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Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation.
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http://dx.doi.org/10.3390/s24051515 | DOI Listing |
Front Artif Intell
August 2025
School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
Introduction: Digital content, including images and videos, is increasingly ruling the online world, and so multimedia services form a part of this modern life. However, the digital resources face significant problems, especially regarding copyright infringement. In such an instance, any modification without authority infringes intellectual property rights.
View Article and Find Full Text PDFMethodsX
December 2025
Department of Computer Science and Engineering, GITAM Deemed to be University, Andhra Pradesh, India.
Cervical cancer is a serious health concern that entails high risks for individuals due to delayed detection and treatment worldwide. Formal screening for the condition is challenging in developing countries due to several factors, including medical costs, access to healthcare facilities, and delayed symptom manifestation. A blockchain-enabled healthcare system for cervical cancer risk prediction ensures data security, privacy, and accurate risk assessment.
View Article and Find Full Text PDFEnviron Manage
September 2025
Department of Computer Applications, JECRC University, Jaipur, Rajasthan, 303905, India.
Groundwater conservation and wastewater management soil depletion, water pollution, and poor resource management. These problems underscore the need for innovative practices that leverage blockchain technology to enhance sustainability, maintain data integrity, and optimize resource utilization in wastewater management. This review systematically analyzes recent advances (2019-2025) in blockchain technology, Machine Learning (ML), and Deep Learning (DL) models applied to sustainable water resource management.
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July 2025
Department of Computer Science and Engineering, Francis Xavier Engineering College, Tirunelveli, India.
The pharmaceutical supply chain has a critical component, the Drug Traceability System, which tracks drugs from manufacturers for further processing and distribution. The integration of blockchain technology yields a secure solution for monitoring drugs throughout the supply chain management process. The paper proposes a novel Blockchain-enabled Secured Vertical Aggregation Algorithm (BSVA) by leveraging the Hyperledger model.
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July 2025
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
The rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities underscores its potential to enable collaborative machine learning without compromising data ownership. However, conventional FL frameworks face several critical challenges: high computational overhead on edge devices, significant communication latency due to frequent model updates, vulnerability to model and data poisoning attacks, and limited privacy-preserving mechanisms that expose systems to inference risks. These issues hinder the scalability, efficiency, and trustworthiness of FL in real-world, large-scale deployments-particularly in domains like Electronic Health Records (EHR) management, where data sensitivity is paramount.
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