Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access

Song, Hao; Bai, Jianan; Yi, Yang; Wu, Jinsong; Liu, Lingjia

doi:10.1109/mci.2019.2954643

Cited by 145 publications

(77 citation statements)

References 29 publications

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“…Therefore, 6G must reinforce URLLC in most or emergency proceedings with traffic designs and sequential device densities. URLLC is essential to support intelligent services, reconfigure intelligent surfaces, and solve problems in 6G by growth data traffic, increasing battery efficiency, and increasing data-driven DL for ultra-reliable are summarized in [59], [60], [224], [225]. The following is needed to address these challenges 6G.…”

Section: A Perform Of 6g Networkmentioning

confidence: 99%

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-toend behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.INDEX TERMS Artificial neural networks, artificial intelligence, Internet of Things, sixth-generation wireless communication and network architecture, URLLC.

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Section: A Perform Of 6g Networkmentioning

confidence: 99%

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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“…For this scenario, many technologies and techniques are involved such as Software Defined Network (SDN) [9][10][11], Network Function Virtualization (NFV) and 5G mobile networking [12]. Moreover, due to the artificial intelligence (AI) and Machine Learning (ML) ability to solve problems and automate tasks at a network level, they become of great interest during IoT system development [13][14][15]. These technologies and techniques could easy-up or complicate finding the right solution for QoS provisioning in IoT systems.…”

Section: Introductionmentioning

confidence: 99%

IoT Provisioning QoS based on Cloud and Fog Computing

Samann

Zeebaree

Askar

2021

JASTT

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The wide-spread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.

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“…Since the arrival of the 'smart era' of vehicular communications where most of vehicles have powerful processing capability and intelligent functionality, this provides a basis to enable multi-carrier relay selection in a more efficient manner based on artificial intelligence (AI) related technologies [21]- [23]. Furthermore, due to the state-of-the-art achievements in channel estimations, channel statistics and relevant estimated states utilized for network optimization are normally labeled data from multiple sources [24].…”

Section: Introductionmentioning

confidence: 99%

Enabling Multicarrier Relay Selection by Sensing Fusion and Cascaded ANN for Intelligent Vehicular Communications

et al. 2021

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Cooperative relaying has been adopted as one of the most important techniques to enhance the energy efficiency and coverage. Multi-carrier relay selection is an efficient method to allocate spatial/spectral resources in cooperative relay networks and provides diversity gain. However, the implementation of multicarrier relay selection is not straightforward, and could render the high system complexity (for centralized implementation schemes) or long processing delay (for distributed implementation schemes). These issues hinder the promotion and implementation of multicarrier relay selection for intelligent vehicular communications. To mitigate aforementioned issues, we propose an enabling technique of multi-carrier relay selection based on sensing fusion (SF) and cascaded artificial neural networks (CANNs) for intelligent vehicular communications. We employ two well-known multicarrier relay selection schemes, i.e. bulk and per-subcarrier relay selection, to verify the effectiveness of the CANN based enabling technique. With the powerful processing ability with intelligent vehicles, the numerical results illustrate a promising vision of applying CANNs to enable multi-carrier relay selection for fast deployment in intelligent vehicular communication networks.

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Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access

Cited by 145 publications

References 29 publications

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

IoT Provisioning QoS based on Cloud and Fog Computing

Enabling Multicarrier Relay Selection by Sensing Fusion and Cascaded ANN for Intelligent Vehicular Communications

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