Regression-Based Law of Energy Efficiency in Wireless Sensor Networks

Abstract

Wireless Sensor Networks play a pivotal role in various applications, ranging from environmental monitoring to industrial automation. Efficient clustering of these network nodes is crucial for optimizing communication and energy consumption. In this study, we compare the performance of two clustering methods, namely k-means and grid-based clustering, in terms of energy efficiency. The nodes are assumed to be uniformly distributed across a two-dimensional area, and clusters are formed using the k-means algorithm. A baseline model is established using grid clustering for comparison purposes. To evaluate the efficiency of these clustering approaches, the energy consumption analysis is based on Friss law, and regression algorithms are employed to analyze energy consumption patterns within the network. Through regression analysis, an energy efficiency law is determined for all the cases analyzed. Our results underscore an approach to optimize energy consumption by formulating a functional relationship for total transmission power, derived from regression analyses conducted across diverse simulation scenarios.