Affiliations: [a] Department of Computer Science and Engineering, SSM Institute of Engineering and Technology, Dindigul, TamilNadu, India
| [b] Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
| [c] Department of Electronics and Communication Engineering, NPR College of Engineering and Technology, Natham, Dindigul, Tamil Nadu, India
| [d] Department of Information Technology, Bannari Amman Institute of Technology, Erode, TamilNadu, India
Correspondence:
[*]
Corresponding author. V. Nivedhitha, Department of Computer Science and Engineering, SSM Institute of Engineering and Technology, Dindigul, TamilNadu, India. E-mail: [email protected].
Abstract: A Wireless Sensor Network (WSN) is divided into groups of sensor nodes for efficient transmission of data from the point of measuring to sink. By performing clustering, the network remains energy-efficient and stable. An intelligent mechanism is needed to cluster the sensors and find an organizer node, the cluster head. The organizer node assembles data from its constituent nodes called member nodes, finds an optimal route to the sink of the network, and transfers the same. The nomination of cluster head is crucial since energy utilization is a major challenge of sensor nodes deployed over a hostile environment. In this paper, a fuzzy-based Improved Harris’s Hawk Optimization Algorithm (IHHO) is proposed to select an able cluster head for data communication. The fuzzy inference model ponders balance energy, distance from self to sink node, and vicinity of nodes from cluster head as input factors and decides if a candidate node is eligible for becoming a cluster head. The IHHO tunes the logic into an energy-efficient network with less complexity and more ease. The novelty of the paper lies in applying the hawk-pack technique based on fuzzy rules. Simulations show that the combination of Fuzzy based IHHO reduces the death of nodes through which network lifetime is enhanced.
Keywords: Harris’s hawk optimization, fuzzification, cluster head election, energy efficient routing
