Sustainable Secure Blockchain Assisted AIoT and Green Multiconstraints Supply Chain System

Abstract

In this era, digital technologies such as artificial intelligence, the Internet of Things (IoT) and blockchain are gaining popularity in research and academia. The supply chain management application is the key to achieving many benefits from AIoT and blockchain technology. However, these technologies have many issues, such as sustainability, a green environment, and multiconstraints (e.g., time, energy, cost, and CO2) for supply chain management applications. This article presents sustainable, secure blockchain-assisted AIoT and green multiconstraint supply chain systems. Initially, we present a secure and sustainable methodology that securely validates the supply chain management system data. For the green environment, we consider the problem a combinatorial problem consisting of different constraints such as time, energy, cost, and carbon dioxide (CO2). To solve this problem for supply chain management jobs, we present a multiconstraint genetic algorithm deep convolutional neural network (MCGA-DCNN) algorithm methodology. The objective is to reduce total processing time, total processing energy consumption, cost, and the CO2 environment as a green environment for supply chain management jobs. The genetic algorithm is evolutionary, where the fitness function optimizes the multiconstraint weights at the runtime based on DCNN and provides the optimal solutions for jobs. Simulation results show that MCGA-DCNN minimized the time, energy, cost, and CO2 and securely validated all transactions for all supply chain management jobs compared to existing schemes.