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Reseach Article

Multi-agent Patrolling: Multi-Objective Approach of the Event Detection by a Mobile Wireless Sensors Network

by Elie Tagne Fute, Emmanuel Tonye, Fabrice Lauri, Abderrafiaa Koukam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 88 - Number 12
Year of Publication: 2014
Authors: Elie Tagne Fute, Emmanuel Tonye, Fabrice Lauri, Abderrafiaa Koukam
10.5120/15401-3845

Elie Tagne Fute, Emmanuel Tonye, Fabrice Lauri, Abderrafiaa Koukam . Multi-agent Patrolling: Multi-Objective Approach of the Event Detection by a Mobile Wireless Sensors Network. International Journal of Computer Applications. 88, 12 ( February 2014), 1-8. DOI=10.5120/15401-3845

@article{ 10.5120/15401-3845,
author = { Elie Tagne Fute, Emmanuel Tonye, Fabrice Lauri, Abderrafiaa Koukam },
title = { Multi-agent Patrolling: Multi-Objective Approach of the Event Detection by a Mobile Wireless Sensors Network },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 88 },
number = { 12 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume88/number12/15401-3845/ },
doi = { 10.5120/15401-3845 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:07:23.886254+05:30
%A Elie Tagne Fute
%A Emmanuel Tonye
%A Fabrice Lauri
%A Abderrafiaa Koukam
%T Multi-agent Patrolling: Multi-Objective Approach of the Event Detection by a Mobile Wireless Sensors Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 88
%N 12
%P 1-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Patrolling an environment involves a team of agents whose goal usually consists in continuously visiting its most relevant areas as frequently as possible. Hence, patrolling consists in continuously visiting relevant areas of an environment, in order to efficiently surpervise or control it. The Ant Colony Optimization (ACO) is adopted as the solution approach that efficiently solves the multiagent patrolling problem. Two stratagies are employed: all agents are located on the same node at the initial time, and the agents are dispersed over the graph, they start to patrol from their new locations. This paper mainly describes the formulation problem of event detection by a multi-objective approach, an ACO and multi-agent approach are used to model and simulate the detection of events. Different parameters are evaluated in order to minimize them. The minimization ofWorst Idleness, Energy consumption and Communicational Idleness are not compatible. It is therefore necessary to seek compromise solutions . The set of compromise solutions is called Pareto Front. The set of solutions and Pareto Front are presented respectively for the cases of non-dispersion and dispersion of agents.

References
  1. M. Basseur, E. Talbi, A. Nebro, and E. Alba. Metaheuristics for Multiobjective Combinatorial Optimization Problems: Review and recent issues. In INRIA Report-ISSN 0249-6399, 2006.
  2. Christina Busing, Kai-Simon Goetzmann, and Jannik Matuschke. Compromise solutions in multicriteria combinatorial optimization. In Matheon, 2012.
  3. Y. Chevaleyre. Theoretical Analysis of the Multi-Agent Patrolling Problem. In , pages 302–308, 2004.
  4. H. N. Chu, A. Glad, O. Simonin, F. Semp, A. Drogoul, and F. Charpillet. Swarm Approaches for the Patrolling Problem, Information Propagation vs. Pheromone Evaporation. In 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI, vol. 1, pages 442–449, 2007.
  5. Y. Colette and P. Siarry. Optimisation multiobjectif. Technical report, Edition Eyrolles, 2002.
  6. E. T. Fute and E. Tonye. Modelling and Self-organizing in MobileWireless Sensor Networks: Application to Fire Detection. In International Journal of Applied Information Systems, IJAIS, New York, USA, Vol. 5 N3, 2013.
  7. E. T. Fute, E. Tonye, A. Koukam, and F. Lauri. Multi- Agent Patrolling Strategy: Application to the Exploration Environment Problem. In International Journal of Automation, Robotics and Autonomous Systems, ICGST-ARAS, ISSN 1687-4811 (Print), 1687-482X (Online), Vol. 9, Issue 2, 2009.
  8. Caroline Gagne, Marc Gravel, andWilson L. Price. Optimisation Multi-Objectifs a l'aide d'un Algorithme de Colonie de Fourmis. In INFOR, vol. 42-1, pages 23–42, 2004.
  9. Arnaud Glad. Etude de l'auto-organisation dans les algorithmes de patrouille multi-agent fondes sur les phromones digitales. PhD thesis, Universit Nancy 2, 2011.
  10. Tristram Grabener and A. Berro. Optimisation multiobjectif discrete par propagation de contraintes. In Actes JFPC, 2008.
  11. F. Lauri and A. Koukam. A Two-Step Evolutionary and ACO Approach for Solving the Multi-Agent Patrolling Problem. In , Hong-Kong, China, 2008.
  12. A. Machado, G. Ramalho, and . Multi-Agent Patrolling : an Empirical Analysis of Alternatives Architectures. In 3rd , pages 155–170, 2002.
  13. J. B. Seung, Gustavo de Veciana, and Xun Su. Minimizing Energy Consumption In Large-scale Sensor Networks Through Distributed Data Compression And Hierarchical Aggregation. In IEEE Journal on Selected Areas in Communications, pages 1–10, 2004.
  14. Elie Fute T. , E. Tonye, A. Koukam, and F. Lauri. Stratgie de Patrouille Multi-Agents : Application au Problme d'Exploration d'un Environnement. In 5th IEEE International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, 2009.
  15. Jurgen Teich. Pareto-Front Exploration with Uncertain Objectives. In Springer-Verlag, Berlin Heidelberg, pages 314–328, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Agent ant mobile sensors optimization patrolling