عنوان مقاله [English]
Wireless sensor networks (WSN) comprise of a large number of low-power but low-cost small sensing nodes which distributed randomly in a specific area far from the human reach , for the purpose of surveillance, recognition and monitoring the nearby environment based on their inter communication. Each node includes units i.e. sensing, processing, transducing, location positioning and power supply. Owing to various features of sensors such as quickness, self-awareness and self configurability, WSNs have various applications in different areas and many methods are being developed to improve their performance in an application specific way. WSNs face many challenges, including energy restrictions, security, communication reliability, design, and so on. It should be mentioned that it is hardly possible to balance all these challenges due to the conflicts they have with each other. Hitherto, researchers have done extensive studies to bridle these concerns. Sensor nodes are small and have often limited and irreplaceable sources of energy.
Furthermore, they can send information at short distances. In long run operations, each node generally does the data collection singly. In this paper, a multi-objective swarm intelligence-based algorithm built on Shuffled frog-leaping and Firefly Algorithm (named MOFSA) is presented as an adaptive clustering-based multi-hop routing protocol for WSNs. MOFSA's multi-objective function regards different criteria (e.g., inter- and intra-cluster distances, residual energy of nodes, distances from the sink, overlap and load of clusters) to select appropriate cluster heads at each round. Moreover, another multi-objective function is proposed to select the forwarder nodes in the routing phase. The controllable parameters of MOFSA in both clustering and multi-hop phases can be adaptively tuned to achieve the best performance based on the network requirements according to the specific application. Simulation outcomes demonstrate average lifetime improvements of 230% compared with LEACH, 100% compared with ERA, 38% compared with SIF and 260% compared with FSFLA in different network scenarios.