Abstract
Received Signal Strength Indicator (RSSI) is commonly considered and is very popular for target localization applications, since it does not require extra-circuitry and is always available on current devices. Unfortunately, target localizations based on RSSI are affected with many issues, above all in indoor environments. In this paper, we focus on the pervasive localization of target objects in an unknown environment. In order to accomplish the localization task, we implement an Associative Search Network (ASN) on the robots and we deploy a real test-bed to evaluate the effectiveness of the ASN for target localization. The ASN is based on the computation of weights, to ”dictate” the correct direction of movement, closer to the target. Results show that RSSI through an ASN is effective to localize a target, since there is an implicit mechanism of correction, deriving from the learning ASN approach.
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References
Vempaty, A., Han, Y.S., Varshney, P.K.: Target Localization in Wireless Sensor Networks using Error Correcting Codes. IEEE Trans. Inf. Theory 60(1), 697–712 (2014)
Barto, A.G., Sutton, R.S., Brouwer, P.S.: Associative Search Network: A Reinforcement Learning Associative Memory. Biol. Cybern. 40, 201–211 (1981)
Shaw, J.A.: Radiometry and the Friis transmission equation. Am. J. Phys. 33(81) (2013)
Minsky, M.L., Papert, S.: Perceptron: an introduction to computational geometry. MIT Press, Cambridge (1969)
Hyytiä, E., Virtamo, J.: Random waypoint model in n-dimensional space. Operations Research Letters (2005)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Acad. Sci. 79, 2554–2558 (1982)
Priwgharm, R., Srivilas, K., Cherntanomwong, P.: Indoor localization system using RSSI measurement in wireless sensor network based on ZigBee standard. In: NCIT (2010)
Deshpande, N., Grant, E., Henderson, T.C.: Target localization in unknown environments using static wireless sensors and mobile robots. In: IEEE MFI, Salt Lake City (2010)
Bota, M., Guazzelli, A.: ”The Associative Search Network: Landmark Learning and Hill Climbing. The Neural Simulation Language A System for Brain Modeling. MIT Press, Cambridge (2002)
Heurtefeux, K., Valois, F.: Is RSSI a good choice for localization in wireless sensor network? In: IEEE AINA, Fukuoka, Japan (2012)
Zhang, X., Li, S., Lin, Z., Wang, H.: Range based target localization using a single mobile robot or multiple cooperative mobile robots. In: Proc. ICCA, June 2013
Dames, P., Kumar, V.: Cooperative multi-target localization with noisy sensors. In: IEEE ICRA (2013)
Zhou, N., Zhao, X., Tan, M.: RSSI-based mobile robot navigation in grid-pattern wireless sensor network. In: CAC (2013)
Gang, L., Wei, Z., Shiyi, M., Shuqin, S.: Sensor selection for target tracking in a wireless sensor network of bearing-only sensor. Inter. J. of Digital Content Technology and its Applications 6-1, 41–48 (2012)
Hamdoun, S., Rachedi, A., Benslimane, A.: RSSI-based localization algorithms using spatial diversity in wireless sensor networks. In: Ad Hoc and Ubiquitous Computing (IJAHUC), January 2014
Zanca, G., Zorzi, F., Zanella, A., Zorzi, M.: Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks. In: REALWSN, Glasgow, UK (2008)
Loscrí, V.: Performance evaluation of novel distributed coverage techniques for swarms of flying robots. In: IEEE WCNC, Istanbul, Turkey (2014)
Tsetlin, M.L.: Automaton Theory and modeling of biological systems. Academic Press, New York (1973)
Narendra, K.S., Thatachar, M.A.L.: Learning Automaton - A Survey. IEEE Trans. Syst. Man Cybern. 4, 323–334 (1972)
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© 2015 Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Loscrí, V., Bonifacio, S.G., Mitton, N., Fiorenza, S. (2015). Associative Search Network for RSSI-Based Target Localization in Unknown Environments. In: Mitton, N., Kantarci, M., Gallais, A., Papavassiliou, S. (eds) Ad Hoc Networks. ADHOCNETS 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 155. Springer, Cham. https://doi.org/10.1007/978-3-319-25067-0_12
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DOI: https://doi.org/10.1007/978-3-319-25067-0_12
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