Fuzzy Election based Optimization Algorithm (FEBOA) And Energy Harvesting Possibilistic FUZZYC-Means (EHFPCM) Clustering for EH-WSN

Abstract

Wireless Sensor Network (WSN) includes of many nodes by restricted energy resources. Energy efficiency and harvested energy are major important issues in the WSN. Studies recently conducted have demonstrated that clustering is an effective way to increase energy efficiency. Energy Harvesting- Wireless Sensor Network (EH-WSN) is a flexible strategy for even clustering and Cluster Head (CH) selection is helpful to maximize network constancy and energy efficiency. In this paper, Energy Harvesting Possibilistic Fuzzy C-Means (EHFPCM) clustering is introduced to improve harvested energy usage by maintaining the consistency, connectivity, and balancing of harvested energy consumption in EH-WSN. It is based on Data Transmission (DT) and Cluster Establishment (CE). During CE, PFCM clustering is introduced for cluster formation. PFCM clustering divides the network into clusters. Each area forms a group and chooses one or more CH based on the multi-criteria like energy, distance to neighbors, distance to the Base Station (BS), and network load. In a cluster, the Fuzzy Election Based Optimization Algorithm (FEBOA) selects the CH according to the multi-criteria. It desires to receive packets from Cluster Member (CM), aggregate the received packets, and subsequently forward it to DT. DT, every CM wakes up during its designated working time and transmits the data it has gathered to the CH in the cluster. Lastly, measures such as Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, and average delay for transmission are used to measure the results of routing protocols.