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Algorithms for Communication Scheduling in Data Gathering Network with Data Compression

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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.

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Notes

  1. The NP-hardness result by Berlińska [4], cited in the Preliminaries, hints that deciding whether or not there is a feasible solution to the problem DG-Compr-OptF is NP-complete.

  2. This upper bound of 2T on the makespan holds regardless of existence of a feasible solution to the problem DG-Compr-OptF.

  3. One could probably further reduce the size and/or the dimensionality of the table. We nonetheless use the current table for the ease of presentation.

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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.

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Authors and Affiliations

  1. Faculty of Science, Ningbo University, Ningbo, 315211, Zhejiang, China

    Wenchang Luo

  2. Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada

    Wenchang Luo, Yao Xu, Boyuan Gu, Randy Goebel & Guohui Lin

  3. Department of Computer Sciences, Georgia Southern University, Statesboro, GA, 30460, USA

    Weitian Tong

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  1. Wenchang Luo
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Correspondence to Guohui Lin.

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Luo, W., Xu, Y., Gu, B. et al. Algorithms for Communication Scheduling in Data Gathering Network with Data Compression. Algorithmica 80, 3158–3176 (2018). https://doi.org/10.1007/s00453-017-0373-6

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