Multi working sets alternate covering scheme for continuous partial coverage in WSNs

Article
Part of the following topical collections:
  1. Special Issue on Network Coverage

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.

Keywords

Partial coverage Wireless sensor networks Sleep scheduling Multi working sets 

Notes

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).

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Liu X (2017) Survivability-aware connectivity restoration for partitioned wireless sensor networks. IEEE Commun Lett 21(11):2444–2447CrossRefGoogle Scholar
  5. 5.
    Zeng D, Li P, Guo S et al (2015) Energy minimization in multi-task software-defined sensor networks. IEEE Trans Comput 64(11):3128–3139MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    Xin H, Liu X (2017) Energy-balanced transmission with accurate distances for strip-based wireless sensor networks. IEEE Access 5:16193–16204CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    Li M, Cheng W, Liu K et al (2011) Sweep coverage with mobile sensors. IEEE Trans Mob Comput 10(11):1534–1545CrossRefGoogle Scholar
  23. 23.
    Slijepcevic S, Potkonjak M (2001) Power efficient organization of wireless sensor networks. IEEE international conference on. Communications 2:472–476Google Scholar
  24. 24.
    Cardei M, Du DZ (2005) Improving wireless sensor network lifetime through power aware organization. Wirel Netw 11(3):333−340CrossRefGoogle Scholar
  25. 25.
    Zorbas D, Glynos D, Kotzanikolaou P et al (2010) Solving coverage problems in wireless sensor networks using cover sets. Ad Hoc Netw 8(4):400–415CrossRefGoogle Scholar
  26. 26.
    Yang Q, He S, Li J et al (2015) Energy-efficient probabilistic area coverage in wireless sensor networks. IEEE Trans Veh Technol 64(1):367–377CrossRefGoogle Scholar
  27. 27.
    Dobrev S, Durocher S, Eftekhari M et al (2015) Complexity of barrier coverage with relocatable sensors in the plane. Theor Comput Sci 579:64–73MathSciNetCrossRefMATHGoogle Scholar
  28. 28.
    Zhao MC, Lei J, Wu MY et al. (2009) Surface coverage in wireless sensor networks. INFOCOM 2009, IEEE 109–117Google Scholar
  29. 29.
    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):984–993CrossRefGoogle Scholar
  30. 30.
    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):1448–1453MathSciNetCrossRefGoogle Scholar
  31. 31.
    Ghosh A, Das SK (2005) A distributed greedy algorithm for connected sensor cover in dense sensor networks. DCOSS 3560:340–353Google Scholar
  32. 32.
    Hochbaum DS, Pathria A (1998) Analysis of the greedy approach in problems of maximum k-coverage. Nav Res Logist 45(6):615–627MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    Liu A, Chen Z, Xiong NN (2017) An adaptive virtual relaying set scheme for loss-and-delay sensitive WSNs. Inf Sci 424:118–136MathSciNetCrossRefGoogle Scholar
  38. 38.
    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
  39. 39.
    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 80:67–78CrossRefGoogle Scholar
  40. 40.
    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):7448–7461CrossRefGoogle Scholar
  41. 41.
    Liu X, Liu A, Li Z et al (2017) Distributed cooperative communication nodes control and optimization reliability for resource-constrained WSNs. Neurocomputing 270:122–136CrossRefGoogle Scholar
  42. 42.
    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

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.School of SoftwareCentral South UniversityChangshaChina
  3. 3.Department of Computer Science and TechnologyHuaqiao UniversityXiamenChina

Personalised recommendations