Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81722
Title: A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks
Authors: Li, Ming
Miao, Chunyan
Leung, Cyril
Keywords: directional sensor network; coverage control; coral reef algorithm; learning automata; multi-objective optimization
Issue Date: 2015
Source: Li, M., Miao, C., & Leung, C. (2015). A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks. Sensors, 15(12), 30617-30635.
Series/Report no.: Sensors
Abstract: Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.
URI: https://hdl.handle.net/10356/81722
http://hdl.handle.net/10220/39649
ISSN: 1424-8220
DOI: 10.3390/s151229820
Rights: © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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