33 research outputs found
Artificial Intelligence, Machine Learning and Neural Networks for Tomography in Smart Grid – Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies
Until now, the neural network identification methodology for the branch number identification (NNIM-BNI) has identified the number of branches for a given overhead low-voltage broadband over powerlines (OV LV BPL) topology channel attenuation behavior [1]. In this extension paper, NNIM-BNI is extended so that the lengths of the distribution lines and branch lines for a given OV LV BPL topology channel attenuation behavior can be approximated; say, the tomography of the OV LV BPL topology. NNIM exploits the Deterministic Hybrid Model (DHM) and the OV LV BPL topology database of Topology Identification Methodology (TIM). By following the same methodology of the original paper, the results of the neural network identification methodology for the distribution line and branch line length approximation (NNIM-LLA) are compared against the ones of the newly proposed TIM-based methodology, denoted as TIM-LLA.Citation: Lazaropoulos, A. G., and Leligou, H. C. (2023). Artificial Intelligence, Machine Learning and Neural Networks for Tomography in Smart Grid – Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Distribution Line and Branch Line Length Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies. Trends in Renewable Energy, 9, 34-77. DOI: 10.17737/tre.2023.9.1.0014
Evaluation of a blockchain-enabled resource management mechanism for NGNs
A new era in ICT has begun with the evolution of Next Generation Networks
(NGNs) and the development of human-centric applications. Ultra-low latency,
high throughput, and high availability are a few of the main characteristics of
modern networks. Network Providers (NPs) are responsible for the development
and maintenance of network infrastructures ready to support the most demanding
applications that should be available not only in urban areas but in every
corner of the earth. The NPs must collaborate to offer high-quality services
and keep their overall cost low. The collaboration among competitive entities
can in principle be regulated by a trusted 3rd party or by a distributed
approach/technology which can guarantee integrity, security, and trust. This
paper examines the use of blockchain technology for resource management and
negotiation among NPs and presents the results of experiments conducted in a
dedicated real testbed. The implementation of the resource management mechanism
is described in a Smart Contract (SC) and the testbeds use the Raft and the
IBFT consensus mechanisms respectively. The goal of this paper is two-fold: to
assess its performance in terms of transaction throughput and latency so that
we can assess the granularity at which this solution can operate (e.g. support
resource re-allocation among NPs on micro-service level or not) and define
implementation-specific parameters like the consensus mechanism that is the
most suitable for this use case based on performance metrics
Extreme Level Crossing Rate: A New Performance Indicator for URLLC Systems
Level crossing rate (LCR) is a well-known statistical tool that is related to
the duration of a random stationary fading process \emph{on average}. In doing
so, LCR cannot capture the behavior of \emph{extremely rare} random events.
Nonetheless, the latter events play a key role in the performance of
ultra-reliable and low-latency communication systems rather than their average
(expectation) counterparts. In this paper, for the first time, we extend the
notion of LCR to address this issue and sufficiently characterize the
statistical behavior of extreme maxima or minima. This new indicator, entitled
as extreme LCR (ELCR), is analytically introduced and evaluated by resorting to
the extreme value theory and risk assessment. Capitalizing on ELCR, some key
performance metrics emerge, i.e., the maximum outage duration, minimum
effective duration, maximum packet error rate, and maximum transmission delay.
They are all derived in simple closed-form expressions. The theoretical results
are cross-compared and verified via extensive simulations whereas some useful
engineering insights are manifested.Comment: Accepted for publication in IEEE TV
Energy efficiency tools for residential users
Residential energy consumption reserves a significant portion of the total energy consumption in modern cities. The rates of construction of new buildings as well as the rates of renovation on existing ones are generally very low. At the same time, unlike centrally operated large commercial buildings, the installation of energy management systems is a rather expensive solution leaving residential users with limited means to improve their energy efficiency as results are not evident. Considering that to drive energy efficient behaviour, we have to first raise awareness, then provide evidence through measurements and then support further, more elaborate, energy efficiency actions, we capitalise on ICT as soft measures towards reaching hard goals. We propose a novel incremental solution starting from a rather simple mobile application exploiting the sensors available in our smartphones and tablets to proceed to more intelligent advice provisioning towards energy efficiency. We present its implementation architecture and discuss certain market-wise challenges to prove its potential
Energy efficiency tools for residential users
Residential energy consumption reserves a significant portion of the total energy consumption in modern cities. The rates of construction of new buildings as well as the rates of renovation on existing ones are generally very low. At the same time, unlike centrally operated large commercial buildings, the installation of energy management systems is a rather expensive solution leaving residential users with limited means to improve their energy efficiency as results are not evident. Considering that to drive energy efficient behaviour, we have to first raise awareness, then provide evidence through measurements and then support further, more elaborate, energy efficiency actions, we capitalise on ICT as soft measures towards reaching hard goals. We propose a novel incremental solution starting from a rather simple mobile application exploiting the sensors available in our smartphones and tablets to proceed to more intelligent advice provisioning towards energy efficiency. We present its implementation architecture and discuss certain market-wise challenges to prove its potential
Optimal resource optimisation based on multi‐layer monitoring
Abstract The satisfaction of the Quality of Service (QoS) levels during an entire service life‐cycle is one of the key targets for Service Providers (SP). To achieve this in an optimal way, it is required to predict the exact amount of the needed physical and virtual resources, for example, CPU and memory usage, for any possible combination of parameters that affect the system workload, such as number of users, duration of each request, etc. To solve this problem, the authors introduce a novel architecture and its open‐source implementation that a) monitors and collects data from heterogeneous resources, b) uses them to train machine learning models and c) tailors them to each particular service type. The candidate solution is validated in two real‐life services showing very good accuracy in predicting the required resources for a large number of operational configurations where a data augmentation method is also applied to further decrease the estimation error up to 32%
5G technologies boosting efficient mobile learning
The needs for education, learning and training proliferate primarily due to the facts that economy becomes more and more knowledge based (mandating continuous lifelong learning) and people migrate among countries, which introduces the need for learning other languages, for training on different skills and learning about the new cultural and societal framework. Given that in parallel, time schedules continuously become tighter, learning through mobile devices continuously gains in popularity as it allows for learning anytime, anywhere. To increase the learning efficiency, personalisation (in terms of selecting the learning content, type and presentation) and adaptation of the learning experience in real time based on the experienced affect state are key instruments. All these user requirements challenge the current network architectures and technologies. In this paper, we investigate the requirements implied by efficient mobile learning scenarios and we explore how 5G technologies currently under design/testing/validation and standardisation meet these requirements