Uma Abordagem para Seleção de Rotas em Redes de Sensores Sem Fio Utilizando Sistemas Fuzzy Genéticos
Resumo
Redes de Sensores Sem Fio (RSSFs) são compostas por um conjunto de nós sensores com o objetivo de detectar e transmitir alguma característica do meio físico. Estes nós sensores, depois de captar algum evento, devem se comunicar com um nó especial, denominado sink node. A abordagem proposta por este trabalho apresenta a aplicação de Sistemas Fuzzy Genéticos (SFGs) para a seleção de rotas em RSSFs, de modo a realizar a comunicação entre múltiplos nós sensores e múltiplos sink nodes. Um Sistema de Inferência Fuzzy de Mamdani é utilizado para estimar o sink node mais adequado para a comunicação em um determinado instante, baseado em algumas características da rede, como a energia e o número de saltos. Algoritmos Genéticos (AGs) são utilizados para ajustar os parâmetros de projeto do sistema de inferência fuzzy de Mamdani. A abordagem proposta para seleção de rotas foi aplicada, por meio de simulações computacionais, para demonstrar a viabilidade da abordagem implementada. Os resultados obtidos apresentam uma rede de sensores com maior tempo de vida, por meio da escolha adequada do sink node utilizado para o envio de pacotes, de forma a encontrar as melhores rotas.Referências
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Homaifar, A. and McCormick, E. (1995). Simultaneous Design of Membership Functions and Rule Sets for Fuzzy Controllers using Genetic Algorithms. IEEE Transactions on Fuzzy Systems, 3(2):129–139.
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Park, D., Kandel, A., and Langholz, G. (1994). Genetic-based New Fuzzy Reasoning Models with Application to Fuzzy Control. IEEE Transactions on Systems, Man and Cybernetics, 24(1):39–47.
Pedrycz, W. and Gomide, F. (1998). An Introduction to Fuzzy Sets. MIT Press Cambridge, MA.
Shi, Y., Eberhart, R., and Chen, Y. (1999). Implementation of Evolutionary Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 7(2):109–119.
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Srikanth, T. and kamala, V. (2008). A real coded genetic algorithm for optimization of cutting parameters in turning. IJCSNS International Journal of Computer Science and Network Security, 8(6):189 – 193.
Xu, N. (2002). A survey of sensor network applications. IEEE Communications Magazine, 40.
Yeh, L., Wang, Y., and Tseng, Y. (2009). ipower: an energy conservation system for intelligent buildings by wireless sensor networks. International Journal of Sensor Networks, 5:1–10.
Zadeh, L. (1973). Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics, 3(1):28–44.
Cordón, O., Herrera, F., and Villar, P. (2000). Analysis and Guidelines to Obtain a Good Uniform Fuzzy Partition Granularity for Fuzzy Rule-Based Systems Using Simulated Annealing*. International Journal of Approximate Reasoning, 25(3):187–215.
Cordón, O., Herrera, F., and Villar, P. (2001). Generating the Knowledge Base of a Fuzzy Rule-Based System by the Genetic Learning of the Data Base. IEEE Transactions on Fuzzy Systems, 9(4):667–674.
García-Hernández, C. F., Ibargüengoytia-González, P. H., García-Hernández, J., and Pérez-Díaz, J. A. (2007). Wireless sensor networks and applications : a survey. Journal of Computer Science, 7(3):264–273.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA.
Haupt, R. L. and Haupt, S. E. (1998). Practical Genetic Algorithms. Wiley New York.
Herrera, F. (2008). Genetic Fuzzy Systems: Taxonomy, Current Research Trends and Prospects. Evolutionary Intelligence, 1(1):27–46.
Herrera, F., Lozano, M., and Snchez, A. M. (2003). A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. International Journal of Intelligent Systems, 18(3):309–338.
Herrera, F., Lozano, M., and Snchez, A. M. (2005). Hybrid crossover operators for real-coded genetic algorithms: an experimental study. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 9(4):280 – 298.
Herrera, F., Lozano, M., and Verdegay, J. (1998). Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis. Artificial Intelligence Review, 12(4):265–319.
Hill, J., Szewczyk, R., Woo, A., Hollar, S., Culler, D., and Pister, K. (2000). System architecture directions for networked sensors. SIGPLAN Not., 35(11):93–104.
Hinterding, R., Gielewski, H., and Peachey, T. (1995). The Nature of Mutation in Genetic Algorithms. In Proceedings of the Sixth International Conference on Genetic Algorithms, pages 65–72. Citeseer.
Hoffmann, F. (2001). Evolutionary Algorithms for Fuzzy Control System Design. Proceedings of the IEEE, 89(9):1318–1333.
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: The University of Michigan Press.
Homaifar, A. and McCormick, E. (1995). Simultaneous Design of Membership Functions and Rule Sets for Fuzzy Controllers using Genetic Algorithms. IEEE Transactions on Fuzzy Systems, 3(2):129–139.
Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., and Silva, F. (2003). Directed diffusion for wireless sensor networking. volume 11, pages 2–16, Piscataway, NJ, USA. IEEE Press.
Mainwaring, A., Culler, D., Polastre, J., Szewczyk, R., and Anderson, J. (2002). Wireless sensor networks for habitat monitoring. In WSNA ’02: Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, pages 88–97, New York, NY, USA. ACM.
Mamdani, E. H. (1974). Application of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proceedings of IEE Control and Science, 121(12):1585–1588.
Michalewicz, Z. (2011). Genetic Algorithms + Data Structures = Evolution Programs. Springer.
Park, D., Kandel, A., and Langholz, G. (1994). Genetic-based New Fuzzy Reasoning Models with Application to Fuzzy Control. IEEE Transactions on Systems, Man and Cybernetics, 24(1):39–47.
Pedrycz, W. and Gomide, F. (1998). An Introduction to Fuzzy Sets. MIT Press Cambridge, MA.
Shi, Y., Eberhart, R., and Chen, Y. (1999). Implementation of Evolutionary Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 7(2):109–119.
sinalgo (2010). [link].
Srikanth, T. and kamala, V. (2008). A real coded genetic algorithm for optimization of cutting parameters in turning. IJCSNS International Journal of Computer Science and Network Security, 8(6):189 – 193.
Xu, N. (2002). A survey of sensor network applications. IEEE Communications Magazine, 40.
Yeh, L., Wang, Y., and Tseng, Y. (2009). ipower: an energy conservation system for intelligent buildings by wireless sensor networks. International Journal of Sensor Networks, 5:1–10.
Zadeh, L. (1973). Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics, 3(1):28–44.
Publicado
19/07/2011
Como Citar
LEAL, Líliam Barroso; LEMOS, Marcus Vinícius de S.; HOLANDAFILHO, Raimir; BORGES, Fábbio Anderson da Silva; RABÊLO, Ricardo de Andrade Lira.
Uma Abordagem para Seleção de Rotas em Redes de Sensores Sem Fio Utilizando Sistemas Fuzzy Genéticos. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 8. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 773-784.
ISSN 2763-9061.
