Proportional switching in FIFO networks

We consider a family of discrete time multihop switched queueing networks where each packet movesalong a xed route. In this setting, BackPressure is the canonical choice of scheduling policy; this policy hasthe virtues of possessing a maximal stability region and not requiring explicit knowledge of tra c arrival rates.BackPressure has certain structural weaknesses because implementation requires information about each route,and queueing delays can grow super-linearly with route length. For large networks, where packets over manyroutes are processed by a queue, or where packets over a route are processed by many queues, these limitationscan be prohibitive.In this article, we introduce a scheduling policy for FIFO networks, the Proportional Scheduler, which isbased on the proportional fairness criterion. We show that, like BackPressure, the Proportional Scheduler hasa maximal stability region and does not require explicit knowledge of tra c arrival rates. The ProportionalScheduler has the advantage that information about the network's route structure is not required for scheduling,which substantially improves the policy's performance for large networks. For instance, packets can be routedwith only next-hop information and new nodes can be added to the network with only knowledge of thescheduling constraintsThe research of the rst author was partially supported by NSF grants DMS-1105668 and DMS-1203201. The research of the second author was partially supported by the Spanish Ministry of Economy and Competitiveness Grants MTM2013-42104-P via FEDER funds; he thanks the ICMAT (Madrid, Spain) Research Institute that kindly hosted him while developing this project

A Dual Sampling Communication Method in Wireless Networks.

PhD ThesisAs mobile wireless data traffic is increasing significantly, the development direction for wireless networks is focusing on very high data rates, extremely low latency, with a large number of connected devices and a reduction in energy usage. To satisfy the rapid rise in user and traffic capacity, raises challenges given the limited bandwidth resource. The main purpose for this research is to find ways to improve spectral efficiency, data transmission rate, and reduce latency. Simultaneous wireless transmissions happening in the same frequency band can help alleviate demand on transmission slots, with methods like network coding to support decoding at the end terminals. However, in general, signal asynchrony harms the transmission performance significantly. The main contribution of this research is the proposal of a Dual Sampling (DS) method, which aims to relieve the impact of signal asynchrony on simultaneous transmissions. The key concept behind the DS method is sampling twice within each symbol period to handle overlapping signals for successful decoding. Simulation results confirm that it manages to support simultaneous transmissions. Moreover, the DS method is implemented in both Information-Centric Networks (ICN) and Unmanned Aerial Vehicles (UAVs) aided wireless networks. Additionally, for ICN, a Cache Migration Protocol (CMP) is proposed to support simultaneous transmissions which reduces the transmission latency. While for UAV-aided wireless networks, by exploiting the DS method, simultaneous transmissions are supported resulting in better optimal max-min throughput along supported by suitableUAV flight trajectory planning. By demonstrating the performance gain in the application scenarios of ICN and UAV-aided wireless networks, the DS method can be regarded as an optional promising transmission mechanism when communicating with multiple users simultaneously

Proportional Switching in First-in, First-out Networks

We consider a family of discrete time multihop switched queueing networks where each packet moves along a fixed route. In this setting, BackPressure is the canonical choice of scheduling policy; this policy has the virtues of possessing a maximal stability region and not requiring explicit knowledge of traffic arrival rates. BackPressure has certain structural weaknesses because implementation requires information about each route, and queueing delays can grow super-linearly with route length. For large networks, where packets over many routes are processed by a queue, or where packets over a route are processed by many queues, these limitations can be prohibitive. In this article, we introduce a scheduling policy for first-in, first-out networks, the ProportionalScheduler, which is based on the proportional fairness criterion. We show that, like BackPressure, the ProportionalScheduler has a maximal stability region and does not require explicit knowledge of traffic arrival rates. The ProportionalScheduler has the advantage that information about the network's route structure is not required for scheduling, which substantially improves the policy's performance for large networks. For instance, packets can be routed with only next-hop information and new nodes can be added to the network with only knowledge of the scheduling constraints

Autonomous Swarm Navigation

Robotic swarm systems attract increasing attention in a wide variety of applications, where a multitude of self-organized robotic entities collectively accomplish sensing or exploration tasks. Compared to a single robot, a swarm system offers advantages in terms of exploration speed, robustness against single point of failures, and collective observations of spatio-temporal processes. Autonomous swarm navigation, including swarm self-localization, the localization of external sources, and swarm control, is essential for the success of an autonomous swarm application. However, as a newly emerging technology, a thorough study of autonomous swarm navigation is still missing. In this thesis, we systematically study swarm navigation systems, particularly emphasizing on their collective performance. The general theory of swarm navigation as well as an in-depth study on a specific swarm navigation system proposed for future Mars exploration missions are covered. Concerning swarm localization, a decentralized algorithm is proposed, which achieves a near-optimal performance with low complexity for a dense swarm network. Regarding swarm control, a position-aware swarm control concept is proposed. The swarm is aware of not only the position estimates and the estimation uncertainties of itself and the sources, but also the potential motions to enrich position information. As a result, the swarm actively adapts its formation to improve localization performance, without losing track of other objectives, such as goal approaching and collision avoidance. The autonomous swarm navigation concept described in this thesis is verified for a specific Mars swarm exploration system. More importantly, this concept is generally adaptable to an extensive range of swarm applications

Swarm intelligence techniques for optimization and management tasks insensor networks

The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted in each case. We propose a self-synchronized duty-cycling mechanism for sensor networks. This mechanism is based on the working and resting phases of natural ant colonies, which show self-synchronized activity phases. The main goal of duty-cycling methods is to save energy by efficiently alternating between different states. In the case at hand, we considered two different states: the sleep state, where communications are not possible and energy consumption is low; and the active state, where communication result in a higher energy consumption. In order to test the model, we conducted an extensive experimentation with synchronous simulations on mobile networks and static networks, and also considering asynchronous networks. Later, we extended this work by assuming a broader point of view and including a comprehensive study of the parameters. In addition, thanks to a collaboration with the Technical University of Braunschweig, we were able to test our algorithm in the real sensor network simulator Shawn (http://shawn.sf.net). The second part of this thesis is devoted to the desynchronization of wireless sensor nodes and its application to the distributed graph coloring problem. In particular, our research is inspired by the calling behavior of Japanese tree frogs, whose males use their calls to attract females. Interestingly, as female frogs are only able to correctly localize the male frogs when their calls are not too close in time, groups of males that are located nearby each other desynchronize their calls. Based on a model of this behavior from the literature, we propose a novel algorithm with applications to the field of sensor networks. More in detail, we analyzed the ability of the algorithm to desynchronize neighboring nodes. Furthermore, we considered extensions of the original model, hereby improving its desynchronization capabilities.To illustrate the potential benefits of desynchronized networks, we then focused on distributed graph coloring. Later, we analyzed the algorithm more extensively and show its performance on a larger set of benchmark instances. The classical minimum energy broadcast (MEB) problem in wireless ad hoc networks, which is well-studied in the scientific literature, considers an antenna model that allows the adjustment of the transmission power to any desired real value from zero up to the maximum transmission power level. However, when specifically considering sensor networks, a look at the currently available hardware shows that this antenna model is not very realistic. In this work we re-formulate the MEB problem for an antenna model that is realistic for sensor networks. In this antenna model transmission power levels are chosen from a finite set of possible ones. A further contribution concerns the adaptation of an ant colony optimization algorithm --currently being the state of the art for the classical MEB problem-- to the more realistic problem version, the so-called minimum energy broadcast problem with realistic antennas (MEBRA). The obtained results show that the advantage of ant colony optimization over classical heuristics even grows when the number of possible transmission power levels decreases. Finally we build a distributed version of the algorithm, which also compares quite favorably against centralized heuristics from the literature.Las principles contribuciones de esta tesis se encuentran en el domino de las redes de sensores inalámbricas. Más en detalle, introducimos algoritmos y protocolos que intentan minimizar el consumo energético para los siguientes problemas: gestión autosincronizada de encendido y apagado de sensores con capacidad para obtener energía del ambiente, coloreado de grafos distribuido y broadcasting de consumo mínimo en entornos con antenas reales. En primer lugar, proponemos un sistema capaz de autosincronizar los ciclos de encendido y apagado de los nodos de una red de sensores. El mecanismo está basado en las fases de trabajo y reposo de las colonias de hormigas tal y como estas pueden observarse en la naturaleza, es decir, con fases de actividad autosincronizadas. El principal objectivo de este tipo de técnicas es ahorrar energía gracias a alternar estados de forma eficiente. En este caso en concreto, consideramos dos estados diferentes: el estado dormido, en el que los nodos no pueden comunicarse y el consumo energético es bajo; y el estado activo, en el que las comunicaciones propician un consumo energético elevado. Con el objetivo de probar el modelo, se ha llevado a cabo una extensa experimentación que incluye tanto simulaciones síncronas en redes móviles y estáticas, como simulaciones en redes asíncronas. Además, este trabajo se extendió asumiendo un punto de vista más amplio e incluyendo un detallado estudio de los parámetros del algoritmo. Finalmente, gracias a la colaboración con la Technical University of Braunschweig, tuvimos la oportunidad de probar el mecanismo en el simulador realista de redes de sensores, Shawn (http://shawn.sf.net). La segunda parte de esta tesis está dedicada a la desincronización de nodos en redes de sensores y a su aplicación al problema del coloreado de grafos de forma distribuida. En particular, nuestra investigación está inspirada por el canto de las ranas de árbol japonesas, cuyos machos utilizan su canto para atraer a las hembras. Resulta interesante que debido a que las hembras solo son capaces de localizar las ranas macho cuando sus cantos no están demasiado cerca en el tiempo, los grupos de machos que se hallan en una misma región desincronizan sus cantos. Basado en un modelo de este comportamiento que se encuentra en la literatura, proponemos un nuevo algoritmo con aplicaciones al campo de las redes de sensores. Más en detalle, analizamos la habilidad del algoritmo para desincronizar nodos vecinos. Además, consideramos extensiones del modelo original, mejorando su capacidad de desincronización. Para ilustrar los potenciales beneficios de las redes desincronizadas, nos centramos en el problema del coloreado de grafos distribuido que tiene relación con diferentes tareas habituales en redes de sensores. El clásico problema del broadcasting de consumo mínimo en redes ad hoc ha sido bien estudiado en la literatura. El problema considera un modelo de antena que permite transmitir a cualquier potencia elegida (hasta un máximo establecido por el dispositivo). Sin embargo, cuando se trabaja de forma específica con redes de sensores, un vistazo al hardware actualmente disponible muestra que este modelo de antena no es demasiado realista. En este trabajo reformulamos el problema para el modelo de antena más habitual en redes de sensores. En este modelo, los niveles de potencia de transmisión se eligen de un conjunto finito de posibilidades. La siguiente contribución consiste en en la adaptación de un algoritmo de optimización por colonias de hormigas a la versión más realista del problema, también conocida como broadcasting de consumo mínimo con antenas realistas. Los resultados obtenidos muestran que la ventaja de este método sobre heurísticas clásicas incluso crece cuando el número de posibles potencias de transmisión decrece. Además, se ha presentado una versión distribuida del algoritmo, que también se compara de forma bastante favorable contra las heurísticas centralizadas conocidas

Distributed Optimization with Application to Power Systems and Control

In many engineering domains, systems are composed of partially independent subsystems—power systems are composed of distribution and transmission systems, teams of robots are composed of individual robots, and chemical process systems are composed of vessels, heat exchangers and reactors. Often, these subsystems should reach a common goal such as satisfying a power demand with minimum cost, flying in a formation, or reaching an optimal set-point. At the same time, limited information exchange is desirable—for confidentiality reasons but also due to communication constraints. Moreover, a fast and reliable decision process is key as applications might be safety-critical. Mathematical optimization techniques are among the most successful tools for controlling systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization control the subsystems in a distributed or decentralized fashion, reducing or avoiding central coordination. These methods have a long and successful history. Classical distributed optimization algorithms, however, are typically designed for convex problems. Hence, they are only partially applicable in the above domains since many of them lead to optimization problems with non-convex constraints. This thesis develops one of the first frameworks for distributed and decentralized optimization with non-convex constraints. Based on the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithm, a bi-level distributed ALADIN framework is presented, solving the coordination step of ALADIN in a decentralized fashion. This framework can handle various decentralized inner algorithms, two of which we develop here: a decentralized variant of the Alternating Direction Method of Multipliers (ADMM) and a novel decentralized Conjugate Gradient algorithm. Decentralized conjugate gradient is to the best of our knowledge the first decentralized algorithm with a guarantee of convergence to the exact solution in a finite number of iterates. Sufficient conditions for fast local convergence of bi-level ALADIN are derived. Bi-level ALADIN strongly reduces the communication and coordination effort of ALADIN and preserves its fast convergence guarantees. We illustrate these properties on challenging problems from power systems and control, and compare performance to the widely used ADMM. The developed methods are implemented in the open-source MATLAB toolbox ALADIN-—one of the first toolboxes for decentralized non-convex optimization. ALADIN- comes with a rich set of application examples from different domains showing its broad applicability. As an additional contribution, this thesis provides new insights why state-of-the-art distributed algorithms might encounter issues for constrained problems

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing