Open AccessEfficient coverage algorithm for mobile sensor network with unknown density function
Author(s) -
Zuo Lei,
Yan Weisheng,
Yan Maode
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0986
Subject(s) - computer science , convergence (economics) , wireless sensor network , stability (learning theory) , lyapunov function , algorithm , function (biology) , mathematical optimization , process (computing) , rate of convergence , probability density function , control theory (sociology) , control (management) , mathematics , artificial intelligence , computer network , channel (broadcasting) , physics , nonlinear system , quantum mechanics , machine learning , evolutionary biology , economics , biology , economic growth , operating system , statistics
This study investigates the coverage control problems for the mobile sensor network in a given domain, in which the density function characterising the distribution of the information of interest is unknown. To approximate the density function, we develop an adaptive spatial estimation algorithm for the mobile sensor network. Then, a distributed coverage control strategy is proposed to drive the sensors to the optimal locations. To further improve the efficiency of the coverage process, the authors apply the consensus mechanism into the spatial estimation algorithm and propose an improved control strategy such that the coverage system can converge to the optimal deployment effectively. The convergences of the proposed coverage systems are proved through Lyapunov stability theorem. Moreover, the authors show that the convergence rate of the consensus‐based spatial estimation algorithm is faster than the distributed case. Finally, numerical simulations are provided to illustrate the proposed approaches.