Energy-Efficient Clustering and Routing Algorithm Using Ant Lion Optimization in WSN

Wireless sensor networks necessitate the usage of low-energy communication networks. The major challenges in WSN include clustering, communication capacity, storage, slow communication speed, high configuration complexity, and limited computing. Furthermore, selecting cluster heads remains a challenge for energy minimization in WSN. The main objective of this research is to optimize cluster head (CH) selection through distance reduction, power stabilization, and node delay minimization. Since these constraints, attaining optimal energy resource usage is a critical concern in wireless sensor networks. This work proposed the Ant lion Optimization (ALO) algorithm to cluster sensor nodes (SN). GEAR-R (Geography-Enhanced Energy Efficient Routing) finds the quickest way and dynamically reduces network overhead. The suggested approach is utilized to evaluate throughput, network lifetime, delay, Energy efficiency and the outcomes beat existing approaches in terms of jitter (0.16 ms) and data throughput. Simulation outcomes of the QoS parameters are packet latency (network time (3.1 ms s) for 100 nodes), energy consumption (0.18 mJ), throughput (0.99 Mbps) and network lifetime (6400 rounds).