Energy-efficient clustering in lossy wireless sensor networks

Recent experimental studies have revealed that a large percentage of wireless links are lossy and unreliable for data delivery in wireless sensor networks (WSNs). Such findings raise new challenges for the design of clustering algorithms in WSNs in terms of data reliability and energy efficiency. In this paper, we propose distributed clustering algorithms for lossy WSNs with a mobile collector, where the mobile collector moves close to each cluster head to receive data directly and then uploads collected data to the base station. We first consider constructing one-hop clusters in lossy WSNs where all cluster members are within the direct communication range of their cluster heads. We formulate the problem into an integer program, aiming at maximizing the network lifetime, which is defined as the number of rounds of data collection until the first node dies. We then prove that the problem is NP-hard. After that, we propose a metric-based distributed clustering algorithm to solve the problem. We adopt a metric called selection weight for each sensor node that indicates both link qualities around the node and its capability of being a cluster head. We further extend the algorithm to multi-hop clustering to achieve better scalability. We have found out that the performance of the one-hop clustering algorithm in small WSNs is very close to the optimal results obtained by mathematical tools. We have conducted extensive simulations for large WSNs and the results demonstrate that the proposed clustering algorithms can significantly improve the data reception ratio, reduce the total energy consumption in the network and prolong network lifetime compared to a typical distributed clustering algorithm, HEED, that does not consider lossy links.