Secure Message Handling in Vehicular Energy Networks using Blockchain and Artificially Intelligent IPFS


The underlying work addresses the issues that arise during the process of message dissemination in conventional Vehicular Energy Networks (VENs). These issues include a lack of security, a breach of personal identities, a lack of trust between vehicle owners, and a number of other issues. A Blockchain (BC)-based announcement system is proposed for use with VENs in this research. The goal of this system is to provide a method that is both secure and reliable for the dissemination of announcements within the proposed network. The system that has been suggested has three layers: the layer for the storage of messages, the layer for the dissemination of messages, and the BC layer. In the first layer of the proposed network, all of the vehicles will first be registered with a Certificate Authority (CA), which will ensure that only legal vehicles will be allowed to become a part of the network and communicate with one another. Later, in the second layer, the data that is being sent by the vehicles is being stored at the artificial intelligence-based Interplanetary File System (IPFS), which is incorporated with the Road Side Units (RSUs). This ensures that both the cost of storage and the availability of data are reduced. In addition, the true identities of the vehicles are concealed, which protects the privacy of the owners of those vehicles. In addition to this, the hashes of the data that are kept in the IPFS are kept in BC within the third layer. This layer is also responsible for performing tasks such as lightweight trustworthiness verification of the vehicles, reputation-based incentivization, and concealing predictable trends in the vehicles' reputation scores. Overall, the novelty of the work that has been proposed lies in the fact that the proposed system efficiently tackles multiple problems that are encountered in existing systems simultaneously. This is what makes the work so novel. When compared to storing actual data on the network, storing a hash of the data rather than the data itself results in a reduction of computational time that ranges from 15–18% and a reduction of storage overhead that ranges from 80–85%, respectively. This conclusion was reached through the use of extensive simulations.

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