Improving energy efficiency of location sensing on smartphones

Zhuang, Zhenyun; Kim, Kyu-Han; Singh, Jatinder

doi:10.1145/1814433.1814464

Cited by 272 publications

(148 citation statements)

References 25 publications

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“…In [4], authors examine the human localization in a building using smartphone sensors and randomly placed audio beacons in the building. In [22], authors identify four factors that waste energy: static use of location sensing mechanism, absence of use of other sensors, lack of cooperation among applications, and finally ignoring battery level while sensing. The paper analyzes aforementioned factors and proposes a middleware location sensing framework which adaptively a) toggles between GPS and network based on the accuracy of the providers, b) suppresses the use of location updates based on the context (i.e.…”

Section: Smartphone Sensingmentioning

confidence: 99%

LineKing: Crowdsourced Line Wait-Time Estimation Using Smartphones

Bulut

Yilmaz

Demirbaş

et al. 2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. This paper describes the design, implementation and deployment of LineKing (LK), a crowdsourced line wait-time monitoring service. LK consists of a smartphone component (that provides automatic, energy-efficient, and accurate wait-time detection), and a cloud backend (that uses the collected data to provide accurate wait-time estimation). LK is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in our university campus. The novel wait-time estimation algorithms deployed at the cloud backend provide mean absolute errors of less than 2-3 minutes.

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Section: Smartphone Sensingmentioning

confidence: 99%

LineKing: Crowdsourced Line Wait-Time Estimation Using Smartphones

Bulut

Yilmaz

Demirbaş

et al. 2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…In this work, a mechanism able to use GPS, Cell-ID, Bluetooth or WLAN positioning based on accuracy requirement was implemented. Energy efficient positioning for smartphones is also described in [28] and [7] by using complementary positioning techniques and by ensuring selective use of GPS. The architectural aspect of an indoor-outdoor location sensing technology has been discussed by Flora et al [5].…”

Section: Combination Of Wlan Gps Cellular and Bluetooth Positioningmentioning

confidence: 99%

Context aware handover algorithms for mobile positioning systems

Khan

Silvadorai²,

Tan

et al. 2014

csci

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This work proposes context aware handover algorithms for mobile positioning systems. The algorithms perform handover among positioning systems based on important contextual factors related to position determination with efficient use of battery. The proposed solution is implemented in the form of an Android application named Locate@nav6. The performance of the proposed solution was tested in selected experimental areas. The handover performance was compared with other existing location applications. The proposed solution performed correct handover among positioning systems in 95 percent of cases studied while two other applications performed correct handover in only 50 percent of cases studied. Battery usage of the proposed solution is less than one third of the battery usage of two other applications. The analysis of the positioning error of the applications demonstrated that, the proposed solution is able to reduce positioning error indirectly by handing over the task of positioning to an appropriate positioning system. This kept the average error of positioning below 42.1 meters for Locate@nav6 while the average error for two other applications namely Google Latitude and Malaysia maps was between 92.7 and 171.13 meters.Keywords handover among mobile positioning systems, power efficient mobile positioning, switching between indoor and outdoor positioning systemsComputer Science • 15 (2) 2014 http://dx

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“…LOBOT requests GPS sampling in an adaptive way that incorporates both location accuracy and energy use. The more frequent GPS sampling likely results in better correction of positioning; but more frequent GPS sampling also means significantly higher cost of power consumption [19], [20], [21]. Roughly, LOBOT adjusts its GPS sampling frequency according to the magnitude of the cumulative error of the local relative positioning.…”

Section: Drift Correctionmentioning

confidence: 99%

LOBOT: Low-Cost, Self-Contained Localization of Small-Sized Ground Robotic Vehicles

Zhan

Shi

2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-It is often important to obtain the real-time location of a small-sized ground robotic vehicle when it performs autonomous tasks either indoors or outdoors. We propose and implement LOBOT, a low-cost, self-contained localization system for small-sized ground robotic vehicles. LOBOT provides accurate real-time, three-dimensional positions in both indoor and outdoor environments. Unlike other localization schemes, LOBOT does not require external reference facilities, expensive hardware, careful tuning or strict calibration, and is capable of operating under various indoor and outdoor environments. LOBOT identifies the local relative movement through a set of integrated inexpensive sensors and well corrects the localization drift by infrequent GPS-augmentation. Our empirical experiments in various temporal and spatial scales show that LOBOT keeps the positioning error well under an accepted threshold.

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Improving energy efficiency of location sensing on smartphones

Cited by 272 publications

References 25 publications

LineKing: Crowdsourced Line Wait-Time Estimation Using Smartphones

LineKing: Crowdsourced Line Wait-Time Estimation Using Smartphones

Context aware handover algorithms for mobile positioning systems

LOBOT: Low-Cost, Self-Contained Localization of Small-Sized Ground Robotic Vehicles

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