Artificial intelligence and cyber-physical systems (CPS) are two of the key technologies of the future that are enabling major global shifts. However, most of the current implementations of AI in CPS are not explainable, which creates serious problems in ethical, legal, regulatory, and other domains. Therefore, it is necessary for explainable artificial intelligence (XAI) to be integrated with cyber-physical systems to meet the vital needs for control, fairness, accountability, safety, cyber-resilience, and cybersecurity. The goal of this review is to demonstrate the need, benefits, challenges, and implementation of XAI for CPS. We review the existing literature about XAI and CPS, discuss the current state of the art, examine applications in different domains, and make recommendations for future research directions. To the best of our knowledge, this is the first peer-reviewed academic article to provide a comprehensive review of general XAI for CPS. We also contribute new research ideas including development of multisensory explanations and outputs for these systems, application of XAI to CPS to decrease occupational burnout and increase employee engagement, and enumeration of the multidisciplinary goals and benefits of XAI as applied to cyber-physical systems.

Today, HVAC (heating, ventilation, and air conditioning) systems have become an integral part of modern buildings and are designed to provide comfortable indoor environments while conserving energy and reducing carbon emissions. With advancement in technology, HVAC systems have a variety of sensors that are used to detect the occupants within a controlled environment. Advancements in computer control systems and the use of smart technology have made HVAC systems even more sophisticated, allowing for approximate temperature control and energy management. This paper will review the historical development of technology and the current state of HVAC systems. With the proper data, development of artificial intelligence models can, in theory, improve the overall optimization and reduce energy consumption This paper will provide a review of HVAC history and the key concepts around the usefulness of using AI from previous research conducted in this field of study.

The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area.

Distributed denial of service (DDoS) attacks pose a significant threat to Software Defined Networking (SDN) and are frequently employed by malicious actors. SDN has emerged as a prominent networking paradigm, providing users with a decoupled control and data plane, which grants greater control and programmability over the network. In comparison to traditional networks, SDN offers dynamic, agile, cost-effective, and manageable solutions. However, a notable drawback of SDN is that the central controller becomes a vulnerable attack surface, rendering it susceptible to complete network takeover through DDoS attacks. The novelty of this paper is to gather resources that will be used to mitigate DDoS attacks in SDN environments. This paper focuses on the exploration of open datasets featuring DDoS attacks, as well as examining attack detection and mitigation techniques and frameworks. By analyzing various detection and mitigation strategies, network administrators and security professionals can make informed decisions to enhance the robustness and resilience of SDN environments in the face of evolving DDoS threats.

COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our modern time. It affected people’s health, education, employment, the economy, tourism, and transportation systems. It will take a long time to recover from these effects and return people’s lives back to normal. The main objective of this study is to investigate the various factors in health and food access, and their spatial correlation and statistical association with COVID-19 spread. The minor aim is to explore regression models on examining COVID-19 spread with these variables. To address these objectives, we are studying the interrelation of various socio-economic factors that would help all humans to better prepare for the next pandemic. One of these critical factors is food access and food distribution as it could be high-risk population density places that are spreading the virus infections. More variables, such as income and people density, would influence the pandemic spread. In this study, we produced the spatial extent of COVID-19 cases with food outlets by using the spatial analysis method of geographic information systems. The methodology consisted of clustering techniques and overlaying the spatial extent mapping of the clusters of food outlets and the infected cases. Post-mapping, we analyzed these clusters’ proximity for any spatial variability, correlations between them, and their causal relationships. The quantitative analyses of the health issues and food access areas against COVID-19 infections and deaths were performed using machine learning regression techniques to understand the multi-variate factors. The results indicate a correlation between the dependent variables and independent variables with a Pearson correlation R²-score = 0.44% for COVID-19 cases and R² = 60% for COVID-19 deaths. The regression model with an R²-score of 0.60 would be useful to show the goodness of fit for COVID-19 deaths and the health issues and food access factors.

Human-in-the-loop Heating, Ventilation and Air Conditioning (HVAC) control-based methodologies have gained much attention due to continual discomfort compliance of occupants in residential and commercial buildings; spawning thermal comfort research interest in leveraging emerging advanced technologies to address the prolonged problem of discomfort and energy efficiency. In the past, thermal comfort studies have been conducted to determine the thermal sensation, preference and comfort based on the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database II and customized dataset through machine learning. The ASHRAE Database II is an open-source database that includes sets of objective indoor climatic observations with corresponding subjective evaluations by the building occupants who were used as subjects in experiments. Environmental parameters and occupants’ skin temperature have been used to develop machine learning algorithms to predict thermal comfort indices in both indoor and outdoor settings. However, none of these studies have investigated merging environmental parameters and thermal images to predict thermal comfort indices of occupants. In this study, the holistic understanding of individuals thermal comfort environment was considered by fusing analog environmental sensors and thermal images captured at the time of the subjective measurement. Wavelet scattering features were obtained from the occupants’ thermal image surroundings and joined to the environmental parameters. This research developed different machine learning models, processing methods and evaluated the results based on the fused dataset. The results show the possibility of real time prediction of occupancy and thermal preference through classical machine learning, and stacked models with high accuracy. The proposed framework achieved an estimated 45% mean energy savings during a ten-day energy analysis.