Deep Learning and Proxy Smart Contract-Based Supply Chain Management for Electric Vehicles

Abstract

In today's world of constant change and instant services, industrialisation has perhaps played the biggest role in the realisation of such a reality. The rapid and large-scale production of various goods is made possible through the concept of a supply chain. At the same time, in the world of automobiles, the advent of electric vehicles (EV) has revolutionalised the entire landscape by providing a much more energy-efficient and sustainable alternative. However, more than a supply chain, its proper management is more crucial to smooth functioning. Efficient supply chain management (SCM) in EVs is a very important step in ensuring that this novel technology can be made more cost-efficient and accessible. A viable remedy for these issues, considering the advancement in artificial intelligence and machine learning (AI/ML), blockchain, and other related technologies can be to integrate these solutions into the existing concepts of SCM and data storage. Encouraged by the aforementioned facts, we propose a deep learning (DL) based intrusion detection system (IDS) for EV manufacturing. We take into consideration artificial neural network (ANN), long short-term memory (LSTM), and recurrent neural network (RNN) algorithms and contrast and compare their performance on the data. The IDS is followed by a proxy smart contract (PSC) based deployment framework for smart contracts (SC), followed by secure storage of the supply chain data in a blockchain. The PSC solves the immutability aspect of blockchains whereas, the blockchain provides a secure and transparent storage solution, with efficient traceability.