Tsunami Detection and forewarning system using Wireless Sensor Network - a Survey
Keywords:
Disaster Management, Sensors, Energy efficiency, WSNAbstract
The Tsunami is a natural disaster which can occur over a rapid period of time. The timely report and the responses are very much important to reduce the losses. Certain methods are being followed to detect and inform the public. One of the reliable methods is the WSN. The wireless node sense the vibration of the earth crust, the changes noted will be given to the controller. The controller sends the information to the base station through the radio waves. The wireless sensor devices are equipped with the micro controller, a small RAM for data storage, a flash memory to hold the program, Wireless transceiver, antennae, ADC Converter, and a power source. Our objective is to study the different sensor network protocols to resolve the issue, thus to identify the energy competent wireless sensor network for the considerable improvement in the tsunami disaster management. We also analyze the WSN protocol based on metrics such as Energy competence, location awareness, and network lifetime.
References
ManeeshaV.Ramesh,Sangeeth Kumar, and P.VenkatRangan.”Wireless sensor Network for Landslide Detection”, International Conference on Wireless Networks (ICWN’09), CSREA Press, 2009.
Kanan Casey, Alvin Lim , and Gerry Dozier.”A Sensor Network Architecture for Tsunami Detection and Response”,International Journal of Distributed Sensor Networks, volume-04 , Issue- 01, page-28-43, January 2008.
Vijay Chandrasekhar, Winston KG Seah.” Localization in underwater Sensor Networks − Survey and Challenges”, WUWNet’06 proceedings of IstACM International Workshop, September 25, 2006,Page -33-40, Los Angeles, California, USA.Copyright 2006 ACM 1-59593-484-7/06/0009...$5.00.
MelikeErol-Kantarci, Hussein T. Mouftah, and SemaOktug” A Survey of Architectures and Localization Techniques for Underwater Acoustic Sensor Networks”,IEEE Communication Magazine, volume-48,Issue-12,Pages-152-158,December 2010.
K. Casey, A. Lim, and G. Dozier, “Evolving general regression neural networks for tsunami detection and response”, in Proceedings of the International Congress on Evolutionary Computation(CEC). IEEE, July 2006.
D. Specht, “A general regression neural network,” IEEE Transactions on Neural Networks, Volume-02, Issue-06, November 1991.
Chenyang Lu, Brian M. Blum, Tarek F. Abdelzaher, John A. Stankovic, and Tian He,” RAP: A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks,” IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2002) Page -05,San Jose, CA, September 2002.
Rui Tan, Guoliang Xing, Jinzhu Chen, Wen-Zhan Song, Renjie Huang. 2010. in inproceedings on “Quality driven Volcanic Earthquake Detection using wireless sensor network”, Real time system symposium(RTSS), Page- 271-280,Nov-30-2010.
Makoto Suzuki, NaritoKurata, Hiroyuki Morikawa,ShunsukeSaruwatari,“A high density earthquakemonitoring system using wireless sensor networks”,International conference on Embedded network sensorSystem, November 6-9, 2007.
Naveed Ahmad, NaveedRiaz, Mureed Hussain.2011.“Ad hoc wirelesses Sensor Network Architecture forDisaster Survivor Detection”, International journal ofadvance science and technology, vol-34, page-9-16, September2011.
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