Developing a disaster surveillance system based on wireless sensor network and cloud platform

Typically, today´s WSN disaster surveillance system operates poorly in its accuracy and timeliness and can only detect a single type of disasters. This owes largely to the congestion brought about by excessive communication traffic and the processing limitation of the server which dramatically restrict the development of disaster surveillance systems. In order to solve these problems, this paper proposes a new scheme in improving the traditional disaster surveillance systems. At the data collection and transmission layer, orthogonal neural network algorithm, which is based on the wavelet transform, is introduced to promote the surveillance accuracy and reduce the network congestion. At the data storage and computing layer, cloud storage and distributed parallel computing are used to overcome the limitation of the previous storage and computation. At last, the paper gives the concrete implementation plan and verifies the superiority of the system.