A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks

Sensors

Ming Li³  Chunyan Miao¹  Cyril Leung² 

[1] School of Computer Engineering, Nanyang Technological University, Nanyang Avenue 639798, Singapore;Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada;;Detection and Control of Integrated Systems Engineering Laboratory in Chongqing Technology and BusinessUniversity, Chongqing 400067, China

关键词: directional sensor network;    coverage control;    coral reef algorithm;    learning automata;    multi-objective optimization;   

DOI  :  10.3390/s151229820

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.