GCCO-ECP: A New Energy Saving Cluster IoT Protocol for Agricultural WSN Information Monitoring System

In modern agriculture, efficient agricultural wireless sensor networks (AWSNs) are crucial for monitoring and management. Traditional algorithms struggle with the NP-hard cluster head selection problem in AWSNs. To address this, GCCO-ECP employs the Gaussian Chaos Opposition-based Learning Cheetah Optimization (GCCO) algorithm. This protocol takes into account various factors, including node energy, node degree, average distance, and delay, to enhance the clustering process. Additionally, it incorporates innovative strategies based on Gaussian mutation, chaos theory, opposition-based learning, and cheetah optimization principles to optimize the clustering scheme. Experimental results demonstrate that GCCO-ECP surpasses the LEACH-C, DMaOWOA, and MC-CRITIC-KM protocols, achieving remarkable improvements in network energy consumption, network lifespan, and transmission delay. Specifically, it optimizes network lifespan by 102.58%, throughput by 92.18%, and transmission delay by 11.73%, thereby showcasing its superiority in agricultural WSN information monitoring systems. The protocol’s design aims to enhance the diversity and convergence of population evolution, ensuring robust and efficient performance in real-world agricultural applications.