TY - JOUR
T1 - Spatial-temporal coverage optimization in wireless sensor networks
AU - Liu, Changlei
AU - Cao, Guohong
N1 - Funding Information:
The authors would like to thank the anonymous reviewers whose insightful comments helped improve the presentation of this paper significantly. This work was supported in part by the US National Science Foundation (CNS-0916171).
PY - 2011/4
Y1 - 2011/4
N2 - 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.
AB - 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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U2 - 10.1109/TMC.2010.172
DO - 10.1109/TMC.2010.172
M3 - Article
AN - SCOPUS:79951903165
SN - 1536-1233
VL - 10
SP - 465
EP - 478
JO - IEEE Transactions on Mobile Computing
JF - IEEE Transactions on Mobile Computing
IS - 4
M1 - 5582097
ER -