Abstract
In many applications of Swarm Robotic Systems (SRS) or Wireless Sensor Networks (WSN), it is necessary to know the position of its devices. A straightforward solution should be endowing each device, i.e. a robot or a sensor, with a Global Positioning System (GPS) receiver. However, this solution is often not feasible due to hardware limitations of the devices, or even environmental constraints present in its operational area. In the search for alternatives to the GPS, some multi-hop localization methods have been proposed. In this paper, it is proposed a novel multi-hop localization method based on Tribes algorithm. The results, obtained through simulations, shows that the algorithm is effective in solving the localization problem, achieving errors of the order of 0.01 m.u. in an area of \(100 \times 100\) m.u.. The performance of the algorithm was also compared with a previous PSO-based localization algorithm. The results indicate that the proposed algorithm obtained a better performance than the PSO-based, in terms of errors in the estimated positions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rubenstein, M.: Self-assembly and self-healing for robotic collectives. Thesis, University of Southern California, California, USA (2009)
Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) Swarm Robotics 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005)
Rodic, A., Katie, D., Mester, G.: Ambient intelligent robot-sensor networks for environmental surveillance and remote sensing. In: 7th International Symposium on IEEE Intelligent Systems and Informatics, SISY 2009, Subotica, Srvia, pp. 39–44 (2009)
Nawaz, S., Hussain, M., Watson, S., Trigoni, N., Green, P.N.: An underwater robotic network for monitoring nuclear waste storage pools. In: Hailes, S., Sicari, S., Roussos, G. (eds.) S-CUBE 2009. LNICST, vol. 24, pp. 236–255. Springer, Heidelberg (2010)
Akkaya, K., Younis, M.: A survey on routing protocols for wireless sensor networks. Ad Hoc Netw. 3(3), 325–349 (2005). Elsevier
Rrodolpu, V., Meng, T.H.: Minimum energy mobile wireless networks. IEEE J. Sel. Areas Commun. 17(8), 1333–1344 (1999)
Li, L., Halpern, J.Y.: Minimum-energy mobile wireless networks revisited. In: IEEE International Conference on Communications, ICC 2001, Helsinki, Finlândia, vol. 1, pp. 278–283. IEEE (2001)
Xu, Y., Heideman, J., Estrin, D.: Geography-informed energy conservation for ad hoc routing. In: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, Rome, Italy, pp. 70–84. ACM (2001)
Tu, Y., Govindan, R., Estrin, D.: Geographical and energy aware routing: a recursive data dissemination protocol for wireless sensor networks, Los Angeles, CA, USA (2001)
de Sá, A.O., Nedjah, N., de Macedo Mourelle, L.: Genetic and backtracking search optimization algorithms applied to localization problems. In: Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2014, Part V. LNCS, vol. 8583, pp. 738–746. Springer, Heidelberg (2014)
de Sá, A.O., Nedjah, N., de Macedo Mourelle, L.: Distributed efficient localization in swarm robotic systems using swarm intelligence algorithms. Neurocomputing, Elsevier, p. aceito para publicao (2015)
de Sá, A.O., Nedjah, N., de Macedo Mourelle, L.: Distributed efficient localization in swarm robotics using min-max and particle swarmoptimization. In: Proceedings of The Latin American Congress on Computational Intelligence, LA-CCI, San Carlos de Bariloche, Argentina, vol. 1, pp. 7–12 (2014)
Clerc, M.: TRIBES-un exemple d’optimisation par essaim particulaire sans parametres de contrle. Optimisation par Essaim Particulaire (OEP 2003), Paris, France 64 (2003)
Clerc, M.: Stagnation analysis in particle swarm optimization or what happens when nothing happens. Department of Computer Science, University Essex, Technical report CSM-460 (2006)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of 1995 IEEE International Conference on Neural Networks, Perth, WA, USA, vol. 4, pp. 1942–1948. IEEE (1995)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, Anchorage, AK, USA, pp. 69–73. IEEE (1998)
Savvides, A., Park, H., Srivastava, M.B.: The bits and flops of the n-hop multilateration primitive for node localization problems. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, GA, USA, pp. 112–121. ACM (2002)
Sun, W., Su, X.: Wireless sensor network node localization based on genetic algorithm. In: 3rd International Conference on Communication Software and Networks, pp. 316–319. IEEE (2011)
Ekberg, P.: Swarm-intelligent localization. Thesis, Uppsala Universitet, Uppsala, Sweden (2009)
Langendoen, K., Reijers, N.: Distributed localization algorithms. In: Zurawski, R. (ed.) Embedded Systems Handbook, pp. 1–23. CRC Press, Boca Raton (2005)
Huanxiang, J., Yong, W., Xiaoling, T.: Localization algorithm for mobile anchor node based on genetic algorithm in wireless sensor network. In: International Conference on Intelligent Computing and Integrated Systems, pp. 40–44. IEEE (2010)
Ekberg, P., Ngai, E.C.: A distributed swarm-intelligent localization for sensor networks with mobile nodes. In: 7th International Wireless Communications and Mobile Computing Conference, pp. 83–88 (2011)
Acknowledgement
We thank the State of Rio de Janeiro (FAPERJ, http://www.faperj.br) for funding this study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
de Sá, A.O., Nedjah, N., de Macedo Mourelle, L., dos Santos Coelho, L. (2016). Multi-hop Localization Method Based on Tribes Algorithm. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9790. Springer, Cham. https://doi.org/10.1007/978-3-319-42092-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-42092-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42091-2
Online ISBN: 978-3-319-42092-9
eBook Packages: Computer ScienceComputer Science (R0)