The Industrial Internet or the Web of Devices, now most commonly known as The Internet of Things (IoT) is the network of physical objects—devices, vehicles, buildings and other items— embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data among each other, mostly without human intervention.
One of the fastest growing segments among emerging Disruptive Technologies of the 21st century, IoT is projected to be a USD 1.7 trillion market by 2020 (IDC projection) with over 30 billion interconnected devices globally. Most of this projected amount will be spent by corporations and institutions on endpoint devices, infrastructure support, connectivity and companion IT services.
Given the widespread availability of broadband, more and more devices with Wi-fi capabilities and sensors built-in, will be getting made. Together with this, smartphone penetration is already quite high in most countries and will be skyrocketing in the future. Consequently, more and more devices with an on/off switch and with a sensor will keep getting connected to each other, to the internet and, on occasions, to people. The list of such things will include everyday devices like cellphones, coffee makers, washing machines, headphones, lamps, various wearable devices as well as components of machines, like a jet engine of an aircraft or the drill of an oil rig.
IoT will essentially be built around cloud computing, networks of data-gathering sensors and mobile, virtual and instantaneous communication. Cloud-based applications to interpret and transmit the leveraged data from the host of sensors would be key to the success of IoT. The endpoint devices deployed will be broadly of two types viz., sensors which would be primarily gathering data and ‘kinetic’ devices which would be capable of executing specific actions. The latter category of devices would consist of alarms, locks and valve actuators among others.
Big Data and Predictive Analytics will play a key role in interpreting and analysing the petabytes of data being generated. Real-time analysis of all this data and more importantly, decision making based on all this data cannot always be manual and will need to be automated in several cases. Machine learning and Artificial Intelligence ( AI ) applications will thus play a key role in automating the process and making the 'right decisions' dynamically.
Several last mile issues need to be worked out before the Internet of Things becomes virtually ubiquitous in a connected future. These would include, among others, standards for interfaces and an universally accepted system of gateways so that all these devices can talk to each other and to applications fairly seamlessly. Uninterrupted power and energy requirements for devices operating on a 24x7 basis would also become an important consideration. Many of the devices today have security vulnerabilities and/or poor privacy controls. Several devices still have weak web interfaces and do not have encrypted transmissions built-in, making them vulnerable to hacking. Inadequate software protection in several cases is another vulnerability which can be exploited by hackers to download and install malware.
Smart cities is an oft-heard buzzword used these days, globally. By itself, the phrase does not mean much unless the realistically attainable goals for a given city or geographical location are defined beforehand. The way these goals can be realized would be through the capture of relevant data and the use of analytics and AI for the interpretation, analysis and initiation of prompt actions based on the data being generated. An earlier piece in this blog touches on several use cases for smart cities.
Some of the most common applications of IoT are illustrated in the Infographic just below. As can be seen, smart homes, wearables, smart cities and smart grids occupy the top four slots in terms of popularity.
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