Salp: Metaheuristic-Based Clustering for Wireless Sensor Networks

Introduction

Wireless Sensor Network (WSN) is the emerging area in the current age. A WSN is a collection of sensor nodes, where a sensor node is a small tiny device with limited energy and limited storage. The basic task for sensor nodes is to sense the physical environment and transfer it to the nearest base station. The applications of wireless sensor network mainly include in health-care, military, environmental monitoring, home automation, & other commercial areas. There are many research challenges in WSN like path planning for data transfer sensor node to the base station, cluster formation and cluster-head selection, energy issues and network lifetime issues, etc.

After deployment of a sensor node in a particular area, nodes have to perform data-communication activities like sensing the data, transfer the sensed data to the base station, and some of them have to work as a relay node. Due to this activity highly energy consumption of a node. As we all know that a sensor node has limited energy resources. To overcome this type of issue Node clustering concept has been introduced. In node clustering, a group of sensor nodes is formed with similar size of sensor nodes. After deployment of nodes, n number of clusters are formed and each cluster has a Master cluster head(MCH). MCH collects the data from its cluster member. Node near to base station has to work as a sensing device as well as work as a relay node, due to this energy hole problem arises. To overcome this problem unequal clustering concept has been introduced. In this clustering, the distance between a node to the cluster head and distance between a node to the base station are the basic parameters to form a cluster. Node near the base station has less number of cluster members. As the distance increases the number of cluster member increases.

Cluster formation and cluster head selection are the major research issues. Many researchers adopt optimization techniques to solve real-time problems. These optimization techniques are based on animal or insects behavior. These algorithms are named as Nature-Inspired Optimization Algorithms. There are many nature-inspired algorithms is developed such as Genetic algorithm (GA) is based on Darwin evolution theory, Ant colony optimization(ACO) and Salp optimization (SO) inspired by the behavior of Ant and Salp insects to searching food and so on. A single real time problem can solve with many optimization techniques. In any optimization technique solution vector intialization is the first step, after some iteration operation are preformed and finally a resultant solution vector has been generated. To achieve the final result some algorithms are updating the solution vectors like in GA and some algorithms are updating the location of each solution vector-like PSO. The major disadvantage of these algorithms is the random selection method to generate a solution vector and the generated solution vector gives an approximate solution.

For clustering, many meta-heuristic based cluster formation and cluster-head section algorithm had proposed by many researchers. Among all, LEACH protocol (Heinzelman et.al, 2005) is the first clustering based protocol. It uses a probabilistic approach to select a CH. The setup phase and the steady-state phase are two basic steps that are involved in the LEACH protocol. An energy-efficient routing protocol that uses unequal clustering by Genetic algorithm (Hussain et. al, 2007) is used to select the cluster head. An energy-efficient particle swarm optimization (PSO) based cluster head selection (Rao et al.,2016) and cluster formation are introduced. Authors presented PSO based cluster head selection method by considering the fault-tolerance problem (Kaur et al.,2018). In this paper, the author selects a surrogate cluster head with the selection of a master cluster head. If master cluster head fails then surrogate node becomes cluster head. Moth Flame Optimization (MFO) is applied for the selection of the least number of CHs and routing (Mittal,2018). In this paper, the author considered multi-hop communication between CHs and the base station. Fuzzy logic approach and Ant Colony Optimization (ACO) meta-heuristic approach is proposed (Arjunan et al., 2018) for the clustering problem. Fuzzy logic is used for unequal clustering and ACO is applied for routing. An uneven clustering and routing protocol based (Xiuwu et al.,2019) on glowworm swarm optimization (GSO). For cluster head selection author considers cluster head density, cluster head proximity distance, the energy of cluster head and compactness of the clusters. For unequal clustering and routing, the author adopted a chemical reaction based optimization technique (Rao et al.2016).