Wireless sensor networks are proved to be effective in long-time localized torrential rain monitoring. However, the existing widely\nused architecture of wireless sensor networks for rain monitoring relies on network transportation and back-end calculation, which\ncauses delay in response to heavy rain in localized areas. Our work improves the architecture by applying logistic regression and\nsupport vector machine classification to an intelligent wireless sensor node which is created by Raspberry Pi.The sensor nodes in\nfront-end not only obtain data from sensors, but also can analyze the probabilities of upcoming heavy rain independently and give\nearly warnings to local clients in time. When the sensor nodes send the probability to back-end server, the burdens of network\ntransport are released. We demonstrate by simulation results that our sensor system architecture has potentiality to increase the\nlocal response to heavy rain. The monitoring capacity is also raised.
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