Optimal Mobility Control of Sensors in the Event of a Disaster
Yuichi Nakamura*,, Masaki Ito*, and Kaoru Sezaki*,**
*Institute of Industrial Science, The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
**Center for Spatial Information Science, The University of Tokyo, Chiba, Japan
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.
-  T. Moroi and M. Takemura, “Generation Process of Casualties During the 1923 Kanto Earthquake,” Rekishi Jishin, Vol.21, pp. 47-58, 2006.
-  S. Mitani, Y. Murakami, and Y. Imamura, “Consideration of Vulnerability Concerning the Elderly on Disaster and Disaster-related Deaths,” J. of the Japan Academy for Health Behavioral Science, Vol.29, pp. 23-30, 2014.
-  H. Ieda, S. Kaminishi, T. Inomata, and T. Suzuki, “Street Blockades in Hanshin Earthquake ’95 and Its Influence on Disaster Relief Activities,” Doboku Gakkai Ronbunshu, Issue 576, pp. 69-82, 1997.
-  T. Osaragi and T. Tsuchiya, “Influence of Word-of-Mouth Communication on Large-Scale Evacuation after a Severe Earthquake,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.IV-4, pp. 171-178, 2018.
-  S. Koshimura, “Establishing the Advanced Disaster Reduction Management System by Fusion of Real-Time Disaster Simulation and Big Data Assimilation,” J. Disaster Res., Vol.11, No.2, pp. 164-174, 2016.
-  V. Sivaraman, J. Carrapetta, K. Hu, and B. G. Luxan, “HazeWatch: A participatory sensor system for monitoring air pollution in Sydney,” IEEE 38th Conf. on Local Computer Networks, Sydney, Australia, pp. 56-64, 2013.
-  C. J. Sullivan, “Radioactive source localization in urban environments with sensor networks and the Internet of Things,” 2016 IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 384-388, 2016.
-  X. Liang, K. Schilling, and Y.-K. Zhang, “Co-Kriging Estimation of Nitrate-Nitrogen Loads in an Agricultural River,” Water Resource Management, Vol.30, pp. 1771-1784, 2016.
-  Y. Lin, Y.-Y. Chiang, F. Pan, D. Stripelis, J. L. Ambite, S. P. Eckel, and R. Habre, “Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution,” Proc. of the 25th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems (SIGSPATIAL’17), pp. 25:1–25:10, 2017.
-  B. Kim, D. J. Seo, S. J. Noh, O. P. Prat, and B. R. Nelson, “Improving multisensor estimation of heavy-to-extreme precipitation via conditional bias-penalized optimal estimation,” J. of Hydrology, Vol.556, pp. 1096-1109, 2018.
-  C. Zhang and Y. Zhao, “High Precision Deep Sea Geomagnetic Data Sampling and Recovery with Three-Dimensional Compressive Sensing,” IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E100-A, No.9, pp. 1760-1762, 2017.
-  S. Aoki, G. Lilu, K. Shimizu, M. Iwai, and K. Sezaki, “Efficient System Operation and User Analysis in Participatory Environmental Sensing,” DICOMO2013 Symp., Vol.2013, pp. 2-7, 2013.
-  R. K. Rana, C. T. Chou, S. S. Kanhere, N. Bulusu, and W. Hu, “Ear-Phone : An End-to-End Participatory Urban Noise Mapping System,” Proc. of the 9th ACM/IEEE Int. Conf. on Information Processing in Sensor Networks, Stockholm, Sweden, pp. 105-116, 2010.
-  T. Kitazato, M. Hoshino, M. Ito, and K. Sezaki, “Detection of Pedestrian Flow Using Mobile Devices for Evacuation Guiding in Disaster,” J. Disaster Res., Vol.13, No.2, pp. 303-312, 2018.
-  M. Sakamura, T. Yonezawa, T. Ito, Y. Kaneko, and J. Nakazawa, “Minarepo: Parcitipatory Sensing System for Daily Work in a Local Gevernment,” IPSJ J. Digital Practice, Vol.9, No.2, pp. 550-572, 2018.
-  L. Xiao, S. Boyd, and S. Lall, “A scheme for robust distributed sensor fusion based on average consensus,” IPSN 2005, 4th Int. Symp. on Information Processing in Sensor Networks, Boise, pp. 63-70, 2005.
-  H. M. La, W. Sheng, and J. Chen, “Cooperative and Active Sensing in Mobile Sensor Networks for Scalar Field Mapping,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol.45, Issue 1, pp. 1-12, 2015.
-  J. Li and A. D. Heap, “A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors,” Ecological Informatics, Vol.6, Issue 3-4, pp. 228-241, 2011.
-  A. Howard, M. J. Matarić, and G. S. Sukhatme, “Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem,” Distributed Autonomous Robotic Systems, Vol.5, pp. 299-308, 2002.
-  P. A. Burrough, R. A. McDonnell, and C. D. Lloyd, ”Principles of Geographical Information Systems,” Oxford University Press, Oxford, United Kingdom, 2015.