Distributed data compression for energy efficiency in wireless sensor networks

In this paper distributed source coding is used to compress data in a wireless sensor network in order to reduce energy consumption in sensor nodes. In the distributed source coding scheme the correlation characteristics of the sensor nodes are utilized for achieving compression in the data transmission. Since the correlation characteristics of the nodes vary with respect to time, correlation tracking is required to maintain the required compression level. The performance of a distributed source coding scheme depends primarily on the correlation tracking algorithm in addition to other factors. In this paper the effect of correlation tracking on the performance of distributed source coding schemes with least mean square algorithm (LMS), normalized LMS algorithm, variable step LMS algorithm and variable step normalized LMS algorithm is studied and from the simulation results it is found that normalized LMS algorithm outperforms the other algorithms in the process of correlation tracking. Distributed source coding is also implemented using correlation based clustering algorithm.