TY - GEN
T1 - FindingMiMo
T2 - 9th International Conference on Mobile Systems, Applications, and Services, MobiSys'11 and Co-located Workshops
AU - Shin, Hyojeong
AU - Chon, Yohan
AU - Park, Kwanghyo
AU - Cha, Hojung
PY - 2011
Y1 - 2011
N2 - With the widespread use of smartphones, the loss of a device is critical, both in disrupting daily communications, and in losing valuable property. When a mobile device is missing, localization techniques may assist in finding the device. Current techniques, however, hardly provide a complete solution because of inaccurate position estimation, especially in indoor environments. In this paper, we describe a software architecture called FindingMiMo, which tracks and locates a missing mobile device in indoor environments. The system consists of a missing mobile which logs diverse environmental features on a daily basis, and a chaser which traces the trail of the device using the observation log. During daily operation, the mobile device does not perform location estimation; it only observes the ambient features such as radio signals to minimize its operation cost. Instead, the chaser determines where the missing device measured the observations. This research implemented the scheme on Android-based smartphones. Real experiments with carefully designed, missing-and-tracking scenarios show that the participants successfully approached their lost phones within four meters distance, on average.
AB - With the widespread use of smartphones, the loss of a device is critical, both in disrupting daily communications, and in losing valuable property. When a mobile device is missing, localization techniques may assist in finding the device. Current techniques, however, hardly provide a complete solution because of inaccurate position estimation, especially in indoor environments. In this paper, we describe a software architecture called FindingMiMo, which tracks and locates a missing mobile device in indoor environments. The system consists of a missing mobile which logs diverse environmental features on a daily basis, and a chaser which traces the trail of the device using the observation log. During daily operation, the mobile device does not perform location estimation; it only observes the ambient features such as radio signals to minimize its operation cost. Instead, the chaser determines where the missing device measured the observations. This research implemented the scheme on Android-based smartphones. Real experiments with carefully designed, missing-and-tracking scenarios show that the participants successfully approached their lost phones within four meters distance, on average.
KW - ambient monitoring
KW - indoor navigation
KW - localization
KW - lost and found
KW - place learning
UR - http://www.scopus.com/inward/record.url?scp=79961050416&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79961050416&partnerID=8YFLogxK
U2 - 10.1145/1999995.1999999
DO - 10.1145/1999995.1999999
M3 - Conference contribution
AN - SCOPUS:79961050416
SN - 9781450306430
T3 - MobiSys'11 - Compilation Proceedings of the 9th International Conference on Mobile Systems, Applications and Services and Co-located Workshops
SP - 29
EP - 42
BT - MobiSys'11 - Compilation Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services and Co-located Workshops
Y2 - 28 June 2011 through 1 July 2011
ER -