Sensors-assisted rescue service architecture in mobile cloud computing

In this paper, we propose a sensors-assisted rescue service architecture to integrate rescue schemes for different purposes, including disaster prediction, evacuation planning, and emergency broadcast. In the proposed architecture, multiple-sensed mobile devices are designed to provide a personalized situational awareness, thereby further enhancing the flexibility and efficiency of rescue services. Reliability and scalability of rescue services are improved by leveraging the dynamical resource provision of cloud computing. The proposed rescue service architecture is implemented to show the advantages of power efficiency and scalability of the proposed rescue service architecture.

[1]  D. Saha,et al.  A neural network based prediction model for flood in a disaster management system with sensor networks , 2005, Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, 2005..

[2]  Kari Pulli,et al.  Mobile Visual Computing , 2009, 2009 International Symposium on Ubiquitous Virtual Reality.

[3]  I.G.M.M. Niemegeers,et al.  Cognitive radio emergency networks - requirements and design , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[4]  Daniel Zappala,et al.  Autonomous and Intelligent Radio Switching for heterogeneous wireless networks , 2008, 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems.

[5]  Wei Zhou,et al.  DistressNet: a wireless ad hoc and sensor network architecture for situation management in disaster response , 2010, IEEE Communications Magazine.

[6]  T. Fujiwara,et al.  A framework for data collection system with sensor networks in disaster circumstances , 2004, International Workshop on Wireless Ad-Hoc Networks, 2004..

[7]  Shih-Jung Wu,et al.  An Integrated Building Fire Evacuation System with RFID and Cloud Computing , 2011, 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[8]  Matt Welsh,et al.  Sensor networks for emergency response: challenges and opportunities , 2004, IEEE Pervasive Computing.

[9]  Theodore S. Rappaport,et al.  An overview of the challenges and progress in meeting the E-911 requirement for location service , 1998, IEEE Commun. Mag..

[10]  Yi-Ming Wei,et al.  Artificial neural network based predictive method for flood disaster , 2002 .

[11]  Li Zhang,et al.  Optimum Transit Operations during the Emergency Evacuations , 2009 .

[12]  Hung-Chin Jang,et al.  Rescue information system for earthquake disasters based on MANET emergency communication platform , 2009, IWCMC.

[13]  Shashi Shekhar,et al.  Evacuation route planning: scalable heuristics , 2007, GIS.

[14]  Massimo Maresca,et al.  An Architecture for a Mashup Container in Virtualized Environments , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[15]  Ying Wang,et al.  The Application of RBF Neural Network in Earthquake Prediction , 2009, 2009 Third International Conference on Genetic and Evolutionary Computing.

[16]  Cong Wang,et al.  Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing , 2010, 2010 Proceedings IEEE INFOCOM.

[17]  Farnoush Banaei Kashani,et al.  Voronoi-Based Geospatial Query Processing with MapReduce , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[18]  Botao Xie,et al.  Typhoon disaster in China: prediction, prevention, and mitigation , 2009 .