RFID is widely used to track the movement of goods through a supply chain. In this paper, we extend the domain of RFID by presenting SixthSense, a platform for RFID-based enterprise intelligence systems. We consider an enterprise setting where people (or rather their employee badges) and their personal objects such as books and mobiles are tagged with cheap, passive RFID tags, and there is good coverage of RFID readers in the workplace. SixthSense combines mobility information obtained from RFID-based sensing with information from enterprise systems such as calendar and presence, to automatically draw inferences about the association and interaction amongst people, objects, and workspaces. For instance, SixthSense is able to automatically distinguish between people and objects, learn the identities of people, and infer the ownership of objects by people.<\/p>\n
We characterize the performance of a state-of-the-art RFID system used in our testbed, present our inference algorithms, and evaluate these both in a small testbed and via simulations. We also present the SixthSense programming model that exposes a rich API to applications. To demonstrate the capabilities of the SixthSense platform, we present a few applications built using these APIs, including a misplaced object alert service, an enhanced calendar service, and rich annotation of video with physical events. We also discuss the issue of safeguarding user privacy in the context of SixthSense.<\/p>\n","protected":false},"excerpt":{"rendered":"
RFID is widely used to track the movement of goods through a supply chain. In this paper, we extend the domain of RFID by presenting SixthSense, a platform for RFID-based enterprise intelligence systems. We consider an enterprise setting where people (or rather their employee badges) and their personal objects such as books and mobiles are […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Association for Computing Machinery, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"ACM Mobisys","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and\/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. 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