The growing popularity of electric vehicles (EVs) has rendered public EV charging stations (EVCSs) vital for alleviating range anxiety and supporting long-distance travel. However, recent studies reveal security vulnerabilities in EVs and EVCSs against attacks. This paper addresses these security concerns by introducing injection attacks on the front-end Vehicle-to-Grid (V2G) communication using the ISO 15118 protocol. Malicious EV owners or compromised EVCS supply equipment can inject harmful packets, potentially leading to runtime modifications and malware attacks. To counter this threat, we propose an innovative bidirectional recurrent attentive graph neural network (BiRAGNN)-based intrusion detection system (IDS) that dynamically captures spatiotemporal aspects and the bidirectional flow of information, while leveraging an attention mechanism to effectively detect injection attacks within EVs and EVCSs. The BiRAGNN model is founded on a probabilistic charging graph of real cities. Other deep learning and graph-based IDSs are also investigated as evaluation benchmarks. The IDSs are examined against a standalone system (from a single EVCS) and multi-node systems (from 8, 50, and 100-node EVCSs), all with packet-level, flow-level, and fused packet and flow-level data. The proposed BiRAGNN-based IDS offers a detection accuracy of 99% on the 100-node fused dataset, surpassing the benchmarks by$5 - 8\%$, offering EVs and public EVCSs resilience against cyber threats.

The integration of the Internet of Things (IoT) in healthcare has enabled intelligent and early heart attack detection (HAD) through artificial intelligence; however, the existing approaches suffer from high computational complexity and suboptimal performance, leading to inaccurate predictions, which can jeopardize patient well-being. Moreover, they fail to provide secure bidirectional communication between patients and medical centers while safeguarding patient privacy. Therefore, this paper addresses these limitations by proposing a novel secure and efficient HAD approach in healthcare. First, we propose a customized consortium blockchain network that leverages group signatures to ensure patient anonymity, data unlinkability, and secure two-way communication, thereby preserving patient privacy. Then, a lightweight, robust HAD model is devised via knowledge distillation by leveraging a novel proposed hybrid deep learning architecture that enables accurate early detection of heart attacks, supporting timely clinical intervention. Experimental results on a real dataset, i.e., the Cleveland dataset, demonstrate the scalability of the proposed approach that can process up to 500,000 patients in under 2.5 minutes, while preserving patient privacy. Moreover, it offers 99.22% accuracy, an F1-score of 99.23%, outperforming state-of-the-art techniques, with an inference time of 90 ms and a model memory footprint of only 0.28 MB.

Healthcare is revolutionized by the integration of the Internet of Medical Things (IoMT) and artificial intelligence (AI), enabling real-time patient monitoring, advanced predictive analytics, and personalized treatment plans. However, the existing AI healthcare models are typically trained offline on static datasets, limiting their adaptability to the dynamic nature of health data. This may result in compromising models' accuracy and healthcare decision-making, rendering them obsolete. Moreover, attackers may exploit concept drift by injecting frequent data shifts, which can exhaust healthcare institutions' resources. To address this research gap, we propose a novel adaptive, secure, and efficient concept drift detection framework for healthcare. First, a robust deep learning (DL) model is devised to leverage its high-confidence probability to detect data drift efficiently without relying on labeled data. Then, we propose a customized consortium blockchain network that leverages group signatures to ensure anonymity and unlinkability of patients' health data. It also utilizes a dualledger structure, facilitating a unified drift detection model and enabling authenticated, drift-specific data sharing among medical centers. This design protects against data tampering and falsely claiming drift incidents. Our experiments, conducted on a real health monitoring dataset, show that our concept drift detection approach achieves comparable drift detection performance to the existing methods while reducing the computational time by 52.35%, and achieving an accuracy of 98.43 with our offline model and a 95% accuracy with the online adaptive model.

Integrating the Internet of Medical Things (IoMT) and artificial intelligence (AI) is revolutionizing healthcare by enabling real-time health monitoring, predictive analytics, and personalized treatment. However, existing AI healthcare models are trained offline on static datasets, making them less adaptable to evolving health data and potentially reducing their accuracy and decision-making. Furthermore, adversaries may exploit this by injecting frequent data shifts, straining healthcare resources. Privacy concerns also arise from the exposure of sensitive patient data. Therefore, we propose a novel AI-driven healthcare methodology with secure, decentralized, and privacy-enhancing adaptive intelligence. First, a deep learning (DL) model is devised to leverage its high-confidence probability to detect data drift efficiently. Next, we propose a privacy-preserving approach leveraging functional encryption to ensure patient data confidentiality during drift detection and model retraining while eliminating reliance on a trusted entity. Lastly, we propose a customized consortium blockchain with group signatures to protect patient anonymity and data tampering and unlinkability while preventing falsely claiming drift incidents. Moreover, to ensure decentralization, it removes the need for a trusted authority in cryptographic key generation. Our experiments, on a real testbed and healthcare datasets, show that the proposed methodology achieves real-time drift detection with performance comparable to existing methods, while reducing the computational time by 52.35%. It also maintains high accuracy, achieving up to 98.43% with the offline health monitoring model and up to 96% with the online adaptive model. Additionally, it preserves patient privacy while reducing computational and communication overhead by 94.26% and 89%, respectively, compared to the state-of-the-art.

Heart attack detection (HAD) and human activity recognition (HAR) rely on wearable sensor data to track heart health and physical activity in real-time, facilitating early detection and monitoring of health issues. However, existing solutions for HAR and HAD face challenges in effectively capturing spatial features, long-term dependencies, and diverse sensor data representations. These shortcomings impact recognition accuracy, memory efficiency, and processing speed, while also demanding substantial computational resources due to their complexity. They also lead to performance degradation, increasing the risk of inaccurate diagnoses and potentially jeopardizing patient lives. To overcome these limitations, a novel lightweight hybrid architecture for HAR and HAD is proposed, leveraging convolutional neural networks (CNNs) with gated recurrent units (GRUs) and multi-head attention (MHA). CNNs capture spatial features, GRUs extract long-term dependencies, and MHA computes attention weights across data segments to highlight the most relevant features for health diagnosis, ensuring both improved performance and practicality for real-time health monitoring. Moreover, a magnitude-based weight pruning technique is adapted to reduce the proposed architecture’s complexity, making it suitable in resource-constrained settings without sacrificing accuracy. Furthermore, our methodology integrates an optimized genetic algorithm for counterfactual explanations, recommending minimal health data changes to lower heart attack risk. Experimental results on a real testbed and datasets, including PAMAP2, WISDM, and Cleveland, demonstrate that the proposed method outperforms the state-of-the-art methods, achieving up to 3% improvement in F1-score and accuracy, while reducing inference time, number of parameters, and memory footprint by over 40%, 70%, and 60%, respectively.

Unmanned Aerial Vehicles (UAVs) play a crucial role in various applications, including detecting environmental hazards, e.g., wildfire smoke detection. However, the limited computational capabilities and battery life of UAVs present barriers to deploying complex artificial intelligence (AI) models onboard. To address this challenge, we propose a novel hybrid deep learning framework for UAVs to carry out light-weight yet efficient smoke detection. The framework combines a lightweight model for initial image assessment and a depth-wise model for selective processing of uncertain cases. Bayesian optimization is employed to determine the optimal threshold values for activating the depth-wise model, striking a balance between accuracy and computational efficiency. The proposed approach eliminates the need for cloud server connectivity, enabling onboard decision-making. Experimental results demonstrate that the hybrid framework achieves significant reductions in processing time and the number of calls to the depth-wise model while maintaining high accuracy. The framework’s adaptability and robustness make it suitable for real-time smoke detection applications in resource-constrained environments.

Dynamic wireless charging (DWC) facilitates energy transfer from the electric grid to moving electric vehicles (EVs) via charging pads (CPs) positioned along roadways. To maximize satisfied charging requests, given the limited supply capacity, dynamic charging coordination is required to determine suitable CPs for mobile EVs. Charging coordination necessitates EV owners to share their information (i.e., the identities and locations) with charging service providers (CSPs) to allocate the best CP for charging. However, charging coordination raises privacy concerns due to the exchange of private information. Moreover, a fast authentication mechanism is then required between EVs and CPs to initiate the charging process. In addition to the privacy limitation, existing DWC strategies lack the presence of multiple CSPs, which is a crucial aspect given the significant growth of the EV market. Consequently, centralization arises, with a single CSP overseeing the entire network. This paper proposes a semi-decentralized privacy-preserving networking strategy utilizing a specially designed consortium blockchain to support dynamic charging coordination, authentication, and billing while ensuring user anonymity and data unlinkability. Our proposed strategy leverages a novel semi-decentralized K-times group signature scheme and distributed random number generators to achieve privacy and decentralization. Simulation results showed that the proposed method reduces the EV authentication time to 0.1 ms while limiting storage requirements to just 4 MB per block at each EV. Additionally, the proposed strategy showed improved security and privacy features when compared with IBM’s privacy-preserving blockchain (Identity Mixer).

Human activity recognition (HAR) leverages data from wearable devices and smartphones to detect actions, improving quality of life in areas like elderly care, health monitoring, and sports training. Current deep learning architectures struggle with extracting spatial features, long-term dependencies, and diverse sensor data representations, impacting recognition performance and posing challenges for resource-constrained IoT devices due to their complexity and parameter count. We propose a novel hybrid HAR architecture, integrating convolutional neural networks (CNN) and gated recurrent units (GRU) with a multi-head attention (MHA) mechanism. This architecture captures spatial features via CNN, extracts long-range dependencies with GRU, and uses MHA to compute attention weights for different data segments. The combined spatial and attention features are fed into a classification module for activity recognition. On the PAMAP2 dataset, our CNN-GRU-MHA model outperforms existing methods, achieving an F1-score of 98.4 %, with an inference time of 0.078 seconds and a memory footprint of 790.02 KB, reducing resource usage by 74.34 % and 62.81 %, respectively.

During pandemics, diagnostic tests are essential to provide quick treatment of patients and limit the disease spread. The high demand for testing resources can stress the healthcare system. Thus, a remote collection of symptoms and reporting the results via an automated diagnostic system is highly desirable. However, such a system is challenged by privacy and scalability issues. Hence, we propose a sharded blockchain-based system that (a) introduces a set of shards that distributes the testing load among a group of local nodes (LNs), hence, offering high scalability for country-wide adoption, (b) uses ring signatures and unique random identifiers to ensure the anonymity of the users and the unlinkability of test requests, hence, supporting privacy-preservation, (c) deploys a detection strategy at the LNs based on deep neural networks, which is implemented on smart contracts, hence, enabling autonomous diagnosis, and (d) provides healthcare entities with authorized access to the symptoms and test results, hence, enabling efficient data sharing that supports future research. We provide an implementation of the proposed system and our experimental results demonstrate the high scalability and privacy of the system while achieving a testing accuracy up to 90%. We present a case study for U.S. wide deployment showing that a total daily test request of 2, 407, 462 can be performed and reported in 11 minutes compared to 63 days in absence of sharding. Moreover, sharding decreased the user storage requirement to be 0.18 MB at maximum instead of 723 MB without sharding.

Dynamic wireless charging of electric vehicles (EVs) enables the exchange of power between a mobile EV and the electricity grid via a set of charging pads (CPs) deployed along the road. Accordingly, dynamic charging coordination can be introduced for a group of mobile EVs to specify where each EV can charge (i.e., from which CPs). This coordination mechanism maximizes the satisfied charging requests given the limited available energy supply. Upon specifying the optimal set of pads for a given EV, a fast authentication mechanism is required between the EV and the CPs to start the charging process. However, both the coordination and authentication mechanisms require exchanging private information, e.g., EV identities and locations. Hence, there is a need for a strategy that enables privacy-preservation in dynamic charging via supporting: (i) user anonymity and (ii) data unlinkability. In this paper, we propose a decentralized and scalable networking strategy based on a specially designed private blockchain that can support the privacy requirements of dynamic charging coordination, authentication, and billing. The proposed networking strategy relies on group signature and distributed random number generators to support the desirable features. Simulation results demonstrate the efficiency and low complexity of the proposed blockchain-based networking strategy.

Demand side management (DSM) presents an effective tool to regulate customers’ energy consumption. However, DSM requires customer-utility interaction based on fine-grained data, which jeopardizes the privacy of the customers. To overcome such a threat, several proposals are made based on noise addition, encryption techniques, or extra hardware. Unfortunately, these solutions either impact the integrity of the data, incur additional computational complexity, or add extra cost. Recently, blockchain has been adopted to support smart grid applications while promoting customer’s anonymity. However, special measures are yet to be taken to support both customer’s anonymity and data unlinkability, hence preserving the customer’s privacy. This paper achieves this goal by proposing a novel networking strategy based on a private blockchain. The proposed strategy employs a group signature to ensure the customer’s anonymity and data unlinkability. Further, a novel decentralized random number generation scheme is proposed to support customer-utility interaction for DSM, billing, and auditing while ensuring data unlinkability. We present an implementation of the proposed blockchain-based strategy, investigate its scalability, and provide low-bound empirical expressions on computational time and storage overhead.