Adaptive self-localized Discrete Quasi Monte Carlo Localization (DQMCL) scheme for wsn based on antithetic markov process

摘要

Most of the localization algorithms in past decade are usually based on Monte Carlo, sequential monte carlo and adaptive monte carlo localization method. In this paper we proposed a new scheme called DQMCL which employs the antithetic variance reduction method to improve the localization accuracy. Most existing SMC and AMC based localization algorithm cannot be used in dynamic sensor network but DQMCL can work well even without need of static sensor network with the help of discrete power control method for the entire sensor to improve the average Localization accuracy. Also we analyse a quasi monte carlo method for simulating a discrete time antithetic markov time steps to improve the life time of the sensor node. Our simulation result shows that overall localization accuracy will be more than 88% and localization error is below 35% with synchronization error observed at different discrete time interval.