Abstract
The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form. Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described. There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique. So, some solutions are provided to overcome the limitations of the fall curve technique. In this paper, a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years. Its novelty is to predict a fault before its occurrence by looking at the fall curve. The sensing of current flow in devices is important to prevent a major loss. So, the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices. The analysis result proved that if any of the current sensors gets faulty, then the fall curve will differ and the value will immediately drop to zero. Various evaluation metrics for fault prediction are also described in this paper. At last, this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.
Highlights
Internet of Things (IoT) technology has shown its productive nature in several sectors like industry, agriculture, healthcare, academia and many more [1,2,3]
Identification and classification of fault patterns in IoT sensor nodes is a challenging issue in the IoT data environment
The extensive use of sensors in the IoT environment has demanded automating the prediction of sensor faults in IoT devices
Summary
Internet of Things (IoT) technology has shown its productive nature in several sectors like industry, agriculture, healthcare, academia and many more [1,2,3]. IoT is gaining importance in almost every new research especially in industrial communities [4,5]. IoT is not just used as a point of communication for obtaining the data of physical objects but it collects a tremendous amount of data to monitor several methods that enhance the profit of companies. IoT is capable to monitor real-time data which opens up a new area for researchers working on different applications. A faulty sensor produces corrupted data and predicting faults in IoT sensors is a quite difficult task [9]. A rigid monitoring process is required to validate the sensors’ performance. This process is sometimes referred to as sensor fault prediction which predicts faults in the sensor’s readings that affect the working of the device. Schemes for the identification of sensor faults use data-centric approach [14,15,16,17,18]
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