Energy efficient continuous location determination for pedestrian information systems

Höpfner, Hagen; Schirmer, Maximilian

doi:10.1145/2258056.2258069

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…is holds not only for devices but also for server-side resources, being closely connected to scalability, transparency, and accuracy. Application-driven systems usually tackle energy e ciency by implementing policies for minimizing the consumption, for example dynamically adapting the rate for acquiring the location based on the user's movements [8,33].…”

Section: Features For Evaluationmentioning

confidence: 99%

“…is is an impediment for the applications that need to acquire the location a er a certain number of steps or traveled distance. e four systems that considered such strategies [8,33,40,58] either used an older, less restrictive platform [40,57] or implemented a prototype used in a small-scale instrumented experiment. Höpfner and Schirmer [33] propose several workarounds, even one based on static movement pro les and show promising results in the evaluation against the SDK's default policy.…”

Section: 1%mentioning

confidence: 99%

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A Survey of Techniques for Automatically Sensing the Behavior of a Crowd

Drăghici

Steen

2018

ACM Comput. Surv.

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Crowd-centric research is receiving increasingly more a ention as data sets on crowd behavior are becoming readily available. We have come to a point that many of the models on pedestrian analytics introduced in the last decade, which have mostly not been validated, can now be tested using real-world data sets. In this survey we concentrate exclusively on automatically gathering such data sets, which we refer to as sensing the behavior of pedestrians. We roughly distinguish two approaches: one that requires users to explicitly use local applications and wearables, and one that scans the presence of handheld devices such as smartphones. We come to the conclusion that despite the numerous reports in popular media, relatively few groups have been looking into practical solutions for sensing pedestrian behavior. Moreover, we nd that much work is still needed, in particular when it comes to combing privacy, transparency, scalability, and ease of deployment. We report on over 90 relevant articles and discuss and compare in detail 30 reports on sensing pedestrian behavior. CCS Concepts: •Information systems → Spatial-temporal systems; •Human-centered computing → Ubiquitous and mobile devices; •Computer systems organization → Sensors and actuators;

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Section: Features For Evaluationmentioning

confidence: 99%

Section: 1%mentioning

confidence: 99%

A Survey of Techniques for Automatically Sensing the Behavior of a Crowd

Drăghici

Steen

2018

ACM Comput. Surv.

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“…Careful selection of these parameters is required, taking into account the accuracy of localization. The power consumption related to GPS measurement and uploading to the server can be reduced by suppressing the number of measurements by collaborating with nearby terminals [33], limiting to road segments [14], and limiting periods of moving [40]. In addition, combining low-power consumption localization techniques with GPS-based positioning contributes to saving energy [16].…”

Section: B System Architecturementioning

confidence: 99%

Smartphone-Based Environmental Sensing Using Device Location as Metadata

Fujinami

2016

International Journal on Smart Sensing and Intelligent Systems

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The people-centric sensing community is paying substantial attention to the smartphone as an ad hoc, low-cost, and dense sensing method because it permits people to participate easily in sensing activities, i.e., just by carrying it as usual. People carry their smartphones in various manners, rendering measurement results unreliable. For example, humidity is typically higher in a trouser pocket than around the neck as a result of sweat. In this article, we propose a platform for peoplecentric sensing that considers the on-body position of a smartphone as metadata. A general architecture is presented, and a universal serial bus-based external sensing module for an Androidbased terminal is developed. A heatstroke alert map that visualizes the heatstroke risk is presented as an application based on both the collected raw data and metadata using the platform.Index terms: environmental sensing, people-centric sensing, heatstroke, smartphone, on-body device localization.Kaori Fujinami, SMARTPHONE-BASED ENVIRONMENTAL SENSING USING DEVICE LOCATION AS METADATA 2258 I.

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“…Hopfner et al [7] discuss the idea of postponing position measures until a phone's semantic location could have changed. Coelho et al [3] discussed a delay-tolerant positioning technique that stores the WiFi scan data needed to calculate a phone's position, sends the data to a server when appropriate, and calculates the positions on the server.…”

Section: Related Workmentioning

confidence: 99%

Detecting and correcting WiFi positioning errors

Tsuda

Kong

Maekawa

2013

Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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Recent advances in GPS andWiFi-based positioning technologies for mobile phones have triggered many locationbased services. However, GPS positioning quickly drains a phone's battery and cannot be used indoors. On the other hand, WiFi positioning provides energy-efficient indoor and outdoor positioning with reasonable accuracy. However, WiFi positioning sometimes makes large errors caused by various reasons, e.g., the movement of reference WiFi access points. In this paper we attempt to detect and correct such errors automatically by performing outlier detection in time series. So, we solve this problem by comparing a user's current measurement at time T with her coordinate point at time T predicted from her past coordinate history, and judging whether the current measurement is correct or not by computing the distance between the measurement location and the predicted location. However, it is difficult to predict the user's coordinates accurately with a single prediction method (predictor) because the user's context (e.g., migration speed and sparseness of past coordinates) greatly affects predictor performance. We thus design a context-aware error detection method by employing an ensemble of predictors that have different strengths and weaknesses.

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Energy efficient continuous location determination for pedestrian information systems

Cited by 5 publications

References 13 publications

A Survey of Techniques for Automatically Sensing the Behavior of a Crowd

A Survey of Techniques for Automatically Sensing the Behavior of a Crowd

Smartphone-Based Environmental Sensing Using Device Location as Metadata

Detecting and correcting WiFi positioning errors

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