Particle swarm optimization: is a branch of Artificial knowledge that spotlights on the aggregate conduct and properties of perplexing, self-composed, decentralized framework with a social structure for example flying creature runs, insect states and fish schools [5]. Particle Swarm Optimization is an optimization approach which have been previously implemented in wireless sensor network. Kulkarni et al.[8] have been studied this technique for maximum deployment, localization, clustering of nodes and data aggregation. PSO provides high quality results, fast merging and inconsequential calculation. Then again PSO requires vast measure of memory which restrict its utilization in rapid constant applications. Soliman and tan et al.[11] have been applied adaptive hybrid optimization, PSO and GA to eliminate sensor location problem for maximum coverage. The method ensure true results for desired coverage. Features of particle swarm optimization: i. Easy implementation on hardware and software. ii. Availability of guidelines for choosing its parameters. iii. High quality solutions. iv. Availability of variants for real, integer and binary domains. Disadvantages of particle swarm optimization: i. Large memory is required ii. It is costly method for real time problems. Neural networks: Neural system is comprised of interconnecting artificial neurons that copy the properties of biological neurons. The human brain which possesses an extraordinary ability to keep, store, and execute is a complex network of over billions of neurons, each connected on average to about 10,000 other neurons. Every neuron gets signals through neurotransmitters, which control the effects of the signals on the neuron. NN learns comprises of a system of neurons sorted out in information, covered up and yield layers. In feed forward NN, the outputs of a layer are connected as the inputs to next layer while in recurrent networks, feedback connections are allowed as fig. 5. In an Elman type recurrent network, a copy of hidden layer output is referred as context layer, is presented as the input to hidden layer.[8] Fig 5 :Architecture of neural network[8]>