Search Results (265)

With the rapid development of vehicular network technology, data sharing and collaborative training among vehicles have become key to enhancing the efficiency of intelligent transportation systems. However, the heterogeneity of data and potential Byzantine attacks cause the model to update in different directions during the iterative process, causing the boundary between benign and malicious gradients to shift continuously. To address these issues, this paper proposes a privacy-preserving Byzantine-tolerant federated learning scheme. Specifically, we design a gradient detection method based on median absolute deviation (MAD), which calculates MAD in each round to set a gradient anomaly detection threshold, thereby achieving precise identification and dynamic filtering of malicious gradients. Additionally, to protect vehicle privacy, we obfuscate uploaded parameters to prevent leakage during transmission. Finally, during the aggregation phase, malicious gradients are eliminated, and only benign gradients are selected to participate in the global model update, which improves the model accuracy. Experimental results on three datasets demonstrate that the proposed scheme effectively mitigates the impact of non-independent and identically distributed (non-IID) heterogeneity and Byzantine behaviors while maintaining low computational cost. Full article

In the deep integration process between digital infrastructure and new economic forms, structural imbalance between the evolution rate of cloud storage technology and the growth rate of data-sharing demands has caused systemic security vulnerabilities such as blurred data sovereignty boundaries and nonlinear surges in privacy leakage risks. Existing academic research indicates current proxy re-encryption schemes remain insufficient for cloud access control scenarios characterized by diversified user requirements and personalized permission management, thus failing to fulfill the security needs of emerging computing paradigms. To resolve these issues, a revocable attribute-based proxy re-encryption scheme supporting policy-hiding is proposed. Data owners encrypt data and upload it to the blockchain while concealing attribute values within attribute-based encryption access policies, effectively preventing sensitive information leaks and achieving fine-grained secure data sharing. Simultaneously, proxy re-encryption technology enables verifiable outsourcing of complex computations. Furthermore, the SM3 (SM3 Cryptographic Hash Algorithm) hash function is embedded in user private key generation, and key updates are executed using fresh random factors to revoke malicious users. Ultimately, the scheme proves indistinguishability under chosen-plaintext attacks for specific access structures in the standard model. Experimental simulations confirm that compared with existing schemes, this solution delivers higher execution efficiency in both encryption/decryption and revocation phases. Full article

ZigBee, a wireless technology standard for the Internet of Things (IoT) devices based on IEEE 802.15.4, faces significant security challenges that threaten the confidentiality, integrity, and availability of its networks. Despite using 128-bit Advanced Encryption Standard (AES) with symmetric keys for node authentication and data confidentiality, ZigBee’s design constraints, such as low cost and low power, have allowed security issues to persist. While ZigBee 3.0 introduces enhanced security features such as install codes and trust centre link key updates, there remains a lack of empirical research evaluating their effectiveness in real-world deployments. This research addresses the gap by conducting a comprehensive, hardware-based analysis of ZigBee 3.0 networks using XBee 3 radio modules and ZigBee-compatible devices. We investigate the following three core security issues: (a) the security of symmetric keys, focusing on vulnerabilities that could allow attackers to obtain these keys; (b) the impact of compromised symmetric keys on network confidentiality; and (c) susceptibility to Denial-of-Service (DoS) attacks due to insufficient protection mechanisms. Our experiments simulate realistic attack scenarios under both Centralised and Distributed Security Models to assess the protocol’s resilience. The findings reveal that while ZigBee 3.0 improves upon earlier versions, certain vulnerabilities remain exploitable. We also propose practical security controls and best practices to mitigate these attacks and enhance network security. This work contributes novel insights into the operational security of ZigBee 3.0, offering guidance for secure IoT deployments and advancing the understanding of protocol-level defences in constrained environments. Full article

The Internet of Vehicles (IoVs) uses vehicles as the main carrier to communicate with other entities, promoting efficient transmission and sharing of traffic data. Using real identities for communication may leak private data, so pseudonyms are commonly used as identity credentials. However, existing anonymous authentication schemes have limitations, including large vehicle storage demands, information redundancy, time-dependent pseudonym updates, and public–private key updates coupled with pseudonym changes. To address these issues, we propose a certificateless strong anonymous privacy protection authentication scheme that allows vehicles to autonomously generate and dynamically update pseudonyms. Additionally, the trusted authority transmits each entity’s partial private key via a session key, eliminating reliance on secure channels during transmission. Based on the elliptic curve discrete logarithm problem, the scheme’s existential unforgeability is proven in the random oracle model. Performance analysis shows that it outperforms existing schemes in computational cost and communication overhead, with the total computational cost reduced by 70.29–91.18% and communication overhead reduced by 27.75–82.55%, making it more suitable for privacy-sensitive and delay-critical IoV environments. Full article

Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing direct risks to patients. However, their integration into healthcare requires careful consideration of ethical, regulatory, and safety constraints in light of the sensitivity and nonlinear nature of human data. In this review, we examine recent progress in DTs over the past seven years and explore broader trends in AI-augmented DTs, focusing particularly on movement rehabilitation. Our goal is to provide a comprehensive understanding of how DTs bolstered by AI can transform healthcare delivery, medical research, and personalized care. We discuss implementation challenges such as data privacy, clinical validation, and scalability along with opportunities for more efficient, safe, and patient-centered healthcare systems. By addressing these issues, this review highlights key insights and directions for future research to guide the proactive and ethical adoption of DTs in healthcare. Full article

As the use of Internet of Things (IoT) devices becomes extensive, ensuring their security has become a critical issue for both individuals and organizations, particularly as these devices collect, transmit, and analyze diverse data. The firmware of IoT devices plays a key role in ensuring system security; any vulnerabilities in the firmware can expose the system to threats such as hacking or malware infections. Consequently, fuzzing is used to analyze firmware vulnerabilities during the update process. However, conventional single-core and random fuzzing-based firmware vulnerability analysis techniques suffer from low efficiency, limited security, and high memory usage. Each time the firmware is updated, the entire file—including previously analyzed code—must be reanalyzed. Moreover, given that the firmware is not layered, unaffected code segments are redundantly reanalyzed. To address these limitations, this study proposes a dual-core-based hierarchical partial fuzzing technique for wireless networks using dual cores. Experimental results show that the proposed technique detects 11 more unique crashes within 300 s and finds 2435 more total crashes than that of the conventional scheme. It also reduces memory usage by 35 KiB. The proposed technique improves the speed, effectiveness, and reliability of firmware updates and vulnerability detection. Full article

Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods that can securely aggregate models have received widespread attention. Federated learning enables clients to train models locally and share their model updates with the server. While this approach allows collaborative model training without exposing raw data, it still risks leaking sensitive information. To enhance privacy protection in federated learning, secure aggregation is considered a key enabling technology that requires further in-depth investigation. This paper summarizes the definition, classification, and applications of federated learning; reviews secure aggregation protocols proposed to address privacy and security issues in federated learning; extensively analyzes the selected protocols; and concludes by highlighting the significant challenges and future research directions in applying secure aggregation in federated learning. The purpose of this paper is to review and analyze prior research, evaluate the advantages and disadvantages of various secure aggregation schemes, and propose potential future research directions. This work aims to serve as a valuable reference for researchers studying secure aggregation in federated learning. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Unmanned Aerial Vehicle (UAV) path planning is critical for ensuring flight safety and enhancing mission execution efficiency. This problem is typically formulated as a complex, multi-constrained, and nonlinear optimization task, often addressed using meta-heuristic algorithms. The Crested Porcupine Optimizer (CPO) has become an excellent method to solve this problem; however, the standard CPO has limitations, such as the lack of adaptive parameter tuning to adapt to complex environments, slow convergence, and the tendency to fall into local optimal solutions. To address these issues, this paper proposes an algorithm named QCPO, which integrates CPO with Q-learning to improve UAV path optimization performance. Q-learning is employed to adaptively adjust the key parameters of the CPO, thereby overcoming the limitations of traditional fixed-parameter settings. Inspired by the porcupine’s defense mechanisms, a novel audiovisual coordination strategy is introduced to balance visual and auditory responses, accelerating convergence in the early optimization stages. A refined position update mechanism is designed to prevent excessive step sizes and boundary violations, enhancing the algorithm’s global search capability. A B-spline-based trajectory smoothing method is also incorporated to improve the feasibility and smoothness of the planned paths. In this paper, we compare QCPO with four outstanding heuristics, and QCPO achieves the lowest path cost in all three test scenarios, with path cost reductions of 30.23%, 26.41%, and 33.47%, respectively, compared to standard CPO. The experimental results confirm that QCPO offers an efficient and safe solution for UAV path planning. Full article

Coordinating vehicle handling stability and energy consumption remains a key challenge for distributed driving electric vehicles (DDEVs). In this paper, a hierarchical torque vectoring control strategy is proposed to address this issue. First, a tire road friction coefficient (TRFC) estimator based on the fusion of vision and dynamic is developed to accurately and promptly obtain the TRFC in the upper layer. Second, a direct yaw moment control (DYC) strategy based on the adaptive model predictive control (MPC) is designed to ensure vehicle stability in the middle layer, where tire cornering stiffness is updated dynamically based on the estimated TRFC. Then, the lower layer develops the torque vectoring allocation controller, which comprehensively considers handling stability and energy consumption and distributes the driving torques among the wheels. The weight between stability and economy is coordinated according to the stability boundaries derived from an extended phase-plane correlated with the TRFC. Finally, Hardware-in-the-Loop (HIL) simulations are conducted to validate the effectiveness of the proposed strategy. The results demonstrate that compared with the conventional stability torque distribution strategy, the proposed control strategy not only reduces the RMSE of sideslip angle by 44.88% but also decreases the motor power consumption by 24.45% under DLC conditions, which indicates that the proposed method can significantly enhance vehicle handling stability while reducing energy consumption. Full article

In visual Simultaneous Localization and Mapping (SLAM) systems, dynamic elements in the environment pose significant challenges that complicate reliable feature matching and accurate pose estimation. To address the issue of unstable feature points within dynamic regions, this study proposes a robust dual-mask filtering strategy that synergistically integrates semantic segmentation information with geometric outlier detection techniques. The proposed method first identifies outlier feature points through rigorous geometric consistency checks, then employs morphological dilation to expand the initially detected dynamic regions. Subsequently, the expanded mask is intersected with instance-level semantic segmentation results to precisely delineate dynamic areas, effectively constraining the search space for feature matching and reducing interference caused by dynamic objects. A key innovation of this approach is the incorporation of a Perspective-n-Point (PnP)-based optimization module. This module dynamically updates the outlier set on a per-frame basis, enabling continuous monitoring and selective removal of dynamic features. Extensive experiments conducted on benchmark datasets demonstrate that the proposed method achieves average accuracy improvements of 3.43% and 11.42% on the KITTI dataset and 24% and 8.27% on the TUM dataset. Compared to traditional methods, this dual-mask collaborative filtering strategy improves the accuracy of dynamic feature removal and enhances the reliability of dynamic object detection, validating its robustness and applicability in complex dynamic environments. Full article

The 8th meeting of the African Advisory Committee on Vaccine Safety (AACVS), constituted in 2021, convened by the Vaccine Research and Innovation Unit within the Vaccine Preventable Diseases Program, WHO Regional Office for Africa, was held virtually from 14 to 16 April 2025. The meeting brought together independent vaccine experts to provide advice to the Regional Director, WHO, on vaccine safety issues critical to the African region. Discussions focused on critical updates regarding ongoing regional outbreaks, safety data, and associated safety concerns, with emphasis on newly introduced vaccines, including the malaria vaccines (RTS, S and R21), the MenFive pentavalent meningitis vaccine, and the Mpox vaccines—MVA-BN and LC16—alongside the ongoing Mpox response. The Committee conducted a deep dive into comprehensive safety considerations for new vaccine introduction, active surveillance strategies, strengthening the responsiveness of pharmacovigilance systems, and advancing vaccine research and development in Africa. Key observations highlighted significant gaps in safety surveillance systems. These included delays in data collection, access, and signal detection; a lack of harmonized real-time monitoring frameworks; the underutilization of digital technologies; and inadequate manufacturer responsibilities and accountability in post-market safety monitoring. The meeting concluded with a call to action emphasizing the need for sustainable pharmacovigilance funding mechanisms, improved regional coordination, real-time data sharing, standardized early safety study protocols, strengthened manufacturer accountability, and investments in risk communication and community engagement to bolster public trust. Strengthening vaccine safety systems and enhancing regional collaboration were recognized as urgent priorities to support the safe and effective deployment of vaccines and protect public health across Africa. Full article

The expansion of Zigbee-enabled IoT networks has generated significant security issues, especially around threat detection and secure key management. Using RBF and blockchain technology, this study shows a smart hybrid framework to find threats early and distribute keys safely on IoT networks enabled by Zigbee. This methodology incorporates Radial Basis Function (RBF) networks for prompt threat detection and a blockchain-based trust framework for decentralized and tamper-proof key distribution. It guarantees safe network access, comprehensive authentication, and effective key updates, reducing risks associated with IoT-related DoS attacks and Man in the Middle Attacks. The Trust-Based Security Provider (TBSP) enhances security by administering critical credentials across diverse networks. Comprehensive simulations and performance assessments illustrate the effectiveness of the framework in increasing threat detection precision, minimizing key distribution delay, and bolstering overall network security. The findings confirm its efficacy in safeguarding IoT settings from new risks while ensuring scalability and resource efficiency. We proposed an RBF-based threat detection framework for network keys using the ZBDS2023 dataset and the J48 decision tree algorithm. In conclusion, we demonstrate the security and efficiency of our proposed work. Full article

With the integration of an increasing number of outward-facing components in intelligent and connected vehicles, the open controller area network (CAN) bus environment faces increasingly severe security threats. However, existing security measures remain inadequate, and CAN bus messages lack effective security mechanisms and are vulnerable to malicious attacks. Although encryption algorithms can enhance system security, their high bandwidth consumption negatively impacts the real-time performance of intelligent and connected vehicles. Moreover, the message authentication mechanism of the CAN bus requires lengthy authentication codes, further exacerbating the bandwidth burden. To address these issues, we propose an improved dynamic compression algorithm that achieves higher compression rates and efficiency by optimizing header information processing during data reorganization. Additionally, we have proposed a novel dynamic key management approach, incorporating a dynamic key distribution mechanism, which effectively resolves the challenges associated with key management. Each Electronic Control Unit (ECU) node independently performs compression, encryption, and authentication while periodically updating its keys to enhance system security and strengthen defense capabilities. Experimental results show that the proposed dynamic compression algorithm improves the average compression rate by 2.24% and enhances compression time efficiency by 10% compared to existing solutions. The proposed security protocol effectively defends against four different types of attacks. In hardware tests, using an ECU operating at a frequency of 30 MHz, the computation time for the security algorithm on a single message was 0.85 ms, while at 400 MHz, the computation time was reduced to 0.064 ms. Additionally, for different vehicle models, the average CAN bus load rate was reduced by 8.28%. The proposed security mechanism ensures the security, real-time performance, and freshness of CAN bus messages while reducing bus load, providing a more efficient and reliable solution for the cybersecurity of intelligent and connected vehicles. Full article

Integrated process planning and scheduling (IPPS) is an intricate and vital issue in smart manufacturing, requiring the coordinated optimization of both process plans and production schedules under multiple resource and precedence constraints. This paper presents a novel optimization framework, symmetry-aware adaptive Ant Colony Optimization (SA-AACO), designed to resolve key limitations in existing metaheuristic approaches. The proposed method introduces three core innovations: (1) a symmetry-awareness mechanism to eliminate redundant solutions arising from symmetrically equivalent configurations; (2) an adaptive pheromone-updating strategy that dynamically balances exploration and exploitation; and (3) a dynamic idle time penalty system, integrated with time window-based machine selection. Benchmarked across ten IPPS scenarios, SA-AACO achieves a superior makespan in 9/10 cases (e.g., 29.1% improvement over CCGA in Problem 1) and executes 18-part processing within 30 min. While MMCO marginally outperforms SA-AACO in Problem 10 (makespan: 427 vs. 483), SA-AACO’s consistent dominance across diverse scales underscores the feasibility of its application in industry to balance quality and efficiency. By unifying symmetry handling and adaptive learning, this work advances the reconfigurability of IPPS solutions for dynamic industrial environments. Full article