Distributed and Efficient Minimum-Latency Data Aggregation Scheduling for Multichannel Wireless Sensor Networks

摘要

Data aggregation is a critical operation in wireless sensor networks (WSNs). Many applications have strict requirements for the latency of data aggregation. This paper focuses on the latency problem of data aggregation. Two factors determine the latency of data aggregation. First, because of the existence of interference, efficient collision-free scheduling is crucial for reducing data aggregation latency. Second, the tree structure has an important impact on data aggregation latency. In this paper, we propose a novel approach called distributed and efficient data aggregation scheduling over multichannel links (DEDAS-MC). DEDAS-MC minimizes the latency in routing the aggregated data to the sink over multichannel links. In DEDAS-MC, we first present a scheduling algorithm to schedule sensors to avoid interference and minimize the latency of data aggregation on a given tree. Then, a distributed algorithm for constructing minimum-latency data aggregation trees is proposed by employing the Markov approximation method. In DEDAS-MC, the value of beta is adaptive. The Markov approximation method-based adaptive- beta is more flexible and efficient than the single beta approximation. The experiments show that DEDAS-MC outperforms the existing competing schemes.