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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aronson, J.E.: A survey of dynamic network flows. Annals of OR 20, 1–66 (1989)
Bodlaender, H., Tan, R., van Dijk, T., van Leeuwen, J.: Integer maximum flow in wireless sensor networks with energy constraint. In: Proc. SWAT (2008)
Burkard, R.E., Dlaska, K., Klinz, B.: The quickest flow problem. ZOR — Methods and Models of Operations Research 37, 31–58 (1993)
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)
Chang, J.-H., Tassiulas, L.: Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking 12(4), 609–619 (2004)
Fekete, S.P., Kröller, A.: Geometry-based reasoning for a large sensor network. In: Proc. SoCG, pp. 475–476 (2006)
Fekete, S.P., Kröller, A., Pfisterer, D., Fischer, S.: Algorithmic aspects of large sensor networks. In: Proc MSWSN, pp. 141–152 (2006)
Fekete, S.P., Schmidt, C., Wegener, A., Fischer, S.: Hovering data clouds for recognizing traffic jams. In: Proc. IEEE-ISOLA, pp. 213–218 (2006)
Fleischer, L., Skutella, M.: Quickest flows over time. SIAM Journal on Computing 36, 1600–1630 (2007)
Fleischer, L.K., Tardos, É.: Efficient continuous-time dynamic network flow algorithms. Operations Research Letters 23, 71–80 (1998)
Ford, L.R., Fulkerson, D.R.: Constructing maximal dynamic flows from static flows. Operations Research 6, 419–433 (1958)
Ford, L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (1962)
Garg, N., Könemann, J.: Faster and simpler algorithms for multicommodity flow and other fractional packing problems. In: Proc. FOCS, p. 300 (1998)
Hall, A., Hippler, S., Skutella, M.: Multicommodity flows over time: Efficient algorithms and complexity. Theoretical Computer Science 379, 387–404 (2007)
Hoppe, B., Tardos, É.: The quickest transshipment problem. Mathematics of Operations Research 25, 36–62 (2000)
Hoppe, B.E.: Efficient dynamic network flow algorithms. PhD thesis, Cornell (1995)
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)
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)
Madan, R., Lall, S.: Distributed algorithms for maximum lifetime routing in wireless sensor networks. IEEE Transactions on Wireless Communications 5(8), 2185–2193 (2006)
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)
Peleg, D.: Distributed computing: a locality-sensitive approach. SIAM, Philadelphia (2000)
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)
Ran, B., Boyce, D.E.: Modelling Dynamic Transportation Networks. Springer, Heidelberg (1996)
Sankar, A., Liu, Z.: Maximum lifetime routing in wireless ad-hoc networks. In: Proc. INFOCOM, pp. 1089–1097 (2004)
Schmid, S., Wattenhofer, R.: Algorithmic models for sensor networks. In: Proc. IPDPS (2006)
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)
Zussman, G., Segall, A.: Energy efficient routing in ad hoc disaster recovery networks. In: Proc. INFOCOM, pp. 682–691 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)