Abstract
Accurate localization of nodes in a Wireless Sensor Network (WSN) is imperative for several important applications. The use of Global Positioning Systems (GPS) for localization is the natural approach in most domains. In WSN, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes’ deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of Received Signal Strength Indicator (RSSI) exist in literature but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a Machine Learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up.
This work was supported in part by the Ministry of Education, Government of India and the DeFries-Bajpai Foundation.
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References
Stoleru, R., He, T., Stankovic, J.A.: Range-free localization. In: Poovendran, R., Roy, S., Wang, C. (eds.) Secure Localization and Time Synchronization for Wireless Sensor and Ad Hoc Networks. Advances in Information Security, vol. 30, pp. 3–21. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-46276-9_1
Dil, B., Dulman, S., Havinga, P.: Range-based localization in mobile sensor networks. In: Römer, K., Karl, H., Mattern, F. (eds.) EWSN 2006. LNCS, vol. 3868, pp. 164–179. Springer, Heidelberg (2006). https://doi.org/10.1007/11669463_14
Zhou, Y., Li, J., Lamont, L.: Multilateration localization in the presence of anchor location uncertainties. In: 2012 IEEE Global Communications Conference (GLOBECOM), pp. 309–314 (2012)
Rappaport, T.S: Wireless Communications: Principles and Practice. Prentice Hall PTR, New Jersey (1996)
Breiman, Leo: Random forests. Mach. Learn. 45, 5–32 (2001)
Liaw, A., Wiener, M.: Classification and regression by Random Forest. R News 2, 18–22 (2002)
Jo, C., Lee, C.: Multilateration method based on the variance of estimated distance in range-free localisation. Electron. Lett. 52, 1078–1080 (2016)
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Agrawal, U., Srivastava, A. (2023). A Novel Hybrid Approach for Localization in Wireless Sensor Networks. In: Agapito, G., et al. Current Trends in Web Engineering. ICWE 2022. Communications in Computer and Information Science, vol 1668. Springer, Cham. https://doi.org/10.1007/978-3-031-25380-5_2
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DOI: https://doi.org/10.1007/978-3-031-25380-5_2
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