Skip to main content

Radio Aggregation Scheduling

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 9536))

Abstract

We consider the aggregation problem in radio networks: find a spanning tree in a given graph and a conflict-free schedule of the edges so as to minimize the latency of the computation. While a large body of literature exists on this and related problems, we give the first approximation results in graphs that are not induced by unit ranges in the plane. We give a polynomial-time \(\tilde{\mathrm {O}}(\sqrt{\overline{d}n})\)-approximation algorithm, where \(\overline{d}\) is the average degree and n the number of vertices in the graph, and show that the problem is \(\varOmega (n^{1-\epsilon })\)-hard (and \(\varOmega ((\overline{d}n)^{1/2-\epsilon })\)-hard) to approximate even on bipartite graphs, for any \(\epsilon > 0\), rendering our algorithm essentially optimal. We target geometrically defined graph classes, and in particular obtain a \(O(\log n)\)-approximation in interval graphs.

Gandhi and Oh are partly supported by NSF grants CCF 1218620 and CCF 1433220. Halldórsson and Konrad are supported by Icelandic Research Fund grant-of-excellence no. 120032011. Kortsarz is partly supported by NSF grant CCF 1218620.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    In [15], unit interval graphs as well as grids and tori are considered, which are all subclasses of unit disc graphs.

  2. 2.

    We use the notation \(\tilde{\mathrm {O}}(.)\), which equals the usual \(\mathrm {O}(.)\) notation where all poly-logarithmic factors are ignored.

  3. 3.

    While the result is surely well known, we were not aware of a reference for this particular version, and thus include the algorithm and a proof in the full version of this paper for completeness.

  4. 4.

    A graph is \(4-\)claw-free, if it doesn’t contain the complete bipartite graph \(K_{1,4}\) as an induced subgraph.

References

  1. Aguayo, D., Bicket, J., Biswas, S., Judd, G., Morris, R.: Link-level measurements from an 802.11 b mesh network. ACM SIGCOMM Comput. Commun. Rev. 34(4), 121–132 (2004)

    Article  Google Scholar 

  2. Alon, N., Bar-Noy, A., Linial, N., Peleg, D.: A lower bound for radio broadcast. J. Comput. Syst. Sci. 43, 290–298 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  3. An, M.K., Lam, N.X., Huynh, D.T., Nguyen, T.N.: Minimum data aggregation schedule in wireless sensor networks. I. J. Comput. Appl. 18(4), 254–262 (2011)

    Google Scholar 

  4. Bar-Noy, A., Guha, S., Naor, J., Schieber, B.: Message multicasting in heterogeneous networks. SIAM J. Comput. 30(2), 347–358 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  5. Bar-Yehuda, R., Goldreich, O., Itai, A.: On the time-complexity of broadcast in multi-hop radio networks: an exponential gap between determinism and randomization. J. Comput. Syst. Sci. 45(1), 104–126 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  6. Chalermsook, P., Laekhanukit, B., Nanongkai, D.: Graph products revisited: tight approximation hardness of induced matching, poset dimension and more. In: SODA, pp. 1557–1576. SIAM (2013)

    Google Scholar 

  7. Chen, X., Hu, X., Zhu, J.: Minimum data aggregation time problem in wireless sensor networks. In: Jia, X., Wu, J., He, Y. (eds.) MSN 2005. LNCS, vol. 3794, pp. 133–142. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Chlamtac, I., Weinstein, O.: Distributed “wave” broadcasting in mobil multi-hop radio networks. In: ICDCS, pp. 82–89 (1987)

    Google Scholar 

  9. Elkin, M., Kortsarz, G.: A combinatorial logarithmic approximation algorithm for the directed telephone broadcast problem. SIAM J. Comput. 35(3), 672–689 (2005)

    Article  MathSciNet  Google Scholar 

  10. Elkin, M., Kortsarz, G.: Polylogarithmic additive inapproximability of the radio broadcast problem. SIAM J. Discrete Math. 19(4), 881–899 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  11. Elkin, M., Kortsarz, G.: Sublogarithmic approximation for telephone multicast. J. Comput. Syst. Sci. 72(4), 648–659 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  12. Fasolo, E., Rossi, M., Widmer, J., Zorzi, M.: In-network aggregation techniques for wireless sensor networks: a survey. IEEE Wirel. Commun. 14(2), 70–87 (2007)

    Article  Google Scholar 

  13. Feige, U., Kilian, J.: Zero knowledge and the chromatic number. J. Comput. Syst. Sci. 57(2), 187–199 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  14. Gagnon, J., Narayanan, L.: Minimum latency aggregation scheduling in wireless sensor networks. In: Gao, J., Efrat, A., Fekete, S.P., Zhang, Y. (eds.) ALGOSENSORS 2014, LNCS 8847. LNCS, vol. 8847, pp. 152–168. Springer, Heidelberg (2015)

    Google Scholar 

  15. Guo, L., Li, Y., Cai, Z.: Minimum-latency aggregation scheduling in wireless sensor network. J. Comb. Optim., 1–32 (2014)

    Google Scholar 

  16. Halldórsson, M.M., Mitra, P.: Wireless connectivity and capacity. In: SODA (2012)

    Google Scholar 

  17. Incel, O.D., Ghosh, A., Krishnamachari, B.: Scheduling algorithms for tree-based data collection in wireless sensor networks. In: Nikoletseas, S., Rolim, J.D.P. (eds.) Theoretical Aspects of Distributed Computing in Sensor Networks, pp. 407–445. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  18. Kesselman, A., Kowalski, D.: Fast distributed algorithm for convergecast in ad hoc geometric radio networks. In: WONS, pp. 119–124. IEEE (2005)

    Google Scholar 

  19. Kleinberg, J., Tardos, É.: Algorithm Design. Pearson Education, Boston (2006)

    Google Scholar 

  20. Kortsarz, G., Peleg, D.: Approximation algorithms for minimum-time broadcast. SIAM J. Discrete Math. 8(3), 401–427 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  21. Kotz, D., Newport, C., Gray, R.S., Liu, J., Yuan, Y., Elliott, C.: Experimental evaluation of wireless simulation assumptions. In: MSWiM, pp. 78–82 (2004)

    Google Scholar 

  22. Kowalski, D.R., Pelc, A.: Optimal deterministic broadcasting in known topology radio networks. Distrib. Comput. 19(3), 185–195 (2007)

    Article  Google Scholar 

  23. Moscibroda, T., Wattenhofer, R.: The complexity of connectivity in wireless networks. In: INFOCOM (2006)

    Google Scholar 

  24. Ravi, R.: Rapid rumor ramification: approximating the minimum broadcast time (extended abstract). In: FOCS, pp. 202–213 (1994)

    Google Scholar 

  25. Wan, P.J., Huang, S.C.H., Wang, L., Wan, Z., Jia, X.: Minimum-latency aggregation scheduling in multihop wireless networks. In: MOBIHOC, pp. 185–194. ACM (2009)

    Google Scholar 

  26. Xu, X., Wang, S., Mao, X., Tang, S., Li, X.Y.: An improved approximation algorithm for data aggregation in multi-hop wireless sensor networks. In: FOWANC, pp. 47–56. ACM (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christian Konrad .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Gandhi, R., Halldórsson, M.M., Konrad, C., Kortsarz, G., Oh, H. (2015). Radio Aggregation Scheduling. In: Bose, P., Gąsieniec, L., Römer, K., Wattenhofer, R. (eds) Algorithms for Sensor Systems. ALGOSENSORS 2015. Lecture Notes in Computer Science(), vol 9536. Springer, Cham. https://doi.org/10.1007/978-3-319-28472-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28472-9_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28471-2

  • Online ISBN: 978-3-319-28472-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics