology and pay you a royalty will promote your profile in the industry.  Further, if you don’t participate in the standard-setting process, a competitor’s technology could end up as the adopted standard, and you’d be the one paying license royalties instead of collecting them.

Large-Scale Litigation May Be Inevitable

While some industries may try to rely on technological standards and FRAND licensing terms to ensure ease of entry into the market, it’s likely that other industries will rely on ecosystem “buy-in” to provide customer loyalty.  Ecosystem buy-in is a species of “prospective cost” logic.

If a company has already outfitted its shipping fleet with trucks that use GPS locaters from System Y, then it makes more sense to buy the fuel monitors or load-reporting systems of System Y as well.  Consequently, multiple “System Y" compatible devices will be used across the fleet, making it inefficient to switch to another system.  At that point, the company has bought into the System Y ecosystem of products and won’t want to pay “prospective costs” to switch.

These ecosystem efficiencies will, over time, create a handful of large, powerful default standards, which will perform analogous functions but will have separate competing families of products.  If this sounds familiar, then it should.  This same storm of factors was partially the reason for the explosion in litigation between tech giants, Apple and Samsung, in the on-going smart-phone wars.  In a way, that was the first wave of large-scale IoT patent wars.

An important distinction for firms entering this expanding field, though, is that the smart-phone wars were constrained to mostly just phones.  The technological players were well defined, and each knew who the others were likely to be.  The wars were as much about carving out territory in the market as about placing entry barriers to new, disruptive firms that might challenge existing players.

IIoT seems destined to be much harder to control.  By its nature, it is set up to encourage disruption, as tech companies move into industrial areas they have never touched before, and industrial companies start developing technologies that widely apply beyond their industry.  Amazon’s constantly morphing role in the American tech space, from online goods purveyor to Amazon Web Services provider, is just one example of the power of the IoT.  As these industries are disrupted, it becomes harder, if not impossible, to predict precisely where the next major legal challenge will come.  Therefore, companies have fallen back to the conventional patent practice of developing or acquiring defensive patent portfolios as an essential part of expanding into IIoT.

Conclusion

IIoT is already changing the way industry operates, and this new frontier has pushed its way into the world of intellectual property law.  For pioneers in any field, monitoring and participating in any standards-setting efforts in their industry area becomes critical.  Further, maintaining a comprehensive IP portfolio will be vital to protecting against the waves of litigation to come.  Even with these legal hurdles, though, the IIoT is an area of massive opportunity and growth.

 

Authors’ Bio

Kenie Ho leads the IoT legal group at Finnegan, Henderson, Farabow, Garrett & Dunner LLP. He is a thought leader on IP issues for IoT and frequently speaks and publishes on IoT legal topics.  He has litigated well over 60 patents, primarily focusing on electrical, software, and consumer electronics technology.  In addition to enforcing and defending against patent infringement lawsuits, he helps startups and large companies strategically develop their patent portfolios and IP rights.

Forrest Jones is an attorney at Finnegan, focusing his practice on patent litigation in federal district courts and prosecution ofpatent applications. He has technical experience in electrical and computer engineering, including computer software, signal processing, power generation, consumer electronics, and business methods.

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A to Z of Analytics

Analytics has taken world by storm & It it the powerhouse for all the digital transformation happening in every industry.

Today everybody is generating tons of data – we as consumers leaving digital footprints on social media,IoT generating millions of records from sensors, Mobile phones are used from morning till we sleep. All these variety of data formats are stored in Big Data platform. But only storing this data is not going to take us anywhere unless analytics is applied on it. Hence it is extremely important to close the loop with Analytics insights.
Here is my version of A to Z for Analytics:
Artificial Intelligence: AI is the capability of a machine to imitate intelligent human behavior. BMW, Tesla, Google are using AI for self-driving cars. AI should be used to solve real world tough problems like climate modeling to disease analysis and betterment of humanity.
Boosting and Bagging: it is the technique used to generate more accurate models by ensembling multiple models together
Crisp-DM: is the cross industry standard process for data mining.  It was developed by a consortium of companies like SPSS, Teradata, Daimler and NCR Corporation in 1997 to bring the order in developing analytics models. Major 6 steps involved are business understanding, data understanding, data preparation, modeling, evaluation and deployment.
Data preparation: In analytics deployments more than 60% time is spent on data preparation. As a normal rule is garbage in garbage out. Hence it is important to cleanse and normalize the data and make it available for consumption by model.
Ensembling: is the technique of combining two or more algorithms to get more robust predictions. It is like combining all the marks we obtain in exams to arrive at final overall score. Random Forest is one such example combining multiple decision trees.
Feature selection: Simply put this means selecting only those feature or variables from the data which really makes sense and remove non relevant variables. This uplifts the model accuracy.
Gini Coefficient: it is used to measure the predictive power of the model typically used in credit scoring tools to find out who will repay and who will default on a loan.
Histogram: This is a graphical representation of the distribution of a set of numeric data, usually a vertical bar graph used for exploratory analytics and data preparation step.
Independent Variable: is the variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable like effect of increasing the price on Sales.
Jubatus: This is online Machine Learning Library covering Classification, Regression, Recommendation (Nearest Neighbor Search), Graph Mining, Anomaly Detection, Clustering
KNN: K nearest neighbor algorithm in Machine Learning used for classification problems based on distance or similarity between data points.
Lift Chart: These are widely used in campaign targeting problems, to determine which decile can we target customers for a specific campaign. Also, it tells you how much response you can expect from the new target base.
Model: There are more than 50+ modeling techniques like regressions, decision trees, SVM, GLM, Neural networks etc present in any technology platform like SAS Enterprise miner, IBM SPSS or R. They are broadly categorized under supervised and unsupervised methods into classification, clustering, association rules.
Neural Networks: These are typically organized in layers made up by nodes and mimic the learning like brain does. Today Deep Learning is emerging field based on deep neural networks.
 
Optimization: It the Use of simulations techniques to identify scenarios which will produce best results within available constraints e.g. Sale price optimization, identifying optimal Inventory for maximum fulfillment & avoid stock outs
PMML: this is xml base file format developed by data mining group to transfer models between various technology platforms and it stands for predictive model markup language.
Quartile: It is dividing the sorted output of model into 4 groups for further action.
R: Today every university and even corporates are using R for statistical model building. It is freely available and there are licensed versions like Microsoft R. more than 7000 packages are now available at disposal to data scientists.
Sentiment Analytics: Is the process of determining whether an information or service provided by business leads to positive, negative or neutral human feelings or opinions. All the consumer product companies are measuring the sentiments 24/7 and adjusting there marketing strategies.
Text Analytics: It is used to discover & extract meaningful patterns and relationships from the text collection from social media site such as Facebook, Twitter, Linked-in, Blogs, Call center scripts.
Unsupervised Learning: These are algorithms where there is only input data and expected to find some patterns. Clustering & Association algorithms like k-menas & apriori are best examples.
Visualization: It is the method of enhanced exploratory data analysis & showing output of modeling results with highly interactive statistical graphics. Any model output has to be presented to senior management in most compelling way. Tableau, Qlikview, Spotfire are leading visualization tools.
What-If analysis: It is the method to simulate various business scenarios questions like what if we increased our marketing budget by 20%, what will be impact on sales? Monte Carlo simulation is very popular.
What do think should come for X, Y, Z?
Read more…

The Evolution of Industry 4.0

Welcome to the future, available now courtesy of Industry 4.0. Dreams not so long ago, today’s realities are an amazing presentation of devices, humans, sensors, and machines working together to achieve what once seemed impossible. Small wonder the result is branded as a “smart factory.”

Who would have guessed that the Industrial Revolution would evolve in such a remarkable fashion? Stage 1.0 introduced the labor-saving method of newly-developed machinery powered by steam. Inventors thought of other ways to improve productivity and introduced 2.0 with electricity, mass production and the assembly line. Stage 3.0 took longer to arrive with computers and the assembly line presence of machines and robots working alongside or replacing humans.

Each stage inspired human imagination about Industry 4.0 and the endless possibilities that exist. The Internet of Things, or IoT, includes cyber- physical systems capable of making decentralized decisions. Videos of driverless cars are featured on Facebook posts. Robots operate commercial floor cleaners and delicate surgical instruments. Sensors warn robots to move out of the way of approaching objects!

What exactly is Industry 4.0? It’s a system or factory that possesses four essential features.

Decentralized decision-making: Cyber-physical systems are as independent as possible and make uncomplicated decisions on their own.

Information transparency: Sensor data lets systems build a virtual copy of the physical world in which it exists based on the concepts that affect activities and awareness.

Technical assistance: Systems aid humans in problem-solving and decision-making as well as assist them with unsafe or dangerous tasks.

Interoperability: Devices, sensors, machines and people connect and communicate with one another.

Questions arise about what it means and how it affects everyday activities. The public is active in learning Industry 4.0 participation, although not everyone is aware of it. Proficiency in using computer programs, tablets, and smartphones prepare people of all ages for this technological change to HMI (human machine interface) devices.

The term “big data” describes the huge amount of information collected from network devices. Industry 4.0 enabled equipment sorts and analyzes data by prescribed search criteria for various users. Information reflects items like production methods and a product’s reaction to certain situations. The system’s ability to present it in different ways makes it useful to separate departments, researchers, and end-users.

Sharing data anywhere at any time requires trust and cooperation up and down the supply chain. Identified concerns can be addressed quickly using the four essential features of Industry 4.0.

About Bill McCabe/ Internet of Things Recruiting - Executive Search/ Retained Search for the Internet of Things/ Machine 2 Machine/ Big Data Markets

IBM IOT Futurist - see you at #IBMInterconnect - March 19-23 Las Vegas

Top 50 IOT Authority on Twitter - per IoT Central

Need Help finding your next Big Data or IOT Employee or If you require the top 5% of IOT talent let’s talk. Drop me a line or use this link to schedule an IOT Search Assessment Call Click Here to Schedule

OR Contact me at 303-337-7871

Read more…

At ELEKS, we run several R&D projects related to IoT, drones and other smart devices. Over the course of these projects, we discovered that there are some tasks that are common for different devices and platforms. Our team has substantial experience with complex enterprise solutions, so when our partners from Cisco Systems suggested that we apply our expertise to IoT and create a framework for IoT solutions, we gladly joined the project.

Currently, there are a lot of IoT tools and solutions (as much as 35 are mentioned in this single article), so why should we create something new? The majority of the existing frameworks focus on programming and controlling separate devices. Yet, we aimed to develop a solution that would allow programming complex systems consisting of multiple devices that interact with each other.

Consequently, the framework had to provide the following set of features:

  • An interface for interaction with hardware;
  • Communication between devices;
  • Automatic search of available devices;
  • Dynamic scaling;
  • Integration with other systems;
  • Fault tolerance.

We saw that the majority of requirements could be implemented with Akka.

Ideation

To understand the problems we wanted to solve with Pragmukko, let’s look at the hypothetical business case that can become a commonplace occurrence in the very near future. Assume we have several drones controlled by Raspberry Pi, a regular PC as a control station and a smart fridge with ice cream controlled by Intel Edison. All the devices are physically located in one room and are connected to one and the same network via Wi-Fi. A drone has to pick up and deliver some ice cream from the fridge when a user orders it to do so. Therefore, the framework’s main goal is to enable easy programming of this function.

Since the project team decided to use Akka, our first idea was to launch an Akka node on every component of our system: drones, control station and fridge, and then build the logic of every component as an actor in the node. This way, our system would look like a regular Akka cluster with some nodes capable of flying and other being fridges with ice cream. Akka enables the drones, the fridge and the control station to communicate with each other, control the state of the cluster, receive the list of the cluster members, etc. Under certain conditions, our system is decentralized and resistant to the failure of individual components. Moreover, we can easily add new components to the cluster – they will immediately get the information about other members and join the operation. This means that we can effortlessly install one more fridge, or, if any drone takes too much ice cream and falls down, we can employ additional drones.

In general, we had to write a hardware interaction layer and bring the whole IoT and drones thing together. That’s what we did, along with some other things.

Implementation

To bring the system to life, the team took the following steps. First of all, we created a mechanism to automatically group the nodes in a cluster. Although Akka has this function, we ignored it because it required preliminary configuration of seed nodes. We also needed to know the network addresses of all cluster members, and these were the kind of complications we wanted to avoid. Instead, the project team developed the node grouping mechanism based on UDP broadcasting.

Then, we programmed the device nodes to interact with hardware by transforming the incoming Akka messages into commands. We named such nodes Embedded Nodes. Schematically, they can be presented in the following way:

Drones and IoT ELEKSLabs

Broadcast Actor is an actor that sends UDP-broadcast messages for the node to be identified and added to the cluster.
Embedded Actor is an actor that processes the messages from other nodes of the cluster and interacts with hardware. This actor can be extended to implement user logic.
Hardware Layer is the layer for hardware interaction. It is an actor that receives binary commands and writes them to the serial port.

Also, we introduced one more type of nodes, Manage Nodes, to work on a server, command the devices and provide interaction with other systems. Pragmukko allows extending the functions of a Manage Node with plugins that could also integrate with other systems. The plugins we have can integrate with MongoDB and HDFS as well as implement the REST interface based on Akka-http. Below is the illustration of this node:

Drones and IoT ELEKSLabs-2

UDP-Responder is an actor that adds new nodes to the cluster.

Sample code

Unfortunately, the project team was unable to implement the business case described above since there’s no fridge with ice cream in our office. Therefore, we tested a simplified case. We had several quadcopters randomly flying over a certain territory. They were controlled by the control station that kept them inside the perimeter.

Drones and IoT ELEKSLabs-3

Here’s the example of the code that works on a quadcopter:

1object EmbeddedMain extends App with DroneCommands {
2 
3  //  Initialize initial speed of the drone
4  var (vx, vy) = (Random.nextFloat(), Random.nextFloat())
5 
6  // Special helper to create Embedded Node
7  EmbeddedPragma {
8    ctx => {
9       
10      // The Start message comes right after the node joins the cluster 
11      case Start =>
12        // Here, we notify the hardware that we want to receive telemetry
13        ctx.subscribeHardwareEvents()
14         
15        // Notifying the hardware layer about the desired initial position and speed of the drone
16        // We tell the drone to rise 10 meters up and start moving in directions vx and vy with the specified              // speed
17 
18        ctx.self ! moveTo(0,0,-10)           
19        ctx.self ! direction(vx, vy, 0)
20 
21      // Here, we process the autopilot data
22      // The data is received in the binary format
23      // The tools for processing are available in the DroneCommands trait
24      case TelemetryBatch(batch) =>  // TelemetryBatch - message contains drone
25        // Checking if the received data contains the information about the drone’s position in space
26        val position = batch.collect { case DronePositionLocal(p) => p }.lastOption
27        // If the position is located, we send the information about it to all listeners
28        ctx.listeners foreach ( _ ! position )
29 
30      // Processing commands from the control station
31 
32      case "turn x" =>
33        vx = -vx
34        ctx.self ! direction(vx, vy, 0)
35 
36      case "turn y" =>
37        vy = -vy
38        ctx.self ! direction(vx, vy, 0)
39    }
40  }
41}

And the code of the control station:

1// plug-in listing
2class DroneControlExt extends GCExtentions with DroneCommands {
3 
4  override def process(manager: ActorRef): Receive = {
5 
6     // Processing messages from a drone
7     //  If the drone crosses the 20x20 m perimeter
8     // we send the command to turn along the required axis
9    case DronePositionLocal(position) =>
10      if (position.x > 10 || position.x < -10) sender() ! "turn x"
11      if (position.y > 10 || position.y < -10) sender() ! "turn y"
12  }
13 
14}

Basically, that’s all with the code. We compiled it and launched the Embedded Node on the drone. As for the control station, it can be launched anywhere: for instance on a laptop, provided that it is located in the same network with drones.

So, what’s happening behind the scenes? A drone, when launched, starts identifying itself with broadcast messages. As soon as a control station gets the message, it adds the drone to a cluster. Finding itself in the cluster, the drone adds the control station to the context.listeners list.

The control station and the drone’s Embedded Node implement the trait DroneCommands. This trait contains utilities for Pixhawk MAVLink protocol. For example, direction(dx:Float, dy:Float, dz:Float) method forms a binary command that sets the drone speed along certain axes. All incoming binary commands are automatically transmitted to the hardware interaction layer that communicates with the drone’s autopilot. Since we implemented the hardware interaction layer as a simple interface to the serial port, other autopilots would work if you change the implementation of DroneCommands.

Here are a few more words about the deployment. As the number of drones and IoT devices we experimented with grew bigger, the time to deploy them also increased. We had to go through the same routine for each device: install Java, upload the app, register it as daemon, and reboot the device. Only after all these steps could we deploy the Manage Node with all the components it interacted with. The process was so complicated that the team immediately wanted to automate it. On the advice of our partners from Cisco, we used Mantl.io. A platform for rapidly deploying globally distributed services, Mantl provides all the necessary components to start fast and improve often. As Cisco’s CTO Zorawar Biri Singh outlines in his exclusive interview to InfoWorld, in the foreseeable future, this lightweight, high-level container PaaS can be used as an orchestrator solution for building tightly coupled systems.

Drones and IoT ELEKSLabs-4

With just a few Ansible scripts, we were capable of deploying the necessary infrastructure, regardless of the number of devices in it. The team really loved this method and, as a result, we highly recommend it.

Performance

Traditionally, Akka is used to build distributed server solutions. So, we faced a question – if Akka-based products are effective enough on devices with limited computing capabilities. We received the answer through an experiment. In our case, we used the Raspberry Pi 2 Model B as the drone’s onboard computer. The device had the following characteristics:

  • A 900MHz quad-core ARM Cortex-A7 CPU
  • 1GB RAM

The characteristics might look quite impressive, but alongside our process, this mini computer also has to run two important processes with nearly real-time priorities. To prevent our software from inhibiting the neighbouring processes, we limited the CPU usage for it to a single core. Additionally, we set one more limitation: the use of memory by the Java process was restricted by the -Xmx32m parameter.

Before the test flight, the team decided to check how our Embedded Node would work on Raspberry Pi with such limitations. Once launched, the process immediately utilised 25% CPU (100% according to top data), or just one core out of four, and in a couple of seconds the CPU use dropped down to an acceptable 10-15%. The memory showed even better results: the process did not utilize all of the allocated 32 Mb, and the garbage collector was starting after a time-out.

During the flight, the performance parameters declined. CPU use reached 20% and the garbage collector worked intensely, because the autopilot generated a lot of telemetry data. If necessary, there is some possibility for optimization by limiting the telemetry stream only to autopilot configuration. We did not introduce such limitations because the parameters were within the normal range and the software did its job well – the control station directed the drone and kept it within the designated perimeter.

The project also revealed a number of challenges we still have to overcome. In particular, the higher the drone’s speed, the harder it is to control it. This is a complex problem and our framework cannot take all the blame for it. The control scheme that we used in the experiment is hardly suitable for real tasks. We can feed the drone the destination coordinates instead of the velocity vectors. Such an approach will add accuracy to the drone positioning, offsetting the dependance on the data link between the drone and the control station.

The second problem is the insufficient speed of our software. Akka is very slow to start. If the actor system is launched, joining the cluster takes from 5 to 20 seconds. Primarily, the speed depends on the quality of the network connection. We still work to improve this aspect.

Emulation

Another interesting feature of Pragmukko is the possibility to emulate hardware cluster members. This can come in handy while testing the business logic that is implemented on the devices. To enable hardware emulation, we extended the framework with the possibility of switching the Hardware Interaction Layer to Mock. For the tests, we saved real telemetry in a file and then ran its instances on the Embedded Node. I can already see how we will run integration tests for our drones on Jenkins. Then, the expression “the tests have crashed” will not be nearly as dramatic as it is now.

Follow the link to learn more about Pragmukko, its features and the project roadmap.

Conclusions

Pragmukko makes the system easy to program, configure and test. In spite of certain complications, we are satisfied with the result. We also believe that our framework has a potential for a widespread use.

I thank all of you who have read up to these lines. No drone was harmed in the making of this experiment.

Read more…
Remember when you were teenager and wanted to go on vacation with parents-you were asked to go to travel agent and get all the printed brochures of exotic locations?  
Then came the dot.com wave and online booking sites like Expedia, Travelocity, Makemytrip paved so much that took travel agencies out of equation.
We used to send holiday postcards to our friends and families back home, which are gone out of business due to social media postings on Facebook, Instagram.
Lonely Planet used to be the traveler’s bible, but now we go to tons of websites like TripAdvisor, Priceline which provide us with advice and reviews on hotels, tours and restaurants.
Now I am able to book my flight online, have my boarding pass on my phone, check in with machines, go through automated clearance gates and even validate my boarding pass to board the plane
The travel industry, like many others, is being disrupted by great ideas powered by digital technology and innovation.
Some of the digital innovations travel industry taken so far:
·     Online booking sites like Expedia, Travelocity, MakeMyTrip, Trivago
·     Mobile optimization with Wi-Fi enablement
·     Targeting and hyper-personalization with Big Data Analytics
·     Digital discounts on travel by Kayak, Tripadvisor
·     Smartphones for research vacations, deals, feedbacks
·     Wearables like Disney band for payments, room keys
·     Bluetooth beacons to guide travelers in the vicinity at airports
·     Virtual reality – see the places without even getting out of home
All such digital footprint of customers are collected and then analyzed by big data analytics to hyper personalized the experience.
With extensively networked digital properties and deep hooks into customer data collected via travel booking sites and social media channels, travel companies are delivering customized dream vacations according to the likes and preferences of today’s travelers.
Today’s trend is towards spending money on memories & experiences instead of material possessions.
Accordingly, travel companies are investing in their digital storefronts and omni-channels to keep today’s hyper-connected travelers snapping, sharing, researching and reviewing on the fly – leaving immense data footprints for marketers to leverage.
Bluesmart is a high-quality carry-on suitcase that you can control from your phone. From the app you can lock and unlock it, weigh it, track its location, be notified if you are leaving it behind and find out more about your travel habits.
Thomas Cook have introduced virtual reality experiences across select stores.
Digital disrupters like Airbnb have already put tremendous pressure on hotels.
Starwood Hotels have launched “Let’s chat”, enabling guests to communicate with its front desk associates via WhatsApp, Blackberry messenger or iPhone before or during their stay.
World has gone from Bullock Cart to Hyperloop today. The future will belong to those using data-based intelligence to offer better experiences, encourage exotic longer and more frequent stays, and build long-term loyalty
Read more…

Are You Real? Bringing Authentication to IoT

Serial entrepreneur Chris Ciabarra is at it again. The co-founder and CTO of Revel Systems, an iPad point-of-sale (POS) disruptor which has a valuation of more than $500 million and landed a global contract to replace all of Shell Oil’s PoS terminals with Revel’s, has helped launch Authenticated Reality, an authenticated secure community that fosters real interactions, comments and online conversations from real people on the internet.

Chris is an anti-hacker and data security expert with a strong background in PCI compliance and P2PE. He has presented across the globe as well as in front of the 5th Annual United States Homeland Security Conference on various security topics including how the Internet needs to change.

While his current company is aimed at getting consumers and business to identify themselves as “real,” we couldn’t help but ask him about what his current endeavor might mean for IoT.

What is Authenticated Reality?

Authenticated Reality is a secure community of users and devices. In order to be accepted into the secure community you must authenticate yourself. With all users authenticated this will keep the online community safe from hackers that often hide behind anonymity .  

You talk a lot about this concept of “The New Internet”. What do you mean by it?  

The New Internet is a secure community of users that connect and see each other's real identity while interacting. The biggest problem we face on the internet today stems from a lack of identification. This problem is widespread across multiple verticals when you look at what is wrong with the current internet. For example, IMDB.com recently disabled their movie boards where fans would comment and engage with other movie fanatics. Why would IMDB.com disable something so popular that millions of their users were actively engaging in? Because they felt it was not longer fostering a positive environment for their millions of users. Too much hatred and spamming from online trolls that hide behind a pseudonym and a computer screen. On The New Internet this hatred and spamming would for the most part go away because once you remove the anonymity, users are going to be much more positive if their comments and interactions will reflect on their reputation that is attached to their real name and identity. On The New Internet you can comment on every single page of the old internet but with your reputation on the line, you will be less likely to post something fictitious or negative.

Tell us how your technology works.

Just download the browser and you will get a sidebar to comment and rate every page on old and New Internet. On The New Internet there will be domains that have never before existed on the old Internet and you will be able to comment and rate those pages as well. Users can purchase any domain name they would like even if it is not available on the old Internet.

As our publication name suggests, we focus on the Internet of Things, specifically the Industrial IoT. How do you plan to roll your product out for IoT devices? Can you provide examples?  

On The New Internet every device will be attached to an authenticated user. This is particularly useful for drones and identifying the owner of the device. We will be able to monitor all IoT devices and if it is acting suspicious we can turn it off the network for further investigation.  

We’ve written about Bruce Schneier and his calls for government regulation to address security issues in the IoT. A part of your offering includes a solution for governments. What’s your take on regulation and where do you see Authenticated Reality playing a role?  

We would like to authenticate all users, entities and devices to enable a safe internet experience.

Do you see any authentication solutions in IoT at this time?  And at what point in the future do you think an IoT solution from Authenticated Reality will be available?   

Yes we have patented a IOT security device that we will release in a few months time that will allow IOT devices to get secured. This IOT security device will have a WiFi access point on it that IOT devices attach and register to and the device will keep them secure.

Anything else you’d like to add?

The New Internet is here and if you had the vision back in 80’s to take advantage of the old internet  you would be rich, now is your chance to have the vision and join the new internet early on.  Join at http://thenewinternet.com

Photo of and credit: Chris Ciabarra

 

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What if you could browse an online grocery store, add products to your shopping cart and complete your order right on your smart fridge’s screen using your voice instead of touch interactions? Does speaking to your fridge sound weird? Not at all. At least when you have a friendly, voice-powered shopping assistant integrated with your refrigerator.
Read more…

IoT Central Digest, April 4, 2017

Here are the latest featured articles from IoT Central members and contributors. In this issue we look at 5 cities that are doing right in smart city development, a fascinating infographic that will get you up to speed on the history of autonomous vehicles, explore who will survive the era of robots, and much more. 

Reminder: All members are free to post on IoT Central. We feature the best content and share across our social networks and other channels. Consider contributing today. Our guidelines are here. If you like what you're ready, considering forwarding this to a friend and encourage them to join our community here.

5 Cities That Are Setting Trends in Smart City Development

Infographic: The Growth of the Autonomous Car Market

The invention of autonomous cars gained widespread public exposure in 1939’s world fair exhibition. Automakers had envisioned the car with an out of box abilities to drive through green valleys and palm trees on its own. Cars with a variety of techniques like radar sensors, video cameras, ultrasonic sensors and processing computers were to be designed to drive on roads.  

Managing the Risk of Dirty Data With a Pull-Based IoT Architecture

IoT Generalist vs IoT Specialist, Who will survive to the era of Robots?

Breaking Down the IDC Top 10 IoT Predictions for 2017

Guest post by Evan Birkhead.

A new IDC FutureScape offers top 10 predictions for the Worldwide IoT in 2017.  The research evaluates 10 emerging trends and ranks them in terms of their likely impact across the enterprise and the time it will take each prediction to go mainstream (meaning the middle of bellcurve of adoption). 

What is cognitive computing and how does it impact your future


Follow us on Twitter | Join our LinkedIn group | Members Only | For Bloggers | Subscribe
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Digital Transformation in Manufacturing

Manufacturing companies have traditionally been slow to react to the advent of digital technologies like intelligent robotsdrones, sensor technology,artificial intelligence, nanotechnology & 3d Printing.
Industry 4.0 has changed manufacturing. At a high-level, Industry 4.0 represents the vision of the interconnected factory where all equipment is online, and in some way, is also intelligent and capable of making its own decisions.
The explosion in connected devices and platforms, abundance of data from field devices and rapidly changing technology landscape has made it imperative for companies to quickly adapt their products and services and move from physical world to a digital world.
Today, Manufacturing is transforming from mass production to the one characterized by mass customization. Not only must the right products be delivered to the right person for the right price, the process of how products are designed and delivered must now be at a level of sophistication.
First step in digitization is to analyze current state of all systems starting R&D, procurement, production, warehousing, logistics, marketing, sales & service.
The digitization of manufacturing impacts every aspect of operations and the supply chain. It starts with equipment design, and continues through product design, production process improvement and, ultimately, monitoring and improving the end-user experience.
Digital transformation revolutionizes the way manufacturers share and manage product & engineering design, specs on the cloud by collaborating across geographies.
Down time and reliability are critical when it comes to the operation of equipment and machines on a shop floor. With Big data Analytics, the quick and easy access to this operation data, production information, inventory, quality data gives ability to quickly adjust to machine status across the enterprise.
Quality and yield is directly related to manufacturing processes as to how raw materials are used, inspected, manufactured, and how everything comes together. This really determines the quality level of the products. Cognitive computing enables earlier identification of nascent quality problems, increased production yield, and reduction of problems that lead to service and warranty costs.
Implementing smarter resource and supply chain optimization strategies helps to improve the cost efficiency of these resources like energy consumption, worker safety, and employee resource efficiency.
Service Excellence is also an important part of the strategy that companies are using to achieve digital transformation in the manufacturing space. Connected Devices (IoT) are changing the paradigm of delivering after-sales service. Some of the advantage are most prevalent in several selected industries, such as industrial equipment, power generation and HVAC providers:
·       Push Service Notifications
      o   How is your asset health?
      o   How is your asset usage?
·       Predictive/ PreventiveMaintenance
·       Break-Down Assistance
·       Usage-based Billing
·       Spares Fulfillment
General Electric’s jet engines combine cloud-based services, analytics and on-line sensors to report usage and status and help predict potential failures. The result is improved uptime and lower cost of ownership.
Additive manufacturing (3D printers) for prototyping help shorten the iteration cycles in the design process and help to turn innovation into value. 3D printing is also quickly gaining ground in the commercial manufacturing of customized products in low volumes.
Smart machines integrated with forklifts, storage shelves and production equipment. These machines are able to tak
e autonomous decisions and communicate with each other to drive material 

replenishment, trigger manufacturing and much more.
Industry 4.0, allowing manufacturers to have more flexible manufacturing processes that can better react to customer demands.

Read more…

"Trends in Smart City Development" is a new report from the National League of Cities featuring case studies about how five cities – Philadelphia, San Francisco, Chicago, Charlotte, N.C., and New Delhi, India – are using different approaches to implement smart city projects.

The report also provides recommendations to help local governments consider and plan smart city projects.

A "smart city" is one that has developed technological infrastructure that enables it to collect, aggregate, and analyze real-time data to improve the lives of its residents. The report suggests that any smart city effort should include explicit policy recommendations regarding smart infrastructure and data, a functioning administrative component, and some form of community engagement.

You can read the full report here. (PDF)

Read more…

By 2021, it is estimated that big data could reach $66.8 billion in net worth. But as the volumes of data that becomes accessible increases, so do concerns with privacy as the data out there might not be exactly what people want to have released.

In fact, more customers have become vocal about their concerns with the data that is being collected by them. With potential security challenges and high profile breaches taking place all the time, people are demanding better protection. With data breaches giving sensitive personal information to thieves that results in millions of people becoming victims to cybercrimes, it’s getting tougher for people to trust the online businesses they are working with.

Perhaps this is why more people are demanding more action being done. With the monetization of big data, there becomes valuable databases that are targeted for attack. Many of which use a single level of protection now to protect the data they contain. This is why automated data transfers with beefed up security might be a better result. This will require new data to be validated, to reduce the risk of anything that isn’t trustworthy or accurate creating problems. Since the data would be monitored and tracked on a regular basis, the likelihood of a breach decreases. After all, the more data contained in a single resource, the more that can be obtained by cyber criminals, and thus there becomes more mistrust with the public, as they see new concerns being brought out against them.

Companies must now accept greater responsibility for the personal information they maintain. While risk can vary, companies must accept that their responsibility is there. If a breach does happen, the public is more likely to be unforgiving, especially if there isn’t transparency with what happened and how the company will ensure that this never happens again. This includes those who attempt to pass the blame by using third party providers to help them store their data in clouds and other areas. As the responsibility for the data isn’t moved when you move the information, despite some misconceptions out there.

Customers are also increasing in curiosity with how their data is being used. When you monetize big data, you are also releasing information to companies that some may be concerned as private. Fortunately, the government is actively reviewing this information and ensuring that better privacy focused measures are taken, so that companies can still benefit from the monetization of big data, without there being as much risk to the individuals that they are collecting the data on.

While unauthorized use and service failures can still occur with the big data, it does seem like more companies are committed to protecting the data that they are handling. This means that even when monetization expands, customers will be provided with their privacy rights through digital databases, while having new avenues of encrypted protection released so there is never any concern with their information getting in the wrong hands. 

About Bill McCabe/ Internet of Things Recruiting - Executive Search/ Retained Search for the Internet of Things/ Machine 2 Machine/ Big Data Markets

IBM IOT Futurist - see you at #IBMInterconnect - March 19-23 Las Vegas

Top 50 IOT Authority on Twitter - per IoT Central

Need Help finding your next Big Data or IOT Employee or If you require the top 5% of IOT talent let’s talk. Drop me a line or use this link to schedule an IOT Search Assessment Call Click Here to Schedule

OR Contact me at 303-337-7871

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The capital city of Ohio has proven to be a true innovator among U.S. Smart Cities, and has even been awarded the winner of the Department of Transportation’s Smart City Challenge. Columbus was chosen thanks to the city being a leader in innovative ideas that will transform the city to become greener, more efficient, and easier to live in. This is the first Smart City award in history, and it comes with some significant benefits for Columbus and its residents.

The most important, is a massive $40 million grant from the Department of Transportation. This will be supplemented by up to $10 million from Vulcan Inc., Paul G. Allen’s project and investment company. This $50 million will go towards a number of city initiatives, and is in addition to around $90 million that the city has already raised from private investors and partner companies.

Plans for the Future

The plans that secured Columbus the win are both ambitious, and holistically minded. Rather than just tackling one or a few different areas of the city for modernization, Columbus plans to invest in a number of different areas to benefit private residents, while supporting important sectors of the economy and attracting new investment.

A new rapid transit system will connect consumers to the main retail district of the city, and self-driving electric shuttles will be a major aspect of this system. This will not only provide easy access for consumers, but it will connect residents to jobs in the central districts of Columbus.

Healthcare is another major focus for smart innovations, and the new rapid transit systems will provide residents with easier access to facilities.

In addition to the rapid transit system, Columbus will implement new RFID technologies that will help to streamline toll payments, monitor traffic flow, and plan for future expansion and improvements based on road usage patterns.

A Worthy Winner of this Unprecedented Award

Columbus is the perfect city to be made the inaugural winner of the Smart City Challenge. Their innovations will help boost the economy and improve quality of life in one of America’s largest business and educational centers.

Merry Christmas and Happy New Year to all.

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