Abstract
In wireless sensor networks (WSNs), prototyping systems facilitate the realization of real node deployment, enabling to test new algorithms, protocols, and networking solutions. This paper investigates the 3D indoor redeployment problem in WSNs by finding the positions where nodes should be added in order to improve an initial deployment while optimizing different objectives. For this purpose, an approach based on a recent evolutionary optimization algorithm (NSGA-III) is used. The latter algorithm is hybridized with a strategy of incorporating of the user preferences (PI-EMO-VF). The major contributions of this work are as follows: testing the NSGA-III efficiency in the case of real world problems, comparing it with another recent many-objective algorithm (MOEA/DD), and incorporating the concept of preferences of users into NSGA-III. The real experiments performed on our testbeds indicate that the results given by the proposed algorithm are better than those given by other recent optimization algorithms such as MOEA/DD.
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References
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014). doi:10.1109/TEVC.2013.2281535
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). doi:10.1109/4235.996017
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007). doi:10.1109/TEVC.2007.892759
Ishibuchi, H., Sakane, Y., Tsukamoto, N., Nojima, Y.: Adaptation of scalarizing functions in MOEA/D: an adaptive scalarizing function-based multiobjective evolutionary algorithm. In: Ehrgott, M., Fonseca, Carlos M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 438–452. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01020-0_35
Jaimes, A.L., Montaño, A.A., Coello, C.A.C.: Preference incorporation to solve many-objective airfoil design problems. In: IEEE Congress of Evolutionary Computation (CEC2011), pp. 1605–1612. New Orleans, LA (2011). doi:10.1109/CEC.2011.5949807
Deb, K., Sinha, A., Korhonen, P., Wallenius, J.: An interactive evolutionary multi-objective optimization method based on progressively approximated value functions. IEEE Trans. Evol. Comput. 14(5), 723–739 (2010). doi:10.1109/TEVC.2010.2064323
Mnasri, S., Nasri, N., Val, T.: An overview of the deployment paradigms in the wireless sensor networks. In: Proceedings International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN 2014), Tunisie, 04–07 November 2014
Li, K., Deb, K., Zhang, Q., Kwong, S.: An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans. Evol. Comput. 19(5), 694–716 (2015). doi:10.1109/TEVC.2014.2373386
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Mnasri, S., Van Den Bossche, A., Nasri, N., Val, T. (2017). The 3D Redeployment of Nodes in Wireless Sensor Networks with Real Testbed Prototyping. In: Puliafito, A., Bruneo, D., Distefano, S., Longo, F. (eds) Ad-hoc, Mobile, and Wireless Networks. ADHOC-NOW 2017. Lecture Notes in Computer Science(), vol 10517. Springer, Cham. https://doi.org/10.1007/978-3-319-67910-5_2
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DOI: https://doi.org/10.1007/978-3-319-67910-5_2
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