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Game Theoretic Approach to Optimize Exploration Parameter in ACO MANET Routing

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020 (AISI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1261))

Abstract

A Mobile Ad hoc Network (MANET) is a set of communicating mobile devices (nodes) without network infrastructure. Finding the optimum path between communicating nodes in MANETs is a challenging issue. This is due to the dynamic nature of nodes and the lack of a central routing authority in the network. The nature of the problem guided many researchers to follow the Ant Colony Optimization (ACO) approach because of the similarity between the two processes. In ACO, communication packets are simulated as real ants that come out of their nest and search for food. ACO protocols have a trade-off between exploring new routes vs. exploiting best routes followed by other ants. The two contradicting behaviors are tuned in the ACO model by a set of parameters. In this research, we introduce a novel approach to determine an online balance between exploration and exploitation of routes in ACO in MANET routing through game theory. This approach combines the benefit of online parameter tuning’s adaptability and -on the other hand- the low computational cost of game theory. Experimental results show higher performance for this approach than competitive algorithms.

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References

  1. Rosas, E., Hidalgo, N., Gil-Costa, V., Bonacic, C., Marin, M., Senger, H., Arantes, L., Marcondes, C., Marin, O.: Survey on simulation for mobile Ad-Hoc communication for disaster scenarios. J. Comput. Sci. Technol. 31(2), 326–349 (2016)

    Article  Google Scholar 

  2. Papakostas, D., Eshghi, S., Katsaros, D., Tassiulas, L.: Energy-aware backbone formation in military multilayer Ad Hoc networks. Ad Hoc Netw. 81, 17–44 (2018)

    Article  Google Scholar 

  3. Boukerche, A., Turgut, B., Aydin, N., Ahmad, M.Z., Bölöni, L., Turgut, D.: Routing protocols in Ad Hoc networks: a survey. Comput. Netw. 55(13), 3032–3080 (2011)

    Article  Google Scholar 

  4. Haque, I.T.: On the overheads of Ad Hoc routing schemes. IEEE Syst. J. 9(2), 605–614 (2015)

    Article  Google Scholar 

  5. Walikar, G.A., Biradar, R.C.: A survey on hybrid routing mechanisms in mobile Ad Hoc networks. J. Netw. Comput. Appl. 77, 48–63 (2017)

    Article  Google Scholar 

  6. Ducatelle, F., Di Caro, G., Gambardella, L.M.: Using ant agents to combine reactive and proactive strategies for routing in mobile Ad-Hoc networks. Int. J. Comput. Intell. Appl. 5(02), 169–184 (2005)

    Article  Google Scholar 

  7. Rathi, P.S., Mallikarjuna Rao, C.H.: Survey paper on routing in MANETs for optimal route selection based on routing protocol with particle swarm optimization and different ant colony optimization protocol. In: Smart Intelligent Computing and Applications, pp. 539–547. Springer, Singapore (2020)

    Chapter  Google Scholar 

  8. Stützle, T., López-Ibánez, M., Pellegrini, P., Maur, M., De Oca, M.M., Birattari, M., Dorigo, M.: Parameter adaptation in Ant colony optimization. In: Autonomous Search, pp. 191–215. Springer, Berlin (2011)

    Chapter  Google Scholar 

  9. Deepalakshmi, P., Radhakrishnan, S.: Online parameter tuning using particle swarm optimization for Ant-based Qos routing in mobile Ad-Hoc networks. Int. J. Hybrid Intell. Syst. 9(4), 171–183 (2012)

    Article  Google Scholar 

  10. Reina, D.G., Toral, S.L., Johnson, P., Barrero, F.: A survey on probabilistic broadcast schemes for wireless Ad Hoc networks. Ad Hoc Netw. 25, 263–292 (2015)

    Article  Google Scholar 

  11. Ducatelle, F., Di Caro, G.A., Gambardella, L.M.: An analysis of the different components of the anthocnet routing algorithm. In: International Workshop on Ant Colony Optimization and Swarm Intelligence, pp. 37–48. Springer (2006)

    Google Scholar 

  12. Sandhya, Goel, R.: Fuzzy based parameter adaptation in ACO for solving VRP. Int. J. Oper. Res. Inf. Syst. 10(2), 65–81 (2019)

    Article  Google Scholar 

  13. Kusyk, J., Sahin, C.S., Zou, J., Gundry, S., Uyar, M.U., Urrea, E.: Game theoretic and bio-inspired optimization approach for autonomous movement of MANET nodes. In: Zelinka, I., Snášel, V., Abraham, A. (eds.) Handbook of Optimization. Intelligent Systems Reference Library, pp. 129–155. Springer, Berlin (2013)

    Google Scholar 

  14. Quy, V.K., Ban, N.T., Nam, V.H., Tuan, D.M., Han, N.D.: Survey of recent routing metrics and protocols for mobile Ad-Hoc networks. J. Commun. 14(2), 110–120 (2019)

    Article  Google Scholar 

  15. . The Network Simulator - ns2. https://www.isi.edu/nsnam/ns/

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Correspondence to Marwan A. Hefnawy .

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Hefnawy, M.A., Darwish, S.M. (2021). Game Theoretic Approach to Optimize Exploration Parameter in ACO MANET Routing. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_42

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