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Efficient Area Coverage in Wireless Sensor Networks Using Optimal Scheduling

  • Ritamshirsa ChoudhuriEmail author
  • Rajib K Das
Article
  • 14 Downloads

Abstract

Wireless sensor networks generally have unique lifetime necessities. In any case, the density of the sensors may not be sufficiently substantial to fulfil the coverage requirement while meeting the lifetime constraint in the mean time. Once in a while coverage has to be traded for network lifetime. The proposed efficient pipeline based spatial temporal optimization scheduling for coverage optimization satisfies the coverage problem while meeting the lifetime constraint at the same time. In the proposed optimal scheduling, initially number of nodes in the network is clustered by using energy based one hop clustering algorithm. After the formation of clusters pipeline based spatial temporal optimization algorithm is used for the optimal scheduling. Here the optimization is improved by using trust of each sensor nodes and the area of clusters. Finally, data is aggregated through the optimally scheduled cluster nodes. The experimental results show that our proposed optimization scheduling substantially outperforms other schemes in terms of network lifetime, coverage redundancy and convergence time.

Keywords

Clustering Trust calculation Coverage optimization Scheduling Network lifetime 

Notes

References

  1. 1.
    Patel, M., & Wang, J. (2010). Applications, challenges, and prospective in emerging body area networking technologies. IEEE Wireless Communications, 17(1), 80–88.CrossRefGoogle Scholar
  2. 2.
    Sobeih, A., Hou, J. C., Kung, L.-C., Li, N., Zhang, H., Chen, W.-P., et al. (2006). J-Sim: A simulation and emulation environment for wireless sensor networks. IEEE Wireless Communications, 13(4), 104–119.CrossRefGoogle Scholar
  3. 3.
    Pedraza, J. M. (2017). Advanced nuclear technologies and its future possibilities. In Small modular reactors for electricity generation (pp. 35–122). Springer International Publishing.Google Scholar
  4. 4.
    Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.CrossRefGoogle Scholar
  5. 5.
    Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., & Correal, N. S. (2005). Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 22(4), 54–69.CrossRefGoogle Scholar
  6. 6.
    Howard, A., Matarić, M. J., & Sukhatme, G. S. (2002). Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In Distributed autonomous robotic systems (vol. 5, pp. 299–308). Springer, Japan.Google Scholar
  7. 7.
    Soro, S., & Heinzelman, W. B. (2009). Cluster head election techniques for coverage preservation in wireless sensor networks. Ad Hoc Networks, 7(5), 955–972.CrossRefGoogle Scholar
  8. 8.
    Younis, O., Krunz, M., & Ramasubramanian, S. (2006). Node clustering in wireless sensor networks: Recent developments and deployment challenges. IEEE Network, 20(3), 20–25.CrossRefGoogle Scholar
  9. 9.
    Cardei, M., & Jie, W. (2006). Energy-efficient coverage problems in wireless ad-hoc sensor networks. Computer Communications, 29(4), 413–420.CrossRefGoogle Scholar
  10. 10.
    Romer, K., & Mattern, F. (2004). The design space of wireless sensor networks. IEEE Wireless Communications, 11(6), 54–61.CrossRefGoogle Scholar
  11. 11.
    Moreira, A., Krieger, G., Hajnsek, I., Papathanassiou, K., Younis, M., Lopez-Dekker, P., et al. (2015). Tandem-L: A highly innovative bistatic SAR mission for global observation of dynamic processes on the Earth’s surface. IEEE Geoscience and Remote Sensing Magazine, 3(2), 8–23.CrossRefGoogle Scholar
  12. 12.
    Yang, Q., He, S., Li, J., Chen, J., & Sun, Y. (2015). Energy-efficient probabilistic area coverage in wireless sensor networks. IEEE Transactions on Vehicular Technology, 64(1), 367–377.CrossRefGoogle Scholar
  13. 13.
    Petrioli, C., Nati, M., Casari, P., Zorzi, M., & Basagni, S. (2014). ALBA-R: Load-balancing geographic routing around connectivity holes in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 25(3), 529–539.CrossRefGoogle Scholar
  14. 14.
    Golrezaei, N., Mansourifard, P., Molisch, A. F., & Dimakis, A. G. (2014). Base-station assisted device-to-device communications for high-throughput wireless video networks. IEEE Transactions on Wireless Communications, 13(7), 3665–3676.CrossRefGoogle Scholar
  15. 15.
    Erol-Kantarci, M., & Mouftah, H. T. (2015). Energy-efficient information and communication infrastructures in the smart grid: A survey on interactions and open issues. IEEE Communications Surveys & Tutorials, 17(1), 179–197.CrossRefGoogle Scholar
  16. 16.
    Sharma, K. P., & Sharma, T. P. (2016). ZBFR: Zone based failure recovery in WSNs by utilizing mobility and coverage overlapping. Wireless Networks, 23(7), 2263–2280.CrossRefGoogle Scholar
  17. 17.
    Younis, M., Senturk, I. F., Akkaya, K., Lee, S., & Senel, F. (2014). Topology management techniques for tolerating node failures in wireless sensor networks: A survey. Computer Networks, 58, 254–283.CrossRefGoogle Scholar
  18. 18.
    Tang, M., Yan, F., Deng, S., Shen, L., Kuang, S., & Xing, S. (2016). Coverage optimization algorithms based on voronoi diagram in software-defined sensor networks. In 2016 8th international conference on wireless communications & signal processing (WCSP) (pp. 1–5). IEEE.Google Scholar
  19. 19.
    Govindan, R., Korre, A., Durucan, S., & Imrie, C. E. (2011). A geostatistical and probabilistic spectral image processing methodology for monitoring potential CO2 leakages on the surface. International Journal of Greenhouse Gas Control, 5(3), 589–597.CrossRefGoogle Scholar
  20. 20.
    Leipold, F., Tassetto, D., & Bovelli, S. (2013). Wireless in-cabin communication for aircraft infrastructure. Telecommunication Systems, 52(2), 1211–1232.Google Scholar
  21. 21.
    Mini, S., Udgata, S. K., & Sabat, S. L. (2014). Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sensors Journal, 14(3), 636–644.CrossRefGoogle Scholar
  22. 22.
    More, A., & Raisinghani, V. (2014). Random backoff sleep protocol for energy efficient coverage in wireless sensor networks. Advanced Computing, Networking and Informatics, 2, 123–131.CrossRefGoogle Scholar
  23. 23.
    Han, G., Liu, L., Jiang, J., Shu, L., & Hancke, G. (2017). Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks. IEEE Transactions on Industrial Informatics, 13(1), 135–143.CrossRefGoogle Scholar
  24. 24.
    Jameii, S. M., Faez, K., & Dehghan, M. (2015). AMOF: Adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks. Telecommunication Systems, 61(3), 515–530.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringUniversity of CalcuttaKolkataIndia

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