Arman Process Journal (APJ)

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.

Abstract Blockchain is a holistic system consisting of peer-to-peer connected and distributed blocks of data that eliminates the need for a centralized management entity to manage technological transactions. Blockchain's open-source, impermeable configuration paves the way for an unparalleled level of transparency. Each piece of data is distributed among millions of computers around the world and its authenticity is verified. This relatively new technology is revolutionizing various industries and providing an automated process for managing processes and interactions. The use of blockchain is a cheap and fast solution and it is very attractive that it has experienced a lot of development in recent years. In today's era, the use of blockchain technology plays a very important role in the development of businesses. This powerful technology improves the quality of business and also increases their income and profit. Maintaining business security, authenticity, speed and increasing quality are among the benefits that arise with the help of blockchain in businesses. According to the importance of the issue, in this article we intend to examine the convergence of blockchain with technology-oriented services and related functions.