Blockchain, Bitcoin and IoT

When you buy coffee beans that’s labeled genuine what makes you feel certain of its origin? If you meet someone online how do you know who he or she say they are? To be sure, really sure you’d need a system where records can be stored, facts could be verified by anyone and security is guaranteed. That way no one can cheat the system by editing the records because everyone using the system could be watching the system.

Systems like these are on the horizon and the software that powers such systems is called as blockchain. Blockchains store information across network of personal computer systems making them not just decentralized but also distributed. This mean no central company or person owns the system yet everyone can use it and help run it. This is important because it means it is difficult for any one person to take down the network or corrupt it. The people who run the system use their computers to hold bundles of records submitted by others known as blocks in a chronological chain. The blockchain uses a formal math called cryptography to ensure records cannot be counterfeited, changed by anyone else.

You have probably heard blockchain for its first killer app a form of digital cash called “bitcoin” that you can send to anyone even a complete stranger. Bitcoin is different from credit cards, paypal or other forms to send money because there isn’t a bank or other financial institution involved. Instead people from all over the world help move digital money by validating others digital bitcoin transactions with their personal computers earning a small fee in the process. Bitcoin uses the blockchain by tracking the record of ownership over this digital cash. So only one person can be the owner of the cash and it cannot be spent twice, like counterfeit money in the physical world can. But bitcoin is just the beginning for the blockchains. In the future blockchains that manage and verify the online data may enable us to launch companies that are entirely run by algorithms making self-driving cars safer. Help us protect our online identification and even track billions of devices on Internet of Things.

Blockchains can serve as a ledger of existence for billions of devices that can autonomously broadcast transactions between peers for e.g. Toaster can broadcast data to refrigerator, connected light can broadcast data to smart wearables etc. And when we look at the bitcoin payment systems today, we see it mainly as an app, a payment system and nothing much more but in truth this protocol of value exchange has innovative potentials for the Internet of Things. And currently when we look at bitcoin we see that the employee of the bitcoin digital economy are machines who are mining the bitcoin network. However when we describe the Internet of Things we see machines anything from refrigerators, televisions to smartphones and everything in between can potentially from IoT perspective become customers of the bitcoin network and in doing so they would use the bitcoin network to poll an exchange information, verify information between themselves autonomously and act upon that information and this network of machines will essentially act as communication or social network of devices that can automatically act based on information which it hosts in the bitcoin ledger. Thus with the help of blockchain and bitcoin transaction processes the distributed IoT systems can ensured to be safe and secure from malicious transactions. Also it would be decentralize the ownership of the network and reduce value extraction by major dominant players in the IoT domain. Ultimately serving the purpose of liberating the ownership of IoT from dominant players in this field.



IoT and Regulation of Data Protection

With the advent of IoT humans will be connected to their objects through wearables, fitness devices, smart homes, connected cars, etc. These connected objects will generate large amount of data. A significant size of this data will predominately constitute personal data.
A new approach needs to evolve to address the challenges of data protection through regulatory framework. An identified thought process for data protection shall comprise of at least the following:
Right to forget (Data):
The data to be deleted when one no longer wants it to be processed and believes there are no legitimate grounds to retain it.
Data Accessibility (Personal):
When switching data between different providers (Cloud services) data portability shall be made easier to transfer the personal data.
Data controllability by user:
To seek explicit permission to process personal data. No assumptions to be made in this regard. After all it is user’s data and the goal should be to make the user responsible for their data.
Data protection should be at forefront:
User data protection should be in built in the products and services offered to the user’s right from early development stage. This shall encompass privacy friendly settings as default. Also privacy by design and privacy by default should also be supported.

Considering the nature of IoT deployment and wide variety of implementations a common consensus needs to be reached through standardization processes and technology solutions for user data protection.


Industrial IOT

What are the motivators for Industrial IOT (IIOT)?

Enlisted below are key motivating factors for IIOT:

Making Money

This is possible by offering innovative products and services. Also by offering maintenance plans and performance based services. On a shop floor by offering predictive replenishment’s of consumables and spare parts.

Saving Money

On a shop floor by improving operational effectiveness, improved logistics which takes care of optimum usage of resources through feedback mechanism. Predictive maintenance of the machines based on the sensor data collected from these machines. This helps reduce the downtime and also wastes due to inefficient feedback mechanisms.

Increase compliance

Meet government regulations for factories producing pharmaceutical, medical, food and beverages, etc. through a smart process implementation using IoT platforms. Internal process controls to meet and exceed defined quality standards. And allow access based on role and improve audit functioning.


Industry transition phase from 1.0 to 4.0

First wave of industrialization started during the 1700’s when mechanical looms were introduced to produce goods (clothing’s). This phase was referred to as Mechanization and generally referred to as Industry 1.0. During this phase all the production was done in a silo. Mostly the factories were run using steam plants.

With the advent of electricity started the second wave of industrialization during the 1800’s when the products started moving over the conveyer belts. The phase of industrial production based on factory electrification is known as Industry 2.0. Again it was a silo based approach with limited ability to optimize the factory production based on forecast feedback received from markets.

As electronics industry started to produce semiconductors during 1960’s it gave birth to Programmable Logic Controllers (PLC’s). This gave way to new and modern phase of automating the factories to run production based on automation and robotic processes. This helps increase productivity and produce high quality products. This phase of Industrial Automation referred to as Industry 3.0 was riddled with silo approach as in Industry 1.0 and 2.0.

Today we are in an era where semiconductor technology is helping achieve connect factories to their customers directly using smart connectivity based on IoT concept. And we are right in the middle of this Industrial revolution referred as Industry 4.0. This is possible using various networking technologies at the disposal of the companies. Industry 4.0 is about connected factories, connected products and connected customers to save money and make money.

As one can infer it took almost a century to bring a revolution from Industry 1.0 to 3.0 while it’s taking only 50 years to move from Industry 3.0 to Industry 4.0. This is being enabled with the support of semiconductor, connectivity, digital technologies.

Industry 3.0 Challenges

Industry 3.0 challenged with host of issues such as disconnected manufacturing assets, disconnected enterprise systems within same factory. This leads to complex heterogeneous systems to be managed within the factory running on different protocols, interfaces from different suppliers.

Consider for example a typical automotive factory, in the current context there are several manufacturing assets such as robots, PLCs, RFID readers, Bar code scanners, etc. They are connected to their own individual enterprise systems running on Oracle, SAP, Microsoft MSMQ, etc. Prima facie it appears the factory is automated whereas in actuality the systems work in disjointed fashion. In order to make the Bar code scanner talk to Robots or RFID scanners talk to PLC equipment’s it would require programming. This becomes a significant challenge. How do we overcome this?

Industry 4.0 Advantages

Industry 4.0 is a concept where in all the manufacturing assets would talk to each other within the same factory floor. The data generated from an RFID reader can be used to communicate with the assembly robot to either pace up the production of slow down. The cumulative data can be collected, monitored and transferred from the intermediate monitoring platform to the enterprise systems. The data collected over the enterprise management systems can be shared with the top management, vendors, supervisors, sales team and field service teams in some cases with the customer for actionable insights. This can happen over the cloud systems.

Over the Horizon Industry 4.0

Industry 4.0 is about connected factories for efficient production planning, inventory control and just in time deliveries. Industry 4.0 is about connected products to take care of the preventive maintenance, to complete R&D feedback loop and warranty tracking once the product reached customers hands. And most important it is about customer experience. Having connected customers to help in mapping products, run biometrics, provide training programs and help in anti-theft tracking.

IoT and Big Data

As we enter into new computing age with connected devices around us with ambient sensing intelligence all pervasive the data generated would run into hundreds of terabytes.

A major chunk of this data would be generated by the IoT devices around us (Sensors,actuators, wearables,smartphones, etc). This data eventually culminates into Big data. Gaining actionable insights would be a major challenge both in real time and non-real time.

For a specific end application once a large number of sensors are deployed and activated, they would start generating data and push them to analytics platforms to gain actionable insights and accordingly send actuating signals.

Consider for example the healthcare wearables like ECG devices they would be generating at least 1000 events every second and millions of such wearables once deployed will be generating a large volume of data running into 10’s of millions of data points.

This data comes in real-time or as stream of data also known as Velocity in Big data definition.

The data also has variety since it is structured, un-structured, having diverse data models from different sources. Data uncertainty referred to as veracity is due to incompleteness, inconsistency, ambiguity, latency, etc.

Of all the four challenges mentioned above with respect to IoT, the big data generated for analytics purpose to gain actionable insights, the velocity with which the data flows in real time and streaming poses a major challenge.

Companies addressing the above challenges of volume, velocity, variety and veracity and gain actionable insights would be at the forefront of IoT Big Data Analytics. Moreover a silo approach should be desisted to share information with augmenting IoT platforms.