Methods for improving resilience in communication networks and P2P overlays

Resilience to failures and deliberate attacks is becoming an essential requirement in most communication networks today. This also applies to P2P Overlays which on the one hand are created on top of communication infrastructures, and therefore are equally affected by failures of the underlying infrastructure, but which on the other hand introduce new possibilities like the creation of arbitrary links within the overlay. In this article, we present a survey of strategies to improve resilience in communication networks as well as in P2P overlay networks. Furthermore, our intention is to point out differences and similarities in the resilience-enhancing measures for both types of networks. By revising some basic concepts from graph theory, we show that many concepts for communication networks are based on well-known graph-theoretical problems. Especially, some methods for the construction of protection paths in advance of a failure are based on very hard problems, indeed many of them are in NP and can only be solved heuristically or on certain topologies. P2P overlay networks evidently benefit from resilience-enhancing strategies in the underlying communication infrastructure, but beyond that, their specific properties pose the need for more sophisticated mechanisms. The dynamic nature of peers requires to take some precautions, like estimating the reliability of peers, redundantly storing information, and provisioning a reliable routing

Multi-layer survivability in IP-over-WDM networks

Ph.DDOCTOR OF PHILOSOPH

Journal of Telecommunications and Information Technology, 2011, nr 4

kwartalni

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

Optimizing total cost of ownership (TCO) for 5G multi-tenant mobile backhaul (MBH) optical transport networks

Legacy network elements are reaching end-of-life and packet-based transport networks are not efficiently optimized. In particular, high density cell architecture in future 5G networks will face big technical and financial challenges due to avalanche of traffic volume and massive growth in connected devices. Raising density and ever-increasing traffic demand within future 5G Heterogeneous Networks (HetNets) will result in huge deployment, expansion and operating costs for upcoming Mobile BackHaul (MBH) networks with flat revenue generation. Thus, the goal of this dissertation is to provide an efficient physical network planning mechanism and an optimized resource engineering tool in order to reduce the Total Cost of Ownership (TCO) and increase the generated revenues. This will help Service Providers (SPs) and Mobile Network Operators (MNOs) to improve their network scalability and maintain positive Project Profit Margins (PPM). In order to meet this goal, three key issues are required to be addressed in our framework and are summarized as follows: i) how to design and migrate to a scalable and reliable MBH network in an optimal cost?, ii) how to control the deployment and activation of the network resources in such MBH based on required traffic demand in an efficient and cost-effective way?, and iii) how to enhance the resource sharing in such network and maximize the profit margins in an efficient way? As part of our contributions to address the first issue highlighted above and to plan the MBH with reduced network TCO and improved scalability, we propose a comprehensive migration plan towards an End-to-End Integrated-Optical-Packet-Network (E2-IOPN) for SP optical transport networks. We review various empirical challenges faced by a real SP during the transformation process towards E2-IOPN as well as the implementation of an as-built plan and a high-level design (HLD) for migrating towards lower cost-per-bit GPON, MPLS-TP, OTN and next-generation DWDM technologies. Then, we propose a longer-term strategy based on SDN and NFV approach that will offer rapid end-to-end service provisioning with costefficient centralized network control. We define CapEx and OpEx cost models and drive a cost comparative study that shows the benefit and financial impact of introducing new low-cost packet-based technologies to carry traffic from legacy and new services. To address the second issue, we first introduce an algorithm based on a stochastic geometry model (Voronoi Tessellation) to more precisely define MBH zones within a geographical area and more accurately calculate required traffic demands and related MBH infrastructure. In order to optimize the deployment and activation of the network resources in the MBH in an efficient and cost-effective way, we propose a novel method called BackHauling-as-a-Service (BHaaS) for network planning and Total Cost of Ownership (TCO) analysis based on required traffic demand and a "You-pay-only-for-what-you-use" approach. Furthermore, we enhance BHaaS performance by introducing a more service-aware method called Traffic-Profile-asa- Service (TPaaS) to further drive down the costs based on yearly activated traffic profiles. Results show that BHaaS and TPaaS may enhance by 22% the project benefit compared to traditional TCO model. Finally, we introduce a new cost (CapEx and OpEx) models for 5G multi-tenant Virtualized MBH (V-MBH) as part of our contribution to address the third issue. In fact, in order to enhance the resource sharing and maximize the network profits, we drive a novel pay-as-yougrow and optimization model for the V-MBH called Virtual-Backhaul-as-a-Service (VBaaS). VBaaS can serve as a planning tool to optimize the Project Profit Margin (PPM) while considering the TCO and the yearly generated Return-on-Investment (ROI). We formulate an MNO Pricing Game (MPG) for TCO optimization to calculate the optimal Pareto-Equilibrium pricing strategy for offered Tenant Service Instances (TSI). Then, we compare CapEx, OpEx, TCO, ROI and PPM for a specific use-case known in the industry as CORD project using Traditional MBH (T-MBH) versus Virtualized MBH (V-MBH) as well as using randomized versus Pareto-Equilibrium pricing strategies. The results of our framework offer SPs and MNOs a more precise estimation of traffic demand, an optimized infrastructure planning and yearly resource deployment as well as an optimized TCO analysis (CapEx and OpEx) with enhanced pricing strategy and generated ROI. Numerical results show more than three times increase in network profitability using our proposed solutions compared with Traditional MBH (T-MBH) methods

Advanced digital signal processing for next-generation flexible optical networks

To keep pace with the rapid expansion in data-exchange traffic around the world, optical networks are anticipated to provide flexibility to maximize utilization of the deployed optical fiber resources. On the other hand, digital signal processing (DSP) has been employed in coherent optical systems to enable 100G and beyond optical fiber networks. The goal of the thesis is to develop advanced DSP techniques for the flexible optical networks. With the reconfigured modulation formats in the systems, modulation classification (MC) is essential in the DSP to facilitate the sequential compensation modules which are modulation format-dependent. Based on the cumulative distribution function (CDF) of received signal's amplitude, an MC algorithm for M-ary quadrature amplitude modulation (M-QAM) formats with M = 4, 8, 16, 32, and 64 is proposed. Results show that the proposed algorithm achieves accurate classification at optical signal-to-noise ratio (OSNR) of interest and is robust to frequency offset and laser phase noise. Relying on the CDF of received signal's amplitude, a non-data-aided (NDA) OSNR estimation algorithm is developed for coherent optical systems employing multilevel constellations. It outperforms the state-of-the-art NDA algorithm in terms of performance and complexity. Furthermore, a joint OSNR estimation and MC algorithm enabled by support vector machine is designed. Compared to deep neural network-based joint estimation approach, the proposed algorithm achieves better performance with comparable complexity. In addition, a low-complexity two-stage carrier phase estimation algorithm is proposed for coherent optical systems with 16-QAM format. The proposed algorithm exploits the second power operation instead of the conventional fourth power to remove the modulation phase, which is enabled by constellation partition and rotation. Optical back-to-back experiments and numerical simulations are carried out to evaluate the performance of the algorithm. Results show that, compared with the conventional fourth power-based CPE algorithm, the proposed algorithm provides comparable tolerance to the carrier phase noise, with reduced complexity. Lastly, a novel transmission scheme is investigated for the open and disaggregated metro coherent optical networks, which impose the requirements for multiple user connectivities on the limited orthogonal frequency resources. Thus, it is desirable to provide connections simultaneously to various users in a non-orthogonal way. A transmission scheme based on the non-orthogonal sparse code multiple access in a digital subcarrier multiplexing is proposed. Compared to power domain-based counterpart, the proposed scheme supports more than 2 users without user pairing and clustering. The feasibility of the proposed scheme is verified through numerical simulations. Three scenarios with 2, 4, and 6 users over 1, 2, and 4 subcarriers, respectively, are considered. Performance evaluations show that in all scenarios, the proposed scheme attains bit error ratio lower than the forward error correction limits with the transmission ranges of interest in metro applications

Impact detection techniques using fibre-optic sensors for aerospace & defence

Impact detection techniques are developed for application in the aerospace and defence industries. Optical fibre sensors hold great promise for structural health monitoring systems and methods of interrogating fibre Bragg gratings (FBG) are investigated given the need for dynamic strain capture and multiplexed sensors. An arrayed waveguide grating based interrogator is developed. The relationships between key performance indicators, such as strain range and linearity of response, and parameters such as the FBG length and spectral width are determined. It was found that the inclusion of a semiconductor optical amplifier could increase the signal-to-noise ratio by ~300% as the system moves to its least sensitive. An alternative interrogator is investigated utilising two wave mixing in erbium-doped fibre in order to create an adaptive system insensitive to quasistatic strain and temperature drifts. Dynamic strain sensing was demonstrated at 200 Hz which remained functional while undergoing a temperature shift of 8.5 °C. In addition, software techniques are investigated for locating impact events on a curved composite structure using both time-of-flight triangulation and neural networks. A feature characteristic of composite damage creation is identified in dynamic signals captured during impact. An algorithm is developed which successfully distinguishes between signals characteristic of a non-damaging impact with those from a damaging impact with a classification accuracy of 93 – 96%. Finally, a demonstrator system is produced to exhibit some of the techniques developed in this thesis

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts