TY - GEN
T1 - Energy-efficient mobile data collection in energy-harvesting wireless sensor networks
AU - Wang, Cong
AU - Guo, Songtao
AU - Yang, Yuanyuan
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - Environmental energy harvesting technologies have provided potential for battery-powered wireless sensor networks to have perpetual network operations. To design a robust network that can adapt to not only temporal but also spatial variations of ambient energy sources, in this paper, we utilize mobility to circumvent communication bottlenecks, by employing a mobile data collector, called SenCar. We propose a two-stage approach for mobile data collection. In the first stage, SenCar makes stops at a subset of selected sensor locations to collect data packets in a multi-hop fashion. We provide a selection algorithm to search for sensor locations with most residual energy while guaranteeing a bounded tour length. Then we design a distributed data gathering algorithm to achieve maximum network utility by adjusting data rates, link scheduling and flow routing that adapts to spatial temporal environmental energy variations. The effectiveness and efficiency of the proposed algorithms are validated by extensive numerical results.
AB - Environmental energy harvesting technologies have provided potential for battery-powered wireless sensor networks to have perpetual network operations. To design a robust network that can adapt to not only temporal but also spatial variations of ambient energy sources, in this paper, we utilize mobility to circumvent communication bottlenecks, by employing a mobile data collector, called SenCar. We propose a two-stage approach for mobile data collection. In the first stage, SenCar makes stops at a subset of selected sensor locations to collect data packets in a multi-hop fashion. We provide a selection algorithm to search for sensor locations with most residual energy while guaranteeing a bounded tour length. Then we design a distributed data gathering algorithm to achieve maximum network utility by adjusting data rates, link scheduling and flow routing that adapts to spatial temporal environmental energy variations. The effectiveness and efficiency of the proposed algorithms are validated by extensive numerical results.
KW - convex optimization
KW - distributed algorithms
KW - energy harvesting
KW - mobile data gathering
KW - Wireless sensor networks
UR - https://www.scopus.com/pages/publications/84988222664
U2 - 10.1109/PADSW.2014.7097791
DO - 10.1109/PADSW.2014.7097791
M3 - Conference contribution
AN - SCOPUS:84988222664
T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
SP - 55
EP - 62
BT - 2014 20th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2014 - Proceedings
PB - IEEE Computer Society
T2 - 20th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2014
Y2 - 16 December 2014 through 19 December 2014
ER -