Energy aware farmland fertility optimization based clustering scheme for wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) encompass an enormous set of spatially dispersed sensor nodes connected via wireless links for monitoring and recording the physical data in the target region. The nodes in WSN operate via an inbuilt battery which depletes the energy after a certain period. The partitioning of networks into clusters or groups is one of the efficient solutions to lengthen the lifespan of the WSN. The clustering process can be treated as an NP-hard optimization problem and the utilization of metaheuristics has drastically raised the ability to resolve continuous optimization problems. This study designs an energy-aware farmland fertility optimization-based clustering scheme (EAFFOCS) for WSN. An objective of the EAFFOCS technique is for determining the optimal nodes as CHs and enhancing the overall network efficiency. The proposed EAFFOCS technique is based on the concepts of farmland fertility. Besides, the EAFFOCS technique derives a fitness function involving three input variables like distance to neighbours, distance to BS, and energy. Using the fitness function values, the EAFFOCS technique chooses the node with maximum fitness as CHs. Then, the nodes placed closer to the CHs are joined together to form a cluster. To demonstrate the improved outcomes of the EAFFOCS technique, a wide range of simulations was carried out and the outcomes are inspected under several aspects. The comparative outcomes reported the better performance of the EAFFOCS technique with the maximum residual energy of 11% under the presence of 1000 sensor nodes whereas the HABC-MBOA, FFCGWO, FFOCR, and HASPSO systems have resulted in least RE of 10%, 5%, 2%, and 1% correspondingly.