Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/95628
Title: STUDIES OF WIRELESS SENSOR NETWORKS TO MITIGATE NATURAL DISASTERS
Researcher: Abhijit Paul
Guide(s): Prodipto Das
Keywords: Computer Science
University: Assam University
Completed Date: 
Abstract: newlineA natural disaster is a consequence when a natural calamity affects humans and the built environment. These disasters are largely unpredictable and occur within very short spans of time. To reduce the number of sufferers and damages due to natural disasters, timely warning is important. To monitor natural disasters, infrastructure network is not a good option because there are some remote and hostile areas where infrastructure network cannot be implemented. Though in some areas infrastructure network can be implemented, but the network may break down during disaster. Wireless Sensor Network (WSN) provides a good solution in these situations because it can be deployed almost everywhere and during disaster can be used to monitor the situation. WSN is used to monitor regional environmental conditions in plains, forests and also in waters. WSN consists of sensors also called nodes where each node has computation and wireless communication capability for signal processing and networking of the data. WSN also consists of a special node called Base Station (BS) which aggregate all data sensed by sensors. Data is transferred from BS to communication server to predict disaster conditions. Unavailability to reach those areas where WSN is deployed requires optimal use of available resources. Battery power is one of the key resources where minimum consumption of power is required. To maximize network lifetime in WSN by minimizing power consumption, different strategies are used such as clustering, low duty cycle etc. These WSN based strategies are implemented to mitigate natural disasters. Among the natural disasters mainly flood is mitigated by predicting river water level using Artificial Neural Network (ANN) technique. The proposed system first collects data in energy efficient way and then with this strategy it predicts river flood.
Pagination: 3331 KB
URI: http://hdl.handle.net/10603/95628
Appears in Departments:Department of Computer Science

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