Algorithmica

pp 1–19 | Cite as

Algorithms for Communication Scheduling in Data Gathering Network with Data Compression

  • Wenchang Luo
  • Yao Xu
  • Boyuan Gu
  • Weitian Tong
  • Randy Goebel
  • Guohui Lin
Article
  • 63 Downloads

Abstract

We consider a communication scheduling problem that arises within wireless sensor networks, where data is accumulated by the sensors and transferred directly to a central base station. One may choose to compress the data collected by a sensor, to decrease the data size for transmission, but the cost of compression must be considered. The goal is to designate a subset of sensors to compress their collected data, and then to determine a data transmission order for all the sensors, such that the total compression cost is minimized subject to a bounded data transmission completion time (a.k.a. makespan). A recent result confirms the NP-hardness for this problem, even in the special case where data compression is free. Here we first design a pseudo-polynomial time exact algorithm, articulated within a dynamic programming scheme. This algorithm also solves a variant with the complementary optimization goal—to minimize the makespan while constraining the total compression cost within a given budget. Our second result consists of a bi-factor \((1 + \epsilon , 2)\)-approximation for the problem, where \((1 + \epsilon )\) refers to the compression cost and 2 refers to the makespan, and a 2-approximation for the variant. Lastly, we apply a sparsing technique to the dynamic programming exact algorithm, to achieve a dual fully polynomial time approximation scheme for the problem and a usual fully polynomial time approximation scheme for the variant.

Keywords

Wireless sensor network Data compression Scheduling Approximation algorithm FPTAS Dual FPTAS 

Notes

Acknowledgements

W.L. was supported by China Scholarship Council (Grant No. 201408330402), the K. C. Wong Magna Fund in the Ningbo University, and the Ningbo Natural Science Foundation (2016A610078). W.L., Y.X., B.G., R.G. and G.L. were supported by NSERC. W.T. was supported by the FY16 Startup Funding from the Georgia Southern University.

References

  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Alfieri, A., Bianco, A., Brandimarte, P., Chiasserini, C.F.: Maximizing system lifetime in wireless sensor networks. Eur. J. Oper. Res. 181, 390–402 (2007)CrossRefMATHGoogle Scholar
  3. 3.
    Berlińska, J.: Communication scheduling in data gathering networks with limited memory. Appl. Math. Comput. 235, 530–537 (2014)MathSciNetMATHGoogle Scholar
  4. 4.
    Berlińska, J.: Scheduling for data gathering networks with data compression. Eur. J. Oper. Res. 246, 744–749 (2015)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Błażewicz, J., Ecker, K.H., Pesch, E., Schmidt, G., Weglarz, J.: Handbook on Scheduling: From Theory to Applications. Springer, Berlin (2007)MATHGoogle Scholar
  6. 6.
    Cheng, J., Ye, Q., Jiang, H., Wang, D., Wang, C.: Stcdg: an efficient data gathering algorithm based on matrix completion for wireless sensor networks. IEEE Trans. Wirel. Commun. 12, 850–861 (2013)CrossRefGoogle Scholar
  7. 7.
    Choi, K., Robertazzi, T.G.: Divisible load scheduling in wireless sensor networks with information utility. In: Proceedings of the 2008 IEEE International Performance, Computing and Communications Conference, pp. 9–17 (2008)Google Scholar
  8. 8.
    Ergen, S.C., Varaiya, P.: TDMA scheduling algorithms for wireless sensor networks. Wirel. Netw. 16, 985–997 (2010)CrossRefGoogle Scholar
  9. 9.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-completeness. W. H. Freeman and Company, San Francisco (1979)MATHGoogle Scholar
  10. 10.
    Hochbaum, D., Shmoys, D.: Using dual approximation algorithms for scheduling problems: theoretical and practical results. J. ACM 34, 144–162 (1987)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kellerer, H., Pferschy, U.: Improved dynamic programming in connection with an FPTAS for the knapsack problem. J. Comb. Optim. 8, 5–11 (2004)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack Problems. Springer, Berlin (2004)CrossRefMATHGoogle Scholar
  13. 13.
    Kellerer, H., Strusevich, V.: Fast approximation schemes for Boolean programming and scheduling problems related to positive convex half-product. Eur. J. Oper. Res. 228, 24–32 (2013)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Kimura, N., Latifi, S.: A survey on data compression in wireless sensor networks. In: Proceedings of the 2005 International Conference on Information Technology: Coding and Computing, pp. 8–13 (2005)Google Scholar
  15. 15.
    Kumar, S., Chauhan, S.: A survey on scheduling algorithms for wireless sensor networks. Int. J. Comput. Appl. 20, 7–13 (2011)Google Scholar
  16. 16.
    Luo, C., Wu, F., Sun, J., Chen, C.W.: Compressive data gathering for large-scale wireless sensor networks. In: Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, pp. 145–156 (2009)Google Scholar
  17. 17.
    Moges, M., Robertazzi, T.G.: Wireless sensor networks: scheduling for measurement and data reporting. IEEE Trans. Aerosp. Electron. Syst. 42, 327–340 (2006)CrossRefMATHGoogle Scholar
  18. 18.
    Rossi, A., Singh, A., Sevaux, M.: Lifetime maximization in wireless directional sensor network. Eur. J. Oper. Res. 231, 229–241 (2013)CrossRefGoogle Scholar
  19. 19.
    Shi, L., Fapojuwo, A.O.: TDMA scheduling with optimized energy efficiency and minimum delay in clustered wireless sensor networks. IEEE Trans. Mob. Comput. 9, 927–940 (2010)CrossRefGoogle Scholar
  20. 20.
    Tauhidul, I.M.: Approximation Algorithms for Minimum Knapsack Problem. Master’s thesis, University of Lethbridge (2009)Google Scholar
  21. 21.
    Wang, J., Tang, S., Yin, B., Li, X.Y.: Data gathering in wireless sensor networks through intelligent compressive sensing. In: INFOCOM 2012, pp. 603–611 (2012)Google Scholar
  22. 22.
    Wu, Y., Li, X.Y., Liu, Y., Lou, W.: Energy-efficient wake-up scheduling for data collection and aggregation. IEEE Trans. Parallel Distrib. Syst. 21, 275–287 (2010)CrossRefGoogle Scholar
  23. 23.
    Xiang, L., Luo, J., Rosenberg, C.: Compressed data aggregation: energy-efficient and high-fidelity data collection. IEEE/ACM Trans. Netw. 21, 1722–1735 (2013)CrossRefGoogle Scholar
  24. 24.
    Xu, L., Wang, Y., Wang, Y.: Major coefficients recovery: a compressed data gathering scheme for wireless sensor network. In: Global Telecommunications Conference (GLOBECOM 2011), pp. 1–5 (2011)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Faculty of ScienceNingbo UniversityNingboChina
  2. 2.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  3. 3.Department of Computer SciencesGeorgia Southern UniversityStatesboroUSA

Personalised recommendations