Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Wireless sensor network (WSN) plays a crucial role in the Internet of Things (IoTs), which assist to produce seamless information that have great impact on lifetime. Despite substantial application WSN numerous challenges like energy, load balancing, security, and storage exist. Energy efficacy is regarded as an integral part design WSN; this can be achieved by clustering multi-hop routing technique using metaheuristic optimization algorithm. This paper concentrates Metaheuristics Cluster-based Routing Technique for Energy-Efficient (MHCRT-EEWSN). The presented MHCRT-EEWSN mainly improvements energy efficiency lifespan via process. For effectual process, model utilizes Whale Moth Flame Optimization utilized use fitness function involving intra-cluster distance, inter-cluster balancing factor. Besides, employs Improved African Buffalo (IABO) based technique. To select optimal routes WSN, IABO algorithm designs comprising multiple parameters residual distance experimental validation tested making series simulations. A wide-ranging comparative study shows promising performances than other recent methods.