Energy aware optimal clustering and reliable routing based on Markov model in Wireless Sensor Networks

Wireless sensor networks are constituted out of a large number of sensor nodes with limited energy resources. Severe shortage of onboard energy resource of WSN necessitates the combined solutions to its energy challenges. We propose an effective, method of clustering and reliable packet routing for the mobile nodes of sensor networks. Typically when nodes show some mobility in deployed network area, localization becomes complex which makes the clustering process even more complicated. In most of the existing works, Bayesian method [12] of predicting the future changes in nodes position is employed, wherein changes in states are based on prior probability distribution. However there is no influence of prior positions of sensor nodes on future states of its position. The node's movement to next location can be predicted using its current state of location and hence node's localization is being modeled as markov chains in our work. Normally the energy levels of sensor nodes can be configured to exhibit discrete dynamic energy values based on its different working modes like active, listen and sleep modes. We also focus an effective energy conservation by sending few number of energy abundant nodes to active states to support reliable data transmission. In this paper, we present an efficient reduction of energy consumption plus reliable packet delivery based on optimal cluster formation and markov model for mobile sensor networks. For implementing optimal clusters, we meticulously use k-medoids algorithm as a primary method and next based on node's communication cost, residual battery energy and its movements, transition probability matrix is obtained. This matrix is further utilized to forecast the reliable path for data transmission from source to destination.