Sezaki & Nishiyama Laboratory
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Yuichi Nakamura, Masaki Ito, Kaoru Sezaki
Optimal Mobility Control of Sensors in the Event of a Disaster Journal Article Open Access
In: Journal of Disaster Research, 14 (3), pp. 500-507, 2019.
Abstract | BibTeX | タグ: active sensing, disaster prevention, participatory sensing | Links:
@article{YuichiNakamura2019,
title = {Optimal Mobility Control of Sensors in the Event of a Disaster},
author = {Yuichi Nakamura and Masaki Ito and Kaoru Sezaki},
doi = {10.20965/jdr.2019.p0500},
year = {2019},
date = {2019-01-01},
journal = {Journal of Disaster Research},
volume = {14},
number = {3},
pages = {500-507},
abstract = {Disasters have caused serious damage on human beings throughout their long history. In a major natural disaster such as an earthquake, a key to mitigate the damage is evacuation. Evidently, secondary collateral disasters is account for more casualty than the initial one. In order to have citizens to evacuate safely for the sake of saving their lives, collecting information is vital. However at times of a disaster, it is a difficult task to gain environmental information about the area by conventional way. One of the solutions to this problem is crowd-sensing, which regards citizens as sensors nodes and collect information with their help. We considered a way of controlling the mobility of such sensor nodes under limitation of its mobility, caused by road blockage, for example. Aiming to make a mobility control scheme that enables high-quality information collection, our method uses preceding result of the measurement to control the mobility. Here it uses kriging variance to do that. We evaluated this method by simulating some measurements and it showed better accuracy than baseline. This is expected to be a method to enable a higher-quality input to the agent-based evacuation simulation, which helps to guide people to evacuate more safely.},
keywords = {active sensing, disaster prevention, participatory sensing},
pubstate = {published},
tppubtype = {article}
}
Yuichi Nakamura, Masaki Ito, Kaoru Sezaki
Mobility Control of Mobile Sensing for Time-Varying Parameter Inproceedings
In: Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications, pp. 159, Association for Computing Machinery, Santa Cruz, CA, USA, 2019, ISBN: 9781450362733.
BibTeX | タグ: active sensing, kriging, Mobile sensing, sensor networks | Links:
@inproceedings{10.1145/3301293.3309553,
title = {Mobility Control of Mobile Sensing for Time-Varying Parameter},
author = {Yuichi Nakamura and Masaki Ito and Kaoru Sezaki},
url = {https://doi.org/10.1145/3301293.3309553},
doi = {10.1145/3301293.3309553},
isbn = {9781450362733},
year = {2019},
date = {2019-01-01},
booktitle = {Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications},
pages = {159},
publisher = {Association for Computing Machinery},
address = {Santa Cruz, CA, USA},
series = {HotMobile ’19},
keywords = {active sensing, kriging, Mobile sensing, sensor networks},
pubstate = {published},
tppubtype = {inproceedings}
}
Yuichi Nakamura, Masaki Ito, Kaoru Sezaki
Planning Placement of Distributed Sensor Nodes to Achieve Efficient Measurement Book Chapter Open Access
In: Streitz, Norbert; Konomi, Shin'ichi (Ed.): International Conference on Distributed, Ambient, and Pervasive Interactions, 10921 , pp. 103–113, Springer International Publishing, 2018, ISBN: 978-3-319-91125-0.
Abstract | BibTeX | タグ: active sensing, kriging, Mobile sensing | Links:
@inbook{nakamura2018planning,
title = {Planning Placement of Distributed Sensor Nodes to Achieve Efficient Measurement},
author = {Yuichi Nakamura and Masaki Ito and Kaoru Sezaki},
editor = {Norbert Streitz and Shin'ichi Konomi},
doi = {10.1007/978-3-319-91125-0_8},
isbn = {978-3-319-91125-0},
year = {2018},
date = {2018-05-30},
booktitle = {International Conference on Distributed, Ambient, and Pervasive Interactions},
volume = {10921},
pages = {103--113},
publisher = {Springer International Publishing},
organization = {Springer},
abstract = {This paper proposes a method to plan a placement of multiple sensors distributed in a certain area to enable an efficient measurement in terms of the confidence of the interpolation of measured data using kriging. We considered a system where we have some sensors that can move and are distributed in a certain area and a static scaler filed of interest such as a map of temperature in a certain city. We propose a method to plan the placement for the next time step using the value measured until that time by calculating the gradient of kriging variance. For the sake of evaluation of this method, we conducted a simulation of two-step measurement where a scaler filed is created and some sensor nodes are virtually placed. Here, the interpolation with the data from sensor node placement with the proposed method showed better accuracy than that from randomly placed sensors.},
keywords = {active sensing, kriging, Mobile sensing},
pubstate = {published},
tppubtype = {inbook}
}