Objects Calling Home: Locating Objects Using Mobile Phones

Locating physical items is a highly relevant application addressed by numerous systems. Many of these systems share the drawback that costly infrastructure must be installed before a significant physical area can be covered, that is, before these systems may be used in practice. In this paper, we build on the ubiquitous infrastructure provided by the mobile phone network to design a wide-area system for locating objects. Sensor-equipped mobile phones, naturally omnipresent in populated environments, are the main elements of our system. They are used to distribute search queries and to report an object's location. We present the design of our object search system together with a set of simple heuristics which can be used for efficient object search. Moreover, such a system can only be successfully deployed if environment conditions (such as the participant density and their mobility) and system settings (such as number of queried sensors) allow to find an object quickly and efficiently. We therefore demonstrate the practicability of our system and obtain suitable system parameters for its execution in a series of simulations. Further, we use a real-world experiment to validate the obtained simulation results.

[1]  Kaoru Sezaki,et al.  ASSOCIATION MANAGEMENT BETWEEN EVERYDAY OBJECTS AND PERSONAL DEVICES FOR PASSENGERS IN URBAN AREAS , 2005 .

[2]  Christian Damsgaard Jensen,et al.  Zero-knowledge Device Authentication: Privacy & Security Enhanced RFID preserving Business Value and Consumer Convenience , 2004, PST.

[3]  Wolfgang Kellerer,et al.  Query Scoping for the Sensor Internet , 2006, 2006 ACS/IEEE International Conference on Pervasive Services.

[4]  Vikram Srinivasan,et al.  MAX: human-centric search of the physical world , 2005, SenSys '05.

[5]  David Kotz,et al.  Extracting a Mobility Model from Real User Traces , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[6]  Uwe Hansmann,et al.  Pervasive Computing , 2003 .

[7]  Gaetano Borriello,et al.  Reminding About Tagged Objects Using Passive RFIDs , 2004, UbiComp.

[8]  Roy Want,et al.  Bridging physical and virtual worlds with electronic tags , 1999, CHI '99.

[9]  Mika Raento,et al.  Adaptive On-Device Location Recognition , 2004, Pervasive.

[10]  Nigel Davies,et al.  UbiComp 2004: Ubiquitous Computing , 2004, Lecture Notes in Computer Science.

[11]  Er Erik Fledderus,et al.  IST-Momentum project public deliverable 1.4: Final report on traffic estimation and services characterisation , 2003 .

[12]  Srinivasan Seshan,et al.  IrisNet: An Architecture for a Worldwide Sensor Web , 2003, IEEE Pervasive Comput..

[13]  Jean-Yves Le Boudec,et al.  Perfect simulation and stationarity of a class of mobility models , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[14]  Dirk Trossen,et al.  Building a ubiquitous platform for remote sensing using smartphones , 2005, The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services.

[15]  Michael Beigl,et al.  Revealing the retail black box by interaction sensing , 2003, 23rd International Conference on Distributed Computing Systems Workshops, 2003. Proceedings..

[16]  Gregory D. Abowd,et al.  Social Disclosure of Place: From Location Technology to Communication Practices , 2005, Pervasive.

[17]  Margo I. Seltzer,et al.  Network-Aware Operator Placement for Stream-Processing Systems , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[18]  Marc Langheinrich,et al.  Distributed Persistence for Limited Devices , 2006 .