Abstract
Wireless sensor networks are proved to be effective in long-time localized torrential rain monitoring. However, the existing widely used architecture of wireless sensor networks for rain monitoring relies on network transportation and back-end calculation, which causes delay in response to heavy rain in localized areas. Our work improves the architecture by applying logistic regression and support vector machine classification to an intelligent wireless sensor node which is created by Raspberry Pi. The sensor nodes in front-end not only obtain data from sensors, but also can analyze the probabilities of upcoming heavy rain independently and give early warnings to local clients in time. When the sensor nodes send the probability to back-end server, the burdens of network transport are released. We demonstrate by simulation results that our sensor system architecture has potentiality to increase the local response to heavy rain. The monitoring capacity is also raised.
Highlights
The development of Internet of Things (IoT) signals a shift in the resources of data
We plot the accuracy of European Centre for Medium-Range Weather Forecasts (ECMWF), support vector machines (SVM), and logistic regression versus the size of training samples
Results of logistic regression on Localized Torrential Rain (LTR) test dataset our environmental sensor networks (ESN) to deal with more LTR situations with more prior knowledge
Summary
The development of Internet of Things (IoT) signals a shift in the resources of data. In most existing ESN systems, the flood of sensorgenerated data pours into the back-end server without processing, which shoves heavy load onto network transmission. In harsh environments such as torrential rain and typhoon, short time network failure leads to serious paralysis in monitoring system. It is desirable to learn the environmental information in the front-end, so that the ESN system can respond to different environmental situations with less help from the back-end This new data processing system, which we call intelligent wireless sensor node, is the core of future ESN systems in front-end. The life-long training enables sensor node to reach better accuracy
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