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

Minimizing movement for network connectivity in mobile sensor networks: an adaptive approach

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Nowadays the most important key point in the design of wireless sensor networks (WSNs) is the sensor coverage point and network connectivity. The main practical issue in designing the essential WSN is the mobility of mobile sensors which consumes more power thereby reduces the network lifetime significantly. In order to avoid these problems, we have investigated the mobile sensor deployment (MSD) problem comprises of network connectivity and target coverage is resolved using Euclidean spanning tree model (ECST) and ECST-adaptive VABC (ECST-AVABC) method respectively. Besides, we proposed an AVABC optimization algorithm by obtaining minimum movement of mobile sensors over the network. Furthermore, the extensive simulation experiments have offered the optimal promising solutions of NCON, to the MSD problem with minimum movement and providing the extended lifetime of WSN. Finally, the experimental results states that the movement distance shown by the proposed ECST-AVABC become 4.2, 10, and 20% lesser than the standard ECST-VABC method.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Blumrosen, G., Hod, B., Anker, T., Dolev, D., Rubinsky, B.: Enhancing rssi-based tracking accuracy in wireless sensor networks. ACM Trans. Sens. Netw. 9(3), 1–29 (2013)

    Article  Google Scholar 

  2. Huang, R., Song, W.-Z., Xu, M., Peterson, N., Shirazi, B., LaHusen, R.: Real-world sensor network for long-term volcano monitoring: design and findings. IEEE Trans. Parallel Distrib. Syst. 23(2), 321–329 (2012)

    Article  Google Scholar 

  3. S. Zhou, Wu, M.-Y., Shu, W.: Finding optimal placements for mobile sensors: wireless sensor network topology adjustment. In: Proc. IEEE 6th Circuits Syst. Symp. Emerging Technol.: Frontiers Mobile Wireless Commun., vol. 2, pp. 529–532 (2004)

  4. Agarwal, A., Agarwal, K.: The next generation mobile wireless cellular networks–4G and beyond. Am. J. Elect. Electron. Eng. 2(3), 92–97 (2014)

    Article  Google Scholar 

  5. Agarwal, A., Misra, G., Agarwal, K.: The 5th generation mobile wireless networks–key concepts, network architecture and challenges. Am. J. Elect. Electron. Eng. 3(2), 22–28 (2015)

    Google Scholar 

  6. Liu, X.-Y., Wu, K.-L., Zhu, Y., Kong, L., Wu, M.-Y.: Mobility increases the surface coverage of distributed sensor networks. Comput. Netw. 57(11), 2348–2363 (2013)

    Article  Google Scholar 

  7. Luo, J., Wang, D., Zhang, Q.: Double mobility: coverage of the sea surface with mobile sensor networks. ACM SIGMOBILE Mobile Comput. Commun. Rev. 13(1), 52–55 (2009)

    Article  Google Scholar 

  8. Somasundara, A.A., Ramamoorthy, A., Srivastava, M.B.: Mobile element scheduling with dynamic deadlines. IEEE Trans. Mobile Comput. 6(4), 395–410 (2007)

    Article  Google Scholar 

  9. Chin, T.L., Ramanathan, P., Saluja, K.K., Wang, K.C.: Exposure for collaborative detection using mobile sensor networks. In: Proc. IEEE 2rd Int. Conf. Mobile Adhoc Sen. Syst., pp. 743–750 (2005)

  10. Bisnik, N., Abouzeid, A.A., Isler, V.: Stochastic event capture using mobile sensors subject to a quality metric. IEEE Trans. Robot. 23(4), 676–692 (2007)

    Article  Google Scholar 

  11. Liu, B., Dousse, O., Nain, P., Towsley, D.: Dynamic coverage of mobile sensor networks. IEEE Trans. Parallel Distrib. Syst. 24(2), 301–311 (2013)

    Article  Google Scholar 

  12. Wang, G., Bhuiyan, M.Z.A., Cao, J., Wu, J.: Detecting movements of a target using face tracking in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 25(4), 939–949 (2014)

    Article  Google Scholar 

  13. He, L., Pan, J., Xu, J.: A progressive approach to reducing data collection latency in wireless sensor networks with mobile elements. IEEE Trans. Mobile Comput. 12(7), 1308–1320 (2013)

    Article  Google Scholar 

  14. Tan, R., Xing, G., Wang, J., So, H.C.: Exploiting reactive mobility for collaborative target detection in wireless sensor networks. IEEE Trans. Mobile Comput. 9(3), 317–332 (2010)

    Article  Google Scholar 

  15. Fu, Z., You, K.: Optimal mobile sensor scheduling for a guaranteed coverage ratio in hybrid wireless sensor networks. Int. J. Distrib. Sens. Netw. 9, 1–11 (2013)

    Google Scholar 

  16. He, S., Chen, J., Li, X., Shen, X., Sun, Y.: Cost-effective barrier coverage by mobile sensor networks. In: Proc. IEEE 31st Annu. Int. Conf. Comput. Commun., pp. 819–827 (2012)

  17. Wang, G., Irwin, M.J., Berman, P., Fu, H., Porta, T.L.: Optimizing sensor movement planning for energy efficiency. In: Proc. Int. Symp. Low Power Electron. Des., pp. 215–220 (2005)

  18. Mahboubi, H., Moezzi, K., Aghdam, A.G., Sayrafian-Pour, K.: Self-deployment algorithms for field coverage in a network of nonidentical mobile sensors. In: Proc. IEEE Int. Conf. Commun., pp. 1–6 (2011)

  19. Mahboubi, H., Moezzi, K., Aghdam, A.G., Pour, K.S.: Distributed deployment algorithms for efficient coverage in a network of mobile sensors with nonidentical sensing capabilities. IEEE Trans. Veh. Technol. (2014). https://doi.org/10.1109/TVT.2014.2302232

  20. Yang, Y., Fonoage, M.I., Cardei, M.: Improving network lifetime with mobile wireless sensor networks. Comput. Commun. 33(4), 409–419 (2010)

    Article  Google Scholar 

  21. Luo, W., Wang, J., Guo, J., Chen, J.: Parameterized complexity of max-lifetime target coverage in wireless sensor networks. Theor. Comput. Sci. 518, 32–41 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  22. Wang, Y.-C., Tseng, Y.-C.: Distributed deployment schemes for mobile wireless sensor networks to ensure multilevel coverage. IEEE Trans. Parallel Distrib. Syst. 19(9), 1280–1294 (2008)

    Article  Google Scholar 

  23. Wang, J., Luo, W., Feng, Q., Guo, J.: Parameterized complexity of min-power multicast problems in wireless ad hoc networks. Theor. Comput. Sci. 508, 16–25 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  24. Mathews, E., Mathew, C.: Deployment of mobile routers ensuring coverage and connectivity. Int. J. Comput. Netw. Commun. 4(1), 175–191 (2012)

    Article  Google Scholar 

  25. Korbi, I.E., Zeadally, S.: Energy-aware sensor node relocation in mobile sensor networks. Ad Hoc Netw. 16(1), 247–265 (2014)

    Article  Google Scholar 

  26. Jagtap, A., Kumar, R.: A hybrid approach using Voronoi partition and swarm intelligence. Commun. J

  27. Lu, M., Wu, J., Cardei, M., Li, M.: Energy-efficient connected coverage of discrete targets in wireless sensor networks. In: Proc. of 3rd Int. Conf. Netw. and Mobile Comput., pp. 43–52 (2005)

  28. Luo, R.C., Chen, O.: Mobile sensor node deployment and asynchronous power management for wireless sensor networks. IEEE Trans. Ind. Electron. 59(5), 2377–2385 (2012)

    Article  Google Scholar 

  29. Bai, X., Kumar, S., Xuan, D., Yun, Z., Lai, T.H.: Deploying wireless sensors to achieve both coverage and connectivity. In: Proc. 7th ACM Int. Symp. Mobile Ad Hoc Netw. Comput., pp. 131–142 (2006)

  30. van de Vel, M.: Theory of Convex Structures, vol. 50. North Holland, Amsterdam (1993)

    MATH  Google Scholar 

  31. Karp, R.M.: Reducibility Among Combinatorial Problems. Springer, New York (1972)

    Book  MATH  Google Scholar 

  32. Lawler, H.E.L.: The quadratic assignment problem. Manage. Sci. 9(4), 586–599 (1963)

    Article  MathSciNet  MATH  Google Scholar 

  33. Liao, Y.Z., Zhang, S., Cao, J., Wang, W., Wang, J.: Minimizing movement for target coverage in mobile sensor networks. In: Proc. 32nd Int. Conf. Distrib. Comput. Syst., pp. 194–200 (2012)

  34. Kuhn, Y.H.W.: The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1/2), 83–97 (1955)

    Article  MathSciNet  MATH  Google Scholar 

  35. Cole, Y.R.: Parallel merge sort. SIAM J. Comput. 17(4), 770–785 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  36. Liao, H.Z., Wang, J., Zhang, S., Cao, J.: Clique partition based relay placement in wimax mesh networks. In: Proc. IEEE Global Commun. Conf., pp. 2566–2571 (2012)

  37. de Berg, H.M., van Kreveld, M., Overmars, M., Schwarzkopf, O.C.: Computational Geometry. Springer, New York (2000)

    Book  MATH  Google Scholar 

  38. Shamos, M.I.: Geometric complexity. In: Proc. 7th Annu. ACM Symp. Theory Comput., pp. 224–233 (1975)

  39. Dorigo, M., Birattari, M.: Ant colony optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 36–39. Springer, New York (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arvind Madhukar Jagtap.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jagtap, A.M., Gomathi, N. Minimizing movement for network connectivity in mobile sensor networks: an adaptive approach. Cluster Comput 22 (Suppl 1), 1373–1383 (2019). https://doi.org/10.1007/s10586-017-1660-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1660-3

Keywords

Navigation