We are undoubtedly becoming ‘smarter’ in various aspects of our daily life, with IoT and machine learning (ML) playing a crucial role in this. Well known thought leaders like Bill Gates and Dr Judith Dayhoff (author of ‘Neural Network Architectures: An Introduction’) say that the IoT has given our physical inanimate world a digital nervous system. IoT has really exploded over the past three years, demonstrating its potential in applications ranging from wearables and automated cars to smart homes and smart cities, creating an impact everywhere. Machine learning is basically a part of computer science that makes any system smart enough to learn on its own without actually being programmed for that task. It helps a system or device learn in the same way as humans learn by themselves. As we learn any type of system on the basis of our experience and the knowledge that is gained after analysing it, machines too can analyse and study the behaviour of a system or its output data and then learn how to take different decisions on that basis.

The main purpose behind ML is to automate the development of different analytical models to enable algorithms to continuously learn with the help of available data. Google’s self-driving vehicle is one such development that uses different ML techniques with IoT to create a completely autonomous vehicle. It combines the advanced features of different modern cars (like speech recognition, lane assistance, adaptive cruise control, parking assistants and navigators). [ref]

Internet of Things has become a global phenomenon that has the potential to change the way we live by revolutionising our interaction with everyday objects. The number of objects covered under IoT is expected to reach a mammoth 50 billion by the year 2025! Just as the smartphone revolution changed our perception of handheld mobile devices, smart cities are set to be the next big upgrade that will change our perception of living in general. A big chunk of the smart city plan ranging from managing traffic and automating household devices to keeping people safe relies heavily of IoT. Needless to say that your house will no longer be made from just brick and mortar but will essentially be a computer system with sensors and actuators which will not just listen to your commands, but also learn from you in order to automate tasks without you lifting so much as a finger! The IoT lab in SPCRC is a congregation of cool nerds who like to eat embedded systems in breakfast and machine learning in lunch so that dumb monotonic systems can be made smarter before dinner!

With the number of IoT devices increasing everyday, a big challenge of providing constant and sufficient power to them has paved its way to the desks of the researchers. The number of connected IoT objects is expected to reach 50 billion by the end of 2025; how will such a vast eco-system be powered and maintained? Surely not all of these devices would draw power from mains! In fact in many IoT use cases, the sensor nodes are deployed in places where mains power is unavailable leaving only batteries available for use. The research on data reduction techniques is therefore a very relevant topic today wherein various signal processing as well as machine learning algorithms are developed to extend the lifetime of IoT nodes without compromising much on the data that is to be collected by the system.

Air pollution is one of the biggest issues in the world leading to death of millions of people while causing serious cardiovascular and respiratory diseases in many more. This makes it necessary to monitor air pollution so that people are aware of the air-pollution around them and can take appropriate action to reduce health risks. Traditionally, pollution monitoring is done using stationary monitoring systems which are highly reliable, accurate and able to measure a wide range of pollutants. However, these are expensive, large, and bulky, which leads to sparse deployment. There are three main objectives of the ongoing research project: (1) Set-up an internet of things (IoT) network for monitoring air pollution in urban conditions in Indian cities. (2) To correlate the pollution measurements to quality of health parameters such as respiratory, cardiovascular and psycho-physiological effects on the health of the people in general and specifically on children and public service personnel. (3) To develop a web-based spatial data platform for dynamic geo-visualization of the data from the IoT network to provide citizen advisory and governance. Data would be gathered on air pollution, along with temperature, humidity, and wind. The testbed pollution data will be collected in Gachibowli area of Hyderabad. This measurement region represents a typical fast growing urban scenario in India. The project will involve use of the state-of-the-art IoT technology for gathering pollution data while surveys will be conducted to gather the health data. Data will be processed with cloud computing and data analytics such as machine learning, signal processing and geostatistical algorithms.

Water monitoring is an essential aspect of water management. It is the process of measuring and analyzing water quality and quantity. The Internet of Things (IoT) has revolutionized water monitoring by providing real-time data on water quality and quantity. IoT sensors can be used to monitor water levels, temperature, pH, turbidity, and other parameters. The data collected by these sensors can be analyzed to identify trends and patterns in water quality and quantity. IoT-based water monitoring systems can help detect leaks, reduce water waste, and improve water quality. These systems can also help identify potential problems before they become serious issues. Here at the Water-IoT branch of SPCRC, we are working on projects which look at monitoring water level in tanks, sumps, borewells, water flow in pipe networks as well as monitoring of STP water. Not just monitoring, but we also aim to gain some useful insights like consumption patterns and early leakage detection using the collected level data. In conclusion, IoT-based water monitoring systems are an effective way to manage water resources. They provide real-time data on water quality and quantity, which can help reduce waste and improve water management practices. By using IoT sensors to monitor water quality and quantity, we can ensure that our water resources are used efficiently and sustainably.