Algorithms for Communication Scheduling in Data Gathering Network with Data Compression

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...

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Vydané v:Algorithmica Ročník 80; číslo 11; s. 3158 - 3176
Hlavní autori: Luo, Wenchang, Xu, Yao, Gu, Boyuan, Tong, Weitian, Goebel, Randy, Lin, Guohui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.11.2018
Springer Nature B.V
Predmet:
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
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Shrnutí: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.
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content type line 14
ISSN:0178-4617
1432-0541
DOI:10.1007/s00453-017-0373-6