Abstract
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.
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References
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):1268–1282
Liu X, Li X, Zhang S, Liu A (2017) Big program code dissemination scheme for emergency software-define wireless sensor networks. Peer-to-Peer Netw Appl. https://doi.org/10.1007/s12083-017-0565-5
Chen X, Ma M, Liu A. (2017) Dynamic Power Management and Adaptive Packet Size Selection for IoT in e-Healthcare. Computers & Electrical Engineering, DOI: https://doi.org/10.1016/j.compeleceng.2017.06.010
Liu X (2017) Survivability-aware connectivity restoration for partitioned wireless sensor networks. IEEE Commun Lett 21(11):2444–2447
Zeng D, Li P, Guo S et al (2015) Energy minimization in multi-task software-defined sensor networks. IEEE Trans Comput 64(11):3128–3139
Zhao S, Liu A. (2017) High performance target tracking scheme with low prediction precision requirement in WSNs. International Journal of Ad Hoc and Ubiquitous Computing, http://www.inderscience.com/info/ingeneral/forthcoming.php
Liu Q, Liu A (2017) On the hybrid using of unicast-broadcast in wireless sensor networks. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.03.004
Liu YX, Liu A, Guo S et al (2017) Context-aware collect data with energy efficient in cyber-physical cloud systems. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2017.05.029
Xin H, Liu X (2017) Energy-balanced transmission with accurate distances for strip-based wireless sensor networks. IEEE Access 5:16193–16204
Wang J, Liu A, Yan T et al (2017) A resource allocation model based on double-sided combinational auctions for transparent computing. Peer-to-Peer Netw Appl. https://doi.org/10.1007/s12083-017-0556-6
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):74–80
Liu X (2017) Node deployment based on extra path creation for wireless sensor networks on mountain roads. IEEE Commun Lett 21(11):2376–2379
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):312–325
Wang T, Peng Z, Liang J et al (2014) Following targets for mobile tracking in wireless sensor networks. ACM Trans Sensor Netw 12(4):31.1–31.24
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):2177–2185
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):139
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):2741–2759
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):2053–2064
Tian D, Georganas ND (2003) A node scheduling scheme for energy conservation in large wireless sensor networks. Wirel Commun Mob Comput 3(2):271–290
Hui Y, Su Z, Guo S. (2017) Utility based data computing scheme to provide sensing Service in Internet of things. IEEE Trans Emerg Topics Comput, https://doi.org/10.1109/TETC.2017.2674023
Su Z, Qi Q, Xu Q, Guo S,Wang X. (2017) Incentive scheme for cyber physical social systems based on user behaviors. IEEE Trans Emerg Topics Comput, https://doi.org/10.1109/TETC.2017.2671843
Li M, Cheng W, Liu K et al (2011) Sweep coverage with mobile sensors. IEEE Trans Mob Comput 10(11):1534–1545
Slijepcevic S, Potkonjak M (2001) Power efficient organization of wireless sensor networks. IEEE international conference on. Communications 2:472–476
Cardei M, Du DZ (2005) Improving wireless sensor network lifetime through power aware organization. Wirel Netw 11(3):333−340
Zorbas D, Glynos D, Kotzanikolaou P et al (2010) Solving coverage problems in wireless sensor networks using cover sets. Ad Hoc Netw 8(4):400–415
Yang Q, He S, Li J et al (2015) Energy-efficient probabilistic area coverage in wireless sensor networks. IEEE Trans Veh Technol 64(1):367–377
Dobrev S, Durocher S, Eftekhari M et al (2015) Complexity of barrier coverage with relocatable sensors in the plane. Theor Comput Sci 579:64–73
Zhao MC, Lei J, Wu MY et al. (2009) Surface coverage in wireless sensor networks. INFOCOM 2009, IEEE 109–117
Zhang C, Bai X, Teng J et al (2010) Constructing low-connectivity and full-coverage three dimensional sensor networks. IEEE J Select Areas Commun 28(7):984–993
Chakrabarty K, Iyengar SS, Qi H et al (2002) Grid coverage for surveillance and target location in distributed sensor networks. IEEE Trans Comput 51(12):1448–1453
Ghosh A, Das SK (2005) A distributed greedy algorithm for connected sensor cover in dense sensor networks. DCOSS 3560:340–353
Hochbaum DS, Pathria A (1998) Analysis of the greedy approach in problems of maximum k-coverage. Nav Res Logist 45(6):615–627
Megerian S, Koushanfar F, Potkonjak M et al (2005) Worst and best-case coverage in sensor networks. IEEE Trans Mob Comput 4(1):84–92
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 32–41
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):1093–1102
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):6346–6355
Liu A, Chen Z, Xiong NN (2017) An adaptive virtual relaying set scheme for loss-and-delay sensitive WSNs. Inf Sci 424:118–136
Liu X, Zhao S, Liu A et al (2017) Knowledge-aware Proactive Nodes Selection approach for energy management in Internet of Things. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2017.07.022
Mostafaei H, Montieri A, Persico V et al (2017) A sleep scheduling approach based on learning automata for WSN partial coverage. J Netw Comput Appl 80:67–78
He S, Shin DH, Zhang J et al (2016) Full-view area coverage in camera sensor networks: dimension reduction and near-optimal solutions. IEEE Trans Veh Technol 65(9):7448–7461
Liu X, Liu A, Li Z et al (2017) Distributed cooperative communication nodes control and optimization reliability for resource-constrained WSNs. Neurocomputing 270:122–136
Huang M, Liu A, Wang T, Huang C (2018) Green data gathering under delay differentiated services constraint for internet of things. Wirel Commun Mob Comput. https://doi.org/10.1155/2018/9715428
Acknowledgements
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).
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This article is part of the Topical Collection: Special Issue on Network CoverageGuest Editors: Shibo He, Dong-Hoon Shin, and Yuanchao Shu
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Huang, M., Liu, A., Zhao, M. et al. Multi working sets alternate covering scheme for continuous partial coverage in WSNs. Peer-to-Peer Netw. Appl. 12, 553–567 (2019). https://doi.org/10.1007/s12083-018-0647-z
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DOI: https://doi.org/10.1007/s12083-018-0647-z