Improving Data Query and Ensuring Security in Mobile Vehicular Networks Using Deep Learning and Blockchain

Dashti, SeyedEbrahim; Moayyedi, Fatemeh

doi:10.22034/apj.2026.2071330.1054

Improving Data Query and Ensuring Security in Mobile Vehicular Networks Using Deep Learning and Blockchain

Document Type : Original Research Article

Authors

SeyedEbrahim Dashti ¹

fatemeh moayyedi ²

¹ Islamic Azad University

² Larestan University

10.22034/apj.2026.2071330.1054

Abstract

Background and Objectives: With the advancement of vehicular networks and the increasing demand for accurate and timely data, challenges such as data retrieval delays and security concerns have garnered significant attention. Traditional cloud-based storage methods are unable to meet temporal and security requirements due to the considerable distance between vehicles and servers. Although edge computing offers a solution for reducing latency, it requires improvements due to limitations in storage and distributed management. Previous research has primarily focused on one aspect of optimization (reducing delay or enhancing security), with less attention paid to combining these two objectives. This paper aims to propose an innovative hybrid optimization model using deep learning and blockchain that considers both security and delay reduction. This is achieved by optimizing caching locations, storage, retrieval processes, and storing critical information on the blockchain, ensuring a scalable and flexible model adaptable to traffic changes and user demands.

Methods: The study population and sample include mobile vehicular networks (VANETs), considering edge servers and vehicular nodes. An LSTM model was used to predict traffic patterns and data popularity, while blockchain with a Proof of Authority (PoA) consensus mechanism and smart contracts was employed for secure data storage. Performance was evaluated based on delay, security, and scalability metrics, and compared with existing methods such as Tabu Search, CCS-AGP, and Random Caching in terms of delay and security.

Findings: The proposed model significantly reduced delay (by 10% to 30% compared to baseline methods). The use of blockchain introduced only an 8% additional delay while elevating security to a "very high" level. The system demonstrated stability and scalability under increasing numbers of nodes and data volume. Simulation results indicated that the combination of deep learning and blockchain achieves an optimal balance between performance and security.

Conclusion: The proposed model, integrating deep learning and blockchain, not only reduces delay but also ensures data security and integrity. This framework can serve as a foundation for developing intelligent systems in domains such as the Internet of Things (IoT), smart cities, and next-generation transportation.

Keywords

deep learning

blockchain

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