Battery recovery aware sensor networks

Authors:
Chi-Kin Chau;Muhammad Husni Wahab;Fei Qin;Yunsheng Wang;Yang Yang
Affiliations:
EE Department, University College London and Computer Laboratory, University of Cambridge;EE Department, University College London;EE Department, University College London;EE Department, University College London;EE Department, University College London
Venue:
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
Year:
2009

Citing 5
Cited 0

Nonideal battery and main memory effects on CPU speed-setting for low power

IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low power electronics and design
Battery Model for Embedded Systems

VLSID '05 Proceedings of the 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design
Opportunistic forwarding in wireless networks with duty cycling

Proceedings of the third ACM workshop on Challenged networks
Improving battery performance by using traffic shaping techniques

IEEE Journal on Selected Areas in Communications
A framework for optimal battery management for wireless nodes

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.01

Visualization

Abstract

Many applications of sensor networks require batteries as the energy source, and hence critically rely on energy optimisation of sensor batteries. But as often neglected by the networking community, most batteries are non-ideal energy reservoirs and can exhibit battery recovery effect -- the deliverable energy in batteries can be replenished per se, if left idling for sufficient duration. We made several contributions towards harnessing battery recovery effect in sensor networks. First, we empirically examine the gain of battery runtime due to battery recovery effect, and found this effect significant and duration-dependent. Second, based on our findings, we model the battery recovery effect in the presence of random sensing activities by a Markov chain model, and study the effect of duty cycling and buffering to harness battery recovery effect. Third, we propose a more energy-efficient duty cycling scheme that is aware of battery recovery effect, and analyse its performance with respect to the latency of data delivery.