Xuan Yang

This study examines the role of Cyber-Physical Systems (CPS) through Internet of Things (IoT) devices, and advanced machine learning in monitoring and managing air pollution in future smart cities, with a focus on enhancing Quality of Life (QoL). CPS, integrated with IoT sensors, enables real-time data collection and analysis to understand the impact of power plant locations and operations on air quality. The study utilizes the Topographic Position Index (TPI) to assess how topography affects pollution, and regression analysis to explore the relationship between power plant output and pollution levels. Long Short-Term Memory (LSTM) networks forecast air pollution trends, while the CA-Markov chain method predicts spatial-temporal patterns. Graphical tools, such as heat maps and trend charts, visualize pollutant distribution and progression, making data more accessible for decision-makers. Findings indicate that flat areas are more susceptible to higher pollution, with February being the peak month for most pollutants. The results of this study also showed that CO and CO2 in spring have a higher correlation with power plant production (R2 =0.85) than other pollutants. The LSTM model shows high accuracy (R2 = 0.84) in predicting air quality trends within the CPS and IoT-enabled framework.

Intrusion detection exerts a crucial influence on securing the IIoT driven by anomaly detection approaches. Dissimilar with the static data, the intrusion detection data is in the form of a dynamic data stream possessing the properties of infiniteness, correlations, and data distribution change.However, these properties cause some issues for current anomaly detection approaches.Firstly, it is impractical to save the whole dataset due to the infiniteness.Secondly, the correlations are hardly considered.Thirdly, the data distribution change cant be appropriately handled due to a lack of model update and change detection strategy. Thus, we propose ASTREAM(anomaly detection in data streams), a novel anomaly detection approach that merges sliding window, model update, and change detection strategies into LSHiForest to achieve accurate and efficient anomaly detection with better scalability. ASTREAM has the following characteristics:(a)the sliding window can be utilized to handle the infiniteness of data streams;(b)the introduced PCA can consider the correlations between different attributes;(c)the change detection and model update can detect data distribution change in time and train the new model. Comprehensive experiments are implemented on the KDDCUP99 dataset to validate ASTREAM performance.Experiment results reveal that ASTREAM outperforms baselines in aspects of accuracy and efficiency and has better scalability.

With the increasing growth of web services shared in various mobile edge platforms, it becomes necessary to evaluate all the candidates based on their quality of services to reduce the users’ service selection cost. However, the service quality data released by service providers cannot be simply deemed as trusted due to various subjective or objective reasons, which further produce a series of serious trust‐aware service evaluation problems, including service quality data sparsity and lack of feedback incentive. In view of this, we summarize the challenging issues existing in the current research field of trusted mobile edge service evaluation. Afterward, we review the current research status of the trusted service evaluation in the mobile edge environment and discuss one of the typical application scenarios based on trusted service evaluation, that is, recommender systems, as well as their diverse categories. We believe this research could be helpful in assisting a mobile edge platform to build a trusted reputation system for various smart applications hosted in the mobile edge platform.