TY - GEN
T1 - Civic Crowdsensing Through Location-Aware Virtual Monsters
AU - Yonezawa, Takuro
AU - Sakamura, Mina
AU - Kawaguchi, Nobuo
AU - Nakazawa, Jin
N1 - Funding Information:
This research is supported by JSPS KAKENHI Grant Number 19K11945 and MIC SCOPE project.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Crowdsensing
KW - Urban computing
UR - http://www.scopus.com/inward/record.url?scp=85088740857&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088740857&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50344-4_33
DO - 10.1007/978-3-030-50344-4_33
M3 - Conference contribution
AN - SCOPUS:85088740857
SN - 9783030503437
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 464
EP - 476
BT - Distributed, Ambient and Pervasive Interactions - 8th International Conference, DAPI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Streitz, Norbert
A2 - Konomi, Shin’ichi
PB - Springer
T2 - 8th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
Y2 - 19 July 2020 through 24 July 2020
ER -