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The Maximum Energy-Constrained Dynamic Flow Problem

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5124))

Abstract

We study a natural class of flow problems that occur in the context of wireless networks; the objective is to maximize the flow from a set of sources to one sink node within a given time limit, while satisfying a number of constraints. These restrictions include capacities and transit times for edges; in addition, every node has a bound on the amount of transmission it can perform, due to limited battery energy it carries. We show that this Maximum energy-constrained dynamic flow problem (ECDF) is difficult in various ways: it is NP-hard for arbitrary transit times; a solution using flow paths can have exponential-size growth; a solution using edge flow values may not exist; and finding an integral solution is NP-hard. On the positive side, we show that the problem can be solved polynomially for uniform transit times for a limited time limit; we give an FPTAS for finding a fractional flow; and, most notably, there is a distributed FPTAS that can be run directly on the network.

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References

  1. Aronson, J.E.: A survey of dynamic network flows. Annals of OR 20, 1–66 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  2. Bodlaender, H., Tan, R., van Dijk, T., van Leeuwen, J.: Integer maximum flow in wireless sensor networks with energy constraint. In: Proc. SWAT (2008)

    Google Scholar 

  3. Burkard, R.E., Dlaska, K., Klinz, B.: The quickest flow problem. ZOR — Methods and Models of Operations Research 37, 31–58 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  4. Busse, M., Haenselmann, T., Effelsberg, W.: A comparison of lifetime-efficient forwarding strategies for wireless sensor networks. In: Proc. PE-WASUN, pp. 33–40 (2006)

    Google Scholar 

  5. Chang, J.-H., Tassiulas, L.: Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking 12(4), 609–619 (2004)

    Article  Google Scholar 

  6. Fekete, S.P., Kröller, A.: Geometry-based reasoning for a large sensor network. In: Proc. SoCG, pp. 475–476 (2006)

    Google Scholar 

  7. Fekete, S.P., Kröller, A., Pfisterer, D., Fischer, S.: Algorithmic aspects of large sensor networks. In: Proc MSWSN, pp. 141–152 (2006)

    Google Scholar 

  8. Fekete, S.P., Schmidt, C., Wegener, A., Fischer, S.: Hovering data clouds for recognizing traffic jams. In: Proc. IEEE-ISOLA, pp. 213–218 (2006)

    Google Scholar 

  9. Fleischer, L., Skutella, M.: Quickest flows over time. SIAM Journal on Computing 36, 1600–1630 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  10. Fleischer, L.K., Tardos, É.: Efficient continuous-time dynamic network flow algorithms. Operations Research Letters 23, 71–80 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  11. Ford, L.R., Fulkerson, D.R.: Constructing maximal dynamic flows from static flows. Operations Research 6, 419–433 (1958)

    Article  MathSciNet  Google Scholar 

  12. Ford, L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (1962)

    MATH  Google Scholar 

  13. Garg, N., Könemann, J.: Faster and simpler algorithms for multicommodity flow and other fractional packing problems. In: Proc. FOCS, p. 300 (1998)

    Google Scholar 

  14. Hall, A., Hippler, S., Skutella, M.: Multicommodity flows over time: Efficient algorithms and complexity. Theoretical Computer Science 379, 387–404 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  15. Hoppe, B., Tardos, É.: The quickest transshipment problem. Mathematics of Operations Research 25, 36–62 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  16. Hoppe, B.E.: Efficient dynamic network flow algorithms. PhD thesis, Cornell (1995)

    Google Scholar 

  17. Klinz, B., Woeginger, G.J.: Minimum cost dynamic flows: The series-parallel case. In: Balas, E., Clausen, J. (eds.) IPCO 1995. LNCS, vol. 920, pp. 329–343. Springer, Heidelberg (1995)

    Google Scholar 

  18. Kröller, A., Fekete, S.P., Pfisterer, D., Fischer, S.: Deterministic boundary recognition and topology extraction for large sensor networks. In: Proc. SODA, pp. 1000–1009 (2006)

    Google Scholar 

  19. Madan, R., Lall, S.: Distributed algorithms for maximum lifetime routing in wireless sensor networks. IEEE Transactions on Wireless Communications 5(8), 2185–2193 (2006)

    Article  Google Scholar 

  20. Madan, R., Luo, Z.-Q., Lall, S.: A distributed algorithm with linear convergence for maximum lifetime routing in wireless networks. In: Proc. Allerton Conference, pp. 896–905 (2005)

    Google Scholar 

  21. Peleg, D.: Distributed computing: a locality-sensitive approach. SIAM, Philadelphia (2000)

    MATH  Google Scholar 

  22. Powell, W.B., Jaillet, P., Odoni, A.: Stochastic and dynamic networks and routing. In: Network Routing, ch. 3. Handbooks in Operations Research and Management Science, vol. 8, pp. 141–295. North–Holland, Amsterdam, The Netherlands (1995)

    Google Scholar 

  23. Ran, B., Boyce, D.E.: Modelling Dynamic Transportation Networks. Springer, Heidelberg (1996)

    Google Scholar 

  24. Sankar, A., Liu, Z.: Maximum lifetime routing in wireless ad-hoc networks. In: Proc. INFOCOM, pp. 1089–1097 (2004)

    Google Scholar 

  25. Schmid, S., Wattenhofer, R.: Algorithmic models for sensor networks. In: Proc. IPDPS (2006)

    Google Scholar 

  26. Wattenhofer, R.: Sensor networks: Distributed algorithms reloaded - or revolutions? In: Flocchini, P., Gąsieniec, L. (eds.) SIROCCO 2006. LNCS, vol. 4056, pp. 24–28. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  27. Zussman, G., Segall, A.: Energy efficient routing in ad hoc disaster recovery networks. In: Proc. INFOCOM, pp. 682–691 (2003)

    Google Scholar 

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Joachim Gudmundsson

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Fekete, S.P., Hall, A., Köhler, E., Kröller, A. (2008). The Maximum Energy-Constrained Dynamic Flow Problem. In: Gudmundsson, J. (eds) Algorithm Theory – SWAT 2008. SWAT 2008. Lecture Notes in Computer Science, vol 5124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69903-3_12

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  • DOI: https://doi.org/10.1007/978-3-540-69903-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69900-2

  • Online ISBN: 978-3-540-69903-3

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