Energy Efficient Routing Protocols for Maximization of Network Lifetime in Wireless Sensor Networks

Abstract

The nodes in Wireless Sensor(WS) networks are sensory nodes that can observe newlineand relay information about local physical phenomena. These Sensors have become newlinewidespread in our daily lives, and they ve been integrated into smart products like newlinesmartphones and smartwatches. As the Internet of Things (IoT) and AI (artificial in- newlinetelligence) technologies improve, we can give increasingly precise and comprehensive newlinesensory data about our daily activities across networks via wireless sensing devices. By 2025, Internet-connected gadgets will generate unprecedented amounts and numbers newlineof data, according to a report by the International Data Corporation (IDC). The rapid expansion of the Internet of Everything (IoE) heralds the dawn of a new age. It works newlineon the trendy principle of the human tendency to less effort and more comfort and ef- newlinefective results. The glut of data presents both benefits and obstacles for building data-driven technologies, but the latter is a more significant issue for computer resources.Low-packet-loss data transfer between normal nodes, cluster head nodes, and motes is proposed using a revised version of LEACH s cluster head selection method. After newline100 iterations, we compare the statistical outcomes of the two current methods to those newlineof our new method, Revise Cluster Head nodes Low Energy Adaptive Clustering Hi- newlineerarchy (RCHLEACH). The results show 5000 rounds for each iteration. The Lemmas newlineand the time complexity of the RCHLEACH justify the suggested protocol. newlineDuring the clustering phase in the LEACH protocol, the challenge of selecting a newlinehead node or cluster head was handled with the use of soft computing techniques, newlinenamely the Type 1 (Mamdani) and Type 2 (Sugeno) Fuzzy approach. Two scenarios newlineare examined using the suggested method using the network s lifespan through low- newlinepower operation and careful selection of cluster leaders according to fuzzy parameters.An Adaptive NeuroFuzzy Inference System (ANFIS) approach in Low energy adap- newlinetive clustering hierarchy (LEACH) was used to design a p