Energy-Efficient Clustering Routing for WSNs Based on Multiobjective Quantum Adaptive Gray Wolf Optimization

Maximizing longevity and improving the communication quality of wireless sensor networks (WSNs) are two pivotal objectives in addressing clustering routing issues. However, existing methods suffer from low quality of service (QoS), and accumulating unnecessary monitoring data results in a reduced network lifetime. In this regard, a new multiobjective quantum adaptive gray wolf optimization-based energy-efficient clustering routing (MQAGWO-EECR) algorithm is proposed, which vigorously prolongs the network lifetime and considerably reduces delay and packet loss rate. Two novel operators named quantum calculation and adaptive adjustment are devised in conjunction with gray wolf optimization (GWO), which significantly enhances solution quality and obviously improves convergence rate. Additionally, a new energy-efficient clustering routing (EECR) model involves sleep scheduling, cluster head (CH) selection, and multihop routing, which accurately captures the data transmission process. To be specific, sleep scheduling alternates nodes between active and sleep modes to reduce unnecessary energy usage. Moreover, clustering routing is designed by concurrently considering energy cost, node density, the distance between CH nodes and the base station (BS), delay, and packet loss rate, ensuring CH selection is rational and significantly guaranteeing QoS performance. The experimental results demonstrate that the proposed MQAGWO-EECR effectively optimizes energy efficiency and further extends network longevity by at least 11.09% compared to energy-efficient multihop LEACH-based clustering (EM-LEACH), KSA-DEEC, and GAPSO-H. Additionally, it decreases transmission delay by at least 5.27% and reduces packet loss rate by at least 4.84% compared to the other three algorithms.