Objects Calling Home: Locating Objects Using Mobile Phones

Abstract

Abstract. 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 widearea 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.

Target Tracking via Crowdsourcing: A Mechanism Design Approach

“…Some example application domains include social networks, environmental monitoring [2], [3], green computing [4], target localization and tracking [5], [6], [7], [8], [9], healthcare [10] (such as predicting and tracking disease patterns/outbreaks), and tracking traffic patterns [11], [12]. For instance, the OpenSense project [2] involves the design of a sensing infrastructure for real-time air quality monitoring using heterogeneous sensors owned by the general public, while [3] involves the design of a similar system to monitor noise levels.…”

“…For instance, work reported in [6], [8] utilizes built-in sensors in smartphones such as camera, digital compass and GPS, to estimate a target location as well as monitor the velocity of moving objects. In [5], [7], [9], proximity sensors in builtin smartphones are used to track objects (such as lost/stolen devices) installed with electronic tags (such as Bluetooth or RFID tags). Such systems have important commercial applications (such as tracking lost/stolen objects or accurately estimating arrival time of buses) as well as defense related applications (estimating the enemy's vehicle position prior to an attack).…”

“…Existing sensing applications and systems assume voluntary participation of users, for example, [13], [11], [5], [6], [7], [8], [9]. While participating in a sensing task, users consume their own resources such as energy and processing power.…”

AnonySense: A system for anonymous opportunistic sensing

“…Examples of such systems include CarTel [1], Mobiscopes [2], Urbanet [3], Urban Atmospheres [4], Urban Sensing [5], SenseWeb [6], and Metrosense [7] at Dartmouth College. Applications of opportunistic sensing include collecting traffic reports or pollution readings from a particular street or part of a university campus [1,7], finding parking spots [3], locating lost Bluetooth-enabled objects with the help of other users' mobile devices [8], and even inferring coffee-shop space availability [9].…”

AnonySense: Opportunistic and Privacy-Preserving Context Collection

“…With opportunistic sensing, applications need not rely on a static sensor deployment, and can instead glean context from any region that mobile nodes visit. Applications of opportunistic sensing include collecting traffic reports or pollution readings from a particular street [19], locating Bluetooth-enabled objects with the help of users' mobile devices [11], and even inferring coffee-shop space availability [35]. Examples of opportunistic-sensing systems include CarTel [19], Mobiscopes [1], Urbanet [32], Urban Sensing [6], SenseWeb [33] and our own MetroSense [5] at Dartmouth College.…”