Kaji Lab




    2 分で読めます

    IWIN2022にて「Implementation of spatio-temporal fencing for crowd sensing in a smartphone applications」を発表してきました. この発表で「IWIN2022 Best Presentation Awards」を受賞しました


    Crowdsensing platforms are available to solve the problems of dedicated system development and operating costs associated with crowdsensing. Among them, there are several crowdsensing platforms that provide incentive elements to motivate and maintain users. We build a crowdsensing platform (Lavlus) to mitigate disincentive factors that can be used in conjunction with incentive factors. This study implements a smartphone application for Lavlus. The Lavlus smartphone app needs to have the following functions: sensing project download, sensing request notification, automatic sensing, and sensor data upload. Among them, sensing request notification and automatic sensing are based on Spatio-temporal fencing. Geofencing used latitude and longitude, which are easy for users to visually recognize, but GPS is unstable indoors and in areas with many buildings. Therefore, a margin is provided on the geofence to determine whether the user has entered or exited the geofence with certainty. Lavlus may also target specific facilities, such as amusement parks or factories, as actual use cases. In this case, the geofence may be arbitrarily polygonal. We then draw a circle with the user at its center and generate geofencing points at eight points on the circumference of the circle. If one or more of the generated points are inside the geofence, the system determines that it is about to enter space-time and issues a notification whether or not it will cooperate with sensing. If all eight points are inside, the application judges that it has definitely entered the area and performs automatic sensing. If all eight points are outside, the application judges that it has definitely exited and terminates sensing. This allows proper geofencing even when the geofence is arbitrarily polygonal. In this paper, we implemented these functions and verified their operation. As a result, the geofence worked properly for arbitrary polygons.


    どうも,M1の須崎翔太です. この度 8/31 ~ 9/3 に開催されたIWIN2022にオフラインで参加してきました. 学会発表は1ヶ月前にあったDICOMOが初めてで,今回は英語かつオフラインとのことで非常に緊張しました. そんな初めての環境での発表でしたが,多くの助力と練習により満足できる発表ができました. 研究室の皆様や,関係者の皆様にはここで御礼申し上げます. 結果,「IWIN2022 Best Presentation Awards」受賞でき,大変嬉しく思います.




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