Enhancing clustering efficiency in wireless sensor networks for smart agriculture using a hybrid PSO-snake-GWO optimization approach

These days, wireless sensor networks, or WSNs, are widely used in agriculture to increase farming’s productivity and quality. In general, sensors used in agriculture are battery-operated and placed outdoors for extended periods of time. Sensors in WSNs must continuously sense data and transmit it, which quickly depletes the battery. As a result, the network becomes fragmented and has less coverage. Therefore, increasing network longevity is crucial for large-scale agricultural monitoring systems. For networks to last longer and use less energy, efficient clustering is crucial. In this paper, the author introduces a new hybrid optimisation method for cluster head selection in Wireless Sensor Networks (WSNs) called Hybrid PSO–Snake–GWO. The proposed work compares with LEACH, HEED, Hybrid PSO-GWO and Hybrid ACO- PSO algorithm. For comparison energy consumption, throughput, average back-off time and the number of lives node has taken into consideration. The simulation results show that the proposed work is superior to the LEACH, HEED, ASO-PSO and PSO-GWO clustering algorithm in terms of energy consumption, throughput, and back-off time, and improves the overall network lifetime. The results proves that the proposed work select the cluster head in efficient manner and improve the network life, to make it suitable for agriculture monitoring system.