Robust synchronization for 2-D discrete-time coupled dynamical networks

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a new synchronization problem is addressed for an array of 2-D coupled dynamical networks. The class of systems under investigation is described by the 2-D nonlinear state space model which is oriented from the well-known Fornasini–Marchesini second model. For such a new 2-D complex network model, both the network dynamics and the couplings evolve in two independent directions. A new synchronization concept is put forward to account for the phenomenon that the propagations of all 2-D dynamical networks are synchronized in two directions with influence from the coupling strength. The purpose of the problem addressed is to first derive sufficient conditions ensuring the global synchronization and then extend the obtained results to more general cases where the system matrices contain either the norm-bounded or the polytopic parameter uncertainties. An energy-like quadratic function is developed, together with the intensive use of the Kronecker product, to establish the easy-to-verify conditions under which the addressed 2-D complex network model achieves global synchronization. Finally, a numerical example is given to illustrate the theoretical results and the effectiveness of the proposed synchronization scheme.This work was supported in part by the National Natural Science Foundation of China under Grants 61028008 and 61174136, the International Science and Technology Cooperation Project of China under Grant No. 2009DFA32050, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

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

Probabilistic Graphical Models: an Application in Synchronization and Localization

Die Lokalisierung von mobilen Nutzern (MU) in sehr dichten Netzen erfordert häufig die Synchronisierung der Access Points (APs) untereinander. Erstens konzentriert sich diese Arbeit auf die Lösung des Problems der Zeitsynchronisation in 5G-Netzwerken, indem ein hybrider Bayesischer Ansatz für die Schätzung des Taktversatzes und des Versatzes verwendet wird. Wir untersuchen und demonstrieren den beträchtlichen Nutzen der Belief Propagation (BP), die auf factor graphs läuft, um eine präzise netzwerkweite Synchronisation zu erreichen. Darüber hinaus nutzen wir die Vorteile der Bayesischen Rekursiven Filterung (BRF), um den Zeitstempel-Fehler bei der paarweisen Synchronisierung zu verringern. Schließlich zeigen wir die Vorzüge der hybriden Synchronisation auf, indem wir ein großes Netzwerk in gemeinsame und lokale Synchronisationsdomänen unterteilen und so den am besten geeigneten Synchronisationsalgorithmus (BP- oder BRF-basiert) auf jede Domäne anwenden können. Zweitens schlagen wir einen Deep Neural Network (DNN)-gestützten Particle Filter-basierten (DePF)-Ansatz vor, um das gemeinsame MU-Sync&loc-Problem zu lösen. Insbesondere setzt DePF einen asymmetrischen Zeitstempel-Austauschmechanismus zwischen den MUs und den APs ein, der Informationen über den Taktversatz, die Zeitverschiebung der MUs, und die AP-MU Abstand liefert. Zur Schätzung des Ankunftswinkels des empfangenen Synchronisierungspakets nutzt DePF den multiple signal classification Algorithmus, der durch die Channel Impulse Response (CIR) der Synchronisierungspakete gespeist wird. Die CIR wird auch genutzt, um den Verbindungszustand zu bestimmen, d. h. Line-of-Sight (LoS) oder Non-LoS (NLoS). Schließlich nutzt DePF particle Gaussian mixtures, die eine hybride partikelbasierte und parametrische BRF-Fusion der vorgenannten Informationen ermöglichen und die Position und die Taktparameter der MUs gemeinsam schätzen.Mobile User (MU) localization in ultra dense networks often requires, on one hand, the Access Points (APs) to be synchronized among each other, and, on the other hand, the MU-AP synchronization. In this work, we firstly address the former, which eventually provides a basis for the latter, i.e., for the joint MU synchronization and localization (sync&loc). In particular, firstly, this work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Specifically, we investigate and demonstrate the substantial benefit of Belief Propagation (BP) running on Factor Graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian Recursive Filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into common and local synchronization domains, thereby being able to apply the most suitable synchronization algorithm (BP- or BRF-based) on each domain. Secondly, we propose a Deep Neural Network (DNN)-assisted Particle Filter-based (DePF) approach to address the MU joint sync&loc problem. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the APs, which provides information about the MUs' clock offset, skew, and AP-MU distance. In addition, to estimate the Angle of Arrival (AoA) of the received synchronization packet, DePF draws on the Multiple Signal Classification (MUSIC) algorithm that is fed by the Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged on to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS (NLoS). Finally DePF capitalizes on particle Gaussian mixtures which allow for a hybrid particle-based and parametric BRF fusion of the aforementioned pieces of information and jointly estimate the position and clock parameters of the MUs

Self-organizing Fast Routing Protocols for Underwater Acoustic Communications Networks

To address this problem, in this thesis we propose a cross-layer proactive routing initialization mechanism that does not require additional measurements and, at the same time, is energy efficient. Two routing protocols are proposed: Self-Organized Fast Routing Protocol for Radial Underwater Networks (SOFRP) for radial topology and Self-organized Proactive Routing Protocol for Non-uniformly Deployed Underwater Networks (SPRINT) for a randomly deployed network. SOFRP is based on the algorithm to recreate a radial topology with a gateway node, such that packets always use the shortest possible path from source to sink, thus minimizing consumed energy. Collisions are avoided as much as possible during the path initialization. The algorithm is suitable for 2D or 3D areas, and automatically adapts to a varying number of nodes. In SPRINT the routing path to the gateway is formed on the basis of the distance, measured by the signal strength received. The data sending node prefers to choose the neighbor node which is closest to it. It is designed to achieve high data throughput and low energy consumption of the nodes. There is a tradeoff between the throughput and the energy consumption: more distance needs more transmission energy, and more relay nodes (hops) to the destination node affects the throughput. Each hop increases the packet delay and decreases the throughput. Hence, energy consumption requires nearest nodes to be chosen as forwarding node whereas the throughput requires farthest node to be selected to minimize the number of hops. Fecha de lectura de Tesis Doctoral: 11 mayo 2020Underwater Wireless Sensor Networks (UWSNs) constitute an emerging technology for marine surveillance, natural disaster alert and environmental monitoring. Unlike terrestrial Wireless Sensor Networks (WSNs), electromagnetic waves cannot propagate more than few meters in water (high absorption rate). However, acoustic waves can travel long distances in underwater. Therefore, acoustic waves are preferred for underwater communications, but they travel very slow compare to EM waves (typical speed in water is 1500 m/s against 2x10^8 m/s for EM waves). This physical effect makes a high propagation delay and cannot be avoided, but the end-to-end packet delay it can be reduced. Routing delay is one of the major factors in end-to-end packet delay. In reactive routing protocols, when a packet arrives to a node, the node takes some time to select the node to which the data packet would be forwarded. We may reduce the routing delay for time-critical applications by using proactive routing protocols. Other two critical issues in UWSNs are determining the position of the nodes and time synchronization. Wireless sensor nodes need to determine the position of the surrounding nodes to select the next node in the path to reach the sink node. A Global Navigation Satellite System (GNSS) cannot be used because of the very short underwater range of the GNSS signal. Timestamping to estimate the distance is possible but the limited mobility of the UWSN nodes and variation in the propagation speed of the acoustic waves make the time synchronization a challenging task. For these reasons, terrestrial WSN protocols cannot be readily used for underwater acoustic networks

Scale invariance in natural and artificial collective systems : a review

Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adaptivity is typically increased and we refer to it as scale-invariant. In this review, we first identify three main types of self-organized scale-invariant systems: scale-invariant spatial structures, scale-invariant topologies and scale-invariant dynamics. We then provide examples of scale invariance from different domains in science, describe their origins and main features and discuss potential challenges and approaches for designing and engineering artificial systems with scale-invariant properties

Optimization and Applications of Modern Wireless Networks and Symmetry

Due to the future demands of wireless communications, this book focuses on channel coding, multi-access, network protocol, and the related techniques for IoT/5G. Channel coding is widely used to enhance reliability and spectral efficiency. In particular, low-density parity check (LDPC) codes and polar codes are optimized for next wireless standard. Moreover, advanced network protocol is developed to improve wireless throughput. This invokes a great deal of attention on modern communications