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Civic Crowdsensing Through Location-Aware Virtual Monsters

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Distributed, Ambient and Pervasive Interactions (HCII 2020)

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We present a new model for encouraging people to get involved with monitoring and taking part in the life of cities. Cities could be smarter if IoT and people could serve as engaged and pro-active data resources (i.e., crowd sensing). This study tackles two challenges: methods by which the privacy of people who act as sensors/actuators can be guaranteed and methods to create a unified programming model for crowd sensors alongside other IoT functions. To achieve these goals, we introduce a new concept called Lokemon (Location Monster). Each sensing space is characterized as a personified target. Lokemon asks users to imagine themselves to be monsters associated with target spots when achieving sensing tasks. Lokemon is also expressed as a PubSub node so that the data from Lokemon can be easily accessed in the same way as data from IoT is assessed. The article explains the concept of Lokemon and its programming model. We report our evaluation of the effectiveness of Lokemon in a campus experiment that was performed for four weeks.

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This research is supported by JSPS KAKENHI Grant Number 19K11945 and MIC SCOPE project.

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Correspondence to Takuro Yonezawa .

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Yonezawa, T., Sakamura, M., Kawaguchi, N., Nakazawa, J. (2020). Civic Crowdsensing Through Location-Aware Virtual Monsters. In: Streitz, N., Konomi, S. (eds) Distributed, Ambient and Pervasive Interactions. HCII 2020. Lecture Notes in Computer Science(), vol 12203. Springer, Cham.

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  • Print ISBN: 978-3-030-50343-7

  • Online ISBN: 978-3-030-50344-4

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