# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 5 Challenges and Future Research Directions
* **Data Quality Assurance for Effective DNNs:** The spatial-temporal data in CPS is characterized as complex (generated from multiple sources of sensors, microcontrollers, etc. ), incomplete (measurement errors, missing values, outliers, etc. ), noisy data (real-time streaming data), challenging to interpret, and unavailability in some cases, among others. There is a need to address these challenges, i.e., by developing better data collection techniques, missing data imputation and normalization methods, and new feature extraction procedures that can effectively capture the relevant latent spatial-temporal information in large and complex datasets. Similarly, explainable AI can be leveraged to develop more precise, interpretable, and explainable DNNs that provide the detailed underlying features and relationships driving results or decisions. On the other hand, transfer learning can be leveraged with existing knowledge and pre-trained models to improve the accuracy and efficiency of DNNs. For example, the transfer of knowledge from related spatial-temporal datasets within or across the different CPS domains would be beneficial for improving ML model efficiency and supporting the CPS co-design initiative. * **Strict Performance Assurance for CPS:** Most models that handle spatial-temporal data in CPS are highly complex, combining two or more DNNs for a given task (CNN-LSTM, GCN-GTN, etc.). This calls for the use of multiple layers and many parameters, leading to a longer training time and hindering real-time performance in practice. In a nutshell, the computational complexity, which translates to communication delays, and the dynamic nature of CPS data are among the factors hindering the achievement of real-time performance in various CPS domains. This calls for the design of new efficient model architectures that require few parameters to handle CPS spatial-temporal data. The targeted model architectures can significantly reduce the computational requirements and memory footprint of DNN models, making them more suitable for real-time tasks. Such architectures can include lightweight models, such as MobileNet and ShuffleNet, with smaller parameters that can be executed on CPS-resource-constrained devices. Alternatively, using specialized hardware, such as field-programmable gate arrays, can further optimize the execution of DNN models. Furthermore, the targeted model architectures can enable the deployment of DNN models in edge devices (sensors, actuators, smart cameras, etc. ), which can process data locally within the network edge to reduce communication latency with the cloud. This can be critical for CPS applications, which require real-time decision-making and control. Similarly, developing and using continuous learning techniques that can adapt to the changing CPS data in real time can improve the performance, accuracy, and reliability of DNN models as well. * **Reliability, Safety, and Security Resilience Insurance for DNNs and CPS:** Reliability entails predicting, detecting, and mitigating failures, while safety guarantees the system by dealing with unexpected failures. Security resilience entails preventing security threats posed by adversaries. As for reliability and safety, CPS could fail due to hardware or software faults resulting in inconsistent spatial-temporal data, which might lead the DNN models to make incorrect predictions or even shut down the system. There is a need for models that adopt spatial-temporal data to predict, detect, and mitigate hardware and software failures in CPS. Similarly, methods for evaluating the reliability and availability of DNN models and systems, such as reliability metrics and failure analysis, are critical too. Developing strategies for implementing fault-tolerant techniques and self-healing CPS cost-effectively and efficiently is critical for the predictive maintenance of CPS. On the other hand, data breaches and theft can compromise the confidentiality and integrity of spatial-temporal data in CPS. Furthermore, threats can lead the models to make incorrect predictions.