Efficient data routing for agricultural landscapes: ensemble fuzzy crossover based golden jackal approach

Precision agriculture involves extensive agricultural landscapes with varying terrains and crop types. An energy-efficient routing protocol ensures that data is efficiently transmitted across the entire agricultural area. However, the ability of clustering routing protocol is based on the cluster formation as well as cluster head selection processes. Traditional methods are impractical for such large-scale deployments. In order to conquer the above-mentioned challenges, this paper proposed a novel Ensemble Fuzzy Crossover based Golden Jackal (EFC-GJ) method for enhancing the formation of cluster and cluster heads selection. In the proposed method, the crossover-based Golden Jackal Optimization, Fuzzy c-means Clustering Method, and Ensemble Q-learning are utilized for cluster center initialization, cluster formation, and cluster head selection respectively. The performance evaluation measures such as throughput, jitter, latency, energy consumption, and network lifetime are utilized for the evaluation of the proposed EFC-GJ method and these results are compared with existing methods. The EFC-GJ method attained a PDR of 0.98, throughput of 0.97 Mbps, end-to-end delay of 1.3 s, network lifetime of 5620 rounds, energy consumption of 0.2 mJ, jitter of 0.36 ms, and latency of 1.7 s. The experimental results illustrate the EFC-GJ method’s effectiveness in forming cluster and selecting cluster head.