This paper explores the possibility of turning the cellular radio modem into a passive body motion sensor. Sensing is herein based on the real-time analysis of cellular radio propagation. Unlike WiFi and Bluetooth, all cellular radios are never disconnected as, even in idle mode state, they perform continuous and autonomous measurements of the propagation conditions, namely the cellular signal quality (CSQ). CSQ is constantly updated while searching for any opportunity to reselect a new camped cell and being responsive to paging operations. Body movements in the surrounding of a cellular device are responsible of small but characteristic variations of the CSQ dynamics that might also trigger new reselections. Preliminary experiments are presented based on commercial off-the-shelf (COTS) smart-phone devices. The use of data analytics tools applied to cellular signals is a new topic that has the potential of opening up new research opportunities.

Is someone moving around my cell-phone? Tracing cellular signals for passive motion detection

Savazzi S;Kianoush S;Rampa V;
2017

Abstract

This paper explores the possibility of turning the cellular radio modem into a passive body motion sensor. Sensing is herein based on the real-time analysis of cellular radio propagation. Unlike WiFi and Bluetooth, all cellular radios are never disconnected as, even in idle mode state, they perform continuous and autonomous measurements of the propagation conditions, namely the cellular signal quality (CSQ). CSQ is constantly updated while searching for any opportunity to reselect a new camped cell and being responsive to paging operations. Body movements in the surrounding of a cellular device are responsible of small but characteristic variations of the CSQ dynamics that might also trigger new reselections. Preliminary experiments are presented based on commercial off-the-shelf (COTS) smart-phone devices. The use of data analytics tools applied to cellular signals is a new topic that has the potential of opening up new research opportunities.
2017
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni - IEIIT
cellular signal quality
machine learning
anomaly detection
occupancy detection
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/332518
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