Treffer: Maximum Target Coverage Problem in Mobile Wireless Sensor Networks.

Title:
Maximum Target Coverage Problem in Mobile Wireless Sensor Networks.
Authors:
Liang D; School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China., Shen H; School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China., Chen L; School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China.
Source:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2020 Dec 29; Vol. 21 (1). Date of Electronic Publication: 2020 Dec 29.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: Basel, Switzerland : MDPI, c2000-
Contributed Indexing:
Keywords: approximation algorithm; integer programming; mobile sensors; target coverage; wireless sensor network
Entry Date(s):
Date Created: 20210101 Latest Revision: 20210111
Update Code:
20250114
PubMed Central ID:
PMC7795209
DOI:
10.3390/s21010184
PMID:
33383935
Database:
MEDLINE

Weitere Informationen

We formulate and analyze a generic coverage optimization problem arising in wireless sensor networks with sensors of limited mobility. Given a set of targets to be covered and a set of mobile sensors, we seek a sensor dispatch algorithm maximizing the covered targets under the constraint that the maximal moving distance for each sensor is upper-bounded by a given threshold. We prove that the problem is NP-hard. Given its hardness, we devise four algorithms to solve it heuristically or approximately. Among the approximate algorithms, we first develop randomized (1-1/e)-optimal algorithm. We then employ a derandomization technique to devise a deterministic (1-1/e)-approximation algorithm. We also design a deterministic approximation algorithm with nearly ▵-1 approximation ratio by using a colouring technique, where denotes the maximal number of subsets covering the same target. Experiments are also conducted to validate the effectiveness of the algorithms in a variety of parameter settings.