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

• Published in: International journal of communication systems Vol. 34; no. 18
• Main Author: Juneja, Kapil
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.12.2021
• Subjects: ACO; Algorithms; Ant colony optimization; balanced; clustered; Clustering; Communication; Experimentation; hierarchical routing; Parameters; Protocol; Route optimization; sensor network; Wireless sensor networks
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.4997

Summary 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 load‐balanced 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. In the proposed DLB‐fACO protocol, the CH selection is accomplished by analyzing the node density, degree density, energy, coverage, and probability vector parameters. The pre‐observation adaptive CH selection method reduced cluster switching. The coverage consideration‐based method reduced the probability of orphan nodes. The cluster members are assigned with degree ratio and energy‐balanced evaluation. This approach reduced the chances of route and cluster switching in the network. The proposed DLB‐ACO protocol handles the issues of orphan nodes, overload, and underload conditions.