Data quality challenges in large-scale cyber-physical systems: A systematic review

Cyber-physical systems (CPSs) are integrated systems engineered to combine computational control algorithms and physical components such as sensors and actuators, effectively using an embedded communication core. Smart cities can be viewed as large-scale, heterogeneous CPSs that utilise technologies like the Internet of Things (IoT), surveillance, social media, and others to make informed decisions and drive the innovations of automation in urban areas. Such systems incorporate multiple layers and complex structure of hardware, software, analytical algorithms, business knowledge and communication networks, and operate under noisy and dynamic conditions. Thus, large-scale CPSs are vulnerable to enormous technical and operational challenges that may compromise the quality of data of their applications and accordingly reduce the quality of their services. This paper presents a systematic literature review to investigate data quality challenges in smart-cities large-scale CPSs and to identify the most common techniques used to address these challenges. This systematic literature review showed that significant work had been conducted to address data quality management challenges in smart cities, large-scale CPS applications. However, still, more is required to provide a practical, comprehensive data quality management solution to detect errors in sensor nodes’ measurements associated with the main data quality dimensions of accuracy, timeliness, completeness, and consistency. No systematic or generic approach was demonstrated for detecting sensor nodes and sensor node networks failures in large-scale CPS applications. Moreover, further research is required to address the challenges of ensuring the quality of the spatial and temporal contextual attributes of sensor nodes’ observations.