Varsha Radhakrishnan
Doctoral researcher
Varsha's aim is to develop her career in academia as her previous teaching experience and research experiences have allowed her to follow her passion.
Areas of Expertise
- IoT
- Artificial Intelligence
- Data mining
- Web designing
Qualifications
- MSc Computer Science, Birmingham City University
Research
Zero Power Energy Management in IoT enabled Smart Water Systems
A serious drop in ensuring the water quality in the distribution system is a factor that affects public health. This could lead to increase in biological and non-biological contents, change in color and odor of the water. These contaminants cause a serious threat to the whole water ecosystem. The conventional methods of analyzing the water quality requires much time and labor. So there is a need to monitor and protect the water with a real time water quality monitoring system in order to make active measurements to reduce contamination. The growth of IoT technology had helped in developing efficient methods to solve many serious issues in real time. But, most of the methods were concentrating on data management in the network than energy management. The main issue with wireless network is its battery life as it depletes after a specific time period and the battery has to be replaced or recharged. Energy harvesting provide a promising solution by harvesting energy from natural sources such as light, vibrations and transforming them into electrical impulses to enhance the battery life time. There are some research of different protocols such as XMAC, BMAC used to reduce the energy and cost of the network, but there is a research gap in managing the energy and cost of the sensor nodes for monitoring the water distribution system in real time. Therefore, the integration of energy harvesting, wireless sensor network and IoT could considerably contribute for monitoring the water quality in the Water distribution system (WDS) in a cost effective and energy efficient manner by developing an energy management scheme using Deep-Reinforcement learning in a self-powered IoT network for near real time monitoring in a water distribution system.