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Controlling Mobile Sink Trajectory for Data Harvesting in Wireless Sensor Networks

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Abstract

This paper proposes a mathematical optimization model to maximize the lifetime of wireless sensor networks through determining the optimal trajectory (OT) of mobile sink (MS). We address deadline and event based applications where by capturing an event, a sensor node has to send its data to MS in a restricted time slot defined as a deadline. We demonstrate that the addressed problem is in NP-hard form and then by dividing the problem into two phases, we propose a heuristic approach based on mathematical optimization. In the first phase, the trajectory of MS is determined through proposing a convex mathematical optimization model; in this step, we specify an optimal line as OT with respect to the current location and constant velocity of MS; moreover, the volume of captured data by sensor nodes, deadline and geographical locations of sensor nodes are taken into account. We extend our work in the second phase by proposing a mixed integer linear programming (MILP) model to relax the constant velocity assumption of MS. To obtain an optimal solution of MILP, subsequently a tabu-based algorithm is proposed. The effectiveness of our approach is validated via the extensive number of simulation runs and comparison with other proposed algorithms.

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Notes

  1. This assumption will be relaxed in Sect. 4.

References

  1. Luo, J., & Hubaux, J.-P. (2010). Joint sink mobility and routing to increase the lifetime of wireless sensor networks: The case of constrained mobility. IEEE/ACM Transactions on Networking, 18(5), 1387–1400.

    Article  Google Scholar 

  2. Gandham, S. R, Dawande, M., et al. (2003). Energy efficient schemes for wireless sensor networks with multiple mobile base stations. In Proceeding of the Globecom’03 (pp. 377–381).

  3. Jain, S., Shah, R. C., Brunette, W., Borriello, G., & Roy, S. (2006). Exploiting mobility for energy efficient data collection in sensor networks. Mobile Networks and Applications, 11(3), 327–339.

    Article  Google Scholar 

  4. Chakrabarti, A., Sabharwal, A., & Aazhang, B. (2006). Communication power optimization in a sensor network with a path-constrained mobile observer. ACM Transactions on Sensor Networks, 2(3), 297–324.

    Article  Google Scholar 

  5. Song, L., & Hatzinakos, D. (2007). Architecture of wireless sensor networks with mobile sinks: Sparsely deployed sensors. IEEE Transactions on Vehicular Technology, 56(4), 1826–1836.

    Article  Google Scholar 

  6. Jea, D., Somasundara, A., & Srivastava, M. (2005). Multiple controlled mobile elements (data mules) for data collection in sensor networks. Distributed computing in sensor systems (pp. 244–257). Berlin: Springer.

    Google Scholar 

  7. Li, Z., Li, M., Wang, J., & Cao, Z. (2011). Ubiquitous data collection for mobile users in wireless sensor networks. In Proc. of IEEEINFOCOM’11. Shanghai.

  8. Shah, R. C., et al. (2003). Data mules: Modeling and analysis of a three-tier architecture for sparse sensor networks. Ad Hoc Networks, 1(2), 215–233.

    Article  Google Scholar 

  9. Tong, L., Zhao, Q., & Adireddy, S. (Oct 2003). Sensor networks with mobile agents. In Proc., IEEE MILCOM 2003, vol. 22 (pp. 688–693). Boston, MA.

  10. Shi, Y., & Hou, Y. T. (2009). Optimal base station placement in wireless sensor networks. ACM Transactions on Sensor Networks, 5(4), 32.

    Article  MathSciNet  Google Scholar 

  11. Yun, Y., & Xia, Y. (2010). Maximizing the lifetime of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Transaction on Mobile, Computing, 9(9), 1308–1318.

    Article  Google Scholar 

  12. Xing, G., Li, M., Wang, T., Jia, W., & Huang, J. (2012). Efficient rendezvous algorithms for mobility-enabled wireless sensor networks. IEEE Transactions on Mobile Computing, 11(1), 47–60.

    Article  Google Scholar 

  13. Wang, Y.-C., Peng, W.-C., & Tseng, Y.-C. (2010). Energy-balanced dispatch of mobile sensors in a hybrid wireless sensor network. IEEE Transactions on Parallel and Distributed Systems, 12, 1836–1850.

    Article  Google Scholar 

  14. Gu, Y., et al. (2013). ESWC: Efficient scheduling for the mobile sink in wireless sensor networks with delay constraint. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1310–1320.

    Article  Google Scholar 

  15. Zhao, M., Ma, M., & Yang, Y. (2010). Efficient data gathering with mobile collectors and space-division multiple access technique in wireless sensor networks. IEEE Transactions on Computers, 60(3), 400–417.

    Article  MathSciNet  Google Scholar 

  16. Basagni, S., et al. (2008). Controlled sink mobility for prolonging wireless sensor networks lifetime. Wireless Networks, 14(6), 831–858.

    Article  Google Scholar 

  17. Shi, Y., & Hou, Y. T. (2008). Theoretical results on base station movement problem for sensor network. In The 27th conference on computer communications. IEEE INFOCOM.

  18. Xing, G., Wang, T., Xie, Z., & Jia, W. (2008). Rendezvous planning in wireless sensor networks with mobile elements. IEEE Transactions on Mobile Computing, 7(11), 1–14.

    Article  Google Scholar 

  19. Tashtarian, F., Yaghmaee Moghaddam, M. H., Sohraby, K., & Effati, S. (2014). On maximizing the lifetime of wireless sensor networks in event-driven applications with mobile sinks. IEEE Transactions on Vehicular Technology, 64(7), 3177–3189.

    Google Scholar 

  20. Konstantopoulos, C., Pantziou, G., Gavalas, D., Mpitziopoulos, A., & Mamalis, B. (2012). A rendezvous-based approach enabling energy-efficient sensory data collection with mobile sinks. IEEE Transaction Parallel Distributed System, 23, 809–817.

    Article  Google Scholar 

  21. Liang, W., Luo, J., & Xu, X. (2010). Prolonging network lifetime via a controlled mobile sink in wireless sensor networks. In Proc. of Globecom’10, IEEE.

  22. Pan, J., Cai, L., Hou, Y. T., Shi, Y., & Shen, S. X. (2005). Optimal base-station locations in two-tiered wireless sensor networks. IEEE Transaction on Mobile Computing, 4(5), 458–473.

    Article  Google Scholar 

  23. MathWorks—MATLAB and simulink for technical computing. http://www.mathworks.com.

  24. Gao, S., et al. (2011). Efficient data collection in wireless sensor networks with path-constrained mobile sinks. IEEE Transaction on Mobile Computing, 10(5), 1–9.

    Article  Google Scholar 

  25. Tashtarian, F., Yaghmaee Moghaddam, M. H., Sohraby, K., & Effati, S. (2015). ODT: Optimal deadline-based trajectory for mobile sinks in WSN: A decision tree and dynamic programming approach. Computer Networks, 77, 128–143.

    Article  Google Scholar 

  26. Ye, F., Luo, H., Cheng, J., Lu, S., & Zhang, L. (Sep 2002). A two-tier data dissemination model for large-scale wireless sensor networks. In Proceedings of ACM the 8th annual international conference on mobile computing and networking, ser. MobiCom’02 (pp. 148–159). Atlanta, Georgia.

  27. Liu, X., Member, S., & Zhao, H. (2013). SinkTrail: A proactive data reporting protocol for wireless sensor networks. IEEE Transactions on Computers, 62(1), 151–162.

    Article  MathSciNet  Google Scholar 

  28. Heinzelman, W. B., et al. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  29. Vavasis, S. A. (1991). Nonlinear optimization: Complexity issues. Oxford: Oxford University Press.

    MATH  Google Scholar 

  30. Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  31. Manyem, P., & Ugon, J. (2012). Computational complexity, NP completeness and optimization duality: A survey. Electronic Colloquium on Computational Complexity (ECCC), 19, 9.

    Google Scholar 

  32. Vapnik, V. N. (1995). The nature of statistical learning theory. NewYork: Springer.

    Book  MATH  Google Scholar 

  33. Glover, F. (1989). Tabu search, part I. ORSA Journal on Computing, 1, 190–206.

    Article  MathSciNet  MATH  Google Scholar 

  34. Glover, F. (1990). Tabu search, part II. ORSA Journal on Computing, 2, 4–32.

    Article  MATH  Google Scholar 

  35. Rhazi, A. E., & Pierre, S. (2009). A Tabu search algorithm for cluster building in wireless sensor networks. IEEE Transactions on Mobile Computing, 8(4), 433–444.

    Article  Google Scholar 

  36. TOMLAB/CPLEX. http://tomopt.com/.

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Acknowledgments

This work was partially supported by “Mashhad Branch, Islamic Azad University, Mashhad, Iran”. Any opinion, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the host institution or funders.

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Authors and Affiliations

  1. Department of Information Technology, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Farzad Tashtarian

  2. Department of Computer Engineering, Pardis Branch, Islamic Azad University, Pardis, Iran

    Mohammad Reza Majma

  3. Amirkabir University of Technology, Tehran, Iran

    Hossein Pedram & Mehdi Dehghan TakhtFooladi

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  1. Farzad Tashtarian
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  2. Mohammad Reza Majma
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  3. Hossein Pedram
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  4. Mehdi Dehghan TakhtFooladi
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Correspondence to Farzad Tashtarian.

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Tashtarian, F., Majma, M.R., Pedram, H. et al. Controlling Mobile Sink Trajectory for Data Harvesting in Wireless Sensor Networks. Wireless Pers Commun 90, 1149–1178 (2016). https://doi.org/10.1007/s11277-016-3383-9

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