What’s Ahead for Internet of Things?

While the Industrial IoT era is already redefining manufacturing, its benefits to business and society are only just beginning. Did you know…

  • Businesses are on track to have 3.1 billion connected devices this year.[1]
  • 2.6 million industrial robots are expected to be working in factories by 2019.[2]
  • Analysts estimate IIoT could add US$14.2 trillion to the global economy by 2030.[3]

With the latest wave of artificial intelligence (AI) driving IIoT, manufacturers will shift to outcome-based services to create new levels of competitiveness as customers require measurable results from the products they buy. Your operations will need to be forward-thinking, responsive, accurate and reliable to compete in this scenario. How will you get there? By getting more value from your data.

The Opportunity Lies in Your Data

Some people are calling data the new currency. Powered by AI, analytics, and algorithms, IIoT is unleashing data-driven insights across the manufacturing value chain. It’s this huge amount of data generated by connected machines that hold the real value of IIoT. Data sent from machine sensors can be used to detect impending machine failures and product quality issues in real time. It can also predict outcomes and inform the supply chain about demand for aftermarket parts.

A World Economic Forum report identified key areas of opportunity enabled by data derived from the IIoT:[4]

  • Vastly improved operational efficiency, such as improved uptime and asset utilization, through predictive maintenance and remote management.
  • The emergence of an outcome economy fueled by software-driven services, innovations in hardware, and the increased visibility into products, processes, customers, and partners.
  • New connected ecosystems, coalescing around software platforms that blur traditional industry boundaries.
  • Collaboration between humans and machines, which will result in unprecedented levels of productivity and more engaging work experiences.



This article was originally published at – Hitachi Vantara Community 


The Evolution of Industrial IoT

Industrial revolution 4.0 technologies drive manufacturing to new heights

The industrial internet of things (IIoT) is the application of IoT technologies in manufacturing. Like the first industrial revolution in the 18th century, IIoT is transforming today’s manufacturing industry. This fourth industrial revolution is built on advancements in artificial intelligence (AI), IoT, 3D printing and robotics, and they’re the foundation for the factories of the future.

Let’s see how this 250-year evolution from mechanism to machine learning and smart factories was built on disruptive technologies that stretched across four revolutionary phases.

Industry 1.0: Mechanization Using Steam Power 

Before Edmund Cartwright introduced the first mechanical loom in 1784, textiles were produced in people’s homes. Cartwright used water and steam to power his mechanical looms, which led to giant leaps in productivity and helped launch the first industrial revolution. The original design was continually refined, and by 1850 there was 250,000 power looms operating in England—and mechanized versions of other equipment like paper machines and threshing machines soon followed.

Industry 2.0: Mass Production Using Electrical Energy

The first assembly lines appeared in the meatpacking industry in 1870 and drastically reduced the time to slaughter and dress a single steer from eight hours to 35 minutes.[1] By 1913, Henry Ford developed a moving assembly line for large-scale manufacturing, producing affordable cars faster than ever before. When cars became available to the masses, thereby creating a more mobile society, many other industries quickly followed suit by adopting the assembly line.

Industry 3.0: Automated Production Using IT

In 1969, Richard Morley developed the first programmable logic controller (PLC) for General Motors. Originally designed to replace hard-wired relay systems, PLC’s hardened embedded processor, running a real-time proprietary operating system, became a mainstay of the industrial automation world.[2] Today, PLCs control a vast array of equipment and can be found in everything from factories to vending machines.



This article was originally published at – Hitachi Vantara Community 

Recent interaction with IED communications: An Extract

It is possible to equip old machines in good running condition with sensors to make them more productive. Is this advisable, how costly is this exercise, or is it better to opt for new machinery?

How about a Fitbit for machines – a typical Fitbit has 7 or more sensors – GPS, 3-axis accelerometers, digital compass, optical heart rate, altimeter, ambient light sensors, vibration motors in a tiny form factor that can run for days together with a single charge, and synchronises with mobile device app which pushes the information to a cloud-based analytics software to provide your custom dashboard to see your health parameters. It’s the same idea behind the Industrial IoT add-on devices.

Legacy hardware assets are prevalent in all industries and any potential IoT solution must include provisioning solution for these assets. Some of these assets don’t have any sensor mounted – these are pure mechanical assets and not digitally connected with any infrastructure. Then there are other legacy assets which have sensors but communicate over legacy operational technology protocols.

In the case of mechanical assets, the question really is how much retrofitting is needed? It would depend on what we are trying to solve for. There are commercial off-the-shelf add-on products available today which can be externally mounted – just like my earlier example of Fitbit. These add-on devices come with pre-installed sensors and network connectivity to sense and capture a wide range of sensor data such as temperature, pressure, humidity, vibration, etc. This data is then made available to on-prem or cloud-based data science solution.

In case of legacy operational technology, there are several edges and fog based solutions that provide protocol translation and data management capabilities. Since there is custom requirement, the type of add-on device and size of integration determines the overall RoI from the investments.

What are the best ways of integrating old machines into modern concepts like IIoT?



This Interview was originally published at – IED Communications 

My thoughts on building a business case that justifies the journey towards a scalable IoT solution.

Manufacturing companies today are challenged with rapidly changing variables in their industries, such as requirements for shorter lead times, mass customisation, the increasing number of product variations and there is an ongoing shift in overall consumers’ mindsets as more people move away from product ownership-based consumption in favor of as-a-service business models. Additionally, conventional models of production planning by experts are no longer efficient due to unforeseen disruptions and changing business environments.

When you speak to any factory operators and plant managers across Industries, some of things you hear are still very basic, e.g., how do I get more visibility in to my operation? How do I plan for what I don’t know? How do I minimise impact of downtime? How do I plan for spares ahead of time to minimise the impact of a breakdown? And at executive levels, you would often hear about higher level objectives that impact share of market, competitive advantage and customer experiences.

The bottom line is overall operations rarely go as planned. This problem gets further amplified with aging equipment and legacy processes which cannot be replaced without significant investments and impact on the ongoing production. No manufacturer is willing to stop producing and replace the entire setup just to gain more operational efficiency.

Internet of Things is expected to address the unique challenge of dealing with legacy assets and processes while optimising for efficiencies and help business innovate. Enterprises are looking for technology solution to deliver product and/or services to its customers in the most predictable and cost effective way without compromising on the overall quality goals and customer experience. Predictable outcome and costs are directly linked to how assets and operations are managed.

Across all Industries whether it is manufacturing, energy, transportation or health care, there are assets that need to be managed smarter. These assets could be a robotic arm in manufacturing, a windmill in energy, a train in transportation or a MRI scanner in health care. Availability and performance of these assets are not only just the need but also a business imperative. Internet of Things offers a potential economic impact of $4 trillion to $ 11 trillion a year in 2025 as per McKinsey report on Unlocking the Potential of the Internet of Things. When you double click on this data point, you realise one of the key industry that’s contributed majorly to this trend is Manufacturing where the impact from operations management to predictive maintenance is around $1 to $3 trillion as per the same report.

The very first step in the IoT journey is to connect factory assets through a gateway or an add-on device that enables flow of sensor data to either an edge and fog systems near asset analytics and prediction or cloud based solution for more powerful analytics and ML driven outcomes. Internet of Things architecture elements leverage the data streams from PLC, sensors, relays, switches or sometime externally mounted instrumentation and blend with data from IT systems and other sources such as weather, demography, manufacturer own data sets, etc., to make predictions that enable use cases such as remaining useful life, prevent equipment failure, reduce maintenance costs, degrading performance indicators and asset safety.

So there is enough business case that justifies the journey towards a scalable IoT solution that not only looks at a specific problem the manufacturer face but also provides future proofing of the investment. Eventually the vision for the manufacture should be moving towards an autonomous economy that senses and responds to demands on a real-time basis.

Expansion of IoT Ecosystem Is Increasing Security Concerns

There is a consensus among IoT research analysts and thought leaders around the potential of IoT market opportunity. As per recent IDC report, the installed base of IoT endpoints will grow from 9.7 billion in 2014 to more than 25.6 billion in 2019, hitting 30 billion in 2020.

However, there is also growing security concerns around this explosion of connected devices. A compromised IoT device is not only risky for the organization but also has long-term implications on overall customer confidence in its products and services. A study by Aruba Networks, covering 3000 companies across 20 countries, has revealed that 84 percent of companies have experienced some sort of IoT breach.

There is an inherent danger when exposing IoT endpoints to receive commands from remote servers. In a recent news,  500 smart locks around the world stopped working after the company mistakenly issued the incorrect OTA update. This shows the potential downside of remote command and control capability if it’s accompanied with flawed security architecture and support capability.

The largest known DDoS (Distributed Denial of Service) attack involved nearly 150,000 compromised internet-connected closed-circuit television devices and digital video recorders. Hackers used these devices to overwhelm the servers of French internet service provider OVH with more than 1 Tbps of data.

Security concerns within the growing IoT ecosystem primarily center on the following:

Fear of being hacked:

Hackers have used the IoT system for a wide variety of nefarious activities. From diverting sewage water into lakes and rivers, killing marine life to taking control of a smart car from more than 10 miles away. While some of these have been conducted by researchers many of them are genuine hacks that raise concern over the use of IoT devices.

Access to sensitive data:

The total volume of data generated by IoT will reach 600 ZB per year by 2020, 275 times higher than projected traffic going from data centers to end users/devices (2.2 ZB); 39 times higher than total projected data center traffic (15.3 ZB). A big chunk of this highly confidential information will flow over public networks. Your asset data may reveal a lot more than you can imagine e.g. how much energy is being generated and traded by looking at your wind turbine, how efficient is your fleet of mining truck? Where are your assets located? And probably even your energy meter may indicate whether occupants are at home or away?

Disrupting Operations:

A 14-year-old in Lodz, Poland hacked the city’s tram system with a homemade transmitter that tripped rail switches and redirected trains, derailing four trams and injuring dozens. Elsewhere in Ukraine, hackers compromised the information systems of three energy distribution companies and cut off electricity for around 230,000 people for one to six hours. These attacks are becoming increasingly common as more devices get connected to the IoT ecosystem.

Safety Risks for the public:

The case of the Lodz teenager proves the danger to the public from IoT attacks. Everything from transportation, to electricity grids to factories are vulnerable to hacks. The damage that can be caused by malicious elements can be imagined.

While the many benefits of an IoT ecosystem far outweigh the risks, it is critical to ensure security concerns are addressed at every layer of the IoT chain. It is business imperative to have an end to end security framework that factors in encryption, identity, access, authentication, authorization, logging, blacklisting, policy, and privacy framework. So, the bottom line is that if you want to draw benefits from IoT, make sure its assets and data pipeline is secured and governed by robust security framework.

Predictive Analytics Is Shaping The Future Of Asset Management

Enterprises are looking for a technology solution to deliver product or services to its customers in the most predictable and cost-effective way without compromising on the overall quality goals and customer experience. Predictable outcome and costs are directly linked to how assets and operations are managed. The term “asset” refers to any “thing” that’s capable of generating data on its own using the sensors or with externally mounted instrumentation.

Across all Industries, whether it is manufacturing, energy, transportation or health care, there are assets that need to be managed smarter. These assets could be a robotic arm in manufacturing, a windmill in energy, a train in transportation or an MRI scanner in Healthcare. Availability and performance of these assets are not only just the need but also a business imperative.

Advances in the Information Technology world – Machine Learning, Analytics, Computing and Storage – has made a strong case for using the Operation Technology data to enable use cases such as remaining useful life, prevent equipment failure, reduce maintenance costs, degrading performance indicators and asset safety. There is the operational need for preventive actions to ensure uptime, contain further losses from degrading asset performance and cascading issues in downstream processes.

Machine learning algorithms use pre-existing data gathered from the assets and blend it with an environment and business data to create a theoretical model. There are several steps involved in this process of preparing the data to get the theoretical model which enables insights – OSEMN (read as awesome) which means obtain, scrub, explore, model and interpreting. Once the theoretical model is created, it needs to tested through unseen data to evaluate the model qualitatively and determine the accuracy (or precision) of the model before it is applied in the real use case. Precision is a really important aspect to remove any bias from the model and use it as a repeatable model.

Some of the commonly used algorithms in Predictive Analytics are:


Statistical modeling for estimating the relationship between variables. It is used in predicting output variable using cause-effect relationship with input variable


Predicting event based on the decision tree. This model is about the likelihood of a specific event.

Time Series Forecasting:

It comprises methods for analyzing time series data to predict future time event given the past history


Similar objects are grouped in clusters so that observations and finding can be associated mathematically

Association rule mining:

It’s application of “if and then” pattern for data occurring together

These algorithms are frequently applied in predictive analytics use cases and used in augmenting expert judgment and create a strong case against the biased manual decision making.

Predictive Analytics is the key to achieving higher operational efficiency and jump start your IoT journey towards data-driven outcomes!

Complexity – A Major IoT Challenge

To the list of greatest inventions of the world such as the wheel, the compass, steam engine, concrete, the automobile, railways, airplane add 21st century’s offering the Internet of Things or IoT. Gartner estimates the total economic value-add from IoT will reach US$1.9 trillion worldwide in 2020. Other reports claim close to half the companies in sectors like oil, gas, and manufacturing industries are already using Instrumented devices capable of providing valuable data.

The major benefit of IoT is the ability to measure, monitor and manage any asset in any location from anywhere at any time. However, companies implementing IoT face some real challenges. At its heart, IoT involves dealing with data streams from a wide variety of sensors. For example, a regular automotive car has roughly 30k parts and the new generation autonomous cars may likely have an even higher number of smarter parts. These assets either by themselves or through connected instrumentation generate about a GB of data per second in operation, Guess what – we are talking about 3600GB of data per hour. All this data must be taken through an intelligent lifecycle from capture to archive and used all along to support the data-driven decision process.

How do you make sense of this? Where do you begin? How do you create an environment that learns by itself to use or discard the insights generated by using these data? Is it about accuracy or precision or both? What impact does it have on real-time operations and decision making?

The primary challenge is handling the massive amounts of data. This includes security of the data and the network as well as the analytics required to derive usable business intelligence from it. IoT technologies have to support this entire process from sensing, transforming, network, analysis, and action. If it were to be a homogeneous environment where we are dealing with assets of one class or type, it would still be a scalability challenge. With a number of asset types and assets of each class multiplying in the process, the complexity is getting multiplied across the layers from edge to core. There are no interoperability standards coupled with crowded as well as siloed solutions and products.

Apart from this, an IoT implementation faces issues like:

Ubiquitous connectivity:

Yes, it is 2017 but network outages do occur even in the most advanced nations leaving the very concept of in-stream data analytics at risk.


The numbers of different systems connected through IoT continue to create interoperability challenges.

Competing standards:

Different IoT vendors are pushing their standards creating a veritable Tower of Babel. This will take time to sort out.

A successful IoT implementation needs to address three issues:


IoT is not a one-size fit all kind of solution. The key is to find a scalable platform that integrates all aspects of the business to ensure seamless information flow across the enterprise.

Data Usage:

Sensors will throw up a huge amount of data. There needs to be sanity across all layers of implementation. The overall platform architecture needs to have a robust data management and distributed analytics framework to create actionable insights where it matters.


Hackers and sensitive data loss created a big risk and interrupt operations. It is therefore a business imperative to have a solution that identifies each and every asset which is locked-down at every tier using authentication and authorization while enabling logging, blacklisting and encryption of data at rest or in transit.


The benefits that accrue from a smart enterprise far outweigh the risk and the complexities involved in its implementation. Customers have the real need of dealing with new product innovation, eliminate wastage in their process, improve product quality, reduce operational costs and create new consumption led business models.

 So the bottom line really is not if you need to make your business smarter, but how soon you can do so. For delaying the process is simply handing your competition an unassailable advantage.

Kaun Tujhe by Palak Muchhal

Tu aata hai seene mein, Jab jab saansein bharti hoon,

Tere dil ki galiyon se, Main har roz guzarti hoon

I love this song and have heard this a thousand times. What I didn’t know until today was the name of the singer. I always assumed this was Shreya Ghoshal’s song and her voice. I never bothered to check.

Why am I blogging this? Why is it so important to know?

What If I told you, Singer name is Palak Muchhal and she is just 25 years old and has saved lives of 1333 children by singing songs?

Palak’s decision to use her voice to help others was triggered when she saw poor children using their clothes to clean train compartments. Around the same time, teachers at Nidhi Vinay Mandir, an Indore based school, approached Muchhal and her parents with a request for a charity show to raise funds for their pupil, Lokesh, who was suffering from a congenital heart defect. His father was poor footwear shop-owner, earning Rs 50 a day and was unable to bear the Rs 80,000 cost of heart surgery.

Muchhal and her parents agreed to arrange a show and in March 2000, she used a street vendor’s cart as a stage for the event and collected Rs 51,000 towards the cost of surgery.

The publicity prompted renowned Bangalore-based cardiologist, Devi Prasad Shetty, to operate on Lokesh free of charge. Muchhal’s parents gave advertisement in local newspapers so that idle funds could be used for heart surgery of some needy child-like Lokesh. The outcome of this was a list of 33 children in need of heart surgery.

Then started the journey of this young girl to dedicate her singing and earnings through her foundation for needy children.

How can one be so strong? so selfless? So beautiful inside out?

May you inspire many more, thank you for doing everything that you do!!

if you haven’t heard her singing, here is my favorite song.