Search

With the fast and rapid pace of developments in wireless technology, energy consumption has become a problem of great significance for future networks. Over the past few years, several energy monitoring policies have been initiated to promote energy efficiency emphasizing the active and important role of consumers to realize this goal. This study resolves the energy consumption issues of broadband networks and determines the energy usage associated with high spectrum allocation in future broadband networks by leveraging clustering from the data mining domain. For analyzing the overall patterns of energy consumption in broadband networks, this study segments the broadband networks based on the similarities of their electrical load profiles and the proportion of energy usage per hour (%) as a common framework and divides the users into different groups. The prime objective for the segmentation is to provide personalized recommendations to each group to reduce the energy consumption and associated costs thereby fostering energy efficiency measures and improving consumer engagement. The segmentation is obtained by an iterative process based on computational clusters calculation which is finalized by a post clustering analysis. Post clustering analysis involves visualization and statistical data mining techniques to analyze the energy consumption patterns and reallocating to a more appropriate group. The K-Means clustering technique is utilized for this purpose which provides the best prediction accuracy of 98.46% for energy load profiles at the spectrum of 100GHz. The energy consumption segmentation of the consumers provides knowledge and a better understanding of the consumer for optimizing energy consumption for future broadband.

In this paper, we present findings from a three-year research project funded by the US Institute of Museum and Library Services that examined how advanced broadband measurement capabilities can support the infrastructure and services needed to respond to the digital demands of public library users across the US. Previous studies have identified the ongoing broadband challenges of public libraries while also highlighting the increasing digital expectations of their patrons. However, few large-scale research efforts have collected automated, longitudinal measurement data on library broadband speeds and quality of service at a local, granular level inside public libraries over time, including when buildings are closed. This research seeks to address this gap in the literature through the following research question: How can public libraries utilize broadband measurement tools to develop a better understanding of the broadband speeds and quality of service that public libraries receive? In response, quantitative measurement data were gathered from an open-source broadband measurement system that was both developed for the research and deployed at 30 public libraries across the US. Findings from our analysis of the data revealed that Ookla measurements over time can confirm when the library's internet connection matches expected service levels and when they do not. When measurements are not consistent with expected service levels, libraries can observe the differences and correlate this with additional local information about the causes. Ongoing measurements conducted by the library enable local control and monitoring of this vital service and support critique and interrogation of the differences between internet measurement platforms. In addition, we learned that speed tests are useful for examining these trends but are only a small part of assessing an internet connection and how well it can be used for specific purposes. These findings have implications for state library agencies and federal policymakers interested in having access to data on observed versus advertised speeds and quality of service of public library broadband connections nationwide., INTRODUCTION The COVID-19 pandemic exposed the severity of the digital divide in the United States. During this time, lack of access to computers and the internet has been highlighted among [...]

Mobile broadband (MBB) services are rapidly growing, causing a massive increase in mobile data traffic growth. This surge in data traffic is due to several factors (such as the massive increase of subscribers, mobile applications, etc.) which have led to the need for more bandwidth. Mobile service providers are constantly improving their network efficiency by upgrading current networks and investing in newer mobile network generations. However, these improvements will not be enough to accommodate the future spectrum demands. This paper proposes a time series forecasting model to analyze future spectrum demands based on the spectrum efficiency growth of MBB networks. This model depends on two key input data: the average spectrum efficiency per site and the number of sites per technology. The model is used to predict the spectrum efficiency growth of three countries (Turkey, Malaysia, and Oman) from 2015 to 2025. The proposed model is compared with various traditional statistical models such as the Moving Average (MA), Auto-Regression (AR), Autoregressive–Moving-Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA). The forecasted results indicate that the average spectrum efficiency and growth will continue to rise multiple times by 2025 compared to 2015. The data from this prediction model can be used as input data to forecast the required spectrum needed in future for any specific country. This study further contributes to the network planning of future mobile networks for Fifth Generation (5G) and Sixth Generation (6G) technology. The proposed model obtains higher accuracy (by 90%) compared to other models. The proposed model is also applicable to any country, especially when new wireless communication technologies emerge in future. It is customizable and scalable since spectrum regulators can add additional metrics that positively contribute towards accurately estimating future spectrum efficiency growth.

Recently, multipath transmission control protocol (MPTCP) was standardized so that data can be transmitted through multiple paths to utilize all available path bandwidths. However, when high-speed long-distance networks are included in MPTCP paths, the traffic transmission performance of MPTCP is severely deteriorated, especially in case the multiple paths' characteristics are heavily asymmetric. In order to alleviate this problem, we propose a "Coupled CUBIC congestion control" that adopts TCP CUBIC on a large bandwidth-delay product (BDP) path in a linked increase manner for maintaining fairness with an ordinary TCP traversing the same bottleneck path. To verify the performance excellence of the proposed algorithm, we implemented the Coupled CUBIC Congestion Control into Linux kernels by modifying the legacy MPTCP linked-increases algorithm (LIA) congestion control source code. We constructed asymmetric heterogeneous network testbeds mixed with large and small BDP paths and compared the performances of LIA and Coupled CUBIC by experiments. Experimental results show that the proposed Coupled CUBIC utilizes almost over 80% of the bandwidth resource in the high BDP path, while the LIA utilizes only less than 20% of the bandwidth for the same path. It was confirmed that the resource utilization and traffic transmission performance have been greatly improved by using the proposed Coupled CUBIC in high-speed multipath networks, as well as maintaining MPTCP fairness with competing single-path CUBIC or Reno TCP flows. [ABSTRACT FROM AUTHOR]