Discrete antithetic Markov Monte Carlo based power mapping localization algorithm for WSN

Localizing and tracking moving stimuli or objects is an essential capability for a sensor network in many applications. Discrete power Management is an efficient way to construct a reliable and energy efficient network topology in WSN. At the same time Location awareness is also important for wireless sensor networks since many applications such as environment monitoring and Tracking. Hence. MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. In this paper we introduce the new power mapping algorithm based on discrete antithetic markov Monte carlo method which variance reduction method for increasing the accuracy of Markov chain Monte Carlo algorithm for computing the dominant eigen pair of a matrix also we are concentrate on power management technique in terms of discrete power levels which allocate the power based on the event of every sensor node. By using this discrete power mapping method we can analyze all the high level parameter of the wireless sensor network especially for RSSI and TOA,also we derive the mathematical model for discrete level distance measurement and reduce the distance error for more than three anchors and redundant distant measurements to account for the error in each individual measurement.