A container-based architecture to provide services from SDR devices

Rádio Definido por Software (SDR) é um dispositivo de rádio programável que, conectado a um computador ou como uma solução embarcada, pode transmitir e receber informações usando ondas de rádio. A característica de programabilidade do SDR e sua largura de banda de rádio frequência (RF) estendem sua aplicação a diversas áreas que incluem aviação, satélite, radar e dispositivos móveis. O emprego do SDR tem despertado grande interesse na provisão de serviços de rede. Atuando como uma interface sem-fio multiprogramável na borda de redes cabeadas, o SDR é capaz de transmitir, receber e decodificar informações de rádio. Estas informações são usadas para fornecer serviços, como por exemplo uma página de internet contendo um mapa de rastreamento de aeronaves em tempo real, e gráficos de monitoramento de sensores. No entanto, para ser usado para esta finalidade, o SDR deve integrar-se às correntes tecnologias dos ambientes de rede, como NFV, SDN, containerização, e a computação em nuvem. Esta dissertação está focada na integração do SDR com a technologia de containerização. É proposta uma arquitetura para geração de serviços usando contâineres e o SDR como dispositivo de borda. Usando diferentes modelos de SDRs (USRP, LimeSDR e RTL-SDR), a plataforma GNURadio e Docker containers, dois cenários de aplicação da arquitetura são apresentados, nos quais a comunicação ADS-B e LoRa são implementadas. A avaliação da solução proposta é realizada comparando-se a geração de serviço com a arquitetura, (com dois níveis de isolação de rede), e sem a arquitetura. O tempo de lançamento e de resposta dos serviços, e a utilização dos recursos computacionais são comparados, mostrando que a arquitetura tem impacto nesses fatores. Este impacto aumenta conforme o nível de isolação de rede utilizado. Por outro lado a arquitetura aplica uma topologia que converte os componentes funcionais do serviço em blocos modulares, tornando possível sua aplicação em diferentes projetos de RF, e oferece benefícios não funcionais, como a capacidade de prover serviços em tempo real, emprego com diferentes modelos de SDR, e isolação de rede. Além disso, a arquitetura adiciona uma série de características de controle herdadas da tecnologia de virtualização.Software Defined Radio is a programmable radio device that, when connected to a computer or as an embedded solution, can transmit and receive data information using radio waves. The programming features of the SDR and its RF bandwidth range extends the application possibility to several areas, including aviation, satellite, radar, and mobile communication. SDR has drawn great attention to network service provision. Acting as a multi-programmable air interface at the edge of wired network environments, SDR can receive, decode and forward radio information, which is used to generate the services. Examples of services including real-time flight tracker web pages, and sensor monitoring data charts. However, to provide network services, SDR must integrate into complex network environments where recent technologies, such as NFV, SDN, containerization and cloud computing, are applied. This thesis addresses the integration of SDRs with containerization. It proposes an easy-to-deploy container-based architecture to provide network services from SDR devices. Using different types of SDR devices (USRP, LimeSDR and RTL-SDR), GNURadio platform and Docker Container, two use cases of the proposed architecture are presented, demonstrating scenarios where ADSB and LoRa communication are implemented in order to provide services to end-users. Evaluation of the proposed solution is performed comparing two models of service provision: with the proposed architecture (two levels of network isolation), and without the architecture. The overhead time added to launch the services, the time response and computational resource utilization are compared, showing that there is an overhead added by the architecture which impacts on the system performance. The overhead increases with the applied network isolation level. Conversely, the architecture converts the service functional components into modular components, its application can be extended to different RF projects and SDR types, and offers non-functional benefits such as, real-time capability, network isolation, fine setting of communication parameters, and a set of control and configuration features inherited from container virtualization platform

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Effectiveness of integration of system-level optimization in concurrent engineering for rocket design

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.Includes bibliographical references (p. 110-111).Integrated concurrent engineering is a method for rapid conceptual design. Previous study has suggested that integration of system-level optimization techniques into integrated concurrent engineering can benefit the design process. In order to confirm and strengthen these results further study was carried out. A two-stage liquid rocket software model was created to serve as a complex multi-disciplinary design problem. Several design session trials were run with the goal of optimizing the rocket in performance and cost. Some design teams used optimization along with integrated concurrent engineering, while others only used integrated concurrent engineering. The results from the two design methods were compared in several metrics, and including optimization alongside concurrent engineering shows a marked benefit in some areas.by Brian Kenichi Bairstow.S.M

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

A study on interaction-driven comparison between analog and digital gaming control interface on smartphone

This study aims to find empirical evidence of effectiveness levels: comfort, efficiency, and accuracy between analog and digital interface on smartphone game control through the two different usability tests: 1) A Pilot Study for measuring a correlation contrast with direct and indirect input control from six participants in a small group; 2) A Main Study for finding the effectiveness of “Tap-only affords” basis between a digital and analog input control. The usability test was analyzed by both qualitative and quantitative research methods. There was a total of the 81 participants who were divided into two big groups for comparing one hand and two hands input control, and nine participants per each group implemented a smartphone game based on different input control tasks. The findings appear that direct touch screen interaction is more effective on two hands input control tasks while using an indirect physical input control was more effective on one hand touchscreen

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Intrusion Detection: Embedded Software Machine Learning and Hardware Rules Based Co-Designs

Security of innovative technologies in future generation networks such as (Cyber Physical Systems (CPS) and Wi-Fi has become a critical universal issue for individuals, economy, enterprises, organizations and governments. The rate of cyber-attacks has increased dramatically, and the tactics used by the attackers are continuing to evolve and have become ingenious during the attacks. Intrusion Detection is one of the solutions against these attacks. One approach in designing an intrusion detection system (IDS) is software-based machine learning. Such approach can predict and detect threats before they result in major security incidents. Moreover, despite the considerable research in machine learning based designs, there is still a relatively small body of literature that is concerned with imbalanced class distributions from the intrusion detection system perspective. In addition, it is necessary to have an effective performance metric that can compare multiple multi-class as well as binary-class systems with respect to class distribution. Furthermore, the expectant detection techniques must have the ability to identify real attacks from random defects, ingrained defects in the design, misconfigurations of the system devices, system faults, human errors, and software implementation errors. Moreover, a lightweight IDS that is small, real-time, flexible and reconfigurable enough to be used as permanent elements of the system's security infrastructure is essential. The main goal of the current study is to design an effective and accurate intrusion detection framework with minimum features that are more discriminative and representative. Three publicly available datasets representing variant networking environments are adopted which also reflect realistic imbalanced class distributions as well as updated attack patterns. The presented intrusion detection framework is composed of three main modules: feature selection and dimensionality reduction, handling imbalanced class distributions, and classification. The feature selection mechanism utilizes searching algorithms and correlation based subset evaluation techniques, whereas the feature dimensionality reduction part utilizes principal component analysis and auto-encoder as an instance of deep learning. Various classifiers, including eight single-learning classifiers, four ensemble classifiers, one stacked classifier, and five imbalanced class handling approaches are evaluated to identify the most efficient and accurate one(s) for the proposed intrusion detection framework. A hardware-based approach to detect malicious behaviors of sensors and actuators embedded in medical devices, in which the safety of the patient is critical and of utmost importance, is additionally proposed. The idea is based on a methodology that transforms a device's behavior rules into a state machine to build a Behavior Specification Rules Monitoring (BSRM) tool for four medical devices. Simulation and synthesis results demonstrate that the BSRM tool can effectively identify the expected normal behavior of the device and detect any deviation from its normal behavior. The performance of the BSRM approach has also been compared with a machine learning based approach for the same problem. The FPGA module of the BSRM can be embedded in medical devices as an IDS and can be further integrated with the machine learning based approach. The reconfigurable nature of the FPGA chip adds an extra advantage to the designed model in which the behavior rules can be easily updated and tailored according to the requirements of the device, patient, treatment algorithm, and/or pervasive healthcare application