Using Machine Learning for Handover Optimization in Vehicular Fog Computing

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long short-term memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set

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

A survey of online data-driven proactive 5G network optimisation using machine learning

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capitaland operational expenditure. Proactive network optimisation is widely acknowledged as on e of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area

Mecanismos para controlo e gestão de redes 5G: redes de operador

In 5G networks, time-series data will be omnipresent for the monitoring of network metrics. With the increase in the number of Internet of Things (IoT) devices in the next years, it is expected that the number of real-time time-series data streams increases at a fast pace. To be able to monitor those streams, test and correlate different algorithms and metrics simultaneously and in a seamless way, time-series forecasting is becoming essential for the pro-active successful management of the network. The objective of this dissertation is to design, implement and test a prediction system in a communication network, that allows integrating various networks, such as a vehicular network and a 4G operator network, to improve the network reliability and Quality-of-Service (QoS). To do that, the dissertation has three main goals: (1) the analysis of different network datasets and implementation of different approaches to forecast network metrics, to test different techniques; (2) the design and implementation of a real-time distributed time-series forecasting architecture, to enable the network operator to make predictions about the network metrics; and lastly, (3) to use the forecasting models made previously and apply them to improve the network performance using resource management policies. The tests done with two different datasets, addressing the use cases of congestion management and resource splitting in a network with a limited number of resources, show that the network performance can be improved with proactive management made by a real-time system able to predict the network metrics and act on the network accordingly. It is also done a study about what network metrics can cause reduced accessibility in 4G networks, for the network operator to act more efficiently and pro-actively to avoid such eventsEm redes 5G, séries temporais serão omnipresentes para a monitorização de métricas de rede. Com o aumento do número de dispositivos da Internet das Coisas (IoT) nos próximos anos, é esperado que o número de fluxos de séries temporais em tempo real cresça a um ritmo elevado. Para monitorizar esses fluxos, testar e correlacionar diferentes algoritmos e métricas simultaneamente e de maneira integrada, a previsão de séries temporais está a tornar-se essencial para a gestão preventiva bem sucedida da rede. O objetivo desta dissertação é desenhar, implementar e testar um sistema de previsão numa rede de comunicações, que permite integrar várias redes diferentes, como por exemplo uma rede veicular e uma rede 4G de operador, para melhorar a fiabilidade e a qualidade de serviço (QoS). Para isso, a dissertação tem três objetivos principais: (1) a análise de diferentes datasets de rede e subsequente implementação de diferentes abordagens para previsão de métricas de rede, para testar diferentes técnicas; (2) o desenho e implementação de uma arquitetura distribuída de previsão de séries temporais em tempo real, para permitir ao operador de rede efetuar previsões sobre as métricas de rede; e finalmente, (3) o uso de modelos de previsão criados anteriormente e sua aplicação para melhorar o desempenho da rede utilizando políticas de gestão de recursos. Os testes efetuados com dois datasets diferentes, endereçando os casos de uso de gestão de congestionamento e divisão de recursos numa rede com recursos limitados, mostram que o desempenho da rede pode ser melhorado com gestão preventiva da rede efetuada por um sistema em tempo real capaz de prever métricas de rede e atuar em conformidade na rede. Também é efetuado um estudo sobre que métricas de rede podem causar reduzida acessibilidade em redes 4G, para o operador de rede atuar mais eficazmente e proativamente para evitar tais acontecimentos.Mestrado em Engenharia de Computadores e Telemátic

Recent Advances in Machine Learning for Network Automation in the O-RAN

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe

The importance of Quality Assurance as a Data Scientist: Commom pitfalls, examples and solutions found while validationand developing supervised binary classification models

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn today’s information era, where Data galvanizes change, companies are aiming towards competitive advantage by mining this important resource to achieve actionable insights, knowledge, and wisdom. However, to minimize bias and obtain robust long-term solutions, the methodologies that are devised from Data Science and Machine Learning approaches benefit from being carefully validated by a Quality Assurance Data Scientist, who understands not only both business rules and analytics tasks, but also understands and recommends Quality Assurance guidelines and validations. Through my experience as a Data Scientist at EDP Distribuição, I identify and systematically report on seven key Quality Assurance guidelines that helped achieve more reliable products and provided three practical examples where validation was key in discerning improvements

Urban wireless traffic evolution: the role of new devices and the effect of policy

The emergence of new wireless technologies, such as the Internet of Things, allows digitalizing new and diverse urban activities. Thus, wireless traffic grows in volume and complexity, making prediction, investment planning, and regulation increasingly difficult. This article characterizes urban wireless traffic evolution, supporting operators to drive mobile network evolution and policymakers to increase national and local competitiveness. We propose a holistic method that widens previous research scope, including new devices and the effect of policy from multiple government levels. We provide an analytical formulation that combines existing complementary methods on traffic evolution research and diverse data sources. Results for a centric area of Helsinki during 2020-2030 indicate that daily volumes increase, albeit a surprisingly large part of the traffic continues to be generated by smartphones. Machine traffic gains importance, driven by surveillance video cameras and connected cars. While camera traffic is sensitive to law enforcement policies and data regulation, car traffic is less affected by transport electrification policy. High-priority traffic remains small, even under encouraging autonomous vehicle policies. We suggest that 5G small cells might be needed around 2025, albeit the utilization of novel radio technology and additional mid-band spectrum could delay this need until 2029. We argue that mobile network operators inevitably need to cooperate in constructing a single, shared small cell network to mitigate the high deployment costs of massively deploying small cells. We also provide guidance to local and national policymakers for IoT-enabled competitive gains via the mitigation of five bottlenecks. For example, local monopolies for mmWave connectivity should be facilitated on space-limited urban furniture or risk an eventual capacity crunch, slowing down digitalization