, Volume 80, Issue 11, pp 3158–3176 | Cite as

Algorithms for Communication Scheduling in Data Gathering Network with Data Compression

  • Wenchang Luo
  • Yao Xu
  • Boyuan Gu
  • Weitian Tong
  • Randy Goebel
  • Guohui LinEmail author


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.


Wireless sensor network Data compression Scheduling Approximation algorithm FPTAS Dual FPTAS 



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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© 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

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