Show simple item record

dc.contributor.authorYao, D.Z.*
dc.contributor.authorYu, C.*
dc.contributor.authorDey, A.K.*
dc.contributor.authorKoehler, C.*
dc.contributor.authorMin, Geyong*
dc.contributor.authorYang, L.T.*
dc.contributor.authorJin, H.*
dc.date.accessioned2016-11-28T15:24:35Z
dc.date.available2016-11-28T15:24:35Z
dc.date.issued2014-10
dc.identifier.citationYao, D. Z., Yu, C., Dey, A. K., Koehler, C., Min, G. Y., Yang, L. T. and Jin, H. (2014) Energy efficient indoor tracking on smartphones. Future Generation Computer Systems-the International Journal of Grid Computing and Escience, 39, 44-54.
dc.identifier.urihttp://hdl.handle.net/10454/10817
dc.descriptionNo
dc.description.abstractContinuously identifying a user’s location context provides new opportunities to understand daily life and human behavior. Indoor location systems have been mainly based on WiFi infrastructures which consume a great deal of energy mostly due to keeping the user’s WiFi device connected to the infrastructure and network communication, limiting the overall time when a user can be tracked. Particularly such tracking systems on battery-limited mobile devices must be energy-efficient to limit the impact on the experience of using a phone. Recently, there have been a lot of studies of energy-efficient positioning systems, but these have focused on outdoor positioning technologies. In this paper, we propose a novel indoor tracking framework that intelligently determines the location sampling rate and the frequency of network communication, to optimize the accuracy of the location data while being energy-efficient at the same time. This framework leverages an accelerometer, widely available on everyday smartphones, to reduce the duty cycle and the network communication frequency when a tracked user is moving slowly or not at all. Our framework can work for 14 h without charging, supporting applications that require this location information without affecting user experience.
dc.subjectMobile computing; WiFi-location; Energy-aware systems; Motion learning
dc.titleEnergy efficient indoor tracking on smartphones
dc.status.refereedYes
dc.date.Accepted2013-12-13
dc.date.application2013-12-22
dc.typeArticle
dc.type.versionNo full-text in the repository
dc.identifier.doihttps://doi.org/10.1016/j.future.2013.12.032


This item appears in the following Collection(s)

Show simple item record