Abstract
Disaster area mapping is critical to guiding evacuees to safety and aiding responders in decision-making. During disasters however, Cloud-based mapping services cannot be relied upon, because network infrastructures may have been damaged. In this study, we propose a disaster area mapping system that functions under challenged-network environments in a disaster area. The system infers a pedestrian map with walking speed information from data gathered by civilians and responders with mobile devices. To generate the map, the system addresses the following challenges: how to collect disaster area data, how to share data without continuous end-to-end networks, and how to generate maps without Cloud-based mapping services. First, the system leverages human mobility to collect disaster area data. Civilians and responders with mobile devices function as sensor nodes and log their GPS and velocity traces while moving based on the Post-Disaster Mobility Model. Second, the system uses mobile devices to establish a Delay-Tolerant Network, through which nodes opportunistically share data. Finally to generate the map, the collected data are routed to Computing Nodes: devices with more computational resources than mobile devices that are spatially-distributed across the disaster area. The Computing Nodes infer the map from the data and share it with evacuees. Through experimental evaluations and computer simulations, we found that the system significantly decreases the time required to generate and deliver a map to an evacuee, compared to a case without the system. Furthermore, the overall reduction in time increases as the size of the data required to generate the map and the number of DTN nodes increase.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509019410 |
| DOIs | |
| Publication status | Published - Apr 19 2016 |
| Event | 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016 - Sydney, Australia Duration: Mar 14 2016 → Mar 18 2016 |
Publication series
| Name | 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016 |
|---|
Conference
| Conference | 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016 |
|---|---|
| Country | Australia |
| City | Sydney |
| Period | 3/14/16 → 3/18/16 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Networks and Communications
- Human-Computer Interaction
Cite this
Disaster area mapping using spatially-distributed computing nodes across a DTN. / Trono, Edgar Marko; Fujimoto, Manato; Suwa, Hirohiko; Arakawa, Yutaka; Takai, Mineo; Yasumoto, Keiichi.
2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7457149 (2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Disaster area mapping using spatially-distributed computing nodes across a DTN
AU - Trono, Edgar Marko
AU - Fujimoto, Manato
AU - Suwa, Hirohiko
AU - Arakawa, Yutaka
AU - Takai, Mineo
AU - Yasumoto, Keiichi
PY - 2016/4/19
Y1 - 2016/4/19
N2 - Disaster area mapping is critical to guiding evacuees to safety and aiding responders in decision-making. During disasters however, Cloud-based mapping services cannot be relied upon, because network infrastructures may have been damaged. In this study, we propose a disaster area mapping system that functions under challenged-network environments in a disaster area. The system infers a pedestrian map with walking speed information from data gathered by civilians and responders with mobile devices. To generate the map, the system addresses the following challenges: how to collect disaster area data, how to share data without continuous end-to-end networks, and how to generate maps without Cloud-based mapping services. First, the system leverages human mobility to collect disaster area data. Civilians and responders with mobile devices function as sensor nodes and log their GPS and velocity traces while moving based on the Post-Disaster Mobility Model. Second, the system uses mobile devices to establish a Delay-Tolerant Network, through which nodes opportunistically share data. Finally to generate the map, the collected data are routed to Computing Nodes: devices with more computational resources than mobile devices that are spatially-distributed across the disaster area. The Computing Nodes infer the map from the data and share it with evacuees. Through experimental evaluations and computer simulations, we found that the system significantly decreases the time required to generate and deliver a map to an evacuee, compared to a case without the system. Furthermore, the overall reduction in time increases as the size of the data required to generate the map and the number of DTN nodes increase.
AB - Disaster area mapping is critical to guiding evacuees to safety and aiding responders in decision-making. During disasters however, Cloud-based mapping services cannot be relied upon, because network infrastructures may have been damaged. In this study, we propose a disaster area mapping system that functions under challenged-network environments in a disaster area. The system infers a pedestrian map with walking speed information from data gathered by civilians and responders with mobile devices. To generate the map, the system addresses the following challenges: how to collect disaster area data, how to share data without continuous end-to-end networks, and how to generate maps without Cloud-based mapping services. First, the system leverages human mobility to collect disaster area data. Civilians and responders with mobile devices function as sensor nodes and log their GPS and velocity traces while moving based on the Post-Disaster Mobility Model. Second, the system uses mobile devices to establish a Delay-Tolerant Network, through which nodes opportunistically share data. Finally to generate the map, the collected data are routed to Computing Nodes: devices with more computational resources than mobile devices that are spatially-distributed across the disaster area. The Computing Nodes infer the map from the data and share it with evacuees. Through experimental evaluations and computer simulations, we found that the system significantly decreases the time required to generate and deliver a map to an evacuee, compared to a case without the system. Furthermore, the overall reduction in time increases as the size of the data required to generate the map and the number of DTN nodes increase.
UR - http://www.scopus.com/inward/record.url?scp=84966621660&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966621660&partnerID=8YFLogxK
U2 - 10.1109/PERCOMW.2016.7457149
DO - 10.1109/PERCOMW.2016.7457149
M3 - Conference contribution
AN - SCOPUS:84966621660
T3 - 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
BT - 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -