arXiv subjects

Edward J. Oughton

Publications and source records attributed to Edward J. Oughton.

29 records · Page 2Linked to original sources

arXiv2023

Global Vulnerability Assessment of Mobile Telecommunications Infrastructure to Climate Hazards using Crowdsourced Open Data

The ongoing change in Earth`s climate is causing an increase in the frequency and severity of climate-related hazards, for example, from coastal flooding, riverine flooding, and tropical cyclones. There is currently an urgent need to quantify the potential impacts of these events on infrastructure and users, especially for hitherto neglected infrastructure sectors, such as telecommunications, particularly given our increasing dependence on digital technologies. In this analysis a global assessment is undertaken, quantifying the number of mobile cells vulnerable to climate hazards using open crowdsourced data equating to 7.6 million 2G, 3G, 4G and 5G assets. For a 0.01% annual probability event under a high emissions scenario (RCP8.5), the number of affected cells is estimated at 2.26 million for tropical cyclones, equating to USD 1.01 billion in direct damage (an increase against the historical baseline of 14% and 44%, respectively). Equally, for coastal flooding the number of potentially affected cells for an event with a 0.01% annual probability under RCP8.5 is 109.9 thousand, equating to direct damage costs of USD 2.69 billion (an increase against the baseline of 70% and 78%, respectively). The findings demonstrate the need for risk analysts to include mobile communications (and telecommunications more broadly) in future critical national infrastructure assessments. Indeed, this paper contributes a proven assessment methodology to the literature for use in future research for assessing this critical infrastructure sector.

econ.GN↗

arXiv2023

What would it cost to connect the unconnected? Estimating global universal broadband infrastructure investment

Roughly 3 billion citizens remain offline, equating to approximately 40 percent of the global population. Therefore, providing Internet connectivity is an essential part of the Sustainable Development Goals (SDGs) (Goal 9). In this paper a high-resolution global model is developed to evaluate the necessary investment requirements to achieve affordable universal broadband. The results indicate that approximately $418 billion needs to be mobilized to connect all unconnected citizens globally (targeting 40-50 GB/Month per user with 95 percent reliability). The bulk of additional investment is for emerging market economies (73 percent) and low-income developing countries (24 percent). To our knowledge, the paper contributes the first high-resolution global assessment which quantifies universal broadband investment at the sub-national level to achieve SDG Goal 9.

econ.GN↗

arXiv2023

Crowdsourced data indicates broadband has a positive impact on local business creation

Broadband connectivity is regarded as generally having a positive macroeconomic effect, but we lack evidence as to how it affects key economic activity metrics, such as firm creation, at a very local level. This analysis models the impact of broadband Next Generation Access (NGA) on new business creation at the local level over the 2011-2015 period in England, United Kingdom, using high-resolution panel data. After controlling for a range of factors, we find that faster broadband speeds brought by NGA technologies have a positive effect on the rate of business growth. We find that in England between 2011-2015, on average a one percentage increase in download speeds is associated with a 0.0574 percentage point increase in the annual growth rate of business establishments. The primary hypothesised mechanism behind the estimated relationship is the enabling effect that faster broadband speeds have on innovative business models based on new digital technologies and services. Entrepreneurs either sought appropriate locations that offer high quality broadband infrastructure (contributing to new business establishment growth), or potentially enjoyed a competitive advantage (resulting in a higher survival rate). The findings of this study suggest that aspiring to reach universal high capacity broadband connectivity is economically desirable, especially as the costs of delivering such service decline.

econ.GN↗

arXiv2023

Quantifying polarization across political groups on key policy issues using sentiment analysis

There is growing concern that over the past decade, industrialized democratic nations are becoming increasingly politically polarized. Indeed, elections in the US, UK, France, and Germany have all seen tightly won races, with notable examples including the 2016 Trump vs. Clinton presidential election and the UK's Brexit referendum. However, while there has been much qualitative discussion of polarization on key issues, there are few examples of formal quantitative assessments examining this topic. Therefore, in this paper, we undertake a statistical evaluation of political polarization for representatives elected to the US congress on key policy issues between 2021-2022. The method is based on applying sentiment analysis to Twitter data and developing quantitative analysis for six political groupings defined based on voting records. Two sets of policy groups are explored, including geopolitical policies (e.g., Ukraine-Russia, China, Taiwan, etc.) and domestic policies (e.g., abortion, climate change, LGBTQ, immigration, etc.). We find that out of the twelve policies explored here, gun control was the most politically polarizing, with significant polarization results found for all groups (four of which were P < 0.001). The next most polarizing issues include immigration and border control, fossil fuels, and Ukraine-Russia. Interestingly, the least polarized policy topics were Taiwan, LGBTQ, and the Chinese Communist Party, potentially demonstrating the highest degree of bipartisanship on these issues. The results can be used to guide future policy making, by helping to identify areas of common ground across political groups.

stat.AP↗

arXiv2022

The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks

Cyber-Physical Systems (CPS) are increasingly complex and frequently integrated into modern societies via critical infrastructure systems, products, and services. Consequently, there is a need for reliable functionality of these complex systems under various scenarios, from physical failures due to aging, through to cyber attacks. Indeed, the development of effective strategies to restore disrupted infrastructure systems continues to be a major challenge. Hitherto, there have been an increasing number of papers evaluating cyber-physical infrastructures, yet a comprehensive review focusing on mathematical modeling and different optimization methods is still lacking. Thus, this review paper appraises the literature on optimization techniques for CPS facing disruption, to synthesize key findings on the current methods in this domain. A total of 108 relevant research papers are reviewed following an extensive assessment of all major scientific databases. The main mathematical modeling practices and optimization methods are identified for both deterministic and stochastic formulations, categorizing them based on the solution approach (exact, heuristic, meta-heuristic), objective function, and network size. We also perform keyword clustering and bibliographic coupling analyses to summarize the current research trends. Future research needs in terms of the scalability of optimization algorithms are discussed. Overall, there is a need to shift towards more scalable optimization solution algorithms, empowered by data-driven methods and machine learning, to provide reliable decision-support systems for decision-makers and practitioners.

math.OC↗

arXiv2022

Surveying 5G Techno-Economic Research to Inform the Evaluation of 6G Wireless Technologies

Techno-economic assessment is a fundamental technique engineers use for evaluating new communications technologies. However, despite the techno-economics of the fifth cellular generation (5G) being an active research area, it is surprising there are few comprehensive evaluations of this growing literature. With mobile network operators deploying 5G across their networks, it is therefore an opportune time to appraise current accomplishments and review the state-of-the-art. Such insight can inform the flurry of 6G research papers currently underway and help engineers in their mission to provide affordable high-capacity, low-latency broadband connectivity, globally. The survey discusses emerging trends from the 5G techno-economic literature and makes five key recommendations for the design and standardization of Next Generation 6G wireless technologies.

eess.SP↗

arXiv2021

Engineering-Economic Evaluation of Diffractive Non-Line-Of-Sight Backhaul (e3nb): A Techno-economic Model for 3D Wireless Backhaul Assessment

Developing ways to affordably deliver broadband connectivity is one of the major issues of our time. In challenging deployment locations with irregular terrain, traditional Clear-Line-Of-Sight (CLOS) wireless links can be uneconomical to deploy, as the number of required towers make infrastructure investment unviable. With new research focusing on developing wireless diffractive backhaul technologies to provide Non-Line-Of-Sight (NLOS) links, this paper evaluates the engineering-economic implications. A Three-Dimensional (3D) techno-economic assessment framework is developed, utilizing a combination of remote sensing and viewshed geospatial techniques, in order to quantify the impact of different wireless backhaul strategies. This framework is applied to assess both Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight strategies for deployment in Peru, as well as the islands of Kalimantan and Papua, in Indonesia. The results find that a hybrid strategy combining the use of Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight links produces a 9-45 percent cost-efficiency saving, relative to only using traditional Clear-Line-Of-Sight wireless backhaul links.

cs.NI↗

arXiv2021

A Techno-Economic Cost Framework for Satellite Networks Applied to Low Earth Orbit Constellations: Assessing Starlink, OneWeb and Kuiper

Delivering broadband connectivity to unconnected areas is extremely challenging. The emergence of Low Earth Orbit (LEO) satellite systems has been seen as a potential solution for connecting remote areas. Despite the hype around these new technologies, we still lack an open-source modeling framework for assessing the techno-economics of satellite broadband connectivity which is therefore the purpose of this paper. Firstly, a generalizable techno-economic model is presented to assess the engineering-economics of satellite constellations. Secondly, the approach is applied to assess the three main competing LEO constellations which include Starlink, OneWeb and Kuiper. This involves simulating the impact on coverage, capacity and cost as both the number of satellites and quantity of subscribers increases. Finally, a global assessment is undertaken visualizing the potential capacity and cost per user via different subscriber scenarios. The results demonstrate how limited the capacity will be once resources are spread across users in each satellite coverage area. For example, if there is 1 user per 10 km^2 we estimate a mean per user capacity of 24.94 Mbps, 1.01 Mbps and 10.30 Mbps for Starlink, OneWeb and Kuiper respectively in the busiest hour of the day. But if the subscriber density increases to 1 user per km^2, then the mean per user capacity drops significantly to 2.49 Mbps, 0.10 Mbps and 1.02 Mbps for Starlink, OneWeb and Kuiper respectively. LEO broadband will be an essential part of the connectivity toolkit, but the results reveal that these mega-constellations will most likely have to operate below 0.1 users per km^2 to provide a service that outcompetes other broadband connectivity options. The open-source codebase which the paper contributes is provided with the hope that other engineers will access, use, and further develop the satellite assessment capability.

eess.SY↗

arXiv2021

Supportive 5G Infrastructure Policies are Essential for Universal 6G: Assessment using an Open-source Techno-economic Simulation Model utilizing Remote Sensing

Work has now begun on the sixth generation of cellular technologies (`6G`) and cost-efficient global broadband coverage is already becoming a key pillar. Indeed, we are still far from providing universal and affordable broadband connectivity, despite this being a key part of the Sustainable Development Goals (Target 9.c). Currently, both Mobile Network Operators and governments still lack independent analysis of the strategies that can help achieve this target with the cellular technologies available (4G and 5G). Therefore, this paper undertakes quantitative assessment demonstrating how current 5G policies affect universal broadband, as well as drawing conclusions over how decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies. As an example, the assessment approach is applied to India, the world`s second-largest mobile market and a country with very high spectrum prices. The results demonstrate the trade-offs between technological decisions. This includes demonstrating how important current infrastructure policy is, particularly given fiber backhaul will be essential for delivering 6G quality of service. We find that by eliminating the spectrum licensing costs, 100% 5G population coverage can viably be achieved using fiber backhaul. Therefore, supportive infrastructure policies are essential in providing a superior foundation for evolution to future cellular generation, such as 6G.

econ.GN↗

arXiv2021

The Economic Impact of Critical National Infrastructure Failure Due to Space Weather

Space weather is a collective term for different solar or space phenomena that can detrimentally affect technology. However, current understanding of space weather hazards is still relatively embryonic in comparison to terrestrial natural hazards such as hurricanes or earthquakes. Indeed, certain types of space weather such as large Coronal Mass Ejections (CMEs) are an archetypal example of a low probability, high severity hazard. Few major events, short time-series data and a lack of consensus regarding the potential impacts on critical infrastructure have hampered the economic impact assessment of space weather. Yet, space weather has the potential to disrupt a wide range of Critical National Infrastructure (CNI) systems including electricity transmission, satellite communications and positioning, aviation and rail transportation. Recently there has been growing interest in these potential economic and societal impacts. Estimates range from millions of dollars of equipment damage from the Quebec 1989 event, to some analysts reporting billions of lost dollars in the wider economy from potential future disaster scenarios. Hence, this provides motivation for this article which tracks the origin and development of the socio-economic evaluation of space weather, from 1989 to 2017, and articulates future research directions for the field.

econ.GN↗

arXiv2021

Predicting cell phone adoption metrics using satellite imagery

Approximately half of the global population does not have access to the internet, even though digital connectivity can reduce poverty by revolutionizing economic development opportunities. Due to a lack of data, Mobile Network Operators and governments struggle to effectively determine if infrastructure investments are viable, especially in greenfield areas where demand is unknown. This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the `digital divide`. In this paper we present a machine learning method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia. Our predictive machine learning approach consistently outperforms baseline models which use population density or nightlight luminosity, with an improvement in data variance prediction of at least 40%. The method is a starting point for developing more sophisticated predictive models of infrastructure demand using machine learning and publicly available satellite imagery. The evidence produced can help to better inform infrastructure investment and policy decisions.

cs.CY↗