Sandpiper optimization algorithm with cosine similarity based cross-layer routing protocol for smart agriculture in wireless sensor network assisted internet of things systems

The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN-assisted IoT applications, even though better data communication is achieved through cross-layer models. This paper proposes a new cross-layer-based clustering and routing model to provide a scalable and energy-efficient long data communication in WSN-assisted IoT systems for smart agriculture. Initially, the fuzzy k-medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy-hole problems in WSN. A cross-layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio-inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end-to-end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime.