Abstract
Wireless sensor networks (WSN) involve large number of sensor nodes distributed at diverse locations. The collected data are prone to be inaccurate and faulty due to internal or external influences, such as, environmental interference or sensor aging. Intelligent failure detection is necessary for the effective functioning of the sensor network. In this paper, we propose a supervised learning method that is named artificial hydrocarbon networks (AHN), to predict temperature in a remote location and detect failures in sensors. It allows predicting the temperature and detecting failure in sensor node of remote locations using information from a web service comparing it with field temperature sensors. For experimentation, we implemented a small WSN to test our sensor in order to measure failure detection, identification and accommodation proposal. In our experiments, 94.18% of the testing data were recovered and accommodated allowing of validation our proposed approach that is based on AHN, which detects, identify and accommodate sensor failures accurately.
Highlights
IntroductionWeather conditions affect the daily life of many people in the world [1]. The need of environmental information for the development of activities around the cities, farms and communities is increasing as technology of meteorological predictions becomes available
Nowadays, weather conditions affect the daily life of many people in the world [1]
We conducted a series of experiments over the Wireless sensor networks (WSN), described in Section 2, to evaluate the performance of our proposed SFDIA strategy based on artificial hydrocarbon networks (AHN)
Summary
Weather conditions affect the daily life of many people in the world [1]. The need of environmental information for the development of activities around the cities, farms and communities is increasing as technology of meteorological predictions becomes available. The weather monitoring stations help to predict and understand the weather conditions in order to monitor and track weather changes. With a right distribution of meteorological sensors, a prediction based on acquired data with information obtained in real time can be created. This information is used to their own ends, and in some cases, shared between third parties through a new rising technology like the Internet of Things (IoT) to keep in hand the needed information to anticipate and be alert to the weather and climate conditions
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