TY - GEN
T1 - QoI-aware energy management for wireless sensor networks
AU - Liu, Chi Harold
AU - Hui, Pan
AU - Branch, Joel W.
AU - Yang, Bo
PY - 2011
Y1 - 2011
N2 - In this paper, we propose an efficient energy-management framework in wireless sensor networks (WSNs) to address the fundamental research challenge imposed by both the maintenance of the energy supply and the support of the quality-of-information (QoI) requirements. By quantifying the QoI benefit the tasks receive in relation to the level of QoI they request as the QoI satisfaction index), we propose a QoI-aware energy-management scheme to distributedly decide the participating state of each sensor. Specifically, by using the mathematical framework of the Gur Game, we propose a novel pay-off structure taking into account the QoI and the energy consumption. We finally evaluate the proposed scheme under an event occurrence detection scenario, where the proposed scheme successfully guarantees less than 7% QoI outage, saves 80% of the energy reserve if compared with the lower bound solution, and achieves the suboptimum with only 4% gap if compared with optimal solution.
AB - In this paper, we propose an efficient energy-management framework in wireless sensor networks (WSNs) to address the fundamental research challenge imposed by both the maintenance of the energy supply and the support of the quality-of-information (QoI) requirements. By quantifying the QoI benefit the tasks receive in relation to the level of QoI they request as the QoI satisfaction index), we propose a QoI-aware energy-management scheme to distributedly decide the participating state of each sensor. Specifically, by using the mathematical framework of the Gur Game, we propose a novel pay-off structure taking into account the QoI and the energy consumption. We finally evaluate the proposed scheme under an event occurrence detection scenario, where the proposed scheme successfully guarantees less than 7% QoI outage, saves 80% of the energy reserve if compared with the lower bound solution, and achieves the suboptimum with only 4% gap if compared with optimal solution.
UR - https://www.scopus.com/pages/publications/79958066974
U2 - 10.1109/PERCOMW.2011.5766978
DO - 10.1109/PERCOMW.2011.5766978
M3 - Conference contribution
AN - SCOPUS:79958066974
SN - 9781612849379
T3 - 2011 IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2011
SP - 8
EP - 13
BT - 2011 IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2011
PB - IEEE Computer Society
T2 - 9th IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2011
Y2 - 21 March 2011 through 25 March 2011
ER -