A robust wireless proximity detection technique based on RSS and ToF measurements

Proximity sensors are increasingly used in distributed monitoring applications, e.g. for surveillance purposes. However, most of existing solutions have two different, usually contrasting requirements, i.e. either they have strong directional features, or they just cover very short distances. In order to tackle such issues, in this paper a new, almost omnidirectional proximity detection technique is described. The proposed approach is explicitly conceived for wearable wireless sensor networks (WSN) nodes and it relies on the fusion of time-of-flight (ToF) and received signal strength (RSS) values. Even though it is not accurate enough for fine-grained indoor localization, the proposed solution is robust in detecting when a given threshold is crossed by a moving object. Also, it is flexible, light from the computational point of view and easy to implement using commercial off-the-shelf (COTS) components.

[1]  M. Malajner,et al.  Using RSSI value for distance estimation in wireless sensor networks based on ZigBee , 2008, 2008 15th International Conference on Systems, Signals and Image Processing.

[2]  G Santinelli,et al.  Self-calibrating indoor positioning system based on ZigBee® devices , 2009, 2009 IEEE Instrumentation and Measurement Technology Conference.

[3]  Andreas Savvides,et al.  An Empirical Characterization of Radio Signal Strength Variability in 3-D IEEE 802.15.4 Networks Using Monopole Antennas , 2006, EWSN.

[4]  Christof Röhrig,et al.  Localization of Sensor Nodes in a Wireless Sensor Network Using the nanoLOC TRX Transceiver , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[5]  Richard P. Martin,et al.  The limits of localization using signal strength: a comparative study , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[6]  Ismail Güvenç,et al.  A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques , 2009, IEEE Communications Surveys & Tutorials.

[7]  David E. Culler,et al.  A practical evaluation of radio signal strength for ranging-based localization , 2007, MOCO.

[8]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[9]  Mani B. Srivastava,et al.  The bits and flops of the n-hop multilateration primitive for node localization problems , 2002, WSNA '02.

[10]  Alessio De Angelis,et al.  A Low-Cost Ultra-Wideband Indoor Ranging System , 2009, IEEE Transactions on Instrumentation and Measurement.

[11]  Andreas F. Molisch,et al.  UWB Systems for Wireless Sensor Networks , 2009, Proceedings of the IEEE.

[12]  Fotini-Niovi Pavlidou,et al.  An overview of the IEEE 802.15.4a Standard , 2010, IEEE Communications Magazine.

[13]  Andrea Conti,et al.  Experimental Results on Indoor Localization Techniques through Wireless Sensors Network , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[14]  Dario Petri,et al.  Experimental assessment of a RSS-based localization algorithm in indoor environment , 2010, 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings.

[15]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .