Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

Abstract

A sensor network is a situation-based critical network defined under certain restrictions and constraints. The clustered architecture is defined over these networks to utilize the criticality vectors and to improve network communication. In this paper, a degree load-balanced and fuzzy-ACO (DLB-fACO) protocol is proposed for optimizing the clustered architecture. In this architecture, the load analysis is performed while forming the clusters. The cluster head selection is performed based on energy, cluster density, and probability vector. The node degree-and energy-balanced analysis is performed for identifying the cluster members. Once the clusters are formed, the ACO approach is applied to perform the cluster-based hierarchical routing. Communication is performed at two levels. In the first level, node-to-cluster head communication is performed by considering energy and degree consideration. In the second level, the fuzzy-integrated ACO method is applied for inter-cluster route formation. The route optimization is here performed under fuzzy-based energy, degree, and distance parameters. These fuzzy parameters are evaluated within the ACO algorithm for generating the optimized route. The proposed loadbalanced protocol is analyzed against the LEACH, LEACH-C, LEACH-CC, M-LEACH, Fuzzy-PSO, Fuzzy-ACO, Fuzzy-Cuckoo, and FMCB-ER protocols.The experimentation results confirm the significant gain in network lifetime and packet communication. The cluster count, clustering switching, and communication failure are also reduced in comparison with existing conventional and optimized protocols.

Enhanced Elman spike neural network for cluster head based energy aware routing in WSN

“…Juneja 29 have presented a design of degree load balanced, fuzzy ant colony optimization (ACO) protocol were used for optimizing the clustering architecture in WSN. The load investigation was achieved while developing the clusters.…”

“…Updating the weight parameters of output layer in T jk , then, the error terms are calculated and it select the optimal cluster head by using Equation (29),…”