A Firefly Optimization Algorithm for Maximizing the Connectivity in Mobile Wireless Sensor Network

Abstract

For the effective functioning of a Mobile Wireless Sensor Networks (MWSN), the connectivity maintenance of the sensor nodes is of significant concern. Otherwise, it may result in an independent node or nodes wholly get detached from the network. Though such detached sensor nodes are functioning correctly and have good energy backup, its service cannot be utilized for the purpose it is intended for as it is isolated from the core network. These sensor nodes are sophisticated tiny devices and costlier depending on the application; therefore, proper care should be taken to keep them connected to the network. Hence, a firefly based algorithm, a Swarm Intelligence technique, referred to as Firefly Algorithm for Connectivity in Mobile WSN (FACM) has been proposed in this article in order to establish proper connectivity among the sensor nodes in MWSN. FACM is based on the insect fireflies, which have a unique feature of producing light, a result of chemical reaction, at different intervals to escape from the predators and most of the time to attract prey. The inevitable feature of insect firefly, attracting the prey, is exploited in the proposed FACM where a brighter sensor node (in terms of energy and distance) will attract the less bright neighboring sensor nodes. Thus, the less bright sensor node can depend on the brighter sensor node for the data transfer, thereby saving its energy. A fitness function has been designed based on the combination of two parameters energy and the distance, which decides the brightness of the sensor node. The effectiveness of the proposed FACM has been theoretically analyzed and verified by simulation through MATLAB. The results obtained are compared with classical FA and are found to be inspiring. © 2020, Springer Nature Switzerland AG.