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Spatial-Temporal Coverage Optimization in Wireless Sensor Networks
April 2011 (vol. 10 no. 4)
pp. 465-478
ASCII Text
x 
 
Changlei Liu, Guohong Cao, "Spatial-Temporal Coverage Optimization in Wireless Sensor Networks," IEEE Transactions on Mobile Computing, vol. 10, no. 4, pp. 465-478, April, 2011.
 
 
BibTex
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@article{ 10.1109/TMC.2010.172,
author = {Changlei Liu and Guohong Cao},
title = {Spatial-Temporal Coverage Optimization in Wireless Sensor Networks},
journal ={IEEE Transactions on Mobile Computing},
volume = {10},
number = {4},
issn = {1536-1233},
year = {2011},
pages = {465-478},
doi = {http://doi.ieeecomputersociety.org/10.1109/TMC.2010.172},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Mobile Computing
TI - Spatial-Temporal Coverage Optimization in Wireless Sensor Networks
IS - 4
SN - 1536-1233
SP465
EP478
EPD - 465-478
A1 - Changlei Liu,
A1 - Guohong Cao,
PY - 2011
KW - Wireless sensor network
KW - coverage
KW - sensor scheduling
KW - distributed protocol
KW - parallel algorithm.
VL - 10
JA - IEEE Transactions on Mobile Computing
ER -
 
Changlei Liu, The Pennsylvania State University, University Park
Guohong Cao, The Pennsylvania State University, University Park
Mission-driven sensor networks usually have special lifetime requirements. However, the density of the sensors may not be large enough to satisfy the coverage requirement while meeting the lifetime constraint at the same time. Sometimes, coverage has to be traded for network lifetime. In this paper, we study how to schedule sensors to maximize their coverage during a specified network lifetime. Unlike sensor deployment, where the goal is to maximize the spatial coverage, our objective is to maximize the spatial-temporal coverage by scheduling sensors' activity after they have been deployed. Since the optimization problem is NP-hard, we first present a centralized heuristic whose approximation factor is proved to be {1\over 2}, and then, propose a distributed parallel optimization protocol (POP). In POP, nodes optimize their schedules on their own but converge to local optimality without conflict with one another. Theoretical and simulation results show that POP substantially outperforms other schemes in terms of network lifetime, coverage redundancy, convergence time, and event detection probability.

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Index Terms:
Wireless sensor network, coverage, sensor scheduling, distributed protocol, parallel algorithm.
Citation:
Changlei Liu, Guohong Cao, "Spatial-Temporal Coverage Optimization in Wireless Sensor Networks," IEEE Transactions on Mobile Computing, vol. 10, no. 4, pp. 465-478, April 2011, doi:10.1109/TMC.2010.172
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