geofencing1i.pdf - Crowd Geofencing Shinichi Konomi Tomoyo Sasao Center for Spatial Information Science The University of Tokyo 5-1-5 Kashiwanoha

geofencing1i.pdf - Crowd Geofencing Shinichi Konomi Tomoyo...

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Crowd Geofencing Shin’ichi Konomi Tomoyo Sasao Center for Spatial Information Science, The University of Tokyo 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-8568, Japan {konomi,sasaotomoyo}@csis.u-tokyo.ac.jp ABSTRACT Geofencing mechanisms allow for timely message delivery to the visitors of predefined target areas. However, conventional geofencing approaches poorly support mobile data collection scenarios in which experts need in situ assistance. In this paper, we propose crowd geofencing environments, in which a large number of crowdworkers generate geofences to support mobile experts. As a first step to open up the possibilities of crowd geofencing , we have tested its feasibility by collecting more than one thousand geofences in an unfamiliar city prior to the visit to look into urban water and air quality issues. Our experience has revealed the strengths and weaknesses of crowd geofencing in terms of geofence quality and crowd-powered situated actions. CCS Concepts Human-centered computing Ubiquitous and mobile computing Ubiquitous and mobile computing systems and tools Keywords Mobile crowdsourcing; Geofencing. 1. INTRODUCTION As the number of mobile and wearable devices increases, it is crucial to develop effective push-based information delivery environments. Geospatial contexts play a key role in delivering the right information at the right place, and they can be efficiently managed by using geofences. Geofences are predefined geospatial areas which smart devices use to monitor whether or not it has entered or exited form them to trigger corresponding actions. Geofences are designed and used in different ways in different systems and applications. Users may create personal geofences by themselves in mobile applications such as IFTTT [7]. In other systems, experts design geofences for crowds [16]. Crowds can then receive timely tips or requests while on the move, upon entering geofences. While expert-designed geofences may be well suited for scenarios in which experts’ knowledge guides mobile crowds (i.e., Expert- to-Crowd or E2C scenarios) , such as delivering experts’ sightseeing tips to all tourists in the vicinity, it may not fit reverse scenarios in which collective knowledge of crowds guides mobile experts (i.e., Crowd-to-Expert or C2E scenarios), such as delivering local knowledge and requests to an environmental researcher or an expert amateur [12] in the field. These two types of scenarios bridge experts and crowds in different ways, who hold asymmetrical knowledge around the issues of interest as well as local environments. It can be difficult to find appropriate crowdworkers in vicinity when requested tasks require certain skill and knowledge. In case of mobile data collection, experts may face difficulty accessing proper workers and potentially fail to collect sufficient data. C2E scenarios are an attractive alternative in this case since one could expect that local crowdworkers would be qualified to design local
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