A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era
Open accessAbstract
The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agri…
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Existing attack monitoring methods generally target a single device or a single scenario.
“Existing attack monitoring methods generally target a single device or a single scenario”
From A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends (2024) · cites this paper as [122]
Cites
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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
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<scp>AI</scp>‐driven adaptive learning for sustainable educational transformation
Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025) · Sustainable Development · doi:10.1002/sd.3221 · Cited by 581 · see citing works
A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends
Sun, P., Shen, S., Wan, Y., Wu, Z., Fang, Z., & Gao, X.-Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3372518 · Cited by 172 · see citing works
- Authors
- Mahmood, M. R., Matin, M. A., Sarigiannidis, P., & Goudos, S. K.
- Affiliations
- North South University (BD)
- University of Western Macedonia (GR)
- Aristotle University of Thessaloniki (GR)
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 87535-87562
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4292348061
- Cited by
- 209
- References
- 3
- Citations by year
- 2026: 33 · 2025: 76 · 2024: 63 · 2023: 30 · 2022: 7
- FWCI
- 43.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- European Union's Horizon 2020 research and innovation programme (957406 (TERMINET))
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Keywords
- artificial intelligence, machine learning algorithms, Internet of Things, 6G, beyond 5G, IoT network security, energy efficiency, quality of service, smart healthcare, smart agriculture, smart transportation, smart grid, smart industry, massive connectivity
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