2,871 research outputs found
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
Fine-Grained Reliability for V2V Communications around Suburban and Urban Intersections
Safe transportation is a key use-case of the 5G/LTE Rel.15+ communications,
where an end-to-end reliability of 0.99999 is expected for a vehicle-to-vehicle
(V2V) transmission distance of 100-200 m. Since communications reliability is
related to road-safety, it is crucial to verify the fulfillment of the
performance, especially for accident-prone areas such as intersections. We
derive closed-form expressions for the V2V transmission reliability near
suburban corners and urban intersections over finite interference regions. The
analysis is based on plausible street configurations, traffic scenarios, and
empirically-supported channel propagation. We show the means by which the
performance metric can serve as a preliminary design tool to meet a target
reliability. We then apply meta distribution concepts to provide a careful
dissection of V2V communications reliability. Contrary to existing work on
infinite roads, when we consider finite road segments for practical deployment,
fine-grained reliability per realization exhibits bimodal behavior. Either
performance for a certain vehicular traffic scenario is very reliable or
extremely unreliable, but nowhere in relatively proximity to the average
performance. In other words, standard SINR-based average performance metrics
are analytically accurate but can be insufficient from a practical viewpoint.
Investigating other safety-critical point process networks at the meta
distribution-level may reveal similar discrepancies.Comment: 27 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
Potential of machine learning/Artificial Intelligence (ML/AI) for verifying configurations of 5G multi Radio Access Technology (RAT) base station
Abstract. The enhancements in mobile networks from 1G to 5G have greatly increased data transmission reliability and speed. However, concerns with 5G must be addressed. As system performance and reliability improve, ML and AI integration in products and services become more common. The integration teams in cellular network equipment creation test devices from beginning to end to ensure hardware and software parts function correctly. Radio unit integration is typically the first integration phase, where the radio is tested independently without additional network components like the BBU and UE. 5G architecture and the technology that it is using are explained further. The architecture defined by 3GPP for 5G differs from previous generations, using Network Functions (NFs) instead of network entities. This service-based architecture offers NF reusability to reduce costs and modularity, allowing for the best vendor options for customer radio products. 5G introduced the O-RAN concept to decompose the RAN architecture, allowing for increased speed, flexibility, and innovation. NG-RAN provided this solution to speed up the development and implementation process of 5G. The O-RAN concept aims to improve the efficiency of RAN by breaking it down into components, allowing for more agility and customization. The four protocols, the eCPRI interface, and the functionalities of fronthaul that NGRAN follows are expressed further. Additionally, the significance of NR is described with an explanation of its benefits. Some benefits are high data rates, lower latency, improved spectral efficiency, increased network flexibility, and improved energy efficiency. The timeline for 5G development is provided along with different 3GPP releases. Stand-alone and non-stand-alone architecture is integral while developing the 5G architecture; hence, it is also defined with illustrations. The two frequency bands that NR utilizes, FR1 and FR2, are expressed further. FR1 is a sub-6 GHz frequency band. It contains frequencies of low and high values; on the other hand, FR2 contains frequencies above 6GHz, comprising high frequencies. FR2 is commonly known as the mmWave band. Data collection for implementing the ML approaches is expressed that contains the test setup, data collection, data description, and data visualization part of the thesis work. The Test PC runs tests, executes test cases using test libraries, and collects data from various logs to analyze the system’s performance. The logs contain information about the test results, which can be used to identify issues and evaluate the system’s performance. The data collection part describes that the data was initially present in JSON files and extracted from there. The extraction took place using the Python code script and was then fed into an Excel sheet for further analysis. The data description explains the parameters that are taken while training the models. Jupyter notebook has been used for visualizing the data, and the visualization is carried out with the help of graphs. Moreover, the ML techniques used for analyzing the data are described. In total, three methods are used here. All the techniques come under the category of supervised learning. The explained models are random forest, XG Boost, and LSTM. These three models form the basis of ML techniques applied in the thesis. The results and discussion section explains the outcomes of the ML models and discusses how the thesis will be used in the future. The results include the parameters that are considered to apply the ML models to them. SINR, noise power, rxPower, and RSSI are the metrics that are being monitored. These parameters have variance, which is essential in evaluating the quality of the product test setup, the quality of the software being tested, and the state of the test environment. The discussion section of the thesis explains why the following parameters are taken, which ML model is most appropriate for the data being analyzed, and what the next steps are in implementation
Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges
With the rapid development of marine activities, there has been an increasing
number of maritime mobile terminals, as well as a growing demand for high-speed
and ultra-reliable maritime communications to keep them connected.
Traditionally, the maritime Internet of Things (IoT) is enabled by maritime
satellites. However, satellites are seriously restricted by their high latency
and relatively low data rate. As an alternative, shore & island-based base
stations (BSs) can be built to extend the coverage of terrestrial networks
using fourth-generation (4G), fifth-generation (5G), and beyond 5G services.
Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs.
Despite of all these approaches, there are still open issues for an efficient
maritime communication network (MCN). For example, due to the complicated
electromagnetic propagation environment, the limited geometrically available BS
sites, and rigorous service demands from mission-critical applications,
conventional communication and networking theories and methods should be
tailored for maritime scenarios. Towards this end, we provide a survey on the
demand for maritime communications, the state-of-the-art MCNs, and key
technologies for enhancing transmission efficiency, extending network coverage,
and provisioning maritime-specific services. Future challenges in developing an
environment-aware, service-driven, and integrated satellite-air-ground MCN to
be smart enough to utilize external auxiliary information, e.g., sea state and
atmosphere conditions, are also discussed
A tourism overcrowding sensor using multiple radio techniques detection
The motivation for this dissertation came from the touristic pressure felt in the historic
neighborhoods of Lisbon. This pressure is the result of the rise in the number of touristic
arrivals and the proliferation of local accommodation. To mitigate this problem the
research project in which this dissertation is inserted aims to disperse the pressure felt
by routing the tourists to more sustainable locations and locations that are not crowded.
The goal of this dissertation is then to develop a crowding sensor to detect, in real-time,
the number of persons in its vicinity by detecting how many smartphones it observes in
its readings. The proposed solution aims to detect the wireless trace elements generated
by the normal usage of smartphones. The technologies in which the sensor will detect
devices are Wi-Fi, Bluetooth and the mobile network.
For testing the results gathered by the sensor we developed a prototype that was deployed
on our campus and in a museum, during an event with strong attendance. The data
gathered was stored in a time-series database and a data visualization tool was used to
interpret the results.
The overall conclusions of this dissertation are that it is possible to build a sensor that
detects nearby devices thereby allowing to detect overcrowding situations. The prototype
built allows to detect crowd mobility patterns. The composition of technologies and
identity unification are topics deserving future research.A motivação para a presente dissertação surgiu da pressão turÃstica sentida nos bairros
históricos de Lisboa. Esta pressão é a consequência de um crescimento do número de
turistas e de uma cada vez maior utilização e proliferação do alojamento local. Para
mitigar este problema o projeto de investigação em que esta dissertação está inserida
pretende dispersar os turistas por locais sustentáveis e que não estejam sobrelotados.
O objetivo desta dissertação é o de desenvolver um sensor que consiga detetar, em tempo
real, detetar quantas pessoas estão na sua proximidade com base nos smartphones que
consegue detetar. A solução proposta tem como objetivo detetar os traços gerados pela
normal utilização de um smartphone. As tecnologias nas quais o sensor deteta traços de
utilização são Wi-Fi, Bluetooth e a rede móvel.
Para realizar os testes ao sensor, foi desenvolvido um protótipo que foi instalado no
campus e num museu durante um evento de grande afluência. Os dados provenientes
destes testes foram guardados numa base de dados de séries temporais e analisados
usando uma ferramenta de visualização de dados.
As conclusões obtidas nesta dissertação são que é possÃvel criar um sensor capaz de detetar
dispositivos na sua proximidade e detetar situações de sobrelotação/apinhamento. O
protótipo contruÃdo permite detectar padrões de mobilidade de multidões. A composição
de tecnologias e a unificação de identidade são problemas que requerem investigação futura
Predictive model creation approach using layered subsystems quantified data collection from LTE L2 software system
Abstract. The road-map to a continuous and efficient complex software system’s improvement process has multiple stages and many interrelated on-going transformations, these being direct responses to its always evolving environment. The system’s scalability on this on-going transformations depends, to a great extent, on the prediction of resources consumption, and systematic emergent properties, thus implying, as the systems grow bigger in size and complexity, its predictability decreases in accuracy. A predictive model is used to address the inherent complexity growth and be able to increase the predictability of a complex system’s performance. The model creation processes are driven by the recollection of quantified data from different layers of the Long-term Evolution (LTE) Data-layer (L2) software system. The creation of such a model is possible due to the multiple system analysis tools Nokia has already implemented, allowing a multiple-layers data gathering flow. The process starts by first, stating the system layers differences, second, the use of a layered benchmark approach for the data collection at different levels, third, the design of a process flow organizing the data transformations from recollection, filtering, pre-processing and visualization, and forth, As a proof of concept, different Performance Measurements (PM) predictive models, trained by the collected pre-processed data, are compared. The thesis contains, in parallel to the model creation processes, the exploration, and comparison of various data visualization techniques that addresses the non-trivial graphical representation of the in-between subsystem’s data relations. Finally, the current results of the model process creation process are presented and discussed. The models were able to explain 54% and 67% of the variance in the two test configurations used in the instantiation of the model creation process proposed in this thesis
- …