The Challenges and Issues Facing the Deployment of RFID Technology

Griffith Sciences, School of Information and Communication TechnologyFull Tex

Identification of Fast Radio Bursts using Transfer Learning Approach with Data Augmentation

The universe has many mysteries, such as pulsars, dying stars, supernovae, and fast radio bursts (FRBs), FRBs are millisecond long radio signals, detected as a spike in radio-telescope data. Identification of Fast Radio Bursts from available data involves manual inspection of exhaustive data/plots. Radio Frequency Interference in pose a major challenge in identification of Fast Radio Bursts due to their abundance in the observatory data. We present a machine-learning-aided system, which screens telescope-generated data and identifies potential Fast Radio Burst candidates in it. Proposed system employs Convolutional Neural Networks and Transfer Learning to classify potential Fast Radio Bursts from Radio Frequency Interference from data recorded by the uGMRT. We have used data simulation tools to synthesize additional samples in order to make up for the paucity of data. The VGG16-based model displayed the best receiver operating characteristics curve with the area under curve being 0.90 along with an accuracy of 90.67%

Machine Learning for Intrusion Detection into Unmanned Aerial System 6G Networks

Progress in the development of wireless network technology has played a crucial role in the evolution of societies and provided remarkable services over the past decades. It remotely offers the ability to execute critical missions and effective services that meet the user\u27s needs. This advanced technology integrates cyber and physical layers to form cyber-physical systems (CPS), such as the Unmanned Aerial System (UAS), which consists of an Unmanned Aerial Vehicle (UAV), ground network infrastructure, communication link, etc. Furthermore, it plays a crucial role in connecting objects to create and develop the Internet of Things (IoT) technology. Therefore, the emergence of the CPS and IoT technologies provided many connected devices, generating an enormous amount of data. Consequently, the innovation of 6G technology is an urgent issue in the coming years. The 6G network architecture is an integration of the satellite network, aerial networks, terrestrial networks, and marine networks. These integrated network layers will provide new enabling technologies, for example, air interfaces and transmission technology. Therefore, integrating heterogeneous network layers guarantees an expansion strategy in the capacity that leads to low latency, ultra-high throughput, and high data rates. In the 6G network, Unmanned Aerial Vehicles (UAVs) are expected to densely occupy aerial spaces as UAV flying base stations (UAV-FBS) that comprise the aerial network layer to offer ubiquitous connectivity and enhance the terrestrial network in remote areas where it is challenging to deploy traditional infrastructure, for example, mountain, ocean deserts, and forest. Although the aerial network layer offers benefits to facilitate governmental and commercial missions, adversaries exploit network vulnerabilities to block intercommunication among nodes by jamming attacks and violating integrity through executing spoofing attacks. This work offers a practical IDS onboard UAV intrusion detection system to detect unintentional interference, intentional interference jamming, and spoofing attacks. Integrating time series data with machine learning models is the main part of the suggested IDF to detect anomalies accurately. This integration will improve the accuracy and effectiveness of the model. The 6G network is expected to handle a high volume of data where non-malicious interference and congestion in the channel are similar to a jamming attack. Therefore, an efficient anomaly detection technique must distinguish behaviors in the drone\u27s wireless network as normal or abnormal behavior. Our suggested model comprises two layers. The first layer has the algorithm to detect the anomaly during transmission. Then it will send the initial decision to the second layer in the model, including two separated algorithms, confirming the initial decision separately (nonintentional interference such as congestion in the channel, intentional interference jamming attack, and classify the type of jamming attack, and the second algorithm confirms spoofing attack. A jamming attack is a stealthy attack that aims to exhaust battery level or block communication to make wireless UAV networks unavailable. Therefore, the UAV forcibly relies on GPS signals. In this case, the adversary triggers a spoofing attack by manipulating the Global Navigation Satellite System (GNSS) signal and sending a fake signal to make UAVs estimate incorrect positions and deviate from their planning path to malicious zones. Hackers can start their malicious action either from malicious UAV nodes or the terrestrial malicious node; therefore, this work will enhance security and pave the way to start thinking about leveraging the benefit of the 6G network to design robust detection techniques for detecting multiple attacks that happen separately or simultaneously

Development of a classification algorithm for vehicle impacts: an anomaly detection approach

Dissertação de mestrado em Engenharia InformáticaIn the past decade, Machine Learning has been heavily applied to automobile industry solutions, the most promising being development of autonomous vehicles. New mobility services are available today as alternatives to owning a car, like ride hailing and carsharing. High costs associated with the maintenance of the vehicle and the reduced rate of vehicle use throughout the day are some of the factors for the popularity of these services. Car-sharing is self-service mode of transport that provides its members with access to a fleet of vehicles parked in various locations throughout a city. Damages are expected to happen when vehicles are used and the required repair implies costs to fleet operators. Systems able to detect these damages will promote a better use of these vehicles by vehicle users. Vehicle damages result from impacts with other objects, for instance, other vehicles or structures of any kind and these impacts inflict deformations to the vehicle exterior structure. Most of these impacts can be perceived or detected by the forces involved as result of the impact. Anomaly Detection is a technique applicable in a variety of domains, such as intrusion detection, fraud detection, event detection in sensor network or detection ecosystem disturbances. The objective of this thesis is the study and development of a semi-supervised intelligent system for detection and classification of vehicle impacts with an Anomaly Detection approach, using the accelerometer data, and following a strategy that would allow exploring a Machine Learning cycle. This thesis was developed under an internship in the company Bosch Car Multimedia S.A, located in Braga.Na última década, Machine Learning tem sido extensamente utilizado em soluções na indústria automóvel, o mais promissor sendo o desenvolvimento de veículos com condução autônoma. Novos serviços de mobilidade estão disponíveis hoje como alternativas à posse de um carro, como ride hailing ou car-sharing. Os elevados custos associados à manutenção do veículo e a sua reduzida taxa de utilização ao longo do dia são alguns dos fatores que contribuem para a popularidade destes serviços. Car-sharing é um modo de transporte self-service que fornece aos seus membros acesso a uma frota de veículos estacionados em vários locais duma cidade. Danos são espectáveis de ocorrer quando os veículos são usados e a reparação necessária implica custos para os operadores da frota. Sistemas capazes de detectar esses danos irão promover um melhor aproveitamento desses veículos pelos utilizadores dos veículos. Os danos de veículos resultam de impactos com outros objetos como, por exemplo, outros veículos ou estruturas e esses impactos provocam deformações na estrutura externa do veículo. A maioria desses impactos podem ser compreendidos ou detetados pelas forças envolvidas do resultado do impacto. Anomaly Detection é uma técnica aplicável em uma variedade de domínios, como deteção de intrusões, deteção de fraude, deteção de eventos numa rede de sensores ou deteção de distúrbios no ecossistema. O objetivo desta dissertação foi o estudo e desenvolvimento de um sistema inteligente semi-supervisionado para detecção e classificação de impactos de veículos a partir de uma abordagem de Anomaly Detection, utilizando os dados de acelerómetro, e seguindo uma estratégia que permitisse explorar um ciclo de Machine Learning. Esta dissertação foi desenvolvida no âmbito de um estágio na empresa Bosch Car Multimedia S.A, situada em Braga

A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts