Abstract
Wireless sensor network (WSN) is an emerging technology with a wide range of potential applications in smart buildings. The measuring process by using WSNs in the actual environment always introduces noise, errors, accidents, and other potential outliers to the data collected by the sensors. It is crucial to establish an effective approach for outlier detection and recovery in the real applications of WSNs. In this paper, we propose an outlier detection and recovery approach using artificial neural network (ANN), which can be used to determine whether the temperature values measured by the sensors in WSNs are outliers. The experimental results in real building show that the proposed ANN-based models can provide a reasonably good prediction of the temperature and high accuracy in buildings compared with the hidden Markov model (HMM)-based approach, which can potentially be used for outlier detecting and thermal controlling in the Internet of Things (IoT) applications.
Highlights
The Internet of Things (IoT) is defined as a system which consists of connected application-specific embedded devices including actuators and sensors, software and network connectivity that enable the new service for meeting people in the community
In this paper, we propose an artificial neural network (ANN)-based model to simulate the relationship between the temperature values of different zones in buildings
The accurate temperature data in different zones can be predicted, and the outliers can be identified by computing the posterior probability of the temperature values measured by the sensors in the Wireless sensor network (WSN) system
Summary
The Internet of Things (IoT) is defined as a system which consists of connected application-specific embedded devices including actuators and sensors, software and network connectivity that enable the new service for meeting people in the community. To improve credibility of sensor read values, reliable detection methods of unusual behavior or outlier/offset are required Many existing works such as classification and clustering techniques have been proposed to detect outliers from observed data. We propose an artificial neural network (ANN) based outlier detection method for detecting the outliers in the temperature values by the WSN system in smart buildings. For the proposed ANN-based outlier detection technique, we need to assume that the sensors, which constitute the WSN system are placed in a limited space randomly. This assumption indicates that there is a relationship between the measured temperature value of each sensor [20].
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