TY - GEN
T1 - Exploiting mobility for quality-maximized data collection in energy harvesting sensor networks
AU - Ren, Xiaojiang
AU - Liang, Weifa
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2014/6/25
Y1 - 2014/6/25
N2 - With the advance of energy harvesting technology, more and more sensors now are powered by ambient energy. Energy harvesting sensor networks are a key step in paving the way for truly green systems that can operate 'perpetually' and do not adversely impact on the environment. In this paper we consider quality data collection in an energy harvesting sensor network by exploring sink mobility. That is, we consider a mobile sink traveling along a to-be-found trajectory for data collection, subject to a specified tolerant delay constraint. We first formulate this optimization problem as a data quality maximization problem. Since the problem is NP-hard, we then devise a scalable heuristic solution. Also, a distributed implementation of the proposed algorithm is developed too. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are promising and very efficient.
AB - With the advance of energy harvesting technology, more and more sensors now are powered by ambient energy. Energy harvesting sensor networks are a key step in paving the way for truly green systems that can operate 'perpetually' and do not adversely impact on the environment. In this paper we consider quality data collection in an energy harvesting sensor network by exploring sink mobility. That is, we consider a mobile sink traveling along a to-be-found trajectory for data collection, subject to a specified tolerant delay constraint. We first formulate this optimization problem as a data quality maximization problem. Since the problem is NP-hard, we then devise a scalable heuristic solution. Also, a distributed implementation of the proposed algorithm is developed too. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are promising and very efficient.
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U2 - 10.1109/PIMRC.2014.7136364
DO - 10.1109/PIMRC.2014.7136364
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781479949120
VL - 2014-June
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
SP - 1274
EP - 1278
BT - 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication, PIMRC 2014
PB - IEEE
T2 - 2014 25th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communication, IEEE PIMRC 2014
Y2 - 2 September 2014 through 5 September 2014
ER -