Coverage of wireless sensor networks is a fundamental problem which has been studied for more than two decades. In duty cycle based wireless sensor networks, the nodes are sleep/wake periodic working, and the sleeping of nodes selected to achieve coverage results in a lack of network coverage, which make the coverage of the research difficult to apply in practice. In this paper, a Multi Working Sets Alternate Covering (MWSAC) scheme is proposed to achieve continuous partial coverage of the network. Firstly, a distributed algorithm is proposed to construct the maximum number of working sets, each working set is required to satisfy the partial coverage requirement of the application. Then, the sleeping time of the working nodes is scheduled, which makes the nodes belonging to the same working set wake up synchronously and nodes between multiple working sets wake up asynchronously. Thus, at any time, as long as the nodes of one working set are in waking state, the nodes of other working sets are adjusted to sleeping state to save energy. Due to multiple working sets are alternately covered under MWSAC, the workload and wake-up time of each working node is greatly reduced, which makes the energy consumption more balanced and the network lifetime longer. Both the theoretical analysis and the experimental results show that, compared with the previous continuous coverage scheme, MWSAC scheme has obvious advantages in terms of coverage, network lifetime and node utilization.
Partial coverage Wireless sensor networks Sleep scheduling Multi working sets
This is a preview of subscription content, log in to check access.
This work was supported in part by the National Natural Science Foundation of China (61772554, 61379110, 61572528, 61572526), The National Basic Research Program of China (973 Program)(2014CB046305).
He S, Chen J, Li X et al (2014) Mobility and intruder prior information improving the barrier coverage of sparse sensor networks. IEEE Trans Mob Comput 13(6):1268–1282CrossRefGoogle Scholar
Li H, Liu D, Dai Y, Luan TH (2015) Engineering searchable encryption of mobile cloud networks: when qoe meets qop. IEEE Wirel Commun 22(4):74–80CrossRefGoogle Scholar
Liu X (2017) Node deployment based on extra path creation for wireless sensor networks on mountain roads. IEEE Commun Lett 21(11):2376–2379CrossRefGoogle Scholar
Li H, Yang Y, Luan TH, Liang X, Zhou L, Shen XS (2016) Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data. IEEE Trans Dependable Secure Comput 13(3):312–325CrossRefGoogle Scholar
Wang T, Peng Z, Liang J et al (2014) Following targets for mobile tracking in wireless sensor networks. ACM Trans Sensor Netw 12(4):31.1–31.24Google Scholar
Zeng D, Gu L, Lian L et al (2016) On cost-efficient sensor placement for contaminant detection in water distribution systems. IEEE Trans Industrial Inform 12(6):2177–2185CrossRefGoogle Scholar
Wang T, Wu Q, Wen S et al (2017) Propagation modeling and defending of mobile sensor worm in wireless sensor and actuator networks. Sensors 17(1):139CrossRefGoogle Scholar
Karyakarte MS, Tavildar AS, Khanna R (2017) Dynamic node deployment and cross layer opportunistic robust routing for PoI coverage using WSNs. Wirel Pers Commun 96(2):2741–2759CrossRefGoogle Scholar
Li H, Lin X, Yang H, Liang X, Lu R, Shen X (2014) EPPDR: an efficient privacy-preserving demand response scheme with adaptive key evolution in smart grid. IEEE Trans Parallel Distrib Syst 25(8):2053–2064CrossRefGoogle Scholar
Tian D, Georganas ND (2003) A node scheduling scheme for energy conservation in large wireless sensor networks. Wirel Commun Mob Comput 3(2):271–290CrossRefGoogle Scholar
Megerian S, Koushanfar F, Potkonjak M et al (2005) Worst and best-case coverage in sensor networks. IEEE Trans Mob Comput 4(1):84–92CrossRefGoogle Scholar
Tian D, Georganas ND (2002) A coverage-preserving node scheduling scheme for large wireless sensor networks. Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, Atlanta, p 32–41Google Scholar
Sengupta S, Das S, Nasir M et al (2012) An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Trans Syst Man Cybern 42(6):1093–1102CrossRefGoogle Scholar
Zhu C, Yang LT, Shu L et al (2014) Sleep scheduling for geographic routing in duty-cycled mobile sensor networks. IEEE Trans Ind Electron 61(11):6346–6355CrossRefGoogle Scholar