Bo Wei

Electric Vehicle Charging Systems (EVCSs) have become a critical component of the global transition toward sustainable and intelligent transportation. However, their tight integration with heterogeneous cyber–physical, vehicular, and cloud-based infrastructures exposes them to an expanding attack surface, including data poisoning, malware injection, denial-of-service, and man-in-the-middle (MITM) attacks. Existing security solutions largely rely on isolated detection mechanisms and lack a unified semantic representation of EVCS assets, attack propagation paths, and mitigation dependencies, limiting their effectiveness in complex and evolving threat scenarios. To address these challenges, this paper proposes EVCS-SecOnt, an ontology-driven cybersecurity framework for modeling, reasoning, and mitigating security threats in EVCS infrastructures. The proposed ontology formalizes relationships across four core modules, namely Attack Surface, Attack Classification, Protection Mechanisms, and Risk and Mitigation, enabling holistic threat representation and TARA-based risk assessment. EVCS-SecOnt incorporates standard semantic namespaces (em:, seas:, uiote:, sch:, and time:) to ensure interoperability and is instantiated using the CICEVSE2024 dataset to support observation-level security reasoning. A unified SPARQL-based analytical workflow is employed to perform global ontology validation, attack–risk–severity correlation, mitigation prioritization, and observation-level inference using statistical feature vectors. Experimental results demonstrate that the ontology captures multiple attack classes, risk levels, severity categories, and mitigation strategies, enabling automated identification of critical attack scenarios and context-aware defense recommendations. The validation demonstrates logical consistency, semantic traceability, and query-based coverage of the ontology across attack classes, risk levels, severity categories, and mitigation strategies. EVCS-SecOnt enhances the interpretability, reusability, and explainability of EVCS cybersecurity management by bridging operational data with semantic intelligence. The proposed framework supports adaptive protection, risk-aware decision-making, and ontology-driven security analytics, providing a semantic foundation for next-generation e-mobility and smart charging infrastructures.

Federated Class-Incremental Learning (FCIL) enables large populations of edge devices to incrementally learn a global model from emerging classes while mitigating catastrophic forgetting. Rehearsal-based methods reduce forgetting by replaying past exemplars, but in cross-device settings they raise memory and privacy concerns: directly porting centralized rehearsal to FCIL can overload device memory and expose raw user data. We propose Exemplar-condensed Replay (ExReplay), a cross-device FCIL method that condenses each client’s local data stream into compact, informative exemplars, reducing memory use and raw-sample exposure. ExReplay adaptively distills streaming local data into exemplars that are continually refined during incremental learning. We further identify and address meta-information heterogeneity, a bias caused by independent exemplar condensation over non-IID client data, through unbiased compensation feature matching with a globally shared conditional VAE. Experiments on CIFAR-100, TinyImageNet, and Caltech-256 show that ExReplay outperforms state-of-the-art methods, especially under severe data heterogeneity and tight memory budgets: on CIFAR-100, it improves average accuracy over the strongest baseline by 5.32% in the 10-task setting and 10.29% in the 50-task setting. These gains require moderate training cost and small inference and communication overhead, including under 0.25 ms per sample and 2.33-9.29 MB per round, keeping ExReplay practical for resource-constrained cross-device deployments.

Federated learning is a promising approach that enables collaborative machine learning (ML) in distributed environments, such as the Internet of Medical Things (IoMT) while preserving consumer privacy. It allows multiple consumers to collaboratively train a model using their own data, sharing only the locally trained model rather than the raw data. Most existing federated learning systems assume a high level of trust in participating nodes, which is unrealistic in real-world consumer-centric scenarios. Involving untrusted nodes can compromise the integrity of the training process and result in potential data breaches. To address these challenges, this paper presents REWARDCHAIN, a novel federated learning framework that leverages blockchain technology to ensure trust and accountability among untrusted IoMT consumers. By recording all model updates and client contributions on an immutable blockchain ledger, REWARDCHAIN allows auditing of the entire training process and attributing any malicious behaviour to specific nodes. Moreover, we design an incentive mechanism that evaluates contributions based on data quality and participant reputation. This system motivates participants to contribute high-quality data through a reputation-constrained reward allocation. Our evaluations show that REWARDCHAIN effectively balances trust, security, and model performance, facilitating a more secure and effective federated learning ecosystem.

Deep learning models have demonstrated significant efficacy across various domains, including healthcare, security, ubiquitous computing, and pervasive intelligence. Long short-term memory and convolutional neural networks, in particular, have revolutionized human activity recognition systems by effectively extracting semantic information directly from raw sensor data. However, the acquisition of large, well-annotated human activity datasets remains a considerable challenge due to privacy concerns, as well as the time and cost associated with labeling. To address this, unsupervised semantic representation learning, which bypasses the need for manual annotations, is crucial for utilizing the vast pool of unlabeled sensor data. In this study, we present a novel self-supervised learning network that extracts meaningful representations from sensor data without relying on predefined semantic labels. To address class imbalances, we enhance the synthetic minority oversampling technique, introducing an improved version called iSMOTE, which accurately labels synthetic samples. Additionally, we propose a random masking technique that removes identity mapping from input data, enabling the construction of generic semantic representations for downstream tasks. By utilizing balanced unlabeled data, our network significantly improves human activity recognition while reducing reliance on annotated data. We conducted extensive evaluations and comparisons of our proposed network against various supervised and self-supervised models using datasets from smart environments and wearable sensors. Remarkably, our network outperforms several existing methods across 12 public datasets. For instance, with just 25% of labeled data, our self-supervised network achieved an accuracy of 93.87% on the Ordonez Home A dataset, surpassing the performance of fully supervised methods such as DeepConvLSTM + Attention (84.97%), HAR + Attention (88.55%), and DCC + MSA (90.78%).

The widespread use of electronic devices has contributed to an increase in poor posture, particularly when it comes to the cervical spine, leading to various cervical vertebral pain disorders. In this paper, we focus on accurately monitoring the motion status of the cervical spine using the accelerometers and gyroscope sensors embedded in earphones. Our aim is to gain a better understanding of cervical spine health. To address the individual differences among subjects, we introduce fuzzy rules to the Re-ISDA method, proposing a novel approach known as FuzRe-ISDA. Unlike traditional domain adaptation methods, the FuzRe-ISDA method offers flexibility in adjusting the contribution from different source domains. It takes into account the collective impact of multiple models on predicting new user behavior. Moreover, this method can quickly adapt to new users without requiring extensive datasets. Experimental results demonstrate that our FuzRe-ISDA approach outperforms popular domain adaptation methods in terms of accuracy when predicting cervical motion. This highlights the effectiveness of our approach in addressing individual differences and improving the reliability of cervical spine motion prediction.

In this paper, we study cost-effective home robot solutions which are designed for home health monitoring. The recent advancements in Artificial Intelligence (AI) have significantly advanced the capabilities of the robots, enabling them to better and efficiently understand and interact with their surroundings. The most common robots currently used in homes are toy robots and cleaning robots. While these are relatively affordable, their functionalities are very limited. On the other hand, humanoid and quadruped robots offer more sophisticated features and capabilities, albeit at a much higher cost. Another category is educational robots, which provide educators with the flexibility to attach various sensors and integrate different design methods with the integrated operating systems. However, the challenge still exists in bridging the gap between affordability and functionality. Our research aims to address this by exploring the potential of developing advanced yet affordable and accessible robots for home robots, aiming for health monitoring, by using edge computing techniques and taking advantage of existing computing resources for home robots, such as mobile phones.

Symmetric key generation methods are recently designed for wireless communication based on similar and unique observations of sensor measurements, such as wireless radio channels, inaudible sound channels, etc. Heartbeats, as unique biometrics, have been used for symmetric key generation. However, current solutions are designed for wearable devices with the integration of the same types of touchable heartbeat measurement equipment and fail because of the significant difference from different devices or the same devices with different deployment locations, which limits its large scale of deployment and application. To solve this problem, we propose a general heartbeat-based symmetric key generation solution by embracing observation variance from different devices, i.e., using an optical heart sensor on one finger and facing the camera of the second device to the user's face. We propose a novel data processing method to mitigate the significant difference and exploit key reconciliation to generate symmetric keys for paring devices and securing wireless communication. We have conducted extensive evaluations and shown our proposed method has good key matching rates up to 100% as well as good randomness. Security analysis has also been conducted to ensure the robustness of the proposed method.