Robust-to-Noise Algorithms for Distributed Resource Allocation and Scheduling

Efficient resource allocation and scheduling algorithms are essential for various distributed applications, ranging from wireless networks and cloud computing platforms to autonomous multi-agent systems and swarm robotic networks. However, real-world environments are often plagued by uncertainties and noise, leading to sub-optimal performance and increased vulnerability of traditional algorithms. This paper addresses the challenge of robust resource allocation and scheduling in the presence of noise and disturbances. The proposed study introduces a novel sign-based dynamics for developing robust-to-noise algorithms distributed over a multi-agent network that can adaptively handle external disturbances. Leveraging concepts from convex optimization theory, control theory, and network science the framework establishes a principled approach to design algorithms that can maintain key properties such as resource-demand balance and constraint feasibility. Meanwhile, notions of uniform-connectivity and versatile networking conditions are also addressed.Comment: IEEE/RSI ICRoM202

Control and game-theoretic methods for secure cyber-physical-human systems

This work focuses on systems comprising tightly interconnected physical and digital components. Those, aptly named, cyber-physical systems will be the core of the Fourth Industrial Revolution. Thus, cyber-physical systems will be called upon to interact with humans, either in a cooperative fashion, or as adversaries to malicious human agents that will seek to corrupt their operation. In this work, we will present methods that enable an autonomous system to operate safely among human agents and to gain an advantage in cyber-physical security scenarios by employing tools from control, game and learning theories. Our work revolves around three main axes: unpredictability-based defense, operation among agents with bounded rationality and verification of safety properties for autonomous systems. In taking advantage of the complex nature of cyber-physical systems, our unpredictability-based defense work will focus both on attacks on actuating and sensing components, which will be addressed via a novel switching-based Moving Target Defense framework, and on Denial-of-Service attacks on the underlying network via a zero-sum game exploiting redundant communication channels. Subsequently, we will take a more abstract view of complex system security by exploring the principles of bounded rationality. We will show how attackers of bounded rationality can coordinate in inducing erroneous decisions to a system while they remain stealthy. Methods of cognitive hierarchy will be employed for decision prediction, while closed form solutions of the optimization problem and the conditions of convergence to the Nash equilibrium will be investigated. The principles of bounded rationality will be brought to control systems via the use of policy iteration algorithms, enabling data-driven attack prediction in a more realistic fashion than what can be offered by game equilibrium solutions. The issue of intelligence in security scenarios will be further considered via concepts of learning manipulation through a proposed framework where bounded rationality is understood as a hierarchy in learning, rather than optimizing, capability. This viewpoint will allow us to propose methods of exploiting the learning process of an imperfect opponent in order to affect their cognitive state via the use of tools from optimal control theory. Finally, in the context of safety, we will explore verification and compositionality properties of linear systems that are designed to be added to a cascade network of similar systems. To obfuscate the need for knowledge of the system's dynamics, we will state decentralized conditions that guarantee a specific dissipativity properties for the system, which are shown to be solved by reinforcement learning techniques. Subsequently, we will propose a framework that employs a hierarchical solution of temporal logic specifications and reinforcement learning problems for optimal tracking.Ph.D

Collective behaviours in the stock market -- A maximum entropy approach

Scale invariance, collective behaviours and structural reorganization are crucial for portfolio management (portfolio composition, hedging, alternative definition of risk, etc.). This lack of any characteristic scale and such elaborated behaviours find their origin in the theory of complex systems. There are several mechanisms which generate scale invariance but maximum entropy models are able to explain both scale invariance and collective behaviours. The study of the structure and collective modes of financial markets attracts more and more attention. It has been shown that some agent based models are able to reproduce some stylized facts. Despite their partial success, there is still the problem of rules design. In this work, we used a statistical inverse approach to model the structure and co-movements in financial markets. Inverse models restrict the number of assumptions. We found that a pairwise maximum entropy model is consistent with the data and is able to describe the complex structure of financial systems. We considered the existence of a critical state which is linked to how the market processes information, how it responds to exogenous inputs and how its structure changes. The considered data sets did not reveal a persistent critical state but rather oscillations between order and disorder. In this framework, we also showed that the collective modes are mostly dominated by pairwise co-movements and that univariate models are not good candidates to model crashes. The analysis also suggests a genuine adaptive process since both the maximum variance of the log-likelihood and the accuracy of the predictive scheme vary through time. This approach may provide some clue to crash precursors and may provide highlights on how a shock spreads in a financial network and if it will lead to a crash. The natural continuation of the present work could be the study of such a mechanism.Comment: 146 pages, PhD Thesi

Effective demand response gathering and deployment in smart grids for intensive renewable integration using aggregation and machine learning

Tesis por compendio de publicaciones.[EN] Distributed generation, namely renewables-based technologies, have emerged as a crucial component in the transition to mitigate the effects of climate change, providing a decentralized approach to electricity production. However, the volatile behavior of distributed generation has created new challenges in maintaining system balance and reliability. In this context, the demand response concept and corresponding programs arise giving the local energy communities prominence. In demand response concept, it is expected an empowerment of the consumer in the electricity sector. This has a significant impact on grid operations and brings complex interactions due to the volatile behavior, privacy concerns, and lack of consumer knowledge in the energy market context. For this, aggregators play a crucial role addressing these challenges. It is crucial to develop tools that allow the aggregators helping consumers to make informed decisions, maximize the benefits of their flexibility resources, and contribute to the overall success of grid operations. This thesis, through innovative solutions and resorting to artificial intelligence models, addresses the integration of renewables, promoting fair participation among all demand response providers. The thesis ultimately results in an innovative decision support system - MAESTRO, the Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. MAESTRO is composed by a set of diversified models that together contribute for handling the complexity of managing energy communities with distributed generation resources, demand response providers, energy storage systems and electric vehicles. This PhD thesis comprises a comprehensive analysis of state-of-the-art techniques, system design and development, experimental results, and key findings. In this research were published twenty-six scientific papers, in both international journals and conference proceedings. Contributions to international projects and Portuguese projects was accomplished. [ES] La generación distribuida, en particular las tecnologías basadas en energías renovables, se ha convertido en un componente crucial en la transición para mitigar los efectos del cambio climático, al proporcionar un enfoque descentralizado para la producción de electricidad. Sin embargo, el comportamiento volátil de la generación distribuida ha generado nuevos desafíos para mantener el equilibrio y la confiabilidad del sistema. En este contexto, surge el concepto de respuesta de la demanda y los programas correspondientes, otorgando prominencia a las comunidades energéticas locales. En el concepto de "respuesta a la demanda" (DR por sus siglas en inglés), se espera un empoderamiento del consumidor en el sector eléctrico. Esto tiene un impacto significativo en la operación de la red y genera interacciones complejas debido al comportamiento volátil, las preocupaciones de privacidad y la falta de conocimiento del consumidor en el contexto del mercado energético. Para esto, los agregadores desempeñan un papel crucial al abordar estos desafíos. Es fundamental desarrollar herramientas que permitan a los agregadores ayudar a los consumidores a tomar decisiones informadas, maximizar los beneficios de sus recursos de flexibilidad y contribuir al éxito general de las operaciones de la red. Esta tesis, a través de soluciones innovadoras y utilizando modelos de inteligencia artificial, aborda la integración de energías renovables, promoviendo una participación justa entre todos los proveedores de respuesta de la demanda. La tesis resulta en última instancia en un sistema de apoyo a la toma de decisiones innovador: MAESTRO, Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. MAESTRO está compuesto por un conjunto de modelos diversificados que contribuyen juntos para manejar la complejidad de la gestión de comunidades energéticas con recursos de generación distribuida, proveedores de respuesta de la demanda, sistemas de almacenamiento de energía y vehículos eléctricos. Esta tesis de doctorado comprende un análisis exhaustivo de las técnicas de vanguardia, el diseño y desarrollo del sistema, los resultados experimentales y los hallazgos clave. En esta investigación se publicaron veintiséis artículos científicos, tanto en revistas internacionales como en actas de conferencias. Se lograron contribuciones a proyectos internacionales y proyectos portugueses. [POR] A produção distribuída, nomeadamente as tecnologias baseadas em energias renováveis, emergiram como um componente crucial na transição para mitigar os efeitos das alterações climáticas, proporcionando uma abordagem descentralizada à produção de eletricidade. No entanto, o comportamento volátil da geração distribuída criou desafios na manutenção do equilíbrio e da fiabilidade do sistema. Nesse contexto, surge o conceito de resposta à procura e os programas correspondentes, conferindo proeminência às comunidades energéticas locais. No conceito de resposta à procura, espera-se um empoderamento do consumidor no setor elétrico. Isso tem um impacto significativo nas operações da rede e gera interações complexas devido ao comportamento volátil, preocupações com a privacidade e falta de conhecimento dos consumidores no contexto do mercado energético. Para isso, os agregadores desempenham um papel crucial ao lidar com esses desafios. É fundamental desenvolver ferramentas que permitam aos agregadores ajudar os consumidores a tomar decisões informadas, maximizar os benefícios de seus recursos de flexibilidade e contribuir para o sucesso global das operações da rede. Esta tese de doutoramento, através de soluções inovadoras e recorrendo a modelos de inteligência artificial, aborda a integração de energias renováveis, promovendo uma participação justa entre todos os fornecedores de resposta à procura. A tese resulta, em última instância, num sistema inovador de apoio à tomada de decisões - MAESTRO, Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. A ferramenta MAESTRO é composta por um conjunto de modelos diversificados que, em conjunto, contribuem para lidar com a complexidade da gestão de comunidades energéticas com recursos de geração distribuída, fornecedores de resposta à procura, sistemas de armazenamento de energia e veículos elétricos. Esta tese de doutoramento abrange uma análise abrangente de técnicas de ponta, design e desenvolvimento do sistema, resultados experimentais e descobertas-chave. Nesta pesquisa, foram publicados vinte e seis artigos científicos, tanto em revistas internacionais como em atas de conferências. Foram realizadas contribuições para projetos internacionais e projetos portugueses

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects