16,777 research outputs found
Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks
Conventional cellular wireless networks were designed with the purpose of
providing high throughput for the user and high capacity for the service
provider, without any provisions of energy efficiency. As a result, these
networks have an enormous Carbon footprint. In this paper, we describe the
sources of the inefficiencies in such networks. First we present results of the
studies on how much Carbon footprint such networks generate. We also discuss
how much more mobile traffic is expected to increase so that this Carbon
footprint will even increase tremendously more. We then discuss specific
sources of inefficiency and potential sources of improvement at the physical
layer as well as at higher layers of the communication protocol hierarchy. In
particular, considering that most of the energy inefficiency in cellular
wireless networks is at the base stations, we discuss multi-tier networks and
point to the potential of exploiting mobility patterns in order to use base
station energy judiciously. We then investigate potential methods to reduce
this inefficiency and quantify their individual contributions. By a
consideration of the combination of all potential gains, we conclude that an
improvement in energy consumption in cellular wireless networks by two orders
of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843
Technologies and solutions for location-based services in smart cities: past, present, and future
Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas
Underlay Drone Cell for Temporary Events: Impact of Drone Height and Aerial Channel Environments
Providing seamless connection to a large number of devices is one of the
biggest challenges for the Internet of Things (IoT) networks. Using a drone as
an aerial base station (ABS) to provide coverage to devices or users on ground
is envisaged as a promising solution for IoT networks. In this paper, we
consider a communication network with an underlay ABS to provide coverage for a
temporary event, such as a sporting event or a concert in a stadium. Using
stochastic geometry, we propose a general analytical framework to compute the
uplink and downlink coverage probabilities for both the aerial and the
terrestrial cellular system. Our framework is valid for any aerial channel
model for which the probabilistic functions of line-of-sight (LOS) and
non-line-of-sight (NLOS) links are specified. The accuracy of the analytical
results is verified by Monte Carlo simulations considering two commonly adopted
aerial channel models. Our results show the non-trivial impact of the different
aerial channel environments (i.e., suburban, urban, dense urban and high-rise
urban) on the uplink and downlink coverage probabilities and provide design
guidelines for best ABS deployment height.Comment: This work is accepted to appear in IEEE Internet of Things Journal
Special Issue on UAV over IoT. Copyright may be transferred without notice,
after which this version may no longer be accessible. arXiv admin note: text
overlap with arXiv:1801.0594
A Comprehensive Survey on Networking over TV White Spaces
The 2008 Federal Communication Commission (FCC) ruling in the United States
opened up new opportunities for unlicensed operation in the TV white space
spectrum. Networking protocols over the TV white spaces promise to subdue the
shortcomings of existing short-range multi-hop wireless architectures and
protocols by offering more availability, wider bandwidth, and longer-range
communication. The TV white space protocols are the enabling technologies for
sensing and monitoring, Internet-of-Things (IoT), wireless broadband access,
real-time, smart and connected community, and smart utility applications. In
this paper, we perform a retrospective review of the protocols that have been
built over the last decade and also the new challenges and the directions for
future work. To the best of our knowledge, this is the first comprehensive
survey to present and compare existing networking protocols over the TV white
spaces.Comment: 19 page
Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling
© 2019 Elsevier Ltd Air pollution impact assessment is a major objective for various community councils in large cities, which have lately redirected their attention towards using more low-cost sensing units supported by citizen involvement. However, there is a lack of research studies investigating real-time mobile air-quality measurement through smart sensing units and even more of any data-driven modelling techniques that could be deployed to predict air quality accurately from the generated data-sets. This paper addresses these challenges by: a) proposing a comparative and detailed investigation of various air quality monitoring devices (both fixed and mobile), tested through field measurements and citizen sensing in an eco-neighbourhood from Lorraine, France, and by b) proposing a machine learning approach to evaluate the accuracy and potential of such mobile generated data for air quality prediction. The air quality evaluation consists of three experimenting protocols: a) first, we installed fixed passive tubes for monitoring the nitrogen dioxide concentrations placed in strategic locations highly affected by traffic circulation in an eco-neighbourhood, b) second, we monitored the nitrogen dioxide registered by citizens using smart and mobile pollution units carried at breathing level; results revealed that mobile-captured concentrations were 3–5 times higher than the ones registered by passive-static monitoring tubes and c) third, we compared different mobile pollution stations working simultaneously, which revealed noticeable differences in terms of result variability and sensitivity. Finally, we applied a machine learning modelling by using decision trees and neural networks on the mobile-generated data and show that humidity and noise are the most important factors influencing the prediction of nitrogen dioxide concentrations of mobile stations
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