Energy Efficient Hybrid Optimization based K-means Clustering and Load balancing using Optimized Ad-hoc on-demand Distance Vector Routing for WSN

Abstract

Nowadays, Wireless Sensor Networks (WSN) play a key role in data transmission depends on the locations of nodes. WSN contains Base Station (BS) with several Sensor Nodes (SNs) and these nodes are randomly arranged inside the region. The BS used to give the commands and direction to the sensor node. Energy is a major issue in WSN as after some transmissions the nodes drain their energy when the information is passed inside the region of interest. According to the distance between the sensor nodes, the energy can be used during the Cluster Head (CH). The energy consumption (EC) is abridged by implementing the protocols of clustering and routing which is used to augment the Network Lifetime (NL). The optimal CH selection for finding the shortest path among the CHs is improved by developing the hybrid K-means with Particle Swarm Optimization (PSO) based hybrid Ad-hoc On-demand Distance Vector (AODV) channeling algorithms. The alive nodes, total packet sending time, throughput and NL are increased by using this hybrid technique, whereas dead nodes and EC are minimized in a network.