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internet of things (14)

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

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

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

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

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

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

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Digital Transformation in Utilities sector

It is easy to take for granted the technology we have at our disposal. We flick a switch and the lights go on, we turn on the tap and clean water comes out. We don’t have to worry about gas for cooking. 
But today the Utilities industry is under pressure to simultaneously reduce costs and improve operational performance.
Utilities sector is a bit late in digital innovations than RetailBanking or Insurance. With energy getting on the digital bandwagon with online customer engagement, smart sensors and better use of analytics, Utilities are now adopting it.
Digital technology gives utility companies the opportunity to collect much richer, customer level data, analyze it for service improvements, and add new services to change the way customers buy their products.
Smart technology will be used to monitor home energy usage, to trigger alerts when previously established maximum limits are being reached, and to offer ‘time of use’ tariffs that reward consumers for shifting demand from peak times. 
Electricity is the most versatile and widely used form of energy and global demand is growing continuously. Smart grids manage the electricity demand in sustainable, reliable and economic manner.
Advantages of Digital Transformation:
  • Digital makes customer self-service easy.
  • Digitally engaged customers trust their utilities.
  • Customer care, provided through digital technology, offers utilities both cost-to-serve efficiencies and improved customer intimacy.
  • Digital technology brings the capability to provide more accurate billing and payment processing, as well as faster response times for changing addresses and bills, removing and adding services, and many other functions
  • Using Mobile as a primary customer engagement channel for tips and alerts
  • Predictive maintenance with outage maps and real time alerts to service engineer helps reduce the downtime and costs
  • Smart meters allows utilities organizations to inform their customers about the energy consumption, tailor products and services to their customers while   achieving significant operational efficiencies at the same time

Meridian, a New Zealand energy company, launched PowerShop, an online energy retail market place that gives customers choice and control over how much power they buy and use. This helped Meridian attract online consumers and extend its reach of core retail offering.
Google’s Nest, an IoT enabled energy efficiency management gives details about consumption patterns and better control.
Thames Water, UK’s largest provider of water uses digital for remote asset monitoring to anticipate equipment failures and respond in near real time.
Big Data analytics and actionable intelligence gives competitive advantage by gained efficiency. 
IBM Watson with its cognitive computing power helped utilities identify trend and pattern analysis, predict which assets or pieces of equipment are most likely to cause points of failure. 
Today more than ever, utilities companies are asking: “How can we be competitive in this digital world?” People, whether they are customers, citizens or employees, increasingly expect a simple, fast and seamless experience. 
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Gone are the days, when companies used to decide strategy and then execute it for next five years as planned. 
Today company’s life on Fortune 500 or S&P 500 is just 15 years. Digital businesses like Uber, Airbnb did not exist before 2008 but now they are multi-billion dollar poster children for digital disruption.
Today due to digital, every business has to change how to operate, interact with their customers every day. Long term strategies are no longer valid or sustainable and change is constant feature.
Culture is a key determinant of this successful digital transformation. We can change our technologies, our infrastructure, and our processes. But without addressing the human element, lasting change will not happen. Culture is the operating system of the organization. It is like air, it is there but you can’t see it.
It's important for leaders to understand the business's current culture to map the right solution and timeline that will work for that business. No two organizational cultures are the same. Executives underestimate the importance of culture in an era of digital.  Most cultures are risk averse at a time, when taking risks is the most direct path to innovation.
But we have to remember that without the involvement, cooperation and feedback of the workforce, any digital transformation will struggle to maintain momentum.
Building an organizational culture for a successful adoption of digital technologies like IoTBig Data AnalyticsMobility requires everyone in the organization, from leaders to front-line employees, to be prepared to work in an open and transparent way. It’s hard for an organization to undergo digital transformation if the culture is one built around silos. In cases like these, cultural change would need to be addressed before the transformation process could begin
Culture leads the adoption of technology. The ability to innovate depends on the impatience of the organizational culture. Organizations have to build the culture and community, making the time for people to share experiences, test and learn what works, brainstorm and collaborate.
It takes time to develop a digital culture; the sooner a company acts, the more quickly it will be in a position to compete in this fast-paced, digitized, multichannel world.
Southwest Airlines, in operation for more than 40 years, brought in culture change and empowered employees to go Digital and help customers.
Imagine how GE, which is more than 130 years old and operating in more than 175 countries now, has a quest for cultural change to be leader in Digital and Industrial Internet of Things.
Coca Cola has reinvented itself with culture change by focusing on digital natives while offering more than 100 flavored drinks.

For Digital Transformation Culture is top most enabler. Without people, tools won’t make any difference!!
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New IoT App Makes Drivers Safer

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

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

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

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

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

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

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What is Edge Computing?

The name edge computing signifies the corner or edge in a network diagram at which traffic enters or exits the network.
Edge computing pushes computing power to the edges of a network, so instead of devices like drones or smart traffic lights needing to call home for instructions or data analysis, they can perform analytics themselves on streaming data and communicate with other devices to accomplish tasks.
In edge computing, the big data analytics happens very close to the IoTdevices and sensors. Edge computing thus can also speed up the analysis process, allowing decision makers to take action on insights faster than before. 
For organizations, this offers significant benefits. They have less data sent over their networks, which can improve performance and save on cloud computing costs. It allows organizations to discard IoT data that is only valuable for a limited amount of time, reducing storage and infrastructure costs. Further edge computing improves time to action and reduces response time down to milliseconds, while also conserving network resources.
In Industrial Internet of Things, applications such as power production, smart traffic lights, or manufacturing, the edge devices capture streaming data that can be used to prevent a part from failing, reroute traffic, optimize production, and prevent product defects.
Coca Cola free style dispensers are using edge computing to quickly understand the consumer behavior and help to be more responsive to needs.
GE locomotives take advantage of edge computing by gathering and processing real-time data about railway conditions, train maintenance, and even crew morale to help railroad companies move trains through crowded railway corridors in as safe and efficient a manner as possible.

With Digital Transformation and emerging technologies that will enable “smart” everything – cities, agriculture, cars, health, etc – in the future require the massive deployment of Internet of Things (IoT) sensors while edge computing will drive the implementations. 
Read more…

Do not stop asking for security in IoT

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).

The Security has been always a tradeoff between cost and benefit

I am honest when I say that I do not known how McKinsey gets calculate the total impact that IoT will have on the world economy in 2025, even on one of the specific sectors, and if they had taking into account the challenge of the Security, but it hardly matters: “The opportunities generated by IoT far outweigh the risks”.

With increased IoT opportunity comes increased security risks and a flourishing IoT Security Market (According with Zion Research the IoT Security Market will growth to USD 464 million in 2020).

A decade of breaches and the biggest attack target yet is looming

We all know the negative impact that news about cyber-attacks has in the society and enterprises. In less than a decade and according to Data Source: ICS- CERT (US) have gone from 39 incidents in 2010 to 295 incidents in 2015.

In a survey published by ATT, the company has logged a 458% increase in vulnerability scans of IoT devices in the last 2 years.

It is a temptation for hackers to test their skills in connected objects, whether connected cars or smart homes appliances. But I'm afraid they will go far beyond attacking smart factories, or smart transportation infrastructure or smart grids.

With the millions of unprotected devices out there, the multitude of IoT networks, IoT Platforms, and developers with lack of security I am one more that believes the biggest attack target yet is looming.

 New Threats

With the Internet of Things, we should be prepared for new attacks and we must design new essential defences.

The complex IoT Security Threat Map from Beecham Research provides an overlayed summary of the full set of threat and vulnerability analyses that is used to help clients shape their strategies. This Threat Map “summary” many of the top 5 features from each of those analyses.

1.       external threats and the top internal vulnerabilities of IoT applications

2.       the needs for robust authentication & authorisation & confidentiality

3.       the features and interactions between multiple networks used together in IoT;

4.       the complexities of combining Service Sector optimised capabilities of differing Service Enablement Platforms;

5.       the implementation and defences of edge device operating systems, chip integration and the associated Root of Trust.

 New Vulnerabilities

The OWASP Internet of Things Project is designed to help manufacturers, developers, and consumers better understand the security issues associated with the Internet of Things, and to enable users in any context to make better security decisions when building, deploying, or assessing IoT technologies.

The project looks to define a structure for various IoT sub-projects such as Attack Surface Areas, Testing Guides and Top Vulnerabilities. Bellow the top IoT Vulnerabilities.

 Subex white paper presenting their IoT solution add some real examples of  these vulnerabilities.

Insecure Web Interface: To exploit this vulnerability, attacker uses weak credentials or captures plain text credentials to access web interface. The impact results in data loss, denial of service and can lead to complete device take over. An insecure web interface was exploited by hackers to compromise Asus routers in 2014 that were shipped with default admin user name and password.

Insufficient Authentication/Authorization: Exploitation of this vulnerability involves attacker brute forcing weak passwords or poorly protected credentials to access a particular interface. The impact from this kind of attack is usually denial of service and can also lead to compromise of device. This vulnerability was exploited by ethical hackers to access head unit of Jeep Cherokee2 via WiFi-connectivity. The WiFi password for Jeep Cherokee unit is generated automatically based upon the time when car and head unit is started up. By guessing the time and using brute force techniques, the hackers were able to gain access to head unit.

Insecure Network Services: Attacker uses vulnerable network services to attack the device itself or bounce attacks off the device. Attackers can then use the compromised devices to facilitate attacks on other devices. This vulnerability was exploited by hackers that used 900 CCTV cameras3 globally to DoS attack a cloud platform service.

Lack of Transport Encryption: A lack of transport encryption allows 3rd parties to view data transmitted over the network. The impact of this kind of attack can lead to compromise of device or user accounts depending upon the data exposed. This weakness was exhibited by Toy Talk’s server domain which was susceptible to POODLE attack. Toy Talk helps Hello Barbie doll4 to talk to a child by uploading the words of a child to server and provide appropriate response after processing it. Though there was no reported hack on this, such a vulnerability could easily lead to one.

Privacy Concerns: Hackers use different vectors to view and/or collect personal data which is not properly protected. The impact of this attack is collection of personal user data. This vulnerability was exemplified by the VTech hack5 wherein in hackers were able to steal personal data of parents as well as children using VTech’s tablet.

Who owns the problem?

With the IoT we are creating a very complicated supply chain with lots of stakeholders so it's not always clear 'who owns the problem'. By way of an example with a simple home application and not Super Installers around; if you buy a central heating system and controller which requires you to push a button to increase the temperature then if it stops working you contact the company who supplied it. But if you buy a central heating boiler from one company, a wireless temperature controller from another, download a mobile App from another and have a weather station from another supplier then whose job is it to make sure it's secure and reliable? The simple cop-out is to say 'the homeowner bought the bits and connected them together therefore it's their responsibility' – well I'm sorry but that isn't good enough! 

Manufacturers can't simply divest themselves of responsibility simply because the home owner bought several component parts from different retailers. As a manufacturer you have a responsibility to ensure that your product is secure and reliable when used in any of the possible scenarios and use cases which means that manufacturers need to work together to ensure interoperability – we all own the problem!

This might come as a shock to some companies/industries but at some level even competitors have to work together to agree and implement architectures and connectivity that is secure and reliable. Standardization is a good example of this, if you look at the companies actively working together in ISO, ETSI, Bluetooth SIG etc. then they are often fierce competitors but they all recognize the need to work together to define common, secure and reliable platforms around which they can build interoperable products.  

If Cybersecurity is already top of mind for many organizations, is justified the alarm of lack of security in IoT?

In this three last years of evangelization of IoT, it has been no event or article not collect questions or comments on IoT Security and Privacy.

The good news is that according with the ATT State of IoT Security survey 2015, 85% of global organizations are considering exploring or implementing an IoT strategy but the bad news is that only 10% are fully confident that their connected devices are secure.

Source: ATT State of IoT Security survey 2015

And if we consider the report of Auth0, it scares me that only 10% of developers believe that most IoT devices on the market right now have the necessary security in place.

 

Source: Auth0

In a publication from EY titled “Cybersecurity and the IoT”, the company define three Stages to classify the current status of organizations in the implementation of IoT Security.

Stage 1: Activate

Organizations need to have a solid foundation of cybersecurity. This comprises a comprehensive set of information security measures, which will provide basic (but not good) defense against cyber-attacks. At this stage, organizations establish their fundamentals — i.e., they “activate” their cybersecurity.

Stage 2: Adapt

Organizations change — whether for survival or for growth. Threats also change. Therefore, the foundation of information security measures must adapt to keep pace and match the changing business requirements and dynamics otherwise they will become less and less effective over time. At this stage, organizations work to keep their cybersecurity up-to-date; i.e., they “adapt” to changing requirements.

Stage 3: Anticipate

Organizations need to develop tactics to detect and detract potential cyber-attacks. They must know exactly what they need to protect their most valuable assets, and rehearse appropriate responses to likely attack/incident scenarios: this requires a mature cyber threat intelligence capability, a robust risk assessment methodology, an experienced incident response mechanism and an informed organization. At this stage, organizations are more confident about their ability to handle more predictable threats and unexpected attacks; i.e., they anticipate cyber-attacks.

 

What enterprises needs to do

If you are thinking only in the benefits of IoT without consider the Security as a key component in your strategy you will probably regret very soon. Here below some recommendations either before start your IoT journey or if you are already started. Hope is not too late for wise advices.

Key Takeaways

With the proliferation and variety of IoT Devices, IoT Networks, IoT Platforms, Clouds, and applications, during the next few years we will see new vulnerabilities and a variety of new attacks. The progress in the security technologies and processes that prevent them will be key for the adoption of IoT in enterprises and consumers.

In the future Internet of Things world an end to end security approach to protect physical and digital assets. The ecosystems of this fragmented market must understand the need of Security by Design and avoid the temptation to reduce cost at the expense of the security.

Do not stop asking for security when you buy a connected product or use an IoT Service, the temptation of time to market, competitive prices and the lack of resources must not be an excuse to offer secure IoT solutions to enterprises, consumers and citizens.

 

Thanks in advance for your Likes and Shares

Thoughts ? Comments ?

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

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

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

The problem with the cloud

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The OpenFog Consortium

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

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

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

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

Benefits of Fog Computing

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

Disadvantages of Fog Computing

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

Examples of Fog Computing

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

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

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

What is the future of fog computing?

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

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

Does the fog eliminate the cloud?

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

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

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

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

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

'The Cloud' is not Over

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

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

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

 

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Using Data Science for Predictive Maintenance

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.
Early identification of these potential issues helps organizations deploy maintenance team more cost effectively and maximize parts/equipment up-time. All the critical factors that help to predict failure, may be deeply buried in structured data like equipment year, make, model, warranty details etc and unstructured data covering millions of log entries, sensor data, error messages, odometer reading, speed, engine temperature, engine torque, acceleration and repair & maintenance reports.
Predictive maintenance, a technique to predict when an in-service machine will fail so that maintenance can be planned in advance, encompasses failure prediction, failure diagnosis, failure type classification, and recommendation of maintenance actions after failure.
Business benefits of Data Science with predictive maintenance:
  • Minimize maintenance costs - Don’t waste money through over-cautious time bound maintenance. Only repair equipment when repairs are actually needed.
  • Reduce unplanned downtime - Implement predictive maintenance to predict future equipment malfunctioning and failures and minimize the risk for unplanned disasters putting your business at risk.
  • Root cause analysis - Find causes for equipment malfunctions and work with suppliers to switch-off reasons for high failure rates. Increase return on your assets.
  • Efficient labor planning — no time wasted replacing/fixing equipment that doesn’t need it
  • Avoid warranty cost for failure recovery – thousands of recalls in case of automakers while production loss in assembly line

TrainItalia has invested 50M euros in Internet of Things project which expects to cut maintenance costs by up to 130M euros to increase train availability and customer satisfaction.

Rolls Royce is teaming up with Microsoft for Azure cloud based streaming analytics for predicting engine failures and ensuring right maintenance.
Sudden machine failures can ruin the reputation of a business resulting in potential contract penalties, and lost revenue. Data Science can help in real time and before time to save all this trouble.
Read more…

Top 5 Industrial IoT use cases

The industrial IoT has already proven its versatility with deployments going live in a number of enterprises, showing off dozens of different use cases. But a few key uses consistently present themselves within the same trade, and even throughout different industries.

Top 5 industrial IoT use cases

It’s important to note that IoT use cases will likely expand in the next few years. That being said, we have compiled the top five industrial IoT use cases of today:

Predictive maintenance

Keeping assets up and running has the potential to significantly decreasing operational expenditures (opex), and save companies millions of dollars. With the use of sensors, cameras and data analytics, managers in a range of industries are able to determine when a piece of equipment will fail before it ever does. These IoT-enabled systems can sense signs of warning, use data to create a maintenance timeline and preemptively service equipment before problems occur.

By leveraging streaming data from sensors and devices to quickly assess current conditions, recognize warning signs, deliver alerts and automatically trigger appropriate maintenance processes, IoT turns maintenance into a dynamic, rapid and automated task.

This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when they are needed. The key is to get the right information in the right time. This will allow managers to know which equipment needs maintenance, maintenance work can be better planned and systems remain online while workers stay on task. Other potential advantages include increased equipment lifetime, increased plant safety and fewer accidents with negative impact on environment.

Smart metering

A smart meter is an internet-capable device that measures energy, water or natural gas consumption of a building or home, according to Silicon Labs.

Traditional meters only measure total consumption, whereas smart meters record when and how much of a resource is consumed. Power companies are deploying smart meters to monitor consumer usage and adjust prices according to the time of day and season.

Smart metering benefits utilities by improving customer satisfaction with faster interaction, giving consumers more control of their energy usage to save money and reduce carbon emissions. Smart meters also give visibility of power consumption all the way to the meter so utilities can optimize energy distribution and take action to shift demand loads.

According to Sierra Wireless, smart metering helps utilities to:

  • Reduce operating expenses by managing manual operations remotely
  • Improve forecasting and streamline power-consumption
  • Improve customer service through profiling and segmentation
  • Reduce energy theft
  • Simplify micro-generation monitoring and track renewable power

Asset tracking

A study on the maturity of asset efficiency practices from Infosys and the Institute for Industrial Management (FIR) at Aachen University revealed that 85% of manufacturing companies globally are aware of asset efficiency, but only 15% of the surveyed firms have implemented it at a systematic level.

source: Actsoft
source: Actsoft

Infosys and other supporting companies including Bosch, GE, IBM, Intel, National Instruments and PTC have launched a testbed with the main goal of collecting asset information efficiently and accurately in real-time and running analytics to allow the firms to make the best decisions.

The goal of asset tracking is to allow an enterprise to easily locate and monitor key assets (e.g. raw materials, final products, and containers) and to optimize logistics, maintain inventory levels, prevent quality issues and detect theft.

One industry that heavily relies on asset tracking is maritime shipping. On a large scale, sensors help track the location of a ship at sea, and on a smaller scale they are able to provide the status and temperature of individual cargo containers. One benefit is real-time metrics on refrigerated containers. These containers must be stored at constant temperatures so that perishable goods remain fresh.

Each refrigerated container needs to be equipped with temperature sensors, a processing unit and a mobile transmitter.

To continue reading, please visit the full article on Industrial IoT & 5G

 

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The Good, The Bad & The Ugly of Internet of Things

The greatest advantage we have today is our ability to communicate with one another.
The Internet of Things, also known as IoT, allows machines, computers, mobile or other smart devices to communicate with each other. Thanks to tags and sensors which collect data, which can be used to our advantage in numerous ways.
IoT has really stormed the Digital Transformation. It is estimated that 50 billion devices connected to the Internet worldwide by 2020.
Let us have the Good news first:
  • Smart Cars will communicate with traffic lights to improve traffic, find a parking spot, lower insurance rates based on telematics data
  • Smart Homes will have connected controls like temperature, electricity, cameras for safety and watch over your kids
  • Smart healthcare devices will remind patients to take their medication, tell doctors when a refill is needed & help curb diabetic attacks, monitor symptoms and help disease prevention in real time, including in remote areas
  • Smart Cities & Smart Industries are the buzz-words in IT policies of many governments
  • With sensors and IoT enabled Robots used in Manufacturing - new products could potentially cost less in the future, which promotes better standards of living up and down all household income levels
  • Hyper-Personalization – with Bluetooth, NFC, and Wi-Fi all the connected devices can be used for specifically tailored advertising based on the preferences of the individual
  • Real time alerts in daily life - The Egg Minder tray holds 14 eggs in your refrigerator. It also sends a wireless signal to your phone to let you know how many eggs are in it and which ones are going bad.

Now here are the Bad things:

  • There are no international standards of compatibility that current exist at the macro level for the Internet of Things
  • No cross-industry technology reference architecture that will allow for true interoperability and ease of deployment
  • All the mundane work can be transferred to Robots and there is potential to loss of jobs
  •  All smart connected devices are expensive – Nest the learning thermostat cost about $250 as against $25 for a standard which gets a job done. Philips wireless controlled light cost $60 so your household will be huge expense to be remotely controlled

And the Ugly part:

  • Remember the Fire Sale of Die Hard movie, a Cyber-attack on nation’s computer infrastructure - shutting down transportation systems, disabling financial systems and turning off public utility systems. Cyber-attacks can become common when devices are sold without proper updated software for connectivity
  • Your life is open to hackers who can intercept your communications with individual devices and encroach your privacy. Imagine a criminal who can hack your smart metering utility system & identify when usage drops and assume that means nobody is home
  • Imagine when you get into your fully connected self-driving car, and with some hacking a stalker’s voice come up from speaker “your have been taken” and you may not find Liam Neeson anywhere nearby, to rescue you.

All the consumer digital footprints can be mined, aggregated, and analyzed via Big Data to predict your presence, intent, sentiment, and behavior, which can be used in a good way and bad way.
We just need to manage the safety and privacy concerns to make sure we can receive the full benefits of this technology without assuming unnecessary risks.
Read more…

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