Abstract

GPS (Global Positioning System) and satellite systems have been in use for wide-area location sensing for a decade. GPS receivers convert satellite signals into position, velocity, and time estimates, to assist people in navigation, positioning and time dissemination. RFID (Radio Frequency Identification Device) refers to technologies and systems that use radio waves to transmit and uniquely identify objects (Finkenzeller, 2002; Heijden, 2006). It generally involves RFID tag, which comprises a chip and an antenna that are together attached to an object that is to be identified or tracked. The antenna is used to send and receive radio waves and signals between tag readers and RFID tags. The tag reader is connected to a back-end server and database for relatively heavy-duty processing. RFID technology is increasingly considered by organizations worldwide to coordinate their supply chains (Tewary, Kosalge and Motwani, 2008). It is also used in retailing industry for shelf management and consumer assistance (Thiesse, Fleisch, Sorensen and Tellkamp, 2007; Stahl and Freudenschuss, 2006; Loebbecke, Fujita and Huyskens, 2007). With GPS and RFID technologies, object movement tracking and herein knowledge discovery of the collected movement data have drawn increasing attention. Various location-based contextual services (LBS) are being proposed and discussed. For example, upon identifying travel patterns of a person, real-time traffic report, or personalized advertising directed along the travel route and even location-based games can be delivered to the person (Dey, 2001). Abnormal activity detection enabled by moving object tracking for identifying suspicious or anomalous moving objects finds applications in supply chain management, homeland security (Li, Han, Kim and Gonzalez, 2007), and senior home health care (Li, Fox and Kautz, 2007). For instance, abnormality detection for RFID-enabled shipment tracking is used to prevent problems related to inefficient shipment or fraudulent actions (Masciari, 2007). Very recently, Yan et al. proposed a shopping trip clustering scheme for instore real-time customer segmentation (Yan and Zeng, 2008). Many online social networking applications are now locationaware. They may either require users’ own input or acquire users’ location sensor readings automatically. Google Latitude lets smart phone and laptop users share their locations with (www.google.com/latitude). GPS readings can be automatically updated directly from an in-car GPS navigator onto an online service Fire Eagle by Yahoo, allowing users to broadcast their instant location information over the web (fireeagle.yahoo.net). With other mashed-up web applications, users can get messages about travel information, nearest business and other interesting places, or social network with friends by sharing and learning their locations in real-time.

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