Enabling Technologies and Applications of 5G/6G-Powered Intelligent Connectivity

Fowdur, Tulsi Pawan; Babooram, Lavesh; Indoonundon, Madhavsingh; Murdan, Anshu P.; Bojković, Zoran; Milovanović, Dragan

doi:10.1201/9781003205494-13

Cited by 4 publications

(4 citation statements)

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“…✓ Cyber robots and driverless cars are becoming increasingly popular, with the 6G system allowing for widespread use of things like aerial vehicle mail delivery systems and robots and even the implementation of self-driving cars [18].…”

Section: Resultsmentioning

confidence: 99%

Advancements and Challenges in Energy-efficient 6G Mobile Communication Network

Slimani,

Khoulji,

Kerkeb

2023

E3S Web Conf.

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The arrival of 6G mobile communication networks is anticipated to revolutionize the technological landscape, bringing about profound innovations. This research paper explores the various technological advancements that will pave the way for the advent of 6G networks, with a particular focus on addressing energy consumption. It is widely recognized that energy efficiency plays a crucial role in the evolution of 6G networks. To enhance network performance, user experience, and resource management, the integration of Artificial Intelligence (AI) is expected to be a pivotal technology. AI-based solutions can effectively optimize energy usage and contribute to the overall efficiency of 6G networks. Furthermore, the incorporation of wireless communication systems, telecommunication, and the Internet of Things (IoT) will be integral to the infrastructure of 6G networks. The need for significant enhancements in 6G networks is also examined in this study. Ensuring the safety and protection of 6G networks from cyber threats becomes increasingly important due to the growing reliance on networked communication and the sensitive nature of transmitted information. Cutting-edge security methods such as homomorphic encryption and blockchain technology may be essential in this regard. Moreover, this research paper explores the impact of 6G networks on various domains and discusses the challenges that must be overcome to unlock the technology’s full potential. To ensure responsible adoption and usage of 6G networks, the development of new business models and regulatory frameworks may be necessary to support their implementation while addressing energy consumption concerns.

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Section: Resultsmentioning

confidence: 99%

Advancements and Challenges in Energy-efficient 6G Mobile Communication Network

Slimani,

Khoulji,

Kerkeb

2023

E3S Web Conf.

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“…This paper's objective was to develop a collaborative network analytics system by expanding the framework of Fowdur et al (2021) where insights about the real-time performance of a network are forecasted. With tailored android and desktop applications, the system can collect real-time network traffic and in turn perform network analytics with respect to both classification and timeseries prediction using algorithms such as KNN, MLP, SVM and MLR.…”

Section: Discussionmentioning

confidence: 99%

“…A more detailed description of these methods is given in Fowdur et al (2021). However (Fowdur et al, 2021), covers only non-collaborative regression.…”

Section: Multiplesource Data Aggregationmentioning

confidence: 99%

Performance analysis of a cloud-based network analytics system with multiple-source data aggregation

Fowdur

Babooram

2022

IJPCC

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Purpose The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters. Design/methodology/approach The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases. Findings Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic. Originality/value Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.

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“…In the context of 6G networks, an enhanced blend of massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) is expected to play a crucial role in shaping the future of wireless communication systems. mMTC focuses on supporting a massive number of connected devices and sensors, enabling the Internet of Things (IoT) to unleash its full potential [73]. On the other hand, URLLC aims to provide ultra-reliable and low-latency communication links critical for applications such as autonomous vehicles [74], remote surgery, and industrial automation.…”

Section: Enhanced Blend Of Massive Machine-type Communication (Mmtc) ...mentioning

confidence: 99%

From Efficiency to Sustainability: Exploring the Potential of 6G for a Greener Future

Kumar,

Gupta,

Wang

et al. 2023

Sustainability

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This article provides a comprehensive examination of sustainable 6G wireless communication systems, addressing the urgent need for environmentally friendly and energy-efficient networks. The background establishes the broader context and significance of the study, emphasizing the escalating concerns surrounding the environmental impact and energy consumption of wireless communication systems. The purpose of this study is to explore and propose sustainable solutions for 6G networks. The methods employed in this research encompass an analysis of various strategies and technologies, including energy-aware network design, dynamic power management, energy harvesting, and green infrastructure deployment. The main findings of this article highlight the effectiveness of these approaches in enhancing energy efficiency, reducing carbon footprint, and optimizing resource management in 6G networks. The conclusions drawn from this study emphasize the importance of sustainable 6G wireless communication systems in achieving a more eco-friendly and energy-efficient future. It is crucial to adopt these sustainable practices to minimize environmental impact and address the increasing energy demands of wireless communication networks. The article provides valuable insights to researchers, industry practitioners, and policymakers, aiding in the development and implementation of sustainable practices for 6G wireless communication systems.

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Enabling Technologies and Applications of 5G/6G-Powered Intelligent Connectivity

Cited by 4 publications

References 0 publications

Advancements and Challenges in Energy-efficient 6G Mobile Communication Network

Advancements and Challenges in Energy-efficient 6G Mobile Communication Network

Performance analysis of a cloud-based network analytics system with multiple-source data aggregation

From Efficiency to Sustainability: Exploring the Potential of 6G for a Greener Future

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