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IEEE PerCom2020にてBest Industry Paper Award (Honorable Mention)を受賞しました
Received the Best Industry Paper Award (Honorable Mention) at IEEE PerCom2020

以下の論文が、ユビキタスコンピューティングのトップカンファレンスの1つ IEEE Percom2020 (18th Annual IEEE Conference on Pervasive Computing and Communications)にて、Best Industry Paper Award (Honorable Mention)を受賞しました!(http://www.percom.org/ (外部サイト))

Parasitic Location Logging: Estimating Users' Location from Context of Passersby
Kota Tsubouchi, Teruhiko Teraoka, Hidehito Gomi, Masamichi Shimosaka (Tokyo Institute of Technology)

The following paper won the Best Industry Paper Award (Honorable Mention) at IEEE PerCom2020(18th Annual IEEE Conference on Pervasive Computing and Communications) (http://www.percom.org/ (external site))

Parasitic Location Logging: Estimating Users' Location from Context of Passersby
Kota Tsubouchi, Teruhiko Teraoka, Hidehito Gomi, Masamichi Shimosaka (Tokyo Institute of Technology)

Abstract:
People often turn off location logging when the batteries of their smartphones get low, to reduce the phone’s power consumption and prolong its operation. Here, we propose an innovative data sharing scheme called as the Parasitic Loca- tion Logging (PLL). PLL can acquire location of such users, what we call parasitic users, without invoking any location functionalities by the GPS and Bluetooth low energy (BLE) sensors of their smartphones. PLL estimates parasitic users’ location and trajectory by relying on other users who pass by the parasitic user, what we call host users, as evidence that they are located in close proximity. The results of field experiments showed that PLL dramatically decreases battery consumption of parasitic users’ smartphones and that the position of para- sitic users can be identified accurately. Moreover, the battery consumption of PLL was rigorously evaluated in a laboratory setting to demonstrate its benefit. An agent simulation evaluating the proposed calculation algorithm under various conditions in realistic environments validated the robustness of PLL.