1. YunTech EOS Lab Crowdsourcing Support System for Disaster Surveillance and Response Edward T.-H Chu, Yi-Lung Chen, Jyun-You Lin National Yunlin University of Science and Technology Jane W. S. Liu, IEEE Fellow Academia Sinica 1 The 15 th International Symposium on Wireless Personal Multimedia Communications(WPMC), Taiwan

2. YunTech EOS Lab Motivation System Architecture Multiple Volunteer Routing Problem Demo Video Conclusion Outline 2

3. YunTech EOS Lab 3 Disaster Surveillance System Functionalities of disaster surveillance system  Collect real-time information  Estimate boundaries of threatened areas Sensor networks play an important role in collecting data  Camera surveillance network  Wireless sensor network

4. YunTech EOS Lab 4 Limitation of Sensor Networks In a major disaster  Sensors can be easily damaged  Official rescue resources are limited Collecting information in major disasters is difficult ？

5. YunTech EOS Lab 5 Social network services have been playing an role in colleting disaster information  Ushahidi, Sahana, Facebook, Twitter, Google Map Growing Trend in Disaster Management

6. YunTech EOS Lab 6 Limitations Existing social network based disaster management systems are limited in  coordinating volunteers efficiently Some places may be visited repeatedly while others may not be visited at all  Cannot provide a comprehensive view  Prolong the response time of exploring the whole disaster area

7. YunTech EOS Lab Contributions A crowdsourcing support system for disaster surveillance (CROSS) Cross plans routes for the volunteers to explore threatened areas A prototype including an emergency APP and sever services 7

8. YunTech EOS Lab CROSS System 8 Crowd Command Center Path Threaten Area Location

9. YunTech EOS Lab Major components of CROSS Broadcast manager  Post a volunteer requirement Path planning manager  Determine a route for each volunteer Crowdsourced map manager  Integrate the messages received from volunteers and display it on a map 9

10. YunTech EOS Lab Broadcast Manager 10 (Broadcast volunteering opportunities)

11. YunTech EOS Lab Broadcast Manager (cont.) 11 (Report volunteer identity) (The locations of volunteers)

12. YunTech EOS Lab Path Planning Manager 12 (The route of a volunteer - smartphone view ) (The routes of volunteers - server view)

13. YunTech EOS Lab Crowdsourced Map Manager 13 (The crowdsourced map ) (Emergency call)

14. YunTech EOS Lab Motivation System Architecture Multiple Volunteer Routing Problem Prototype Demo Conclusion Outline 14

15. YunTech EOS Lab Graph Model of Threatened Area CROSS uses a graph to characterize the threatened area 15

16. YunTech EOS Lab Multiple Volunteer Routing Problem Find routes for volunteers such that the number of visited nodes is maximized Constraints  Go back to the starting point  Cannot exceed traveling limit Similar to multiple traveling salesman problem (NP-Hard) 16

17. YunTech EOS Lab Multiple Volunteer Routing Algorithm (MVR) Step 1: finds a smallest Hamilton cycle containing the starting node with three nodes Step 2: based on the initial solution, MVR includes one of nearest nodes to form a new Hamiltonian cycle Step 3: if more than two new Hamiltonian cycles are found, MVR selects the one with the minimum total traveling time Step 4: stop until no more Hamilton cycle is found Step 5: repeat the process to find a route for the next volunteer 17

18. YunTech EOS Lab Multiple Volunteer Routing Algorithm An example  The starting node = B, the traveling distance = 30  The final route of MVR: B->A->C->E->D->B 18

19. YunTech EOS Lab Demo video Prototype demo  http://youtu.be/xCddMzGKyfohttp://youtu.be/xCddMzGKyfo 19

20. YunTech EOS Lab Conclusion We developed a strategy of crowdsourcing disaster information We designed a heuristic algorithm to d etermine a route for each volunteer to explore threatened areas We built up a prototype to demonstrate the applicability 20

21. YunTech EOS Lab Publications 1.Jane W. S. Liu, C. S. Shih, Edward T.-H. Chu, “Cyber-Physical Elements of Disaster Prepared Smart Environment,” accepted for publication in IEEE Computer Magazine. 2012. 2.Edward T.-H. Chu, Yi-Lung Chen, Jyun-You Lin and Jane W. S. Liu,"Crowdsourcing Support System for Disaster Surveillance and Response," In the 15th International Symposium on Wireless Personal Multimedia Communications (WPMC), 2012. (Invited paper) 3.W. P. Liao, Y. Z. Ou, Edward T.-H. Chu, C. S. Shih, J. W. S. Liu,"Ubiquitous Smart Devices and Applicaftions for Disaster Preparedness", In the 2012 International Symposium on UbiCom Frontiers - Innovative Research, Systems and Technologies (UFirst 2012), Japan, 2012. (Invited paper) 4.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. 21

22. YunTech EOS Lab Opinions on newspaper (Chinese) 2012.8 朱宗賢，聯合報 ，「警政 APP 獨漏緊急治安簡訊」  警政署公開回應 : 「警政 App 加治安簡訊 恐涉隱私權 」 2012.8 朱宗賢，中國時 ，「災難資訊 勿忘來台旅人」  新北市消防局來電回應 : 「納入參考」 2012.8 朱宗賢，聯合報 ， 「預約警報 防颱有效率」 2012.8 方祺瑋，聯合報 ， 「預約氣象 何不改傳簡訊？」  氣象局公開回應 : 「推緊急氣象簡訊？納入參考」 2012.6 朱宗賢，聯合報民意論壇 - 「善用社群 擴大救災能量」 22

23. YunTech EOS Lab RITMAN Workshop 2012 – Call for paper Workshop on Resilient ICT for Management of Mega Disasters (RITMAN), Taipei, Taiwan, December 17, 2012 (In conjunction with IEEE International Conference on SOCA 2012) Paper Submission Due: October 5, 2012 (extended) Acceptance Notification: October 22, 2012 Camera Ready Submission: November 2, 2012 Workshop: December 19, 2012 Accepted papers will be included in the IEEE Digital Library 23

24. YunTech EOS Lab Topics (not limited) Virtual repository for disaster information Security and privacy for rescue/emergency information Humanitarians issues in disaster management Assessment and Reduction of Uncertainty in Disaster information Crowdsourcing technologies (social sensing ) Case studies in disaster situation analysis and information fusion Resilient communication protocol and infrastructure recovery Configuration and management of faults and failures during big disasters Networking issues in disaster recovery Disaster risk assessment and management Business service continuity management 24 Welcome to RITMAN 2012!

25. YunTech EOS Lab 25 Assistant Prof. Edward Chu at Yuntech University ( 國立雲林科技大學資工系 朱宗賢 助理教授 ) http://eos.yuntech.edu.tw/ OpenISDM project at SINICA ( 中央研究院 OpenISDM 主題研究計畫，張韻詩教授 ) http://openisdm.iis.sinica.edu.tw/

26. YunTech EOS Lab Case Study: Typhoon Morkot in Yunlin We compare MVR algorithm with three commonly-used route planning algorithms:  Greedy  NN (Nearest Neighbor)  HBF(Exhaustive search) 26

27. YunTech EOS Lab Effect of Starting Locations 27 Experiment Setup: Traveling distance (time) limit at 2 hours and consider only one volunteer

28. YunTech EOS Lab Effect of Traveling Distance - Single Volunteer 28 Experiment Setup: Varying the maximum traveling hours from 120 minutes to 200 minutes

29. YunTech EOS Lab Effect of Traveling Distance - Multiple Volunteers Experiment Setup: 3 volunteers come from three major cities in Yunlin County: Douliu, Huwei and Beigang. We varied the traveling time from 120 min to 200 min 29

30. YunTech EOS Lab Multiple Volunteer Routing Problem Goal:  We aim to s olve the multiple volunteer routing problem (mVoRP) to find routes for m responded volunteers such that the number of visited nodes by all of them is maximized Constraints (Given Parameters)  A directed graph  The number of participant  The traveling distance  Start points – Back to starting point  Similar to multiple traveling salesman problem Hamilton cycle  A path in an undirected graph that visits each vertex exactly once and the start node is the same as the end node 30

31. YunTech EOS Lab Hamilton Brute Force Algorithm Step 1: Search the one of connection node of start node and search the all Hamilton cycle that the path start from the start node and end in the connection node Step 2: Repeat Step 1 until all connection nodes of start node is chosen and calculation Step 3: Choose the most visited nodes of Hamilton cycle to set the path of volunteer Step 4: Update the visited nodes Step 5: Repeat Step 2 to 4 until each volunteer’s path is determined or all nodes are visited 31

32. YunTech EOS Lab Hamilton Brute Force Algorithm An example  The start node: B, The traveling distance:30  The final route of HBF: B->A->C->E->F->G->D->B 32