Priyanka1,*, Ritu Makani2, Hemant Verma3
Priyanka
1 Research Scholar, GJUST, Hisar, 125001, Haryana, India
2 Associate Professor, GJUST, Hisar, 125001, Haryana, India
3 Assistant Professor, CBLU, Bhiwani, 127032 Haryana, India
*Corresponding Author
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.
Blockchain, Artificial Intelligence, Internet of things, Security, Privacy, Integration
Priyanka, Ritu Makani, Hemant Verma (2024). Blockchain Integration with AIoT Data Security and Privacy for Sustainability. Journal of Artificial Intelligence and Systems, 6, 112–122. https://doi.org/10.33969/AIS.2024060108.
[1] Z. Xiong, Z. Cai, D. Takabi, and W. Li, “Privacy threat and defence for federated learning with non-i.i.d. data in AIoT,” IEEE Transactions on Industrial Informatics, p. 1, 2021.
[2] J. Chen, Y.-Y. Huang, Y.-S. Li, C.-Y. Chang, and Y.-M. Huang, “An AIoT based smart agricultural system for pests’ detection,” IEEE Access, vol. 8, pp. 180750–180761, 2020.
[3] X. Zhang, M. Hu, J. Xia, T. Wei, M. Chen, and S. Hu, “Efficient federated learning for cloud-based AIoT applications,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020.
[4] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Pearson, London, UK, 2021.
[5] K. F. Lee, AI Superpowers China, Silicon Valley, and the New World Order, Houghton Mifflin Harcourt, Boston, New York, USA, 2018.
[6] A. Barto and R. S. Sutton, Reinforcement Learning: An Introduction, The MIT Pres, Cambridge, Massachusetts US, London, UK, 2014.
[7] T. Poongodi, A. Rathee, R. Indra Kumari, and P. Suresh, “IoT sensing capabilities: sensor deployment and node discovery, wearable sensors, wireless body area network (WBAN), data acquisition,” in Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm. Intelligent Systems Reference Library, S. L. Peng, S. Pal, and L. Huang, Eds., vol. 174, pp. 127–151, Springer, Cham, 2020.
[8] K. L.-M. Ang and J. K. P. Seng, “Application Specific Internet of Things (ASIoTs): taxonomy, applications, use case and future directions,” IEEE Access, vol. 7, pp. 56577–56590, 2019.
[9] S. S. Seshadri, D. Rodriguez, M. Subedi et al., “IoT Cop: a blockchain-based monitoring framework for detection and isolation of malicious devices in Internet-of-Things systems,” IEEE Internet of Things Journal, vol. 80, no. 5, pp. 3346–3359, 2021.
[10] Y. Lu and L. D. Xu, “Internet of Things (IoT) cybersecurity research: a review of current research topics,” IEEE Internet of Things Journal, vol. 60, no. 2, pp. 2103–2115, 2019.
[11] M. Zichichi, S. Ferretti, and G. D’Angelo, “A framework based on distributed ledger technologies for data management and services in intelligent transportation systems,” IEEE Access, vol. 8, pp. 100384–100402, 2020.
[12] P. Dass, S. Misra, and C. Roy, “T-safe: trustworthy service provisioning for IoT-based intelligent transport systems,” IEEE Transactions on Vehicular Technology, vol. 690, no. 9, pp. 9509–9517, 2020.
[13] M. Frustaci, P. Pace, G. Aloi, and G. Fortino, “Evaluating critical security issues of the IoT world: present and future challenges,” IEEE Internet of Things Journal, vol. 50, no. 4, pp. 2483–2495, 2018.
[14] D. Jeong, “Artificial intelligence security threat, crime, and forensics: taxonomy and open issues,” IEEE Access, vol. 8, pp. 184560–184574, 2020.
[15] A. Alwarafy, K. A. Al-Thelaya, M. Abdallah, J. Schneider, and M. Hamdi, “A survey on security and privacy issues in edgecomputing- assisted Internet of Things,” IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4004–4022, 2021.
[16] L. Ye, Z. Wang, Y. Liu et al., “The challenges and emerging technologies for low-power artificial intelligence IoT systems,” IEEE Transactions on Circuits and Systems I: Regular Papers, pp. 1–14, 2021.