An Energy-Efficient River Water Pollution Monitoring System in Internet of Things
Swati Chopade, Hari Prabhat Gupta, Rahul Mishra, Preti Kumari, Tanima Dutta
Abstract
An important research issue in river water pollution monitoring is to correctly estimate and transfer the pollution data from a river to the base station by consuming minimum energy. In this paper, we propose an energy-efficient river water pollution monitoring system by using deep neural network and long-range communication technology. Firstly, we design a compressed deep neural network for monitoring the river water pollution. Next, we use a knowledge distillation technique to train the compressed deep neural network. The compressed network can be successfully deployed on a limited resources Internet of Things device and achieves an acceptable accuracy. Further, we propose a game theory-based approach to estimate the time duration for using the suitable spreading factor of the long-range network to transmit the river water data. Such game theory-based approach helps in reducing energy consumption and ensures the successful transmission of the data to the base station. Finally, we present experimental and real-world evaluations that demonstrate the effectiveness of the propose system.