Abstract
This paper presents elephant herding optimization algorithm (EHO) adopted for solving localization problems in wireless sensor networks. EHO is a relatively new swarm intelligence metaheuristic that obtains promising results when dealing with NP hard problems. Node localization problem in wireless sensor networks, that belongs to the group of NP hard optimization, represents one of the most significant challenges in this domain. The goal of node localization is to set geographical co-ordinates for each sensor node with unknown position that is randomly deployed in the monitoring area. Node localization is required to report the origin of events, assist group querying of sensors, routing and network coverage. The implementation of the EHO algorithm for node localization problem was not found in the literature. In the experimental section of this paper, we show comparative analysis with other state-of-the-art algorithms tested on the same problem instance.
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After the 2004 Indian Ocean tsunami when 228000 people lost their lives, the problem of early detection of tsunamis using underwater sensor networks to detect the generation of a tsunami wave has received much attention [16].
References
Akyildiz, I.F., Vuran, M.: Wireless sensor networks. No. 978-0-470-03601-3, Wiley (2010)
Rawat, P., Singh, K.D., Chaouchi, H., Bonnin, J.M.: Wireless sensor networks: a survey on recent developments and potential synergies. J. Supercomput. 68, 1–48 (2014)
Goyal, S., Patterh, M.S.: Wireless sensor network localization based on cuckoo search algorithm. Wireless Pers. Commun. 79, 223–234 (2014)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)
Wang, G.-G., Deb, S., dos Santos Coelho, L.: Elephant herding optimization. In: Proceedings of the 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), pp. 1–5, December 2015
Wang, G.-G., Deb, S., Gao, X.-Z., dos Santos Coelho, L.: A new metaheuristic optimization algorithm motivated by elephant herding behaviour. Int. J. Bio-Inspired Comput. 8, 394–409 (2017)
Tuba, V., Beko, M., Tuba, M.: Performance of elephant herding optimization algorithm on CEC 2013 real parameter single objective optimization. WSEAS Trans. Syst. 16, 100–105 (2017)
Tuba, E., Stanimirovic, Z.: Elephant herding optimization algorithm for support vector machine parameters tuning. In: Proceedings of the 2017 International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–5, June 2017
Tuba, E., Alihodzic, A., Tuba, M.: Multilevel image thresholding using elephant herding optimization algorithm. In: Proceedings of 14th International Conference on the Engineering of Modern Electric Systems (EMES), pp. 240–243, June 2017
Gupta, S., Singh, V.P., Singh, S.P., Prakash, T., Rathore, N.S.: Elephant herding optimization based PID controller tuning. Int. J. Adv. Technol. Eng. Explor. 3, 194–198 (2016)
Strumberger, I., Bacanin, N., Beko, M., Tomic, S., Tuba, M.: Static drone placement by elephant herding optimization algorithm. In: Proceedings of the 24th Telecommunications Forum (TELFOR), November 2017
Sun, Z., Tao, L., Wang, X., Zhou, Z.: Localization algorithm in wireless sensor networks based on multiobjective particle swarm optimization. Int. J. Distrib. Sens. Netw. 2015, 1–9 (2015)
Harikrishnan, R., Kumar, J.S., Ponmalar, P.S.: A comparative analysis of intelligent algorithms for localization in wireless sensor networks. Wireless Pers. Commun. 87, 1057–1069 (2016)
Goyal, S., Patterh, M.S.: Modified bat algorithm for localization of wireless sensor network. Wireless Pers. Commun. 86, 657–670 (2015)
Tuba, E., Tuba, M., Beko, M.: Node localization in ad hoc wireless sensor networks using fireworks algorithm. In: Proceedings of the 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 223–229, September 2016
Comfort, L.K., Boin, A., Demchak, C.C.: Designing Resilience: Preparing for Extreme Events. University of Pittsburgh Press, Pittsburgh (2010)
Barsocchi, P., Chessa, S., Furfari, F., Potorti, F.: Evaluating aal solutions through competitive benchmarking: the localization competition. IEEE Pervasive Comput. Mag. 12, 72–79 (2013)
Tomic, S., Beko, M., Dinis, R., Montezuma, P.: Distributed algorithm for target localization in wireless sensor networks using RSS and AoA measurements. Pervasive Mobile Comput. 37, 63–77 (2017)
Lavanya, D., Udgata, Siba K.: Swarm intelligence based localization in wireless sensor networks. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds.) MIWAI 2011. LNCS (LNAI), vol. 7080, pp. 317–328. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25725-4_28
Liu, Y., Yang, Z., Wang, X., Jian, L.: Location, localization, and localizability. J. Comput. Sci. Technol. 25(2), 274–297 (2010)
Sukumar, R.: The Asian Elephant: Ecology and Management. Cambridge University Press, Cambridge Studies in Applied Ecology and Resource Management (1993)
Acknowledgements
This research is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006. The work of M. Beko was supported in part by Fundação para a Ciência e a Tecnologia under Projects PEst-OE/EEI/UI0066/2014 (UNINOVA) and Program Investigador FCT (IF/00325/2015).
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Strumberger, I., Beko, M., Tuba, M., Minovic, M., Bacanin, N. (2018). Elephant Herding Optimization Algorithm for Wireless Sensor Network Localization Problem. In: Camarinha-Matos, L., Adu-Kankam, K., Julashokri, M. (eds) Technological Innovation for Resilient Systems. DoCEIS 2018. IFIP Advances in Information and Communication Technology, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-78574-5_17
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