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Virtual Disaster Management Information Repository Based on Linked Open Data Yi-Lung Chen 1, Jyun-You Lin 1, Tsung-Hsien Chu 1, Jane Win-Shih Liu 2, IEEE Fellow National Yunlin University of Science and Technology 1, Academia Sinica 2 Experiment As stated in our paper on strategies for crowdsourcing sensor data [1], in-situ sensor networks and remote sensor systems used to collect data for this purpose may not always provide the system with sufficiently complete and detailed view of the threatened area. Remote sensors may be ineffective due to weather. The area coverage of sensor networks and density of sensors deployed are often limited by costs, and existing sensors may be damaged. This is the primary reason that Southern Taiwan region were not adequately monitored by surveillance cameras and other sensors during Typhoon Morakot 2009. Disaster Surveillance System Crowd Command Center Path Threaten Area A disaster surveillance and response system must estimate boundaries of threatened areas, assess the threat potential and acquire situation awareness to support decisions on what alerts and warnings to issue when a disaster seems imminent. Crowdsourcing of Data Collection Motivation We simulated the scenario of Typhoon Morkot 2009, which swept through Taiwan over two days and caused the worst flooding in 50 years. In our simulation, we broadcast the request for data collection of Yunlin County to volunteers from two major universities in Yunlin County: National Yunlin University of Science Technology and National Formosa University. All participants start from either Touliu or Huwei. The speed of each participant is set at 0.3 km/min. In order to demonstrate the effectiveness of our multiple rescuer route algorithm, we compare it with a greedy algorithm (GRA) which first uses half of traveling distance to find a route that can visit the most number of nodes. It then adopts the same route back to the original point. Scenario of Typhoon Morakot 2009 The Number of Visited Nodes The Exploration Time We investigated the number of visited nodes of each algorithm under the constraint of traveling distance. The starting point of all participant is Douliu, Huwei, Dounan or Mailiao. According to our simulation results, our algorithm can visit more nodes than GRA algorithm. The maximum improvement can reach 70%. We also found that the starting point of participants can greatly affect the number of visited nodes. To evaluate the exploration time of each algorithm under the resource and equipment constraints, we simulated a 24-hour relay exploration in which only one participant is assigned to explore the threatened area at one time. We fixed the number of visited nodes. The exploration time is shown in the following figures. According to our simulation results, the proposed method always consumes less time than GRA in exploring the threatened areas. We design a crowdsourcing strategy to collect eye- witness reports needed to acquire situation awareness in the shortest time for given number, quality and mobility of human sensors in the threaten area. According to our experimental results, the proposed method performs better than GRA algorithm in both the number of visited nodes and exploration time. Limitation of Surveillance System in Major Disasters A Video Surveillance System [2] Reference [2] http://www.th.gov.bc.ca/gateway/prb-aryhill/images/pitt_river_aerial3crop.JPGhttp://www.th.gov.bc.ca/gateway/prb-aryhill/images/pitt_river_aerial3crop.JPG The Natural Hazard of Southern Taiwan after Typhoon Morakot 2009 Experiences with past major disasters tell us that people with wireless devices and social network services can be effective mobile human sensors. Eye- witness reports taken at right locations can provide invaluable information and enable a disaster warning and response system to mend blind regions in surveillance sensor coverage. System Architecture Crowdsourcing Flow A sensor data collection crowdsourcing process carries out three steps. First, the command center broadcasts a data collection request to a crowd via popular social networks (e.g. facebook, twitter, MSN, etc.) Second, the command center plans an exploration path for each participant in the crowd. Third, the paths are sent to the crowd. Multiple Disaster Rescuer Problem Broadcast a data collection request to a crowd through Web 2.0 applications Collect the traveling distance and location of each participant Use a directed graph to represent the threatened areas and determine an exploration path for each participant Each volunteer follows the path determined by the command center to explore the threatened areas The communication between the system and the crowd repeats until the system has a complete view Multiple Rescuer Route Algorithm We use a directed graph to represent the threatened area. Our goal is to maximize the number of visited nodes while satisfying the constraints of the direct graph, the number of participants, the traveling distance, starting and end points. If the starting points of all participants are the same, we found that the multiple disaster rescuer problem can be reduced to the traditional multiple traveling salesman problem which is known to be NP-hard. For this reason, we develop an heuristic algorithm, named multiple rescuer route algorithm to obtain an approximate solution. [3] http://www.adfero.com/wp-content/uploads/2010/11/Crowdsourcing.jpghttp://www.adfero.com/wp-content/uploads/2010/11/Crowdsourcing.jpg Web 2.0 Crowdsourcing [3] V i : the nodes of graph m : the number of participants n : the number of nodes S i : the starting point of the i th participant D : the maximum traveling distance R i : the route of the i th participant Initialize the weight of each node; Initialize each R i ; For i = 1 to m For j = 1 to n If S i and node v j are connected R = Hamiton (S i, v j, D ); If R is better than R i R i = R ; End if End for Update the weight of visited nodes; End for V i : the nodes of graph m : the number of participants n : the number of nodes S i : the starting point of the i th participant D : the maximum traveling distance R i : the route of the i th participant Initialize the weight of each node; Initialize each R i ; For i = 1 to m For j = 1 to n If S i and node v j are connected R = Hamiton (S i, v j, D ); If R is better than R i R i = R ; End if End for Update the weight of visited nodes; End for Step 1: Initialize the weight of each node Step 2: Search all Hamilton paths and select the one with highest value Step 3: Update the weight of visited nodes Step 4: Repeat Step 2 and 3 until each participant’s path is determined or all nodes are visited Route Acknowledgement Location [1] Edward T.-H. Chu, Y.-L. Chen, J. W. S. Liu and J. K. Zao, 「 Strategies for Crowdsourcing for Disaster Situation Information, 」 In the 2nd International Conference on Disaster Management and Human Health: Reducing Risk, Improving Outcomes, Orlando, Florida, USA, May, 2011. This work was supported in part by Academia Sinica Taiwan under Grant AS-101-TP2-A01. Determine a path for the i-th participant Conclusion (Douliu, Huwei)(Dounan, Mailiao) (Douliu, Huwei)(Dounan, Mailiao)
![Virtual Disaster Management Information Repository Based on Linked Open Data Yi-Lung Chen 1, Jyun-You Lin 1, Tsung-Hsien Chu 1, Jane Win-Shih Liu 2, IEEE Fellow National Yunlin University of Science and Technology 1, Academia Sinica 2 Experiment As stated in our paper on strategies for crowdsourcing sensor data [1], in-situ sensor networks and remote sensor systems used to collect data for this purpose may not always provide the system with sufficiently complete and detailed view of the threatened area.](http://images.slideplayer.com/36/10531641/slides/slide_1.jpg "Remote sensors may be ineffective due to weather. The area coverage of sensor networks and density of sensors deployed are often limited by costs, and existing sensors may be damaged. This is the primary reason that Southern Taiwan region were not adequately monitored by surveillance cameras and other sensors during Typhoon Morakot Disaster Surveillance System Crowd Command Center Path Threaten Area A disaster surveillance and response system must estimate boundaries of threatened areas, assess the threat potential and acquire situation awareness to support decisions on what alerts and warnings to issue when a disaster seems imminent. Crowdsourcing of Data Collection Motivation We simulated the scenario of Typhoon Morkot 2009, which swept through Taiwan over two days and caused the worst flooding in 50 years. In our simulation, we broadcast the request for data collection of Yunlin County to volunteers from two major universities in Yunlin County: National Yunlin University of Science Technology and National Formosa University. All participants start from either Touliu or Huwei. The speed of each participant is set at 0.3 km/min. In order to demonstrate the effectiveness of our multiple rescuer route algorithm, we compare it with a greedy algorithm (GRA) which first uses half of traveling distance to find a route that can visit the most number of nodes. It then adopts the same route back to the original point. Scenario of Typhoon Morakot 2009 The Number of Visited Nodes The Exploration Time We investigated the number of visited nodes of each algorithm under the constraint of traveling distance. The starting point of all participant is Douliu, Huwei, Dounan or Mailiao. According to our simulation results, our algorithm can visit more nodes than GRA algorithm. The maximum improvement can reach 70%. We also found that the starting point of participants can greatly affect the number of visited nodes. To evaluate the exploration time of each algorithm under the resource and equipment constraints, we simulated a 24-hour relay exploration in which only one participant is assigned to explore the threatened area at one time. We fixed the number of visited nodes. The exploration time is shown in the following figures. According to our simulation results, the proposed method always consumes less time than GRA in exploring the threatened areas. We design a crowdsourcing strategy to collect eye- witness reports needed to acquire situation awareness in the shortest time for given number, quality and mobility of human sensors in the threaten area. According to our experimental results, the proposed method performs better than GRA algorithm in both the number of visited nodes and exploration time. Limitation of Surveillance System in Major Disasters A Video Surveillance System [2] Reference [2] The Natural Hazard of Southern Taiwan after Typhoon Morakot 2009 Experiences with past major disasters tell us that people with wireless devices and social network services can be effective mobile human sensors. Eye- witness reports taken at right locations can provide invaluable information and enable a disaster warning and response system to mend blind regions in surveillance sensor coverage. System Architecture Crowdsourcing Flow A sensor data collection crowdsourcing process carries out three steps. First, the command center broadcasts a data collection request to a crowd via popular social networks (e.g. facebook, twitter, MSN, etc.) Second, the command center plans an exploration path for each participant in the crowd. Third, the paths are sent to the crowd. Multiple Disaster Rescuer Problem Broadcast a data collection request to a crowd through Web 2.0 applications Collect the traveling distance and location of each participant Use a directed graph to represent the threatened areas and determine an exploration path for each participant Each volunteer follows the path determined by the command center to explore the threatened areas The communication between the system and the crowd repeats until the system has a complete view Multiple Rescuer Route Algorithm We use a directed graph to represent the threatened area. Our goal is to maximize the number of visited nodes while satisfying the constraints of the direct graph, the number of participants, the traveling distance, starting and end points. If the starting points of all participants are the same, we found that the multiple disaster rescuer problem can be reduced to the traditional multiple traveling salesman problem which is known to be NP-hard. For this reason, we develop an heuristic algorithm, named multiple rescuer route algorithm to obtain an approximate solution. [3] Web 2.0 Crowdsourcing [3] V i : the nodes of graph m : the number of participants n : the number of nodes S i : the starting point of the i th participant D : the maximum traveling distance R i : the route of the i th participant Initialize the weight of each node; Initialize each R i ; For i = 1 to m For j = 1 to n If S i and node v j are connected R = Hamiton (S i, v j, D ); If R is better than R i R i = R ; End if End for Update the weight of visited nodes; End for V i : the nodes of graph m : the number of participants n : the number of nodes S i : the starting point of the i th participant D : the maximum traveling distance R i : the route of the i th participant Initialize the weight of each node; Initialize each R i ; For i = 1 to m For j = 1 to n If S i and node v j are connected R = Hamiton (S i, v j, D ); If R is better than R i R i = R ; End if End for Update the weight of visited nodes; End for Step 1: Initialize the weight of each node Step 2: Search all Hamilton paths and select the one with highest value Step 3: Update the weight of visited nodes Step 4: Repeat Step 2 and 3 until each participant’s path is determined or all nodes are visited Route Acknowledgement Location [1] Edward T.-H. Chu, Y.-L. Chen, J. W. S. Liu and J. K. Zao, 「 Strategies for Crowdsourcing for Disaster Situation Information, 」 In the 2nd International Conference on Disaster Management and Human Health: Reducing Risk, Improving Outcomes, Orlando, Florida, USA, May, This work was supported in part by Academia Sinica Taiwan under Grant AS-101-TP2-A01. Determine a path for the i-th participant Conclusion (Douliu, Huwei)(Dounan, Mailiao) (Douliu, Huwei)(Dounan, Mailiao).")
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Path Planning for Volunteers to Construct Crowdsourced Maps in Mega Disasters Yi-Lung Chen 1, Jyun-You Lin 1, Tsung-Hsien Chu 1, Jane Win-Shih Liu 2, IEEE Fellow National Yunlin University of Science and Technology 1, Academia Sinica 2 Experiment As stated in our paper on strategies for crowdsourcing sensor data [1], in-situ sensor networks and remote sensor systems used to collect data for this purpose may not always provide the system with sufficiently complete and detailed view of the threatened area. Remote sensors may be ineffective due to weather. The area coverage of sensor networks and density of sensors deployed are often limited by costs, and existing sensors may be damaged. This is the primary reason that Southern Taiwan region were not adequately monitored by surveillance cameras and other sensors during Typhoon Morakot 2009. Disaster Surveillance System Crowd Command Center Path Threaten Area A disaster surveillance and response system must estimate boundaries of threatened areas, assess the threat potential and acquire situation awareness to support decisions on what alerts and warnings to issue when a disaster seems imminent. Crowdsourcing of Data Collection Motivation We simulated the scenario of Typhoon Morkot 2009, which swept through Taiwan over two days and caused the worst flooding in 50 years. In our simulation, we broadcast the request for data collection of Yunlin County to volunteers from two major universities in Yunlin County: National Yunlin University of Science Technology and National Formosa University. All participants start from either Touliu or Huwei. The speed of each participant is set at 0.3 km/min. In order to demonstrate the effectiveness of our multiple rescuer route algorithm, we compare it with a greedy algorithm (GRA) which first uses half of traveling distance to find a route that can visit the most number of nodes. It then adopts the same route back to the original point. Scenario of Typhoon Morakot 2009 The Number of Visited Nodes The Exploration Time We investigated the number of visited nodes of each algorithm under the constraint of traveling distance. The starting point of all participant is Douliu, Huwei, Dounan or Mailiao. According to our simulation results, our algorithm can visit more nodes than GRA algorithm. The maximum improvement can reach 70%. We also found that the starting point of participants can greatly affect the number of visited nodes. To evaluate the exploration time of each algorithm under the resource and equipment constraints, we simulated a 24-hour relay exploration in which only one participant is assigned to explore the threatened area at one time. We fixed the number of visited nodes. The exploration time is shown in the following figures. According to our simulation results, the proposed method always consumes less time than GRA in exploring the threatened areas. We design a crowdsourcing strategy to collect eye- witness reports needed to acquire situation awareness in the shortest time for given number, quality and mobility of human sensors in the threaten area. According to our experimental results, the proposed method performs better than GRA algorithm in both the number of visited nodes and exploration time. Limitation of Surveillance System in Major Disasters A Video Surveillance System [2] Reference [2] http://www.th.gov.bc.ca/gateway/prb-aryhill/images/pitt_river_aerial3crop.JPGhttp://www.th.gov.bc.ca/gateway/prb-aryhill/images/pitt_river_aerial3crop.JPG The Natural Hazard of Southern Taiwan after Typhoon Morakot 2009 Experiences with past major disasters tell us that people with wireless devices and social network services can be effective mobile human sensors. Eye- witness reports taken at right locations can provide invaluable information and enable a disaster warning and response system to mend blind regions in surveillance sensor coverage. System Architecture Crowdsourcing Flow A sensor data collection crowdsourcing process carries out three steps. First, the command center broadcasts a data collection request to a crowd via popular social networks (e.g. facebook, twitter, MSN, etc.) Second, the command center plans an exploration path for each participant in the crowd. Third, the paths are sent to the crowd. Multiple Disaster Rescuer Problem Broadcast a data collection request to a crowd through Web 2.0 applications Collect the traveling distance and location of each participant Use a directed graph to represent the threatened areas and determine an exploration path for each participant Each volunteer follows the path determined by the command center to explore the threatened areas The communication between the system and the crowd repeats until the system has a complete view Multiple Rescuer Route Algorithm We use a directed graph to represent the threatened area. Our goal is to maximize the number of visited nodes while satisfying the constraints of the direct graph, the number of participants, the traveling distance, starting and end points. If the starting points of all participants are the same, we found that the multiple disaster rescuer problem can be reduced to the traditional multiple traveling salesman problem which is known to be NP-hard. For this reason, we develop an heuristic algorithm, named multiple rescuer route algorithm to obtain an approximate solution. [3] http://www.adfero.com/wp-content/uploads/2010/11/Crowdsourcing.jpghttp://www.adfero.com/wp-content/uploads/2010/11/Crowdsourcing.jpg Web 2.0 Crowdsourcing [3] V i : the nodes of graph m : the number of participants n : the number of nodes S i : the starting point of the i th participant D : the maximum traveling distance R i : the route of the i th participant Initialize the weight of each node; Initialize each R i ; For i = 1 to m For j = 1 to n If S i and node v j are connected R = Hamiton (S i, v j, D ); If R is better than R i R i = R ; End if End for Update the weight of visited nodes; End for V i : the nodes of graph m : the number of participants n : the number of nodes S i : the starting point of the i th participant D : the maximum traveling distance R i : the route of the i th participant Initialize the weight of each node; Initialize each R i ; For i = 1 to m For j = 1 to n If S i and node v j are connected R = Hamiton (S i, v j, D ); If R is better than R i R i = R ; End if End for Update the weight of visited nodes; End for Step 1: Initialize the weight of each node Step 2: Search all Hamilton paths and select the one with highest value Step 3: Update the weight of visited nodes Step 4: Repeat Step 2 and 3 until each participant’s path is determined or all nodes are visited Route Acknowledgement Location [1] Edward T.-H. Chu, Y.-L. Chen, J. W. S. Liu and J. K. Zao, 「 Strategies for Crowdsourcing for Disaster Situation Information, 」 In the 2nd International Conference on Disaster Management and Human Health: Reducing Risk, Improving Outcomes, Orlando, Florida, USA, May, 2011. This work was supported in part by Academia Sinica Taiwan under Grant AS-101-TP2-A01. Determine a path for the i-th participant Conclusion (Douliu, Huwei)(Dounan, Mailiao) (Douliu, Huwei)(Dounan, Mailiao)
![Path Planning for Volunteers to Construct Crowdsourced Maps in Mega Disasters Yi-Lung Chen 1, Jyun-You Lin 1, Tsung-Hsien Chu 1, Jane Win-Shih Liu 2, IEEE Fellow National Yunlin University of Science and Technology 1, Academia Sinica 2 Experiment As stated in our paper on strategies for crowdsourcing sensor data [1], in-situ sensor networks and remote sensor systems used to collect data for this purpose may not always provide the system with sufficiently complete and detailed view of the threatened area.](http://images.slideplayer.com/36/10531641/slides/slide_2.jpg "Remote sensors may be ineffective due to weather. The area coverage of sensor networks and density of sensors deployed are often limited by costs, and existing sensors may be damaged. This is the primary reason that Southern Taiwan region were not adequately monitored by surveillance cameras and other sensors during Typhoon Morakot Disaster Surveillance System Crowd Command Center Path Threaten Area A disaster surveillance and response system must estimate boundaries of threatened areas, assess the threat potential and acquire situation awareness to support decisions on what alerts and warnings to issue when a disaster seems imminent. Crowdsourcing of Data Collection Motivation We simulated the scenario of Typhoon Morkot 2009, which swept through Taiwan over two days and caused the worst flooding in 50 years. In our simulation, we broadcast the request for data collection of Yunlin County to volunteers from two major universities in Yunlin County: National Yunlin University of Science Technology and National Formosa University. All participants start from either Touliu or Huwei. The speed of each participant is set at 0.3 km/min. In order to demonstrate the effectiveness of our multiple rescuer route algorithm, we compare it with a greedy algorithm (GRA) which first uses half of traveling distance to find a route that can visit the most number of nodes. It then adopts the same route back to the original point. Scenario of Typhoon Morakot 2009 The Number of Visited Nodes The Exploration Time We investigated the number of visited nodes of each algorithm under the constraint of traveling distance. The starting point of all participant is Douliu, Huwei, Dounan or Mailiao. According to our simulation results, our algorithm can visit more nodes than GRA algorithm. The maximum improvement can reach 70%. We also found that the starting point of participants can greatly affect the number of visited nodes. To evaluate the exploration time of each algorithm under the resource and equipment constraints, we simulated a 24-hour relay exploration in which only one participant is assigned to explore the threatened area at one time. We fixed the number of visited nodes. The exploration time is shown in the following figures. According to our simulation results, the proposed method always consumes less time than GRA in exploring the threatened areas. We design a crowdsourcing strategy to collect eye- witness reports needed to acquire situation awareness in the shortest time for given number, quality and mobility of human sensors in the threaten area. According to our experimental results, the proposed method performs better than GRA algorithm in both the number of visited nodes and exploration time. Limitation of Surveillance System in Major Disasters A Video Surveillance System [2] Reference [2] The Natural Hazard of Southern Taiwan after Typhoon Morakot 2009 Experiences with past major disasters tell us that people with wireless devices and social network services can be effective mobile human sensors. Eye- witness reports taken at right locations can provide invaluable information and enable a disaster warning and response system to mend blind regions in surveillance sensor coverage. System Architecture Crowdsourcing Flow A sensor data collection crowdsourcing process carries out three steps. First, the command center broadcasts a data collection request to a crowd via popular social networks (e.g. facebook, twitter, MSN, etc.) Second, the command center plans an exploration path for each participant in the crowd. Third, the paths are sent to the crowd. Multiple Disaster Rescuer Problem Broadcast a data collection request to a crowd through Web 2.0 applications Collect the traveling distance and location of each participant Use a directed graph to represent the threatened areas and determine an exploration path for each participant Each volunteer follows the path determined by the command center to explore the threatened areas The communication between the system and the crowd repeats until the system has a complete view Multiple Rescuer Route Algorithm We use a directed graph to represent the threatened area. Our goal is to maximize the number of visited nodes while satisfying the constraints of the direct graph, the number of participants, the traveling distance, starting and end points. If the starting points of all participants are the same, we found that the multiple disaster rescuer problem can be reduced to the traditional multiple traveling salesman problem which is known to be NP-hard. For this reason, we develop an heuristic algorithm, named multiple rescuer route algorithm to obtain an approximate solution. [3] Web 2.0 Crowdsourcing [3] V i : the nodes of graph m : the number of participants n : the number of nodes S i : the starting point of the i th participant D : the maximum traveling distance R i : the route of the i th participant Initialize the weight of each node; Initialize each R i ; For i = 1 to m For j = 1 to n If S i and node v j are connected R = Hamiton (S i, v j, D ); If R is better than R i R i = R ; End if End for Update the weight of visited nodes; End for V i : the nodes of graph m : the number of participants n : the number of nodes S i : the starting point of the i th participant D : the maximum traveling distance R i : the route of the i th participant Initialize the weight of each node; Initialize each R i ; For i = 1 to m For j = 1 to n If S i and node v j are connected R = Hamiton (S i, v j, D ); If R is better than R i R i = R ; End if End for Update the weight of visited nodes; End for Step 1: Initialize the weight of each node Step 2: Search all Hamilton paths and select the one with highest value Step 3: Update the weight of visited nodes Step 4: Repeat Step 2 and 3 until each participant’s path is determined or all nodes are visited Route Acknowledgement Location [1] Edward T.-H. Chu, Y.-L. Chen, J. W. S. Liu and J. K. Zao, 「 Strategies for Crowdsourcing for Disaster Situation Information, 」 In the 2nd International Conference on Disaster Management and Human Health: Reducing Risk, Improving Outcomes, Orlando, Florida, USA, May, This work was supported in part by Academia Sinica Taiwan under Grant AS-101-TP2-A01. Determine a path for the i-th participant Conclusion (Douliu, Huwei)(Dounan, Mailiao) (Douliu, Huwei)(Dounan, Mailiao).")
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