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Human Indoor Localization for AAL Applications: An RSSI Based Approach

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Ambient Assisted Living (ForItAAL 2016)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 426))

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Abstract

Ambient intelligence technologies have the objective to improve the quality of life of people in daily living, by providing user-oriented services and functionalities. Many of the services and functionalities provided in Ambient Assisted Living (AAL) require the user position and identity to be known, and thus user localization and identification are two prerequisites of utmost importance. In this work we focus our attention on human indoor localization. Our aim is to investigate how Received Signal Strength (RSS) based localization can be performed in an easy way by exploiting common Internet of Things (IoT) communication networks, which could easily integrate with custom networks for AAL purposes. We thus propose a plug and play solution where the Beacon Nodes (BNs) are represented by smart objects located in the house, while the Unknown Node (UN) can be any smart object held by the user. By using real data from different environments (i.e., with different disturbances), we provide a one-slope model and test localization performances of three different algorithms.

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Acknowledgements

This work was supported by the company Apio srl that provided the hardware components and the IoT objects. Thanks to the students Andrea Generosi, Andrea Marchetti and Giammarco Righi for their support during the experiments.

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche, 60131, Ancona, Italy

    L. Ciabattoni, F. Ferracuti, G. Ippoliti, S. Longhi, A. Monteriù & L. Pepa

  2. SMARTEST Research Centre, Università degli Studi eCampus, Via Isimbardi 10, 22060, Novedrate, Como, Italy

    A. Freddi

Authors
  1. L. Ciabattoni
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  2. F. Ferracuti
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  3. A. Freddi
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  4. G. Ippoliti
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  5. S. Longhi
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  6. A. Monteriù
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  7. L. Pepa
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Corresponding author

Correspondence to L. Ciabattoni .

Editor information

Editors and Affiliations

  1. Scuola Superiore Sant'Anna , Pontedera, Italy

    Filippo Cavallo

  2. DIEEI, Università degli Studi di Catania , Catania, Italy

    Vincenzo Marletta

  3. Università Politecnica delle Marche , Ancona, Italy

    Andrea Monteriù

  4. Campus Ekotecne, IMM-CNR, Lecce, Italy

    Pietro Siciliano

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Ciabattoni, L. et al. (2017). Human Indoor Localization for AAL Applications: An RSSI Based Approach. In: Cavallo, F., Marletta, V., Monteriù, A., Siciliano, P. (eds) Ambient Assisted Living. ForItAAL 2016. Lecture Notes in Electrical Engineering, vol 426. Springer, Cham. https://doi.org/10.1007/978-3-319-54283-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-54283-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54282-9

  • Online ISBN: 978-3-319-54283-6

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