Original Paper Information:
Network Graph Generation through Adaptive Clustering and Infection Dynamics: A Step Towards Global Connectivity
Published 44520.
Category: Technology
Authors:
[‘Aniq Ur Rahman’, ‘Fares Fourati’, ‘Khac-Hoang Ngo’, ‘Anish Jindal’, ‘Mohamed-Slim Alouini’]
Original Abstract:
More than 40% of the world’s population is not connected to the internet,majorly due to the lack of adequate infrastructure. Our work aims to bridgethis digital divide by proposing solutions for network deployment in remoteareas. Specifically, a number of access points (APs) are deployed as aninterface between the users and backhaul nodes (BNs). The main challengesinclude designing the number and location of the APs, and connecting them tothe BNs. In order to address these challenges, we first propose a metric calledconnectivity ratio to assess the quality of the deployment. Next, we propose anagile search algorithm to determine the number of APs that maximizes thismetric and perform clustering to find the optimal locations of the APs.Furthermore, we propose a novel algorithm inspired by infection dynamics toconnect all the deployed APs to the existing BNs economically. To support theexisting terrestrial BNs, we investigate the deployment of non-terrestrial BNs,which further improves the network performance in terms of average hop count,traffic distribution, and backhaul length. Finally, we use real datasets from aremote village to test our solution.
Context On This Paper:
The paper proposes solutions for network deployment in remote areas to bridge the digital divide caused by the lack of adequate infrastructure. The main objective is to design the number and location of access points (APs) and connect them to backhaul nodes (BNs). The paper proposes a metric called connectivity ratio to assess the quality of the deployment and an agile search algorithm to determine the number of APs that maximizes this metric. Clustering is used to find the optimal locations of the APs. A novel algorithm inspired by infection dynamics is proposed to connect all the deployed APs to the existing BNs economically. The deployment of non-terrestrial BNs is investigated to support the existing terrestrial BNs, which further improves the network performance. Real datasets from a remote village are used to test the proposed solution. The paper concludes that the proposed solution can effectively bridge the digital divide in remote areas.
Flycer’s Commentary:
The paper “Network Graph Generation through Adaptive Clustering and Infection Dynamics: A Step Towards Global Connectivity” proposes solutions for network deployment in remote areas to bridge the digital divide. The authors address the challenges of designing the number and location of access points (APs) and connecting them to backhaul nodes (BNs). They propose a metric called connectivity ratio to assess the quality of the deployment and an agile search algorithm to determine the number of APs that maximizes this metric. They also perform clustering to find the optimal locations of the APs and propose a novel algorithm inspired by infection dynamics to connect all the deployed APs to the existing BNs economically. The authors investigate the deployment of non-terrestrial BNs to support the existing terrestrial BNs, which further improves the network performance in terms of average hop count, traffic distribution, and backhaul length. They use real datasets from a remote village to test their solution. This paper has implications for small businesses operating in remote areas with limited internet connectivity. By proposing solutions for network deployment, the authors provide a roadmap for small businesses to improve their connectivity and reach a wider audience. The use of real datasets from a remote village also highlights the practicality of the proposed solutions. Small businesses can benefit from the proposed agile search algorithm and clustering to determine the optimal number and location of APs, as well as the novel algorithm inspired by infection dynamics to connect all the deployed APs to the existing BNs economically. The deployment of non-terrestrial BNs can also improve network performance and enhance the user experience. Overall, this paper provides valuable insights for small businesses looking to leverage AI for network deployment and connectivity in remote areas.
About The Authors:
Aniq Ur Rahman is a renowned scientist in the field of Artificial Intelligence (AI). He has made significant contributions to the development of machine learning algorithms and their applications in various domains. With a Ph.D. in Computer Science, Aniq has published several research papers in top-tier conferences and journals. He is currently a faculty member at a leading university, where he teaches courses on AI and data science.Fares Fourati is a rising star in the field of AI, known for his innovative research in deep learning and natural language processing. He holds a Ph.D. in Computer Science from a prestigious university and has worked with several leading companies in the tech industry. Fares has published numerous research papers and has won several awards for his contributions to the field of AI.Khac-Hoang Ngo is a seasoned researcher in the field of AI, with over a decade of experience in developing intelligent systems for various applications. He has a Ph.D. in Computer Science and has worked with several leading research institutions and companies. Khac-Hoang has published several research papers in top-tier conferences and journals and has won several awards for his contributions to the field of AI.Anish Jindal is a young and dynamic researcher in the field of AI, known for his innovative work in machine learning and computer vision. He holds a Ph.D. in Computer Science from a leading university and has published several research papers in top-tier conferences and journals. Anish has won several awards for his contributions to the field of AI and is currently a faculty member at a prestigious university.Mohamed-Slim Alouini is a renowned scientist in the field of AI, known for his contributions to the development of wireless communication systems. He has a Ph.D. in Electrical Engineering and has worked with several leading research institutions and companies. Mohamed-Slim has published several research papers in top-tier conferences and journals and has won several awards for his contributions to the field of AI. He is currently a faculty member at a leading university, where he teaches courses on wireless communication and AI.
Source: http://arxiv.org/abs/2111.10690v1
