Abstract
Mobility awareness and energy efficiency are two indispensable optimization problems in mobile ad hoc networks (MANETs) where nodes move unpredictably in any direction with restricted battery life, resulting in frequent change in topology. These constraints are widely studied to increase the lifetime of such networks. This paper focuses on the problems of mobility as well as energy efficiency to develop a clustering algorithm inspired by multiagent stochastic parallel search technique of particle swarm optimization. The election of cluster heads takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster heads for longer duration of time. The cluster formation is presented by taking multiobjective fitness function using particle swarm optimization. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The results show the effectiveness of our proposed algorithm in terms of network lifetime, average number of clusters formed, average number of reclustering required, energy consumption, and packet delivery ratio.
Highlights
With the advancement of many nature inspired techniques for different optimization problems, researchers are motivated to develop solutions for two important optimization problems in mobile ad hoc networks (MANETs), that is, clustering and routing
In our proposed work we have developed a mobility aware energy efficient clustering for MANET based on the particle swarm optimization approach (ME-Particle swarm optimization (PSO))
We have presented a mobility aware energy efficient clustering for MANET based on a bio-inspired approach of particle swarm optimization
Summary
With the advancement of many nature inspired techniques for different optimization problems, researchers are motivated to develop solutions for two important optimization problems in MANET, that is, clustering and routing. As the mobility of nodes is the major cause of link failure, so, in a clustering scheme, the situation when a selected CH is comparatively more mobile than other nodes, it will frequently dissolve a well formed cluster and require a new intercluster path setup from source to destination or in worst situation there is a need for reclustering This recurrent link failure due to the movement of CHs accelerates the routing overheads and degrades the reliability of data transmission because in both situations there is an increase in control message overheads resulting in reducing the effectiveness and overall the network lifetime. The cluster formation algorithm defines a new fitness function based on the strength of CHs and the average distance of nodes to its corresponding CHs. For cluster formation, a bio-inspired PSO search technique is used which is a robust stochastic optimization technique based on the movement and intelligence of the swarm.
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