Cross-Layer based Energy Efficient Wireless Sensor Network for Large Farms

Abstract

In recent years, researchers have focused their efforts on improving agricultural production by automating farming applications using wireless sensor networks (WSNs). The agricultural information must be accurate and updated in order to take the necessary actions. Sensor nodes (SNs) deployed in agricultural fields are powerconstrained and require lot of energy to transmit data on regular basis. As a result, robust approaches are required to manage energy-related concerns for long-distance communication in large farms. To address long-distance agricultural needs, a cross-layer-based energy-efficient wireless sensor network model called CL-EEWSN that employs the wild horse optimizer (WHO) has been proposed. The primary goal of this research is to select the optimal cross-layer based cluster heads (CH) in terms of energy efficiency, communication delay, and latency. The physical layer, medium access control layer and network layer properties of each SN are used to evaluate and select the best CH. Further, a deep learning-based approach is used to determine the best data transmission path. The CL-EEWSN reduces energy consumption, end-to-end delay, and improves packet delivery ratio when compared to existing state of the-art protocols. The average energy consumption of CL-EEWSN for 100 nodes and 400 nodes is 0.16 nJ and 0.31 nJ respectively and is lowest in comparison to existing techniques