A Novel Natural Heuristic Duty Cycle Optimization Method for Industrial IOT

In the realm of Industrial Wireless Sensor Networks (IWSN), formulating an effective duty cycle is a pivotal technological aspect. Considering that the core components of IWSN networks are sensor nodes powered by energy-restricted microsensors, optimizing the duty cycle is essential for prolonging IWSN's operational duration. Nonetheless, devising an optimal duty cycle configuration for IWSNs represents a computationally challenging NP-hard task. Therefore, a new Elite Adaptive Clonal Ant Colony Algorithm (EACACO) is proposed that can find the ideal duty cycle design to extend the network lifetime in IWSN. First, a newly designed adaptive operator exhibits strong capability for adaptive adjustment and migration exploration, which helps in avoiding local optima. Furthermore, the innovative elite and clone operators significantly expand the population solution space, effectively increasing solution diversity and accelerating algorithm convergence. Additionally, this paper develops a new sensor duty cycle model, thoroughly considering sensor longevity and complete coverage of monitoring targets. Based on this, an objective function is proposed to enhance the overall network lifetime of IWSNs. Finally, extensive simulation experiments were conducted, comparing EACACO with other duty cycle optimization algorithms, including Genetic Algorithm (GA), Ant Colony Algorithm (ACO), and Particle Swarm Optimization Algorithm (PSO), across various scenarios. The simulation results demonstrate that compared to GA, ACO, and PSO, EACACO increases the IWSN network lifetime by at least 8.64%, 31.34%, and 35.38%, respectively.