Milk Carton: Family Tracing and Reunification system using Face Recognition over a DTN with Deployed Computing Nodes

During the recovery period after disasters, Family Tracing and Reunification (FTR) is the process by which separated family members are reunited. Traditional FTR methods rely on paper-based registries and notice boards, which cannot automatically match missing person queries with existing records and cannot be efficiently disseminated. Furthermore, lost children or people with disabilities may not be capable of supplying the text-based personal information required by registry forms. Finally, current digital FTR systems require the Internet for data delivery and storage, which may be unavailable during a disaster scenario. To overcome these limitations, we propose the Milk Carton FTR system. Milk Carton uses the Eigenfaces face recognition algorithm to automatically match missing person queries with existing records, without requiring text-based personal information. The system uses Computing Nodes, commodity devices deployed in evacuation centers, to handle record and query creation, data storage, and matching. To handle data delivery without the Internet, Milk Carton leverages response team vehicles as data ferries. The ferries store, carry, and forward records and queries across the system. In this study, we present the design of the Milk Carton system and initial performance evaluations.

[1]  Yutaka Arakawa,et al.  Milk Carton: A Face Recognition-Based FTR System Using Opportunistic Clustered Computing , 2016, 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS).

[2]  Md. Yusuf Sarwar Uddin,et al.  A post-disaster mobility model for Delay Tolerant Networking , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[3]  Ellen W. Zegura,et al.  A message ferrying approach for data delivery in sparse mobile ad hoc networks , 2004, MobiHoc '04.

[4]  Akihiro Fujihara,et al.  Disaster Evacuation Guidance Using Opportunistic Communication: The Potential for Opportunity-Based Service , 2014, Big Data and Internet of Things.

[5]  Jon Crowcroft,et al.  Evaluating opportunistic networks in disaster scenarios , 2013, J. Netw. Comput. Appl..

[6]  Yutaka Arakawa,et al.  DTN MapEx: Disaster area mapping through distributed computing over a Delay Tolerant Network , 2015, 2015 Eighth International Conference on Mobile Computing and Ubiquitous Networking (ICMU).

[7]  Hirozumi Yamaguchi,et al.  Survey of Real-time Processing Technologies of IoT Data Streams , 2016, J. Inf. Process..

[8]  Ellen W. Zegura,et al.  Serendipity: enabling remote computing among intermittently connected mobile devices , 2012, MobiHoc '12.

[9]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[10]  Kevin R. Fall,et al.  A delay-tolerant network architecture for challenged internets , 2003, SIGCOMM '03.

[11]  Yutaka Arakawa,et al.  Disaster area mapping using spatially-distributed computing nodes across a DTN , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[12]  Radu Stoleru,et al.  DistressNet: A disaster response system providing constant availability cloud-like services , 2013, Ad Hoc Networks.

[13]  Lars C. Wolf,et al.  IBR-DTN: A lightweight, modular and highly portable Bundle Protocol implementation , 2011, Electron. Commun. Eur. Assoc. Softw. Sci. Technol..

[14]  Keiichi Yasumoto,et al.  Disaster Information Collection with Opportunistic Communication and Message Aggregation , 2014, J. Inf. Process..