Mid Sweden University

With the development of intelligent edge computing (IEC) in industrial IoT (IIoT), there is a growing number of service providers trying to leverage computing resources in the cloud and at the edge to meet the users’ demand for low latency and high reliability in diversified applications. This evolving landscape necessitates innovative approaches to manage and process the vast amounts of data generated by IIoT devices. Among these approaches, distributed learning frameworks, such as federated learning (FL), have emerged as popular solutions. However, compared to computing, communication remains the primary bottleneck that constrains the speed of federated model training. Most of the previous solutions have focused on reducing communication overhead. Differently, we propose a transmission-centric approach by designing an efficient communication architecture for FL with cloud-edge collaboration, specifically aimed at enhancing communication capabilities through multi-path transmission. We deploy this FL system in a real environment and conduct extensive testing. The results demonstrate that the new approach can significantly reduce communication time in FL setting, thereby enhancing model aggregation efficiency and shortening the overall training duration. Compared to conventional single-path transmission, the proposed solution improves training efficiency by up to 26.4%. 

In the rapidly evolving cybersecurity landscape, detecting and preventing network attacks has become crucial within the industrial sector. This study aims to explore the potential of intrusion detection by employing deep learning within edge computing, especially for the Industrial Internet of Things. Specifically, TinyML converted CNN, LSTM, Transformer-LSTM, and GCN models on the UNSW-NB15 dataset. A comprehensive dataset analysis gained insights into the nature of attack behavior data. Subsequently, a comparative analysis in an edge computing setup using Raspberry Pi units revealed that the GCN model, with its accuracy of 97.5%, was the best suited of the compared models for this application. However, the study also explored variables like time consumption, where the CNN model was the fastest out of the compared models. This research also highlights the need for continued exploration, especially in addressing dataset imbalances and enhancing model generalizability. By recognizing each model's strengths and areas of improvement, this research serves as a step toward bolstering digital safety and security in an increasingly interconnected industrial world.

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machine learning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios. 

Cellular networks are becoming increasingly complex, requiring careful optimization of parameters such as antenna propagation pattern, tilt, direction, height, and transmitted reference signal power to ensure a high-quality user experience. In this paper, we propose a new method to optimize antenna direction in a cellular network using Q-learning. Our approach involves utilizing the open-source quasi-deterministic radio channel generator to generate radio frequency (RF) power maps for various antenna configurations. We then implement a Q-learning algorithm to learn the optimal antenna directions that maximize the signal-to-interference-plus-noise ratio (SINR) across the coverage area. The learning process takes place in the constructed open-source OpenAI Gym environment associated with the antenna configuration. Our tests demonstrate that the proposed Q-learning-based method outperforms random exhaustive search methods and can effectively improve the performance of cellular networks while enhancing the quality of experience (QoE) for end users.

Collaborative augmented reality (AR), which enables interaction and consistency in multi-user AR scenarios, is a promising technology for AR-guided remote monitoring, optimization, and troubleshooting of complex manufacturing processes. However, for uplink high data rate demands in collaborative-AR, the design of an efficient transmission and resource allocation scheme is demanding in resource-constrained wireless systems. To address this challenge, we propose a collaborative non-orthogonal multiple access (C-NOMA)-enabled transmission scheme by exploiting the fact that multi-user interaction often leads to common and individual views of the scenario (e.g., the region of interest). C-NOMA is designed as a two-step transmission scheme by treating these views separately and allowing users to offload the common views partially. Further, we define an optimization problem to jointly optimize the time and power allocation for AR users, with an objective of minimizing the maximum rate-distortion of the individual views for all users while guaranteeing a target distortion of their common view for its mutual significance. For its inherent non-linearity and non-convexity, we solve the defined problem using a primal-dual interior-point algorithm with a filter line search as well as by developing a successive convex approximation (SCA) method. The simulation results demonstrate that the optimized C-NOMA outperforms the non-collaborative baseline scheme by 23.94% and 77.28% in terms of energy consumption and achievable distortion on the common information, respectively. 

Anomaly-based In-Vehicle Intrusion Detection System (IV-IDS) is one of the protection mechanisms to detect cyber attacks on automotive vehicles. Using artificial intelligence (AI) for anomaly detection to thwart cyber attacks is promising but suffers from generating false alarms and making decisions that are hard to interpret. Consequently, this issue leads to uncertainty and distrust towards such IDS design unless it can explain its behavior, e.g., by using eXplainable AI (XAI). In this paper, we consider the XAI-powered design of such an IV-IDS using CAN bus data from a public dataset, named “Survival”. Novel features are engineered, and a Deep Neural Network (DNN) is trained over the dataset. A visualization-based explanation, “VisExp”, is created to explain the behavior of the AI-based IV-IDS, which is evaluated by experts in a survey, in relation to a rule-based explanation. Our results show that experts’ trust in the AI-based IV-IDS is significantly increased when they are provided with VisExp (more so than the rule-based explanation). These findings confirm the effect, and by extension the need, of explainability in automated systems, and VisExp, being a source of increased explainability, shows promise in helping involved parties gain trust in such systems. Author