An energy efficient routing protocol with fuzzy neural networks in wireless sensor network

Abstract

The extension of wireless sensor network (WSN) lifetime and reduction of power consumption are now important objectives in sensor network research. Energy-efficient communication networks are required when using a WSN. WSNs are additionally constrained in terms of energy by clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computing. Furthermore, choosing a cluster head is still difficult when minimizing WSN energy. In this study, the Bacterial Foraging Optimization with Harmony Search Algorithm (BFO-HSA) is used to cluster sensor nodes (SNs). Eliminating latency, reducing distance, and stabilizing energy consumption are the main goals of research in order to maximize the choice of cluster heads. In WSNs, maximizing the use of energy resources is a crucial issue due to these limitations. The quickest path is found dynamically by decreasing network overhead through the use of a cross-layer-based opportunistic routing protocol (CORP). PDR, packet latency, throughput, power consumption, network lifetime, packet loss rate, and error estimation are all assessed using the suggested method; the outcomes outperformed those of previous approaches. Results for quality-of-service parameters include PDR (98.5 %), packet latency (0.019 s), throughput (0.98 Mbps), power consumption (9.75 mJ), network lifespan (5250 cycles), and PLR (1.5 %) for 100 nodes.

Keywords

• Cross-layer-based opportunistic routing protocol (CORP)
• K-medoid
• Energy-efficient routing fuzzy neural network (ERFN)
• Hybrid Bacteria Foraging Harmony Search Algorithm (BFHSA)