Optimizing Cluster Head Selection for Enhanced Energy Efficiency in WSNs through AHP and TOPSIS Techniques

Abstract

Wireless sensor networks (WSNs) describe an infinite number of low-power wireless nodes that are used to monitor and record environmental events and activities, like temperature, humidity readings and fire detection. These days, WSN lifespan and energy consumption are thought to be difficult problems. Numerous routing protocols have been put forth to increase network lifetime and promote energy-efficient wireless communication. When it comes to these protocols, network design is key to enhancing network performance. Net-work design parameters determine how the sensor nodes communicate with one another. In this study, we present an Optimizing Cluster Head Selection through Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution Preference Ranking Organization (TOPSIS) Techniques (OCHSAT) to lengthen the network’s lifespan and use less energy. Cluster heads (CHs) are spread, and cluster creation is centralized in the clustering phase. A centralized K-means approach is utilized to create the stationary clustering, and the resulting clusters stay static during the operation. AHP and TOPSIS are used to rank and choose the CHs in the best possible way. TOPSIS is a model for Multi-Attribute Decision Making (MADM) that chooses the optimal option by weighing several competing criteria. Rather than altering CHs with dynamic clustering at every interval, increasing the sensor network’s lifespan is our goal by postulating CH dynamicity based on present energy levels using an energy threshold. A customized simulator built on Python was used, the suggested OCHSAT greatly lengthens the network’s lifetime and successfully tackles the issue of energy usage.