A novel intelligent theory-based energy-efficient cluster routing protocol for industrial internet of things

In Industrial Wireless Sensor Networks (IWSNs), energy-efficient clustering remains a critical challenge due to the NP-hard nature of optimal cluster formation, which conventional clustering methods struggle to resolve efficiently. To address this challenge, we propose CLCPA-CRP, a novel energy-efficient cluster-based routing protocol. First, we formulate a multi-objective clustering optimization model integrating four key metrics: (1) minimization of intra-cluster communication distance, (2) reduction of cluster head-to-base station transmission distance, (3) maximization of residual energy in cluster heads, and (4) balanced energy distribution among cluster members. Second, we design the Chaotic Lévy Carnivorous Plant Algorithm (CLCPA), an enhanced bio-inspired optimizer that introduces two innovative operators—chaotic map-driven population initialization and Lévy flight-guided local search—to strengthen convergence speed, escape local optima, and expand global search capabilities. Finally, extensive simulations across four industrial scenarios demonstrate that CLCPA-CRP outperforms state-of-the-art protocols (LEACH, LEACH-C, DEEC, SEP, TS-I-LEACH, SOTA EEM-CRP), achieving 10.55% higher energy savings and 18.04% extended network lifetime on average. The proposed protocol provides a scalable and robust solution for energy-constrained industrial IoT deployments, effectively balancing spatiotemporal energy consumption in dynamic IWSN environments.