# Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study
## 1 Introduction  
Nowadays, the precise knowledge of the most appropriate sensor operating conditions and fault detection systems are among the cornerstones of scientific and technical studies for automated systems [1]. These are based upon on-line monitoring processes and additional comprehensive interpretation of sensor data by assessing sensor reliability. Sensors are driving the rapid growth of Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) [2]. Both paradigms are pushing towards the next generation of sensor networks and unpredictable future applications, meaning that sensor reliability has become one of the most important and desirable performance indicators in the design, implementation, and deployment of future sensor networks [3, 4]. An important reliability-related issue to be detected in autonomous systems, in order to self-correct problems such as lost data packages, and data collision, among others, is the failure of one network element [5, 6, 7]. One possible solution is to build real-time prediction models that maximize robustness and lifetime [8]. There are, in fact, several methods for the evaluation of sensor reliability. Each issue ofreliability, or that might affect it, can be assessed individually and as a whole, through a total error band figure. There are important features to be considered, such as sensitivity, range, precision, resolution, accuracy, offset, linearity, dynamic linearity, hysteresis and response time [9; 10]. Evaluating sensor reliability includes probabilistic and statistical data that increase estimation reliability [11]. Evidence theory can be used, such as the Dempster-Shafer theory of belief functions. Quantifying reliability implies predictions concerning sensor lifetime and failure probability. Reliability can therefore be based on both statistical and Artificial Intelligence (AI) models. Suitable probability functions must be defined, which will be used to calculate the future behavior of devices, based either on carefully controlled laboratory experiments or on thorough failure analysis while in use. A typical product will be liable to various failure modes that change over time in a characteristic manner, so that the probability functions are themselves time dependent. Currently, two of the most widely applied sensors are the 3D stereoscopic camera and the LiDAR [12; 13]. Dozens of new applications have recently been reported [14; 15; 16]. However, the computational load and the processing time for data fusion of 3D stereoscopic camera and the LiDAR sensors in automotive applications are very high [17]. Therefore, a possible solution is to use only the point cloud provided by the LiDAR with a model for predicting the reliability of the sensor data and only activate the support sensors when a possible failure is anticipated [18]. Indeed, it is very difficult to guarantee the real topology and distance of objects with less uncertainty due to dead zones, object transparency, light reflection, weather conditions, and sensor failures, among others [19]. It is mandatory to obtain data in parallel from other connected LiDAR sensors to guarantee reliability. Therefore, new IoT-based technologies open up a wide range of methodologies for estimating and ensuring reliability in key emerging applications [20]. The most widely used techniques for modelling predictions in product lifetime and failure probability are probabilistic methods. Probabilistic methods for uncertain reasoning represent another group of techniques. Probability theory predicts events from a state of partial knowledge, while possibilistic models, represented by fuzzy systems, are applied to situations with intrinsic vagueness and uncertainty. However, the prediction techniques are hardly limited to those mentioned above. Several clustering techniques such as nearest neighbor methods have been explored, in order to enable self-detection and self-correction capabilities [21].