Ant colony optimisation-based algorithms for optical burst switching networks

This research developed two novel distributed algorithms inspired by Ant Colony Optimisation (ACO) for a solution to the problem of dynamic Routing and Wavelength Assignment (RWA) with wavelength continuity constraint in Optical Burst Switching (OBS) networks utilising both the traditional International Telecommunication Union (ITU) Fixed Grid Wavelength Division Multiplexing (WDM) and Flexible Spectrum scenarios. The growing demand for more bandwidth in optical networks require more efficient utilisation of available optical resources. OBS is a promising optical switching technique for the improved utilisation of optical network resources over the current optical circuit switching technique. The development of newer technologies has introduced higher rate transmissions and various modulation formats, however, introducing these technologies into the traditional ITU Fixed Grid does not efficiently utilise the available bandwidth. Flexible Spectrum is a promising approach offering a solution to the problem of improving bandwidth utilisation, which comes with a potential cost. Transmissions have the potential for impairment with respect to the increased traffic and lack of large channel spacing. Proposed routing algorithms should be aware of the linear and non-linear Physical Layer Impairments (PLIs) in order to operate closer to optimum performance. The OBS resource reservation protocol does not cater for the loss of transmissions, Burst Control Packets (BCPs) included, due to physical layer impairments. The protocol was adapted for use in Flexible Spectrum. Investigation of the use of a route and wavelength combination, from source to destination node pair, for the RWA process was proposed for ACO-based approaches to enforce the establishment and use of complete paths for greedy exploitation in Flexible Spectrum was conducted. The routing tuple for the RWA process is the tight coupling of a route and wavelength in combination intended to promote the greedy exploitation of successful paths for transmission requests. The application of the routing tuples differs from traditional ACO-based approaches and prompted the investigation of new pheromone calculation equations. The two novel proposed approaches were tested and experiments conducted comparing with and against existing algorithms (a simple greedy and an ACO-based algorithm) in a traditional ITU Fixed Grid and Flexible Spectrum scenario on three different network topologies. The proposed Flexible Spectrum Ant Colony (FSAC) approach had a markably improved performance over the existing algorithms in the ITU Fixed Grid WDM and Flexible Spectrum scenarios, while Upper Confidence Bound Routing and Wavelength Assignment (UCBRWA) algorithm was able to perform well in the traditional ITU Fixed Grid WDM scenario, but underperformed in the Flexible Spectrum scenario. The results show that the distributed ACO-based FSAC algorithm significantly improved the burst transmission success probability, providing a good solution in the Flexible Spectrum network environment undergoing transmission impairments

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Bio-inspired route estimation in cognitive radio networks

Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms

A congestion aware ant colony optimisation-based routing and wavelength assignment algorithm for transparent flexi-grid optical burst switched networks

Optical Burst Switching (OBS) over transparent exi-grid optical networks, is considered a potential solution to the increasing pressure on backbone networks due to the increase in internet use and widespread adoption of various high bandwidth applications. Both technologies allow for more e cient usage of a networks resources. However, transmissions over exi-grid networks are more susceptible to optical impairments than transmissions made over xed-grid networks, and OBS suers from high burst loss due to contention. These issues need to be solved in order to reap the full benets of both technologies. An open issue for OBS whose solution would mitigate both issues is the Routing and Wavelength Assignment (RWA) algorithm. Ant Colony Optimisation (ACO) is a method of interest for solving the RWA problem on OBS networks. This study aims to improve on current dynamic ACO-based solutions to the Routing and Wavelength Assignment problem on transparent exi-grid Optical Burst Switched networks

Improving relay based cellular networks performance in highly user congested and emergency situations

PhDRelay based cellular networks (RBCNs) are the technologies that incorporate multi-hop communication into traditional cellular networks. A RBCN can potentially support higher data rates, more stable radio coverage and more dynamic services. In reality, RBCNs still suffer from performance degradation in terms of high user congestion, base station failure and overloading in emergency situations. The focus of this thesis is to explore the potential to improve IEEE802.16j supported RBCN performance in user congestion and emergency situations using adjustments to the RF layer (by antenna adjustments or extensions using multi-hop) and cooperative adjustment algorithms, e.g. based on controlling frequency allocation centrally and using distributed approaches. The first part of this thesis designs and validates network reconfiguration algorithms for RBCN, including a cooperative antenna power control algorithm and a heuristic antenna tilting algorithm. The second part of this thesis investigates centralized and distributed dynamic frequency allocation for higher RBCN frequency efficiency, network resilience, and computation simplicity. It is demonstrated that these benefits mitigate user congestion and base station failure problems significantly. Additionally, interweaving coordinated dynamic frequency allocation and antenna tilting is investigated in order to obtain the benefits of both actions. The third part of this thesis incorporates Delay Tolerate Networking (DTN) technology into RBCN to let users self-organize to connect to functional base station through multi-hops supported by other users. Through the use of DTN, RBCN coverage and performance are improved. This thesis explores the augmentation of DTN routing protocols to let more un-covered users connect to base stations and improve network load balancin

Cross-layer modeling and optimization of next-generation internet networks

Scaling traditional telecommunication networks so that they are able to cope with the volume of future traffic demands and the stringent European Commission (EC) regulations on emissions would entail unaffordable investments. For this very reason, the design of an innovative ultra-high bandwidth power-efficient network architecture is nowadays a bold topic within the research community. So far, the independent evolution of network layers has resulted in isolated, and hence, far-from-optimal contributions, which have eventually led to the issues today's networks are facing such as inefficient energy strategy, limited network scalability and flexibility, reduced network manageability and increased overall network and customer services costs. Consequently, there is currently large consensus among network operators and the research community that cross-layer interaction and coordination is fundamental for the proper architectural design of next-generation Internet networks. This thesis actively contributes to the this goal by addressing the modeling, optimization and performance analysis of a set of potential technologies to be deployed in future cross-layer network architectures. By applying a transversal design approach (i.e., joint consideration of several network layers), we aim for achieving the maximization of the integration of the different network layers involved in each specific problem. To this end, Part I provides a comprehensive evaluation of optical transport networks (OTNs) based on layer 2 (L2) sub-wavelength switching (SWS) technologies, also taking into consideration the impact of physical layer impairments (PLIs) (L0 phenomena). Indeed, the recent and relevant advances in optical technologies have dramatically increased the impact that PLIs have on the optical signal quality, particularly in the context of SWS networks. Then, in Part II of the thesis, we present a set of case studies where it is shown that the application of operations research (OR) methodologies in the desing/planning stage of future cross-layer Internet network architectures leads to the successful joint optimization of key network performance indicators (KPIs) such as cost (i.e., CAPEX/OPEX), resources usage and energy consumption. OR can definitely play an important role by allowing network designers/architects to obtain good near-optimal solutions to real-sized problems within practical running times