Ultra‐reliability and low‐latency communications on the internet of things based on<scp>5G</scp>network: Literature review, classification, and future research view

Sefati, Seyed Salar; Halunga, Simona

doi:10.1002/ett.4770

Cited by 22 publications

(6 citation statements)

References 138 publications

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“…Other than that, because it prioritizes reliability and latency, URLLC could use less power and bandwidth than other communication technologies [ 43 ]. This means that URLLC networks are optimized to deliver small amounts of data quickly and reliably, even in challenging conditions [ 44 ]. Those benefits of URLLC make it suitable for several advanced technologies, such as AI-driven optimization, which will optimize the URLLC networks in case of predictive analytics, resource allocation, security, and network troubleshooting.…”

Section: 6g Visions and Requirementsmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

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Section: 6g Visions and Requirementsmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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“…The IEEE 802.11ax, a sixth-generation protocol popularly known as High Efficiency (HE), reports a 30% improvement in throughput over the older IEEE 802.11ac protocol known as very high throughput (VHT) [93]. A 5G-enabled energyefficient routing protocol (EERP) that uses Wi-Fi 6 makes it easier for medical devices to communicate with one another, while multi-user, multiple input, multiple outputs (MU-MIMO) allows parallel communications [94]. A datacentric protocol prioritizes the transmission of critical health information, and content-centric networking (CCN) improves the efficiency of the distribution of healthcare information between IoT devices [95].…”

Section: ) the Network Layermentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

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“…Heterogeneous wireless networks, composed of diverse communication technologies and multiple paths, have become increasingly common due to the rapid growth of mobile devices, the Internet of Things (IoT), and the Internet of Medical Things (IoMT) [1][2][3][4][5]. The proliferation of IoT has led to heightened demand for efficient data transmission and seamless connectivity among various devices such as smartphones, wearable devices, and smart home appliances [6].…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware MPTCP Scheduling in Heterogeneous Wireless Networks Using Multi-Agent Deep Reinforcement Learning Techniques

Arain,

Qiu,

et al. 2023

Electronics

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This paper proposes an energy-efficient scheduling scheme for multi-path TCP (MPTCP) in heterogeneous wireless networks, aiming to minimize energy consumption while ensuring low latency and high throughput. Each MPTCP sub-flow is controlled by an agent that cooperates with other agents using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. This approach enables the agents to learn decentralized policies through centralized training and decentralized execution. The scheduling problem is modeled as a multi-agent decision-making task. The proposed energy-efficient scheduling scheme, referred to as EE-MADDPG, demonstrates significant energy savings while maintaining lower latency and higher throughput compared to other state-of-the-art scheduling techniques. By adopting a multi-agent deep reinforcement learning approach, the agents can learn efficient scheduling policies that optimize various performance metrics in heterogeneous wireless networks.

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Ultra‐reliability and low‐latency communications on the internet of things based on5Gnetwork: Literature review, classification, and future research view

Cited by 22 publications

References 138 publications

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Energy-Aware MPTCP Scheduling in Heterogeneous Wireless Networks Using Multi-Agent Deep Reinforcement Learning Techniques

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