NetSim: The framework for complex network generator

Networks are everywhere and their many types, including social networks, the Internet, food webs etc., have been studied for the last few decades. However, in real-world networks, it's hard to find examples that can be easily comparable, i.e. have the same density or even number of nodes and edges. We propose a flexible and extensible NetSim framework to understand how properties in different types of networks change with varying number of edges and vertices. Our approach enables to simulate three classical network models (random, small-world and scale-free) with easily adjustable model parameters and network size. To be able to compare different networks, for a single experimental setup we kept the number of edges and vertices fixed across the models. To understand how they change depending on the number of nodes and edges we ran over 30,000 simulations and analysed different network characteristics that cannot be derived analytically. Two of the main findings from the analysis are that the average shortest path does not change with the density of the scale-free network but changes for small-world and random networks; the apparent difference in mean betweenness centrality of the scale-free network compared with random and small-world networks

Implementation and evaluation of the sensornet protocol for Contiki

Sensornet Protocol (SP) is a link abstraction layer between the network layer and the link layer for sensor networks. SP was proposed as the core of a future-oriented sensor node architecture that allows flexible and optimized combination between multiple coexisting protocols. This thesis implements the SP sensornet protocol on the Contiki operating system in order to: evaluate the effectiveness of the original SP services; explore further requirements and implementation trade-offs uncovered by the original proposal. We analyze the original SP design and the TinyOS implementation of SP to design the Contiki port. We implement the data sending and receiving part of SP using Contiki processes, and the neighbor management part as a group of global routines. The evaluation consists of a single-hop traffic throughput test and a multihop convergecast test. Both tests are conducted using both simulation and experimentation. We conclude from the evaluation results that SP's link-level abstraction effectively improves modularity in protocol construction without sacrificing performance, and our SP implementation on Contiki lays a good foundation for future protocol innovations in wireless sensor networks

A Dynamical Graph Prior for Relational Inference

Relational inference aims to identify interactions between parts of a dynamical system from the observed dynamics. Current state-of-the-art methods fit a graph neural network (GNN) on a learnable graph to the dynamics. They use one-step message-passing GNNs -- intuitively the right choice since non-locality of multi-step or spectral GNNs may confuse direct and indirect interactions. But the \textit{effective} interaction graph depends on the sampling rate and it is rarely localized to direct neighbors, leading to local minima for the one-step model. In this work, we propose a \textit{dynamical graph prior} (DYGR) for relational inference. The reason we call it a prior is that, contrary to established practice, it constructively uses error amplification in high-degree non-local polynomial filters to generate good gradients for graph learning. To deal with non-uniqueness, DYGR simultaneously fits a ``shallow'' one-step model with shared graph topology. Experiments show that DYGR reconstructs graphs far more accurately than earlier methods, with remarkable robustness to under-sampling. Since appropriate sampling rates for unknown dynamical systems are not known a priori, this robustness makes DYGR suitable for real applications in scientific machine learning

DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems

In a world where Artificial Intelligence revolutionizes inference, prediction and decision-making tasks, Digital Twins emerge as game-changing tools. A case in point is the development and optimization of Cooperative Intelligent Transportation Systems (C-ITSs): a confluence of cyber-physical digital infrastructure and (semi)automated mobility. Herein we introduce Digital Twin for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles shortcomings of traditional vehicular and network simulators. It provides a flexible, modular, and scalable implementation to ensure large-scale, city-wide experimentation with a moderate computational cost. The defining feature of our Digital Twin is a unique architecture allowing for submission of sequential queries, to which the Digital Twin provides instantaneous responses with the "state of the world", and hence is an Oracle. With such bidirectional interaction with external intelligent agents and realistic mobility traces, DRIVE provides the environment for development, training and optimization of Machine Learning based C-ITS solutions.Comment: Accepted for publication at IEEE ISCC 202

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network

PyDistSim - distributed system simulation library using SimPy

An easy to use and flexible distributed system simulation framework would be useful for beginners to learn about distributed systems or for researchers to prototype distributed system algorithms. This paper proposes a framework called PyDistSim for distributed system simulation implemented in Python. PDS focuses on providing a set of tools and libraries to make it easy and intuitive to set up simulations for distributed systems. Our framework is written in Python and is designed to be simple and user-friendly, while still being flexible and can be adapted to a wide variety of use cases. The simulation is deterministic and can be easily controlled. Moreover, the framework provides a variety of tools that can be used to conveniently collect and organize the data during simulation

Modelação e simulação de equipamentos de rede para Indústria 4.0

Currently, the industrial sector has increasingly opted for digital technologies in order to automate all its processes. This development comes from notions like Industry 4.0 that redefines the way these systems are designed. Structurally, all the components of these systems are connected in a complex network known as the Industrial Internet of Things. Certain requirements arise from this concept regarding industrial communication networks. Among them, the need to ensure real-time communications, as well as support for dynamic resource management, are extremely relevant. Several research lines pursued to develop network technologies capable of meeting such requirements. One of these protocols is the Hard Real-Time Ethernet Switch (HaRTES), an Ethernet switch with support for real-time communications and dynamic resource management, requirements imposed by Industry 4.0. The process of designing and implementing industrial networks can, however, be quite time consuming and costly. These aspects impose limitations on testing large networks, whose level of complexity is higher and requires the usage of more hardware. The utilization of network simulators stems from the necessity to overcome such restrictions and provide tools to facilitate the development of new protocols and evaluation of communications networks. In the scope of this dissertation a HaRTES switch model was developed in the OMNeT++ simulation environment. In order to demonstrate a solution that can be employed in industrial real-time networks, this dissertation presents the fundamental aspects of the implemented model as well as a set of experiments that compare it with an existing laboratory prototype, with the objective of validating its implementation.Atualmente o setor industrial tem vindo cada vez mais a optar por tecnologias digitais de forma a automatizar todos os seus processos. Este desenvolvimento surge de noções como Indústria 4.0, que redefine o modo de como estes sistemas são projetados. Estruturalmente, todos os componentes destes sistemas encontram-se conectados numa rede complexa conhecida como Internet Industrial das Coisas. Certos requisitos advêm deste conceito, no que toca às redes de comunicação industriais, entre os quais se destacam a necessidade de garantir comunicações tempo-real bem como suporte a uma gestão dinâmica dos recursos, os quais são de extrema importância. Várias linhas de investigação procuraram desenvolver tecnologias de rede capazes de satisfazer tais exigências. Uma destas soluções é o "Hard Real-Time Ethernet Switch" (HaRTES), um switch Ethernet com suporte a comunicações de tempo-real e gestão dinâmica de Qualidade-de-Serviço (QoS), requisitos impostos pela Indústria 4.0. O processo de projeto e implementação de redes industriais pode, no entanto, ser bastante moroso e dispendioso. Tais aspetos impõem limitações no teste de redes de largas dimensões, cujo nível de complexidade é mais elevado e requer o uso de mais hardware. Os simuladores de redes permitem atenuar o impacto de tais limitações, disponibilizando ferramentas que facilitam o desenvolvimento de novos protocolos e a avaliação de redes de comunicações. No âmbito desta dissertação desenvolveu-se um modelo do switch HaRTES no ambiente de simulação OMNeT++. Com um objetivo de demonstrar uma solução que possa ser utilizada em redes de tempo-real industriais, esta dissertação apresenta os aspetos fundamentais do modelo implementado bem como um conjunto de experiências que o comparam com um protótipo laboratorial já existente, no âmbito da sua validação.Mestrado em Engenharia Eletrónica e Telecomunicaçõe