Journal Article


Chris J. Bleakley
Jong Chern Lim


Computer Science

wireless sensor networks temporal correlation spatial correlation scheduling prediction wireless sensor networks wsns environmental conditions environmental monitoring

Adaptive WSN Scheduling for Lifetime Extension in Environmental Monitoring Applications (2012)

Abstract Wireless sensor networks (WSNs) are often used for environmental monitoring applications in which nodes periodically measure environmental conditions and immediately send the measurements back to the sink for processing. Since WSN nodes are typically battery powered, network lifetime is a major concern. A key research problem is how to determine the data gathering schedule that will maximize network lifetime while meeting the user's application-specific accuracy requirements. In this work, a novel algorithm for determining efficient sampling schedules for data gathering WSNs is proposed. The algorithm differs from previous work in that it dynamically adapts the sampling schedule based on the observed internode data correlation as well as the temporal correlation. The performance of the algorithm has been assessed using real-world datasets. For two-tier networks, the proposed algorithm outperforms a highly cited previously published algorithm by up to 512% in terms of lifetime and by up to 30% in terms of prediction accuracy. For multihop networks, the proposed algorithm improves on the previously published algorithm by up to 553% and 38% in terms of lifetime and accuracy, respectively.
Collections Ireland -> University College Dublin -> CASL Research Collection
Ireland -> University College Dublin -> Complex and Adaptive Systems Laboratory
Ireland -> University College Dublin -> Computer Science Research Collection

Full list of authors on original publication

Chris J. Bleakley, Jong Chern Lim

Experts in our system

Chris Bleakley
University College Dublin
Total Publications: 105