Algorithms for Communication Scheduling in Data Gathering Network with Data Compression

• Published in: Algorithmica Vol. 80; no. 11; pp. 3158 - 3176
• Main Authors: Luo, Wenchang, Xu, Yao, Gu, Boyuan, Tong, Weitian, Goebel, Randy, Lin, Guohui
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2018; Springer Nature B.V
• Subjects: Algorithm Analysis and Problem Complexity; Algorithms; Approximation; Completion time; Computer Science; Computer Systems Organization and Communication Networks; Data compression; Data Structures and Information Theory; Data transmission; Dynamic programming; Mathematical analysis; Mathematics of Computing; Polynomials; Production scheduling; Remote sensors; Scheduling; Sensors; Set theory; Theory of Computation; Wireless communications; Wireless sensor networks; Wireless sensor network; Scheduling; Approximation algorithm; FPTAS; Data compression; Dual FPTAS
• ISSN: 0178-4617; 1432-0541
• DOI: 10.1007/s00453-017-0373-6

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 + ϵ , 2 ) -approximation for the problem, where ( 1 + ϵ ) 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.