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SurroundSense: Mobile Phone Localization via Ambience
"... A growing number of mobile computing applications are centered around the user’s location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, ther ..."
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Cited by 156 (3 self)
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A growing number of mobile computing applications are centered around the user’s location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone’s camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87 % when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization.
Energyaccuracy trade-off for continuous mobile device location.
- In MobiSys,
, 2010
"... ABSTRACT Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones do include a GPS receiver, its frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile dev ..."
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Cited by 60 (4 self)
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ABSTRACT Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones do include a GPS receiver, its frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. ALoc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, using war-driving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors.
Did You See Bob?: Human Localization using Mobile Phones
"... Finding a person in a public place, such as in a library, conference hotel, or shopping mall, can be difficult. The difficulty arises from not knowing where the person may be at that time; even if known, navigating through an unfamiliar place may be frustrating. Maps and floor plans help in some occ ..."
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Cited by 47 (1 self)
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Finding a person in a public place, such as in a library, conference hotel, or shopping mall, can be difficult. The difficulty arises from not knowing where the person may be at that time; even if known, navigating through an unfamiliar place may be frustrating. Maps and floor plans help in some occasions, but such maps may not be always handy. In a small scale poll, 80 % of users responded that the ideal solution would be “to have an escort walk me to the desired person”. This paper identifies the possibility of using mobile phone sensors and opportunistic user-intersections to develop an electronic escort service. By periodically learning the walking trails of different individuals, as well as how they encounter each other in space-time, a route can be computed between any pair of persons. The problem bears resemblance to routing packets in delay tolerant networks, however, its application in the context of human localization raises distinct research challenges. We design and implement Escort, a system that guides a user to the vicinity of a desired person in a public place. We only use an audio beacon, randomly placed in the building, to enable a reference frame. We do not rely on GPS, WiFi, or war-driving to locate a person – the Escort user only needs to follow an arrow displayed on the phone. Evaluation results from experiments in parking lots and university buildings show that, on average, the user is brought to within 8m of the destination. We believe this is an encouraging result, opening new possibilities in mobile, social localization.
Accurate, Low-Energy Trajectory Mapping for Mobile Devices
"... CTrack is an energy-efficient system for trajectory mapping using raw position tracks obtained largely from cellular base station fingerprints. Trajectory mapping, which involves taking a sequence of raw position samples and producing the most likely path followed by the user, is an important compon ..."
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Cited by 33 (1 self)
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CTrack is an energy-efficient system for trajectory mapping using raw position tracks obtained largely from cellular base station fingerprints. Trajectory mapping, which involves taking a sequence of raw position samples and producing the most likely path followed by the user, is an important component in many locationbased services including crowd-sourced traffic monitoring, navigation and routing, and personalized trip management. Using only cellular (GSM) fingerprints instead of power-hungry GPS and WiFi radios, the marginal energy consumed for trajectory mapping is zero. This approach is non-trivial because we need to process streams of highly inaccurate GSM localization samples (average error of over 175 meters) and produce an accurate trajectory. CTrack meets this challenge using a novel two-pass Hidden Markov Model that sequences cellular GSM fingerprints directly without converting them to geographic coordinates, and fuses data from low-energy sensors available on most commodity smart-phones, including accelerometers (to detect movement) and magnetic compasses (to detect turns). We have implemented CTrack on the Android platform, and evaluated it on 126 hours (1,074 miles) of real driving traces in an urban environment. We find that CTrack can retrieve over 75% of a user’s drive accurately in the median. An important by-product of CTrack is that even devices with no GPS or WiFi (constituting a significant fraction of today’s phones) can contribute and benefit from accurate position data. 1
Balancing Energy, Latency and Accuracy for Mobile Sensor Data Classification
- ACM Sensys’11
, 2011
"... Sensor convergence on the mobile phone is spawning a broad base of new and interesting mobile applications. As applications grow in sophistication, raw sensor readings often require classification into more useful applicationspecific high-level data. For example, GPS readings can be classified as ru ..."
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Cited by 22 (1 self)
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Sensor convergence on the mobile phone is spawning a broad base of new and interesting mobile applications. As applications grow in sophistication, raw sensor readings often require classification into more useful applicationspecific high-level data. For example, GPS readings can be classified as running, walking or biking. Unfortunately, traditional classifiers are not built for the challenges of mobile systems: energy, latency, and the dynamics of mobile. Kobe is a tool that aids mobile classifier development. With the help of a SQL-like programming interface, Kobe performs profiling and optimization of classifiers to achieve an optimal energy-latency-accuracy tradeoff. We show through experimentation on five real scenarios, classifiers on Kobe exhibit tight utilization of available resources. For comparable levels of accuracy traditional classifiers, which do not account for resources, suffer between 66 % and 176% longer latencies and use between 31 % and 330 % more energy. From the experience of using Kobe to prototype two new applications, we observe that Kobe enables easier development of mobile sensing and classification apps.
IODetector: A generic service for indoor outdoor detection
- In SenSys’ 12
"... The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. In this paper, we present IODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment ..."
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Cited by 19 (5 self)
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The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. In this paper, we present IODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner. Constrained by the energy budget, IODetector leverages primarily lightweight sensing resources including light sensors, magnetism sensors, celltower signals, etc. For universal applicability, IODetector assumes no prior knowledge (e.g., fingerprints) of the environment and uses only on-board sensors common to mainstream mobile phones. Being a generic and lightweight service component, IODetector greatly benefits many location-based and context-aware applications. We prototype the IODetector on Android mobile phones and evaluate the system comprehensively with data collected from 19 traces which include 84 different places during one month period, employing different phone models. We further perform a case study where we make use of IODetector to instantly infer the GPS availability and localization accuracy in different indoor/outdoor environments.
Energy-Accuracy Aware Localization for Mobile Devices
"... Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smart-phones do include a GPS receiver, its frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-L ..."
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Cited by 15 (0 self)
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Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smart-phones do include a GPS receiver, its frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. A-Loc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, using wardriving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors. 1.
ParkSense: A Smartphone Based Sensing System For On-Street Parking
"... Studies of automotive traffic have shown that on average 30 % of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city i ..."
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Cited by 13 (0 self)
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Studies of automotive traffic have shown that on average 30 % of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city is a considerable investment. In this paper, we present ParkSense, a smartphone based sensing system that detects if a driver has vacated a parking spot. ParkSense leverages the ubiquitous Wi-Fi beacons in urban areas for sensing unparking events. It utilizes a robust Wi-Fi signature matching approach to detect driver’s return to the parked vehicle. Moreover, it uses a novel approach based on the rate of change of Wi-Fi beacons to sense if the user has started driving. We show that the rate of change of the observed beacons is highly correlated with actual user speed and is a good indicator of whether a user is in a vehi-cle. Through empirical evaluation, we demonstrate that our approach has a significantly smaller energy footprint than traditional location sensors like GPS and Wi-Fi based posi-tioning while still maintaining sufficient accuracy.
Supporting Energy-Efficient Uploading Strategies for Continuous Sensing Applications on Mobile Phones
"... Abstract. Continuous sensing applications (e.g., mobile social networking applications) are appearing on new sensor-enabled mobile phones such as the Apple iPhone, Nokia and Android phones. These applications present significant challenges to the phone’s operations given the phone’s limited computat ..."
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Cited by 12 (1 self)
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Abstract. Continuous sensing applications (e.g., mobile social networking applications) are appearing on new sensor-enabled mobile phones such as the Apple iPhone, Nokia and Android phones. These applications present significant challenges to the phone’s operations given the phone’s limited computational and energy resources and the need for applications to share real-time continuous sensed data with back-end servers. System designers have to deal with a trade-off between data accuracy (i.e., application fidelity) and energy constraints in the design of uploading strategies between phones and back-end servers. In this paper, we present the design, implementation and evaluation of several techniques to optimize the information uploading process for continuous sensing on mobile phones. We analyze the cases of continuous and intermittent connectivity imposed by low-duty cycle design considerations or poor wireless network coverage in order to drive down energy consumption and extend the lifetime of the phone. We also show how location prediction can be integrated into this forecasting framework. We present the implementation and the experimental evaluation of these uploading techniques based on measurements from the deployment of a continuous sensing application on 20 Nokia N95 phones used by 20 people for a period of 2 weeks. Our results show that we can make significant energy savings while limiting the impact on the application fidelity, making continuous sensing a viable application for mobile phones. For example, we show that it is possible to achieve an accuracy of 80 % with respect to ground-truth data while saving 60% of the traffic sent over-the-air. 1
Guoguo: Enabling fine-grained indoor localization via smartphone
- in Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services (ACM MobiSys
, 2013
"... Using smartphones for accurate indoor localization opens a new frontier of mobile services, offering enormous oppor-tunities to enhance users ’ experiences in indoor environ-ments. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accura ..."
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Cited by 12 (1 self)
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Using smartphones for accurate indoor localization opens a new frontier of mobile services, offering enormous oppor-tunities to enhance users ’ experiences in indoor environ-ments. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), there are several stringent requirements for an indoor localization system: highly accurate that can dif-ferentiate massive users ’ locations (foot-level); no additional hardware components or extensions on users ’ smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. WiFi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accu-racy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to trans-mit modulated localization beacons using high-band acous-tic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based ser-vices. We further propose approaches to improve its cover-age, accuracy, and location update rate with low-power con-sumption. Our prototype shows centimeter-level localiza-tion accuracy in an office and classroom environment. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.
