Robust In-Network Data Processing for Target Tracking in WSNs

摘要

Target tracking is becoming a common and important application of large-scale wireless sensor networks. Tree-based in-network processing for tracking data is an efficient technique to prevent packet overflowing and minimize communication energy consumption. But existing tree-based approaches have a serious problem which is non-robustness under unreliable link conditions in wireless sensor networks. In this paper, we propose a Conditional Multicasting (CM) technique and an h-distance Common Ancestor Tree (hCAT) algorithm for robust in-network processing for target tracking data via tree topology. CM is a hop by hop data loss inference based multicasting scheme, and hCAT is a special tree for CM to maximize data recovery with the use of redundancies while minimizing the existence of redundancies within networks. CM and hCAT provide higher successful data arrival rate than existing tree-based approaches. Moreover, CM and hCAT employ low extra communication cost and minimal increase of traffic overhead.