[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Activities scheduling algorithms based on probabilistic coverage models for wireless sensor networks

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

Abstract

Area monitoring using Internet and barrier coverage is a typical application of wireless sensor networks. The main concerns in this type of applications are coverage efficiency and sensor energy conservation. For that, many activities scheduling algorithms are proposed in the literature. Unlike prior efforts based on an unrealistic binary sensor coverage model, this paper proposes three efficient activities scheduling algorithms based on realistic sensor coverage models. The first algorithm (C1L-PBC) is centralized and it is based on a coverage graph. The second algorithm (D1L-PBC) is distributed and it ensures 1-barrier coverage; whereas, the third one (D2L-PBC) is also distributed and it guarantees 2-barrier coverage. The obtained experimental results show that the proposed algorithms can effectively guarantee the barrier coverage and prolong the sensor network lifetime.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Mirsadeghi M, Mahani A (2015) Energy efficient fast predictor for WSN-based target tracking. Ann Telecommun 70(1–2):63–71

    Article  Google Scholar 

  2. Wang B (2010) Coverage control in sensor networks. Springer, London

  3. Kumar S et al. (2007) Optimal sleep-wake-up algorithms for barriers of wireless sensors. in Broadband Communications, Networks and Systems, 2007. BROADNETS 2007. Fourth International Conference on. IEEE

  4. Kumar S, Lai TH, Arora A (2005) Barrier coverage with wireless sensors. In Proceedings of the ACM 11th annual international conference on Mobile computing and networking

  5. Kumar S (2006) Foundations of coverage in wireless sensor networks. The Ohio State University

  6. Chen A, Kumar S, Lai TH (2007) Designing localized algorithms for barrier coverage. in Proceedings of the 13th annual ACM international conference on Mobile computing and networking. ACM

  7. Shen C et al. (2008) Barrier coverage with mobile sensors. in Parallel Architectures, Algorithms, and Networks, 2008. I-SPAN 2008. International Symposium on. IEEE

  8. Saipulla A, Liu B, Wang J (2008) Barrier coverage with airdropped wireless sensors. in Military Communications Conference, 2008. MILCOM 2008. IEEE

  9. Bhattacharya B et al (2009) Optimal movement of mobile sensors for barrier coverage of a planar region. Theor Comput Sci 410(52):5515–5528

    Article  MathSciNet  MATH  Google Scholar 

  10. Ssu K-F et al (2009) K-barrier coverage with a directional sensing model. International Journal on Smart Sensing and Intelligent Systems 2(1):75–93

    Google Scholar 

  11. Ban D et al. (2011) Distributed scheduling algorithm for barrier coverage in wireless sensor networks. in Communications and Mobile Computing (CMC), 2011 Third International Conference on. IEEE

  12. Yang T, Fan P, Mu D (2011) Sliding the barriers in wireless sensor networks. in Computing, Control and Industrial Engineering (CCIE), 2011 I.E. 2nd International Conference on. IEEE

  13. Yamamoto K et al. (2011) Barrier Coverage Constructions for Border Security Systems Using Wireless Sensors. in Parallel Processing Workshops (ICPPW), 2011 40th International Conference on. IEEE

  14. Tao D et al. (2011) Strong Barrier Coverage Using Directional Sensors with Arbitrarily Tunable Orientations. in Mobile Ad-hoc and Sensor Networks (MSN), 2011 Seventh International Conference on. IEEE

  15. Cao Y et al. (2011) Local maximum lifetime algorithms for strong k-barrier coverage with coordinated sensors. in Communication Software and Networks (ICCSN), 2011 I.E. 3rd International Conference on. IEEE

  16. Chen J, Li J, Lai TH (2013) Energy-efficient intrusion detection with a barrier of probabilistic sensors: global and local. Wireless Communications, IEEE Transactions on 12(9):4742–4755

    Article  Google Scholar 

  17. Du J et al (2013) Maximizing the lifetime of k-discrete barrier coverage using mobile sensors. Sensors Journal, IEEE 13(12):4690–4701

    Article  Google Scholar 

  18. Deng X et al. (2013) Mending barrier gaps via mobile sensor nodes with adjustable sensing ranges. in Wireless Communications and Networking Conference (WCNC), 2013 IEEE. IEEE

  19. Wang Z et al (2014) Achieving k-barrier coverage in hybrid directional sensor networks. Mobile Computing, IEEE Transactions on 13(7):1443–1455

    Article  Google Scholar 

  20. Zhang X et al. (2015) Multi-objective Optimization of Barrier Coverage with Wireless Sensors. in Evolutionary Multi-Criterion Optimization. Springer

  21. Zhao L et al. (2015) Energy efficient barrier coverage in hybrid directional sensor networks. in Wireless Communications & Signal Processing (WCSP), 2015 International Conference on. IEEE

  22. Yu Z et al. (2015) Local face-view barrier coverage in camera sensor networks. In Computer Communications (INFOCOM), 2015 I. E. Conference on. IEEE. doi:10.1109/INFOCOM.2015.7218437

  23. Senouci MR, Mellouk A, Oukhellou L, Aissani A (2012) An evidence-based sensor coverage model. IEEE Commun Lett 16(9):1462–1465

  24. Senouci MR, Mellouk A, Senouci MA, Oukhellou L (2014) Belief functions in telecommunications and network technologies: an overview. Ann Telecommun 69(3–4):135–145

  25. Levis P, Gay D (2009) TinyOS programming. Cambridge University Press

  26. Levis P et al. (2003) TOSSIM: Accurate and scalable simulation of entire TinyOS applications. in Proceedings of the 1st international conference on Embedded networked sensor systems. ACM

Download references

Author information

Authors and Affiliations

  1. Ecole Militaire Polytechnique (EMP), Algiers, Algeria

    Mohammed Boudali, Mustapha Reda Senouci & Mohamed Aissani

  2. Ecole nationale Supérieure d’Informatique d’Alger (E.S.I), Algiers, Algeria

    Walid-Khaled Hidouci

Authors
  1. Mohammed Boudali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mustapha Reda Senouci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Aissani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Walid-Khaled Hidouci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed Boudali.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boudali, M., Senouci, M.R., Aissani, M. et al. Activities scheduling algorithms based on probabilistic coverage models for wireless sensor networks. Ann. Telecommun. 72, 221–232 (2017). https://doi.org/10.1007/s12243-017-0564-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-017-0564-9

Keywords