This paper presents a forecasting model designed using WSNs (Wireless Sensor
Networks) to predict flood in rivers using simple and fast calculations to
provide real-time results and save the lives of people who may be affected by
the flood. Our prediction model uses multiple variable robust linear regression
which is easy to understand and simple and cost effective in implementation, is
speed efficient, but has low resource utilization and yet provides real time
predictions with reliable accuracy, thus having features which are desirable in
any real world algorithm. Our prediction model is independent of the number of
parameters, i.e. any number of parameters may be added or removed based on the
on-site requirements. When the water level rises, we represent it using a
polynomial whose nature is used to determine if the water level may exceed the
flood line in the near future. We compare our work with a contemporary
algorithm to demonstrate our improvements over it. Then we present our
simulation results for the predicted water level compared to the actual water
level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc,
Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple
Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201
A rectangular microstrip patch antenna using conventional Poly Tetra Fluride Ethelene (PTFE) substrate with air cavity is proposed and theoretically investigated. Considerably high gain along with improved front to back radiation isolation is demonstrated using such proposed antenna. The radiation performance of this new antenna has been compared to a conventional microstrip patch for some commonly used aspect ratios (width to length ratio). Compared to conventional microstrip antenna the proposed configuration shows more than 12% increment in peak gain and more than 10% increment in front to back radiation performance in each set of aspect ratio. The elucidation of such improvement in the radiation characteristics of the proposed antenna is also presented
Very recently work has been done to develop efficient disaster forecasting systems utilizing WSN technology. Such networks pose a tremendous design challenge such as the ability to cope with node failure, limited power, distributed prediction, wide variety of sensors and the need for communication over a large area. Our paper introduces a Predictive Environmental Sensor Network (PESN) Architecture which employs a minimal deployment scheme to ensure connectivity among the nodes involved within the network. On this connected network we run our distributed statistical model for forecasting. The statistical process used for this real time prediction uses multiple variable regression method providing the advantages of simplicity and robustness much needed in low power and limited ability sensor nodes. The versatility of the forecasting model is proved on its independence on the number of parameters, as it can incorporate as many variables into the algorithm as required, as long as there is sufficient positive correlation with the instantaneous river water level.
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