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Source localization in resource-constrained sensor networks based on deep learning

Neural Computing and Applications volume 33, pages 4217–4228 (2021)Cite this article

Abstract

Source localization with a network of low-cost motes with limited processing, memory, and energy resources is considered in this paper. The state-of-the-art methods are mostly based on complicated signal processing approaches in which motes send their (processed) data to a fusion center (FC) wherein the source is localized. These methods are resource-demanding and mostly do not meet the limitations of motes and network. In this paper, we consider distributed detection where each mote performs a binary hypothesis test to detect locally the existence of a desired source and sends its (potentially erroneous) decision to FC during just one bit (1 indicates source existence and 0 otherwise). Hence, both processing and bandwidth constraints are met. We propose to use an artificial neural network (ANN) to correct erroneous local decisions. After error correction, the region affected by the source is specified by nodes with decision 1. Moreover, we propose to localize the source by deep learning in FC which converts the network of decisions 1 and 0 to a black and white image with white pixels in the locations of motes with decision 1. The proposed schemes of error correction by ANN (ECANN) and source localization with deep learning (SoLDeL) were evaluated in a fire detection application. We showed that SoLDeL performs appropriately and scales well into large networks. Moreover, the applicability of ECANN in delineation of farm management zones was illustrated.

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Notes

  1. Hereinafter, motes are referred to as nodes as it is more common in the literature of WSN.

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Acknowledgements

Authors acknowledge the financial support received from the European Commission for SIEUSOIL project (No. 818346).

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Authors and Affiliations

  1. Department of Environment, Faculty of Bioscience Engineering, Ghent University, 9000, Gent, Belgium

    S. Hamed Javadi, Angela Guerrero & Abdul M. Mouazen

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  1. S. Hamed Javadi
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  2. Angela Guerrero
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  3. Abdul M. Mouazen
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Correspondence to S. Hamed Javadi.

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Javadi, S.H., Guerrero, A. & Mouazen, A.M. Source localization in resource-constrained sensor networks based on deep learning. Neural Comput & Applic 33, 4217–4228 (2021). https://doi.org/10.1007/s00521-020-05253-3

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