Techniques for Decentralized and Dynamic Resource Allocation

abstract: This thesis investigates three different resource allocation problems, aiming to achieve two common goals: i) adaptivity to a fast-changing environment, ii) distribution of the computation tasks to achieve a favorable solution. The motivation for this work relies on the modern-era proliferation of sensors and devices, in the Data Acquisition Systems (DAS) layer of the Internet of Things (IoT) architecture. To avoid congestion and enable low-latency services, limits have to be imposed on the amount of decisions that can be centralized (i.e. solved in the ``cloud") and/or amount of control information that devices can exchange. This has been the motivation to develop i) a lightweight PHY Layer protocol for time synchronization and scheduling in Wireless Sensor Networks (WSNs), ii) an adaptive receiver that enables Sub-Nyquist sampling, for efficient spectrum sensing at high frequencies, and iii) an SDN-scheme for resource-sharing across different technologies and operators, to harmoniously and holistically respond to fluctuations in demands at the eNodeB' s layer. The proposed solution for time synchronization and scheduling is a new protocol, called PulseSS, which is completely event-driven and is inspired by biological networks. The results on convergence and accuracy for locally connected networks, presented in this thesis, constitute the theoretical foundation for the protocol in terms of performance guarantee. The derived limits provided guidelines for ad-hoc solutions in the actual implementation of the protocol. The proposed receiver for Compressive Spectrum Sensing (CSS) aims at tackling the noise folding phenomenon, e.g., the accumulation of noise from different sub-bands that are folded, prior to sampling and baseband processing, when an analog front-end aliasing mixer is utilized. The sensing phase design has been conducted via a utility maximization approach, thus the scheme derived has been called Cognitive Utility Maximization Multiple Access (CUMMA). The framework described in the last part of the thesis is inspired by stochastic network optimization tools and dynamics. While convergence of the proposed approach remains an open problem, the numerical results here presented suggest the capability of the algorithm to handle traffic fluctuations across operators, while respecting different time and economic constraints. The scheme has been named Decomposition of Infrastructure-based Dynamic Resource Allocation (DIDRA).Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Collective Behaviour: From Cells to Humans

Living in organised groups is a strategy that can be observed in a multitude of diverse species. Among such species, the behaviour of an individual on their own is not the same as within a group: the environment is modified by the presence of more subjects, individuals interact with each other, and from those interactions complex patterns of behaviour can emerge. Some species of animals almost exclusively exist as groups, and as a consequence, studying them in a social context is the only way to understand their behaviour in nature. This is the idea that drives all the research presented in this thesis: the particular behaviour exhibited by the group is so robust that it will emerge even in a very simplified environment. By observing the individual and the group in those simplified experimental conditions, it is possible to deduce rules that might govern the interaction. The importance of interactions in the group’s behaviour can then be demonstrated by implementing a computer model of agents following those rules and comparing it with natural and experimental behaviour. This thesis presents different examples of such analyses, and gives illustrations of the range of questions that can be answered through this method. Groups of stem cells, juvenile sea bass and human beings were successively observed and tracked in suitable environments, with or without perturbation. The data extracted from those experiments were then processed so as to correct recording errors, and individual and collective behaviours were derived from those data, returning new insights on the nature of the interaction at the individual level, their consequences at the global level, as well as the effects of the interaction on both. Finally, I present the computer models derived from those analyses. Many systems in nature share this property of global behaviours emerging from deterministic local interaction, and as a consequence studies of this kind could shed light on important questions, of which cancer treatment, ocean acidification and human organisations are but a few examples