Assessing the Effectiveness of DL-Clustering for Energy Optimization in Wireless Sensor Networks

Abstract

WSNs experience due to densely dispersed nodes and high flow rates near sinks. However, few researches focus on node and channel traffic, increasing energy consumption and complexity, to alleviate energy efficient traffic using mobile nodes. The proposed method identifies and characterizes energy efficient traffic areas using a unique Water wave game theory algorithm.  Determining the fitness function allows us to estimate the player's stability over these variables. Mobile sinks and neighboring nodes are alerted if the fitness is low, which also predicts energy efficient traffic and creates an implicit alarm threshold. To address multi-energy efficient traffic situations, a novel LAFLC algorithm is used, which uses Learning Automata with Water wave game theory to learn the nature of the energy efficient traffic. In order to find the ideal choice for the input, the algorithm classifies system decisions, mobile data collectors, routing, and mobility. This eliminates the need to reroute data when moving and replacing several traffic nodes for mobile data collectors. The result reveals that the suggested approach attained high PDR, Throughput and Energy efficiency when contrasted with existing techniques.