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Reseach Article

A Simulation Model and a Hybrid Genetic Algorithm for Energy-Aware MANET Routing and Planning

by Ivana Cardial De Miranda Pereira, Nelson Francisco Favilla Ebecken
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 11
Year of Publication: 2015
Authors: Ivana Cardial De Miranda Pereira, Nelson Francisco Favilla Ebecken
10.5120/ijca2015905682

Ivana Cardial De Miranda Pereira, Nelson Francisco Favilla Ebecken . A Simulation Model and a Hybrid Genetic Algorithm for Energy-Aware MANET Routing and Planning. International Journal of Computer Applications. 124, 11 ( August 2015), 42-50. DOI=10.5120/ijca2015905682

@article{ 10.5120/ijca2015905682,
author = { Ivana Cardial De Miranda Pereira, Nelson Francisco Favilla Ebecken },
title = { A Simulation Model and a Hybrid Genetic Algorithm for Energy-Aware MANET Routing and Planning },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 11 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 42-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number11/22152-2015905682/ },
doi = { 10.5120/ijca2015905682 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:10.912154+05:30
%A Ivana Cardial De Miranda Pereira
%A Nelson Francisco Favilla Ebecken
%T A Simulation Model and a Hybrid Genetic Algorithm for Energy-Aware MANET Routing and Planning
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 11
%P 42-50
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a new model developed to aid the planning and the analysis of communications-intensive Mobile Ad Hoc Networks (MANET), with respect to the allocation of energy-critical equipment. A graphical simulation tool and a new hybrid genetic algorithm (HGA) are introduced. They work together to estimate the required amount of deployed battery supplies and the probability of success of real operations. At each period, a hybrid genetic algorithm with reparation of individuals and heuristic crossover and mutation operators finds efficient routes that preserve maximum energy availability at network level, reducing the probability of communications disruption. The simulation tool implements mobility models derived from experts’ advices and may be used in missions like military and search-and-rescue operations. One may easily include new models to represent the movement of nodes in other specific missions, including trace data. The system is flexible and customizable, providing a means to mission planning, including the provision of adequate power supply for the large number of devices typically included within a MANET.

References
  1. Z. Haider and F. Shabbir, "Genetic Based Approach for Optimized Routing in Maritime Tactical MANETs," in Proceedings of 11th International Bhurban Conference on Applied Sciences & Technology - IBCAST, Islamabad, Pakistan, 2014.
  2. I. C. M. Pereira, Análise do Roteamento em Redes Móveis Ad Hoc em Cenários de Operações Militares, UFRJ, Rio de Janeiro, 2004.
  3. C. E. Perkins, E. M. Belding-Royer and S. R. Das, "Ad Hoc On-Demand Distance Vector (AODV) Routing," November 2002. [Online]. http://www.ietf.org/internet-drafts/draft-ietf-manet-aodv-12.txt.
  4. C. Perkins and P. Bhagwat, "Highly dynamic destination-sequenced distance vector routing (DSDV) for mobile computers," in ACM SIGCOMM Conference on Communications Architecture, Protocols and Applications, 1994.
  5. D. B. Johnson and D. A. Maltz, "The dynamic source routing protocol for mobile ad hoc networks," Intenet Draft, draft-ietf-manet-dsr-06.txt, 2002.
  6. V. Park and S. Corson, "A Highly Adaptive Distributed Routing Algorithm for Mobilie Wireless Networks," in INFOCOM, 1997.
  7. Z. Huang, R. Yamamoto and Y. Tanaka, "A Multipath Energy-Efficient Probability Routing Protocol in Ad Hoc Networks," in IEEE International Conference on Advanced Communications Tecnology, Seul, 2014.
  8. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, New York: Springer, 1996.
  9. D. S. Kumar and V. B. Kumar, "Energy-aware Multicast Routing in MANETs based on Genetic Algorithms," in 16th IEEE International Conference on Networks (ICON’ 08), New Delhi, India, 2008.
  10. J. Zhao, X. Jiang and J. Sha, "A Study of the Relationship between Mobility Model of Ad Hoc Network and its Connectivity," Journal of Computers, vol. 9, no. 4, April 2014.
  11. J. Abdullah, "Multiobjectives GA-based QOS Routing Protocol for MANET," International Journal of Grid and Distributed Computing, vol. 3, no. 4, Dec. 2010.
  12. T. Camp, J. Boleng and V. Davies, "A Survey of Mobility Models for Ad Hoc Network Research," Wireless Communication and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, vol. 2, no. 5, pp. 483-502, 2002.
  13. F. Fitzek, F. H. P. Fitzek, T. K. Madsen, R. Prasad and M. Katz, "Impact of Node Mobility on the Protocol Design of Selforganizing Networks," in Proceedings of Wireless World Research Forum, 2004.
  14. A. Jardosh, E. M. Belding-Royer, K. C. Almeroth and S. E Suri, "Towards Realistic Mobility Models for MANET," in MobiCom´03, San Diego, CA, 2003.
  15. D. V. Campos and R. B. Seixas, "Command and Control: a Low Cost Framework to Remotely Monitor Military Training," in Proceedingds of Spring Simulation Multiconference in Military Modeling and Simulation Symposium, Boston, 2011.
  16. D. V. Campos, Sistema de Jogos Didáticos: uma Abordagem Prática de Simulação Construtiva a Serviço dos Jogos de Guerra do Corpo de Fuzileiros Navais, Rio de Janeiro: Escola de Guerra Naval, 2015.
  17. S. Brush, "A history of random processes: Brownian movement from Brown to Perrin Arch," History of Exact Science, vol. 5, pp. 1-36, 1968.
  18. X. Hong, T. J. Kwon, M. Gerla and D. Lihu, "A Mobility Framework for Ad Hoc Wireless networks," in MDM'01: Proceedings of the Second International Conference on Mobile Data Management, 2001.
  19. B. Liang and Z. Haas, "Predictive distance-based mobility management for PCS networks," in Proceedings of the Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), New York, 1999.
  20. J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence, Ann Arbor, USA: University of Michigan Press, 1975.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithms Simulation MANET Energy Efficiency.