# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 1 Introduction  
Cyber-physical systems (CPS) are intended to integrate communication, control, and computational elements with physical processes, aiming to improve the effective monitoring and control of physical components. As the systems are designed to interact with the physical world, monitoring and controlling the physical processes shall generate a variety of data. The data is used to make decisions and control the behavior of CPS [1; 2; 3]. As shown in Figure 1, examples of CPS include autonomous vehicles in smart transportation, the industrial control system (ICS) in smart manufacturing, wearable sensors in medical CPS, etc. [4; 5; 6; 7; 8]. In CPS, vast amounts of data will be generated, representing space and time while operating themselves. Such data is generally denoted as CPS spatial-temporal data [9; 10; 11]. In CPS, the spatial-temporal data represents the physical world, and its evolution over time and space. CPS generates large amounts of data as they monitor and control physical processes in real-world objects. This data is used in CPS to make decisions and control behavior, such as predicting future events, detecting anomalies, and optimizing resource allocation, among others. For example, the spatial-temporal data collected by sensors in the smart transportation system detects obstacles and makes vehicle trajectory decisions. ICS, a crucial component of smart manufacturing systems, utilizes spatial-temporal data to monitor and manage physical processes. The effective management and analysis of spatial-temporal data in CPS require sophisticated techniques and algorithms, including spatial data mining, temporal data analysis, and geographic information systems. The effective use of spatial-temporal data in CPS can significantly improve its performance, reliability, safety, and security [12]. Several techniques have evolved over the last decades to learn the complex patterns and changing dynamics of spatial-temporal data. This includes temporal time-series analysis (ARIMA, SARIMA, etc.) [13], spatial data analysis (spatial regression and auto-correlation, kriging, etc.) [14], signal processing approaches (Fourier and wavelet analysis, Kalman filtering, etc.) [15], and machine learning approaches (regression analysis, support vector machine, etc.) [16; 17]. Nonetheless, most of these methods are not well-suited for handling large, dynamic, non-stationary data, which depicts its space and how it changes over time [18; 19; 20; 21]. Deep Neural Networks (DNNs), on the other hand, are applicable due to their ability to handle immense amounts of data and their capability of modeling complex relationships, both spatially and temporally [22]. For example, DNNs predict future events based on spatial-temporal data in CPS, including traffic patterns in smart cities, resource demand, and fault or intrusion detection in smart manufacturing systems, among others [23; 24; 25; 26]. The existing research efforts revealed numerous successes in the application of DNNs, including anomaly detection [27; 28], resource management [24], predictive maintenance [29; 30; 31; 32], multi-modal data fusion [33], real-time decision making [34; 35], and spatial-temporal data processing [36; 37], among others. For example, [PERSON] et al. [28] reviewed the applications of deep learning for anomaly detection in CPS, outlining areas, where deep learning has achieved promising results and areas that need improvement. Likewise, [PERSON] et al. [38] reviewed the historical and state-of-the-art applications of deep learning in energy CPS (i.e., frequency analysis and control in power systems). [PERSON] et al. [39] systematically reviewed machine learning applications in general to predictive maintenance of industrial CPS to determine the best-performing models and the areas of challenge. However, more research needs to be done to review the applications of DNNs in CPS with spatial-temporal datasets. In line with [PERSON] et al.