Abstract

Modern communication networks offer advance and diverse applications, which require huge usage of network resources while providing quality of services to the users. Advance communication is based on multiple switched networks that cannot be handle by traditional IP (internet protocol) networks. GMPLS (Generalized multiprotocol label switched) networks, an advance version of MPLS (multiprotocol label switched networks), are introduced for multiple switched networks. Traffic engineering in GMPLS networks ensures traffic movement on multiple paths. Optimal path(s) computation can be dependent on multiple objectives with multiple constraints. From optimization prospective, it is an NP (non-deterministic polynomial-time) hard optimization problem, to compute optimal paths based on multiple objectives having multiple constraints. The paper proposed a metaheuristic Pareto based Bat algorithm, which uses two objective functions; routing costs and load balancing costs to compute the optimal path(s) as an optimal solution for traffic engineering in MPLS/GMPLS networks. The proposed algorithm has implemented on different number of nodes in MPLS/GMPLS networks, to analysis the algorithm performance.

Abstract

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT. Document type: Part of book or chapter of book

Abstract

The focus of the paper is an investigation and evaluation of energy efficient solutions in IP over WDM core networks using as a foundation, a hierarchy of hibernation modes implementing different degrees of node groupings and fibre links establishment that support a sleep state. It seeks to embed this groups-nodes strategy into an intelligent control plane implementing routing schemes targeting energy consumption, adaptive signalling and traffic engineering. A Group-Nodes mechanism is proposed as a function of topology and node distribution based on a fixed (or geographical) and random (or ownership) principle. The impact of the proposed technique on energy saving and network performance is assessed; results are presented and evaluated for various scenarios. Evaluation of this methodology indicates potential reduction in power consumption from 7 % up to 15 % at the expense of reduced network performance.

Abstract

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a $17$ node charging network equipped with Level $2$ chargers at a major North American University campus. The data is recorded for $166$ weeks starting from late $2011$. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities.

Abstract

The objective of this thesis was to investigate the management of electric vehicles (EVs) battery charging in distribution networks.\\ud Real EVs charging event data were used to investigate their charging demand profiles in a geographical area. A model was developed to analyse their charging demand characteristics and calculate their potential medium term operating risk level for the distribution network of the corresponding geographical area. A case study with real charging and weather data from three counties in UK was presented to demonstrate the modelling framework.\\ud The effectiveness of a charging control algorithm is dependent on the early knowledge of future EVs charging demand and local generation. To this end, two models were developed to provide this knowledge. The first model utilised data mining principles to forecast the day ahead EVs charging demand based on historical charging event data. The performance of four data mining methods in forecasting the charging demand of an EVs fleet was evaluated using real charging data from USA and France. The second model utilised a data fitting approach to produce stochastic generation forecast scenarios based only on the historical data. A case study was presented to evaluate the performance of the model based on real data from wind generators in UK.\\ud An agent-based control algorithm was developed to manage the EVs battery charging, according to the vehicles’ owner preferences, distribution network technical constraints and local distributed generation. Three agent classes were considered, a EVs/DG aggregator and “Responsive” or “Unresponsive” EVs. The real-time operation of the control system was experimentally demonstrated at the Electric Energy Systems Laboratory hosted at the National Technical University of Athens. A series of experiments demonstrated the adaptive behaviour of “Responsive” EVs agents and proved their ability to charge preferentially from renewable energy sources.

Abstract

Multiprotocol label switched (MPLS) networks were introduced to enhance the network`s service provisioning and optimize its performance using multiple protocols along with label switched based networking technique. With the addition of traffic engineering entity in MPLS domain, there is a massive increase in the networks resource management capability with better quality of services (QoS) provisioning for end users. Routing protocols play an important role in MPLS networks for network traffic management, which uses exact and approximate algorithms. There are number of artificial intelligence-based optimization algorithms which can be used for the optimization of traffic engineering in MPLS networks. The paper presents an optimization model for MPLS networks and proposed dolphin-echolocation algorithm (DEA) for optimal path computation. For Network with different nodes, both algorithms performance has been investigated to study their convergence towards the production of optimal solutions. Furthermore, the DEA algorithm will be compared with the bat algorithm to examine their performance in MPLS network optimization. Various parameters such as mean, minimum /optimal fitness function values and standard deviation. Document type: Conference object

Abstract

This paper describes the verification plan on data hazard detection and handling for a 32-bit MIPS ISA (Microprocessor without Interlocked Pipeline Stages Instruction Set Architecture) compatible 5-stage pipeline processor, RISC32. Our work can be used as a reference for RISC32 processor developers to identify and resolve every possible data hazard that might arise during execution phase within the range of the basic MIPS core instruction set. The techniques used to resolve data hazard in this paper are data forwarding and pipeline stages stalling. When data hazard arises, it is first resolve by using data forwarding. If the problem persists, we use pipeline stages stalling then only follow by another data forwarding to resolve the data hazard. This combination will reduce the impact of data hazard on the processor throughput, instead of only using the pipeline stages stalling. This paper delivers a comprehensive analysis and the development of the data hazard resolving blocks that are able to resolve data hazard arises.

Abstract

Minghini, M., Coetzee, S., Grinberger, A. Y., Yeboah, G., Juhász, L., & Mooney, P. (2020). Editorial: OpenStreetMap research in the COVID-19 era In: Minghini, M., Coetzee, S., Juhász, L., Yeboah, G., Mooney, P., Grinberger, A.Y. (Eds.). Proceedings of the Academic Track at the State of the Map 2020 Online Conference, July 04-05 2020. Available at https://zenodo.org/communities/sotm-2020

Abstract

the number of electric vehicles increases, the impact of their charging on distribution networks is being investigated using different load profiles. Due to the lack of real charging data, the majority of these load impact studies are making assumptions for the electric vehicle charging demand profiles. In this paper a two-step modelling framework was developed to extract the useful information hidden in real EVs charging event data. Real EVs charging demand data were obtained from Plugged-in Midlands (PiM) project, one of the eight ‘Plugged-in Places’ projects supported by the UK Office for Low Emission Vehicles (OLEV). A data mining model was developed to investigate the characteristics of electric vehicle charging demand in a geographical area. A Fuzzy-Based model aggregates these characteristics and estimates the potential relative risk level of EVs charging demand among different geographical areas independently to their actual corresponding distribution networks. A case study with real charging and weather data from three counties in UK is presented to demonstrate the modelling framework. Document type: Article

Abstract

The need to develop and deploy Beyond Visual Line of Sight (BVLOS) aerial vehicles has intensified over the last decade. As the demand for Unmanned Aircraft Systems (UAS) has increased, so too has the regulations that surrounds the industry. Strict regulations are currently in place but differ from country to country. Due to these regulations BVLOS innovators have been posed the task of exploring the means of operating flight missions with the UAV out of the sight of the pilot. Autonomous flight capability is not only fundamental to BVLOS operations for UAS but also likely to have a significant impact on the future development of passenger carrying autonomous aircraft. This review explores the technologies that have been developed to date that enable BVLOS applications. BVLOS flight operations have the potential to open a huge area of commercial opportunity however, there remain many concerns about the current capabilities of UAS to detect and avoid manned and unmanned airborne hazards that may pose a significant safety risk.