期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 64, 期 2, 页码 714-727出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2014.2322356
关键词
Approximation algorithm; data collection; lifetime maximization; NP-hard; target coverage; wireless sensor network (WSN)
资金
- National Science Foundation [NSF-1137732, NSF-1241626, NSF-1343361]
- Air Force Office of Scientific Research [FA8650-13-C-5800]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1613682, 1343361] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1350230] Funding Source: National Science Foundation
- Division Of Graduate Education
- Direct For Education and Human Resources [1241626] Funding Source: National Science Foundation
- Division Of Human Resource Development
- Direct For Education and Human Resources [1137732] Funding Source: National Science Foundation
Target coverage and data collection are two fundamental problems for wireless sensor networks (WSNs). Target coverage is needed to select sensors in a given area that can monitor a set of interesting points. Data collection is needed to transmit the sensed data from sensors to a sink. Since, in many applications, sensors are battery powered, it is expected that a WSN can work untended for a long period. This paper addresses the scheduling problems for both target coverage and data collection in WSNs with the objective of maximizing network lifetime. First, a polynomial-time approximation scheme is developed for the case where the density of target points is bounded, and then, a polynomial-time constant-factor approximation algorithm is developed for the general case. It is also proved that it is NP-hard to find a maximum lifetime scheduling of target cover and data collection for a WSN, even if all the sensors have the same sensing radius and the same transmission radius. Further, the practical efficiency of our algorithms is analyzed through simulation. These extensive simulation results show better performances of our algorithms compared with other research findings.
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