With the rapid growth in wireless communication and IoT technologies, Radio Frequency Identification (RFID) is applied to the Internet of Vehicles (IoV) to ensure the security of private data and the accuracy of identification and tracking. However, in traffic congestion scenarios, frequent mutual authentication increases the overall computing and communication overhead of the network. For this reason, in this work, we propose a lightweight RFID security fast authentication protocol for traffic congestion scenarios, designing an ownership transfer protocol to transfer access rights to vehicle tags in non-congestion scenarios. The edge server is used for authentication, and the elliptic curve cryptography (ECC) algorithm and the hash function are combined to ensure the security of vehicles' private data. The Scyther tool is used for the formal analysis of the proposed scheme, and this analysis shows that the proposed scheme can resist typical attacks in mobile communication of the IoV. Experimental results show that, compared to other RFID authentication protocols, the calculation and communication overheads of the tags proposed in this work are reduced by 66.35% in congested scenarios and 66.67% in non-congested scenarios, while the lowest are reduced by 32.71% and 50%, respectively. The results of this study demonstrate a significant reduction in the computational and communication overhead of tags while ensuring security.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256077 | PMC |
http://dx.doi.org/10.3390/s23115198 | DOI Listing |
J Biomed Semantics
December 2024
Medical BioSciences Department, Radboud University Medical Center, Nijmegen, The Netherlands.
Motivation: We are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and can be misused to de-anonymize and (re-)identify individuals.
View Article and Find Full Text PDFBMC Endocr Disord
December 2024
Departemnt of Pediatrics and Child Health, School of Medicine, University of Gondar, Gondar, Ethiopia.
Background: Diabetes mellitus is one of the most common chronic illnesses in children with multiple psychosocial, economic and developmental effects. Psychiatric disorders such as depression, anxiety, psychological distress, and eating disorders are more common in diabetic patients than the non-diabetic once. The main objective of our study was to assess Prevalence and associated factors of psychiatric problems in children aged 6-18 years with type 1 diabetes mellitus in Gondar, Ethiopia.
View Article and Find Full Text PDFSci Rep
December 2024
School of Electronic and Nanoscale Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
In the era of the Internet of Things (IoT), the transmission of medical reports in the form of scan images for collaborative diagnosis is vital for any telemedicine network. In this context, ensuring secure transmission and communication is necessary to protect medical data to maintain privacy. To address such privacy concerns and secure medical images against cyberattacks, this research presents a robust hybrid encryption framework that integrates quantum, and classical cryptographic methods.
View Article and Find Full Text PDFSci Rep
December 2024
Computer Engineering Department, Umm Al-Qura University, Mecca, 24381, Saudi Arabia.
Efficient traffic management solutions in 6G communication systems face challenges as the scale of the Internet of Things (IoT) grows. This paper aims to yield an all-inclusive framework ensuring reliable air pollution monitoring throughout smart cities, capitalizing on leading-edge techniques to encourage large coverage, high-accuracy data, and scalability. Dynamic sensors deployed to mobile ad-hoc pieces of fire networking sensors adapt to ambient changes.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Computer Science , Applied College, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Over the past two decades, cloud computing has experienced exponential growth, becoming a critical resource for organizations and individuals alike. However, this rapid adoption has introduced significant security challenges, particularly in intrusion detection, where traditional systems often struggle with low detection accuracy and high processing times. To address these limitations, this research proposes an optimized Intrusion Detection System (IDS) that leverages Graph Neural Networks and the Leader K-means clustering algorithm.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!