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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.
Ace: exploiting correlation for energy-efficient and continuous context sensing
- In Proceedings of the 10th international conference on Mobile systems, applications, and services
, 2012
"... We propose ACE (Acquisitional Context Engine), a middle-ware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. ACE provides user’s current context to applications running on it. In addition, it dynamically learns relationships among var-ious c ..."
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Cited by 38 (1 self)
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We propose ACE (Acquisitional Context Engine), a middle-ware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. ACE provides user’s current context to applications running on it. In addition, it dynamically learns relationships among var-ious context attributes (e.g., whenever the user is Driving, he is not AtHome). ACE exploits these automatically learned relationships for two powerful optimizations. The first is in-ference caching that allows ACE to opportunistically infer one context attribute (AtHome) from another already-known attribute (Driving), without acquiring any sensor data. The second optimization is speculative sensing that enables ACE to occasionally infer the value of an expensive attribute (e.g., AtHome) by sensing cheaper attributes (e.g., Driving). Our experiments with two real context traces of 105 people and a Windows Phone prototype show that ACE can reduce sens-ing costs of three context-aware applications by about 4.2×, compared to a raw sensor data cache shared across applica-tions, with a very small memory and processing overhead.
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
permission. Carat: Collaborative Energy Diagnosis for Mobile Devices
, 2013
"... 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. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires pri ..."
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Cited by 19 (3 self)
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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. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific
Adaptive GPS Duty Cycling and Radio Ranging for Energy-efficient Localization
"... This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empir ..."
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Cited by 16 (5 self)
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This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empirical GPS and radio contact data from a largescale animal tracking deployment to model node mobility, GPS and radio performance. These models are used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting applicationspecific positioning criteria. 1
eDoctor: Automatically Diagnosing Abnormal Battery Drain Issues on Smartphones
"... The past few years have witnessed an evolutionary change in the smartphone ecosystem. Smartphones have gone from closed platforms containing only pre-installed applications to open platforms hosting a variety of thirdparty applications. Unfortunately, this change has also led to a rapid increase in ..."
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Cited by 15 (0 self)
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The past few years have witnessed an evolutionary change in the smartphone ecosystem. Smartphones have gone from closed platforms containing only pre-installed applications to open platforms hosting a variety of thirdparty applications. Unfortunately, this change has also led to a rapid increase in Abnormal Battery Drain (ABD) problems that can be caused by software defects or misconfiguration. Such issues can drain a fully-charged battery within a couple of hours, and can potentially affect a significant number of users. This paper presents eDoctor, a practical tool that helps regular users troubleshoot abnormal battery drain issues on smartphones. eDoctor leverages the concept of execution phases to capture an app’s time-varying behavior, which can then be used to identify an abnormal app. Based on the result of a diagnosis, eDoctor suggests the most appropriate repair solution to users. To evaluate eDoctor’s effectiveness, we conducted both in-lab experiments and a controlled user study with 31 participants and 17 realworld ABD issues together with 4 injected issues in 19 apps. The experimental results show that eDoctor can successfully diagnose 47 out of the 50 use cases while imposing no more than 1.5 % of power overhead.
Energy Management Techniques in Modern Mobile Handsets
, 2012
"... Managing energy efficiently is paramount in modern smartphones. The diverse range of wireless interfaces and sensors, and the increasing popularity of power-hungry applications that take advantage of these resources can reduce the battery life of mobile handhelds to few hours of operation. The rese ..."
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Cited by 13 (1 self)
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Managing energy efficiently is paramount in modern smartphones. The diverse range of wireless interfaces and sensors, and the increasing popularity of power-hungry applications that take advantage of these resources can reduce the battery life of mobile handhelds to few hours of operation. The research community, and operating system and hardware vendors found interesting optimisations and techniques to extend the battery life of mobile phones. However, the state of the art of lithium-ion batteries clearly indicates that energy efficiency must be achieved both at the hardware and software level. In this survey, we will cover the software solutions that can be found in the research literature between 1999 and May 2011 at six different levels: energy-aware operating systems, efficient resource management, the impact of users ’ interaction patterns with mobile devices and applications, wireless interfaces and sensors management, and finally the benefits of integrating mobile devices with cloud computing services.
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.
Mobile apps: It’s time to move up to condOS
, 2011
"... Sensing is a significant contributor to the current mobile computing revolution. Today’s typical smartphone has more than eight sensors, including multiple mics, cameras, accelerometers, gyroscopes, a GPS, a digital compass, ..."
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Cited by 11 (2 self)
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Sensing is a significant contributor to the current mobile computing revolution. Today’s typical smartphone has more than eight sensors, including multiple mics, cameras, accelerometers, gyroscopes, a GPS, a digital compass,
The Latency, Accuracy, and Battery (LAB) abstraction: Programmer productivity and energy efficiency for continuous mobile context sensing
- In OOPSLA
, 2013
"... Abstract Emerging mobile applications that sense context are poised to delight and entertain us with timely news and events, health tracking, and social connections. Unfortunately, sensing algorithms quickly drain the phone's battery. Developers can overcome battery drain by carefully optimizi ..."
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Cited by 10 (1 self)
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Abstract Emerging mobile applications that sense context are poised to delight and entertain us with timely news and events, health tracking, and social connections. Unfortunately, sensing algorithms quickly drain the phone's battery. Developers can overcome battery drain by carefully optimizing context sensing but that makes programming with context arduous and ties applications to current sensing hardware. These types of applications embody a twist on the classic tension between programmer productivity and performance due to their combination of requirements. This paper identifies the latency, accuracy, battery (LAB) abstraction to resolve this tension. We implement and evaluate LAB in a system called Senergy. Developers specify their LAB requirements independent of inference algorithms and sensors. Senergy delivers energy efficient context while meeting the requirements and adapts as hardware changes. We demonstrate LAB's expressiveness by using it to implement 22 context sensing algorithms for four types of context (location, driving, walking, and stationary) and six diverse applications. To demonstrate LAB's energy optimizations, we show often an order of magnitude improvements in energy efficiency on applications compared to prior approaches. This relatively simple, priority based API, may serve as a blueprint for future API design in an increasingly complex design space that must tradeoff latency, accuracy, and efficiency to meet application needs and attain portability across evolving, sensor-rich, heterogeneous, and power constrained hardware.
