A Generalized Data Preservation Problem in Sensor Networks–A Network Flow Perspective

  • Bin TangEmail author
  • Rajiv Bagai
  • FNU Nilofar
  • Mehmet Bayram Yildirim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8629)


Many emerging sensor network applications require sensor node deployment in challenging environments that are remote and inaccessible. In such applications, it is not always possible to deploy base stations in or near the sensor field to collect sensory data. Therefore, the overflow data generated by some nodes is first offloaded to other nodes inside the network to be preserved, then gets collected when uploading opportunities become available. In this paper, we study a generalized data preservation problem in sensor networks, whose goal is to minimize the total energy consumption of preserving data inside sensor networks, given that each node has limited battery power. With an intricate transformation of the sensor network graph, we demonstrate that this problem can be modeled and solved as a minimum cost flow problem. Also, using data preservation in sensor networks as an example, we show that seemingly equivalent maximum flow techniques can result in dramatically different network performance. Much caution thus needs to be exercised while adopting classic network flow techniques into sensor network applications, despite successful application of network flow theory to many existing sensor network problems. Finally, we present a load-balancing data preservation algorithm, which not only minimizes the total energy consumption, but also maximizes the minimum remaining energy of nodes that receive distributed data, thereby preserving data for longer time. Simulation results show that compared to the existing techniques, this results in much evenly distributed remaining energy among sensor nodes.


Data preservation Network flow Sensor networks 



This work was supported in part by the NSF Grant CNS-1116849.


  1. 1.
    Vasilescu, I., Kotay, K., Rus, D., Dunbabin, M., Corke, P.: Data collection, storage, and retrieval with an underwater sensor network. In: Proceedings of SenSys 2005, pp. 154–165 (2005)Google Scholar
  2. 2.
    Li, S., Liu, Y., Li, X.: Capacity of large scale wireless networks under gaussian channel model. In: Proceedings of MOBICOM 2008, pp. 140–151 (2008)Google Scholar
  3. 3.
    Luo, L., Cao, Q., Huang, C., Wang, L., Abdelzaher, T., Stankovic, J.: Design, implementation, and evaluation of enviromic: a storage-centric audio sensor network. ACM Trans. Sens. Netw. 5(3), 1–35 (2009)CrossRefGoogle Scholar
  4. 4.
    Werner-Allen, G., Lorincz, K., Johnson, J., Lees, J., Welsh, M.: Fidelity and yield in a volcano monitoring sensor network. In: Proceedings of OSDI 2006, pp. 381–396 (2006)Google Scholar
  5. 5.
    Martinez, K., Ong, R., Hart, J.: Glacsweb: a sensor network for hostile environments. In: Proceedings of SECON 2004, pp. 81–87 (2004)Google Scholar
  6. 6.
    Jain, S., Shah, R., Brunette, W., Borriello, G., Roy, S.: Exploiting mobility for energy efficient data collection in wireless sensor networks. MONET 11(3), 327–339 (2006)Google Scholar
  7. 7.
    Jea, D., Somasundara, A., Srivastava, M.B.: Multiple controlled mobile elements (data mules) for data collection in sensor networks. In: Prasanna, V.K., Iyengar, S.S., Spirakis, P.G., Welsh, M. (eds.) DCOSS 2005. LNCS, vol. 3560, pp. 244–257. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  8. 8.
    Mathioudakis, I., White, N.M., Harris, N.R.: Wireless sensor networks: applications utilizing satellite links. In: Proceedings of the IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2007), pp. 1–5 (2007)Google Scholar
  9. 9.
    Tang, B., Jaggi, N., Wu, H., Kurkal, R.: Energy efficient data redistribution in sensor networks. ACM Trans. Sens. Netw. 9(2), 1–28 (2013)CrossRefGoogle Scholar
  10. 10.
    Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications. Prentice Hall, Englewood Cliffs (1993)zbMATHGoogle Scholar
  11. 11.
    Hou, X., Sumpter, Z., Burson, L., Xue, X., Tang, B.: Maximizing data preservation in intermittently connected sensor networks. In: Proceedings of IEEE MASS 2012, pp. 448–452 (2012)Google Scholar
  12. 12.
    Patel, M.S., Venkatesan, R.C.: Energy efficient capacity constrained routing in wireless sensor networks. Int. J. Pervasive Comput. Commun. 2, 69–80 (2006)CrossRefGoogle Scholar
  13. 13.
    Bodlaender, H.L., Tan, R.B., Dijk, T.C., Leeuwen, J.: Integer maximum flow in wireless sensor networks with energy constraint. In: Proceedings of the 11th Scandinavian Workshop on Algorithm Theory, SWAT 08, pp. 102–113 (2008)Google Scholar
  14. 14.
    Hong, B., Prasanna, V.K.: Maximum data gathering in networked sensor systems. Int. J. Distrib. Sens. Netw. 1, 57–80 (2005)CrossRefGoogle Scholar
  15. 15.
    Xue, Y., Cui, Y., Nahrstedt, K.: Maximizing lifetime for data aggregation in wireless sensor networks. Mob. Netw. Appl. 10(6), 853–864 (2005)CrossRefGoogle Scholar
  16. 16.
    Ghiasi, S., Srivastava, A., Yang, X., Sarrafzadeh, M.: Optimal energy aware clustering in sensor networks. Sensors 2(7), 258–269 (2002)CrossRefGoogle Scholar
  17. 17.
    Xue, X., Hou, X., Tang, B., Bagai, R.: Data preservation in intermittently connected sensor networks with data priorities. In: Proceedings of IEEE SECON 2013, pp. 65–73 (2013)Google Scholar
  18. 18.
    Ha, R.W., Ho, P.H., Shen, X.S., Zhang, J.: Sleep scheduling for wireless sensor networks via network flow model. Comput. Commun. 29, 2469–2481Google Scholar
  19. 19.
    Papadimitriou, C., Yannakakis, M.: Optimization, approximation and complexity classes. J. Comput. Syst. Sci. 43, 425–440 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of HICSS (2000)Google Scholar
  21. 21.
    Goldberg, A.V.: An efficient implementation of a scaling minimum-cost flow algorithm. J. Algorithms 22(1), 1–29 (1997)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Goldberg, A.V.: Andrew Goldberg’s network optimization library.
  23. 23.
    Hershberger, J., Maxel, M., Suri, S.: Finding the \(k\) shortest simple paths: a new algorithm and its implementation. ACM Trans. Algorithms 3(4), 1–19 (2007)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Eppstein, D.: Finding the \(k\) shortest paths. SIAM J. Comput. 28(2), 652–673 (1998)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Bin Tang
    • 1
    Email author
  • Rajiv Bagai
    • 2
  • FNU Nilofar
    • 2
  • Mehmet Bayram Yildirim
    • 3
  1. 1.Department of Computer ScienceCalifornia State UniversityDominguez HillsUSA
  2. 2.Department of Electrical Engineering and Computer ScienceWichita State UniversityWichitaUSA
  3. 3.Department of Industrial and Manufacturing EngineeringWichita State UniversityWichitaUSA

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