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3 results on '"Network lifetime maximization problem"'

2. Heuristics for lifetime maximization in camera sensor networks.

Author

Singh, Alok, Rossi, André, and Sevaux, Marc

Subjects
*SENSOR networks, *HEURISTIC algorithms, *MULTIMEDIA systems, *CAMERAS, *LINEAR programming, *NP-hard problems
Abstract

Due to increasing threat perception and cheap availability of multimedia technologies, camera sensor networks are becoming more and more popular these days. Camera sensor networks pose some unique challenges in addition to the usual difficulties associated with any directional sensor network. This paper addresses the problem of maximizing the network lifetime in camera sensor networks under the full and the partial target coverage models. In the full target coverage model, all the targets are assumed to be covered during the entire lifetime, whereas in the partial target coverage model, targets are supposed to have weights according to their importance, and only a fraction of targets with sum of weights above a certain threshold need to be covered during the entire lifetime. Three heuristics are presented for this problem. The first heuristic is an improved version of an already existing heuristic. Other two heuristics are based on column generation and utilize a linear programming solver to solve the master problem, whereas a genetic algorithm is used to solve the NP -hard subproblem. Computational results show the effectiveness of our proposed heuristics. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
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3. Heuristics for lifetime maximization in camera sensor networks

Author

Alok Singh, Marc Sevaux, Andr Rossi, School of Computer and Information Sciences, University of Hyderabad, Laboratoire d'Etudes et de Recherche en Informatique d'Angers (LERIA), Université d'Angers (UA), Lab-STICC_UBS_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) (Lab-STICC), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université européenne de Bretagne - European University of Brittany (UEB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), and Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, Information Systems and Management, Linear programming, Computer science, 0211 other engineering and technologies, Heuristic, 02 engineering and technology, Theoretical Computer Science, Artificial Intelligence, Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, Fraction (mathematics), 021103 operations research, Maximization, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Solver, Computer Science Applications, Network lifetime maximization problem, Control and Systems Engineering, Camera sensor network, 020201 artificial intelligence & image processing, Heuristics, Wireless sensor network, Software
Abstract

International audience; Due to increasing threat perception and cheap availability of multimedia technologies, camera sensor networks are becoming more and more popular these days. Camera sensor networks pose some unique challenges in addition to the usual difficulties associated with any directional sensor network. This paper addresses the problem of maximizing the network lifetime in camera sensor networks under the full and the partial target coverage models. In the full target coverage model, all the targets are assumed to be covered during the entire lifetime, whereas in the partial target coverage model, targets are supposed to have weights according to their importance, and only a fraction of targets with sum of weights above a certain threshold need to be covered during the entire lifetime. Three heuristics are presented for this problem. The first heuristic is an improved version of an already existing heuristic. Other two heuristics are based on column generation and utilize a linear programming solver to solve the master problem, whereas a genetic algorithm is used to solve the NP-hard subproblem. Computational results show the effectiveness of our proposed heuristics.

Published
2017
Full Text
View/download PDF

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