Blockchain Integration with AIoT Data Security and Privacy for Sustainability

Abstract

As a primary goal, AIoT (Artificial Intelligence of Things) is the fusion of AI (artificial intelligence) methods with IoT (Internet of Things) infrastructure, which is deployed there to enhance the overall system performance of AIoT. Artificial Intelligence of Things can be used to make Internet of Things operations more efficient, which will enhance data analysis and human-machine interactions. The system's general usefulness is further increased by applying artificial intelligence techniques to convert Internet of Things data into relevant information for improved decision-making processes. The Artificial Intelligence of Things frameworks have a wide range of applications, including eCommerce, logistics operations and control, smart homes, smart farms, intelligent transportation systems, industrial automation and control, eCommerce, secure as well as safe healthcare monitoring, and many more. AIoT frameworks, however, are susceptible to a variety of information security-related assaults, which could result in problems with data security and privacy. Serious repercussions, such as unapproved data updates and leaks, are also brought on by these problems. One particular kind of database is the blockchain. It's a digital record of all the transactions that's distributed throughout the whole network of systems. Data is stored in the blocks that are linked in a chained manner. Compared to conventional security methods, blockchain technology offers greater security and is impervious to tampering. Therefore, to increase security, blockchain can be used in a variety of AIoT applications. A safe authentication architecture for AIoT has been suggested, modelled after a generalised blockchain. The adversarial model, which handles most potential security threats in this kind of communication environment, is also highlighted. This framework is part of the blockchain-envisioned safe authentication framework for the Internet of Things. The suggested framework's numerous applications are also covered. Additionally, certain problems and difficulties with the suggested framework are emphasised. Finally, we also offer some suggestions for future research that are related to the framework that has been suggested.