The plummeting cost of Bluetooth tags and the ubiquity of mobile devices are revolutionizing the traditional lost-and-found service. This paper presents SecureFind, a secure and privacy-preserving object-finding system via mobile crowdsourcing. In SecureFind, a unique Bluetooth tag is attached to every valuable object, and the owner of a lost object submits an object-finding request to many mobile users via the SecureFind service provider. Each mobile user involved searches his vicinity for the lost object on behalf of the object owner who can infer the location of his lost object based on the responses from mobile users. SecureFind is designed to ensure strong object security such that only the object owner can discover the location of his lost object as well as offering strong location privacy to mobile users involved. The high efficacy and efficiency of SecureFind are confirmed by extensive simulations.
Several schemes have been proposed for tracking and locating lost objects. AutoWitness @cite_33 is a personal asset tracking system that uses an embedded tag with inertial sensor to estimate asset's position change and proactively transmit trajectory data to an external server via cellular link to facilitate asset retrieval. In contrast, SecureFind depends on low-cost Bluetooth tags without any inertial sensor or cellular communication capabilities, thus more suitable for wide adoption. Moreover, Sherlock @cite_23 is a system designed to localize objects with embedded RFID tags in some closed space, which cannot be applied to find lost object in outdoor and is thus orthogonal to SecureFind.
@cite_33 @cite_23
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We present AutoWitness, a system to deter, detect, and track personal property theft, improve historically dismal stolen property recovery rates, and disrupt stolen property distribution networks. A property owner embeds a small tag inside the asset to be protected, where the tag lies dormant until it detects vehicular movement. Once moved, the tag uses inertial sensor-based dead reckoning to estimate position changes, but to reduce integration errors, the relative position is reset whenever the sensors indicate the vehicle has stopped. The sequence of movements, stops, and turns are logged in compact form and eventually transferred to a server using a cellular modem after both sufficient time has passed (to avoid detection) and RF power is detectable (hinting cellular access may be available). Eventually, the trajectory data are sent to a server which attempts to match a path to the observations. The algorithm uses a Hidden Markov Model of city streets and Viterbi decoding to estimate the most likely path. The proposed design leverages low-power radios and inertial sensors, is immune to intransit cloaking, and supports post hoc path reconstruction. Our prototype demonstrates technical viability of the design; the volume market forces driving machine-to-machine communications will soon make the design economically viable. Over the course of a day a human interacts with tens or hundreds of individual objects. Many of these articles are nomadic, relying on human memory to manually index, inventory, organize, search, and locate them. However, Radio Frequency Identification (RFID) tags hold great promise for automating these tasks. While originally envisioned for managing supply chains and store inventories, RFID tags support the properties necessary for helping humans to manage their objects. This paper presents Sherlock, a system that leverages RFID tags for human-object interaction. Sherlock combines concepts from sensors, radar technology, and computer graphics to implement a novel localization and visualization system for everyday objects. At the heart of Sherlock is a new RFID localization technique that uses steerable antennas to sweep a room, discovering, localizing and indexing tagged objects. In response to user queries, Sherlock displays the locations of matching objects using images from a video camera. We have implemented a prototype of Sherlock to conduct experiments in a real office environment. Our results demonstrate the effectiveness of Sherlock in localizing to a volume of less than 0.55 cubic meters for 90 of objects.