Recently, researchers have applied blockchain technology in vehicular networks to take benefit of its security features, such as confidentiality, authenticity, immutability, integrity, and non-repudiation. The resource-intensive nature of the blockchain consensus algorithm makes it a challenge to integrate it with vehicular networks due to the time-sensitive message dissemination requirements. Moreover, most of the researchers have used the Proof-of-Work consensus algorithm, or its variant to add a block to a blockchain, which is a highly resource-intensive process with greater latency. In this paper, we propose a consensus algorithm for vehicular networks named as Vehicular network Based Consensus Algorithm (VBCA) to ensure data security across the network using blockchain that maintains a secured pool of confirmed messages exchanged in the network. The proposed scheme, based on a consortium blockchain, reduces average transaction latency, and increases the number of confirmed transactions in a decentralized manner, without compromising the integrity and security of data. The simulation results show improved performance in terms of confirmed transactions, transaction latency, number of blocks, and block creation time.
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http://dx.doi.org/10.1038/s41598-023-31442-w | DOI Listing |
Sensors (Basel)
January 2025
Netcom Engineering S.p.A., Via Nuova Poggioreale, Centro Polifunzionale, Tower 7, 5th Floor, 80143 Naples, Italy.
This paper explores the development and testing of two Internet of Things (IoT) applications designed to leverage Vehicle-to-Infrastructure (V2I) communication for managing intelligent intersections. The first scenario focuses on enabling the rapid and safe passage of emergency vehicles through intersections by notifying approaching drivers via a mobile application. The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities.
View Article and Find Full Text PDFSci Rep
January 2025
Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, 11586, Riyadh, Saudi Arabia.
The Internet of Vehicles (IoV) transforms the automobile industry through connected vehicles with communication infrastructure that improves traffic control, safety and information, and entertainment services. However, some issues remain, like data protection, privacy, compatibility with other protocols and systems, and the availability of stable and continuous connections. Specific problems are related to energy consumption for transmitting information, distributing energy loads across the vehicle's sensors and communication units, and designing energy-efficient approaches to processing received data and making decisions in the context of the IoV environment.
View Article and Find Full Text PDFPLoS One
January 2025
Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, Saudi Arabia.
The performance of drones, especially for time-sensitive tasks, is critical in various applications. Fog nodes strategically placed near IoT devices serve as computational resources for drones, ensuring quick service responses for deadline-driven tasks. However, the limited battery capacity of drones poses a challenge, necessitating energy-efficient Internet of Drones (IoD) systems.
View Article and Find Full Text PDFFront Bioeng Biotechnol
January 2025
Rehabilitation Department, Jilin Electric Power Hospital, Changchun, China.
Rehabilitation assessments hold an irreplaceable role in the field of rehabilitative therapy. However, due to the subjectivity of traditional physicians and the variability of patient conditions, this leads to a lack of detailed grading and inaccurate assessment results. To address this issue, we developed an upper limb rehabilitation evaluation model.
View Article and Find Full Text PDFEnviron Technol
January 2025
Department of Sanitary and Environmental Engineering, Federal University of Santa Catarina, Florianópolis, Brazil.
Precise estimates of vehicular emissions at fine spatial scales are essential for effective emission reduction strategies. Achieving high-resolution vehicular emission inventories necessitates detailed data on traffic flow, driving patterns, and vehicle speeds for each road network segment. However, in developing countries, the lack of comprehensive traffic data, limited infrastructure, and insufficient monitoring systems constrains the development of high-resolution inventories.
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