Mobile agent based distributed network management : modeling, methodologies and applications

The explosive growth of the Internet and the continued dramatic increase for all wireless services are fueling the demand for increased capacity, data rates, support of multimedia services, and support for different Quality of Services (QoS) requirements for different classes of services. Furthermore future communication networks will be strongly characterized by heterogeneity. In order to meet the objectives of instant adaptability to the users\u27 requirements and of interoperability and seamless operation within the heterogeneous networking environments, flexibility in terms of network and resource management will be a key design issue. The new emerging technology of mobile agent (MA) has arisen in the distributed programming field as a potential flexible way of managing resources of a distributed system, and is a challenging opportunity for delivering more flexible services and dealing with network programmability. This dissertation mainly focuses on: a) the design of models that provide a generic framework for the evaluation and analysis of the performance and tradeoffs of the mobile agent management paradigm; b) the development of MA based resource and network management applications. First, in order to demonstrate the use and benefits of the mobile agent based management paradigm in the network and resource management process, a commercial application of a multioperator network is introduced, and the use of agents to provide the underlying framework and structure for its implementation and deployment is investigated. Then, a general analytical model and framework for the evaluation of various network management paradigms is introduced and discussed. It is also illustrated how the developed analytical framework can be used to quantitatively evaluate the performances and tradeoffs in the various computing paradigms. Furthermore, the design tradeoffs for choosing the MA based management paradigm to develop a flexible resource management scheme in wireless networks is discussed and evaluated. The integration of an advanced bandwidth reservation mechanism with a bandwidth reconfiguration based call admission control strategy is also proposed. A framework based on the technology of mobile agents, is introduced for the efficient implementation of the proposed integrated resource and QoS management, while the achievable performance of the overall proposed management scheme is evaluated via modeling and simulation. Finally the use of a distributed cooperative scheme among the mobile agents that can be applied in the future wireless networks is proposed and demonstrated, to improve the energy consumption for the routine management processes of mobile terminals, by adopting the peer-to-peer communication concept of wireless ad-hoc networks. The performance evaluation process and the corresponding numerical results demonstrate the significant system energy savings, while several design issues and tradeoffs of the proposed scheme, such as the fairness of the mobile agents involved in the management activity, are discussed and evaluated

Automatic control program creation using concurrent Evolutionary Computing

Over the past decade, Genetic Programming (GP) has been the subject of a significant amount of research, but this has resulted in the solution of few complex real -world problems. In this work, I propose that, for some relatively simple, non safety -critical embedded control applications, GP can be used as a practical alternative to software developed by humans. Embedded control software has become a branch of software engineering with distinct temporal, interface and resource constraints and requirements. This results in a characteristic software structure, and by examining this, the effective decomposition of an overall problem into a number of smaller, simpler problems is performed. It is this type of problem amelioration that is suggested as a method whereby certain real -world problems may be rendered into a soluble form suitable for GP. In the course of this research, the body of published GP literature was examined and the most important changes to the original GP technique of Koza are noted; particular focus is made upon GP techniques involving an element of concurrency -which is central to this work. This search highlighted few applications of GP for the creation of software for complex, real -world problems -this was especially true in the case of multi thread, multi output solutions. To demonstrate this Idea, a concurrent Linear GP (LGP) system was built that creates a multiple input -multiple output solution using a custom low -level evolutionary language set, combining both continuous and Boolean data types. The system uses a multi -tasking model to evolve and execute the required LGP code for each system output using separate populations: Two example problems -a simple fridge controller and a more complex washing machine controller are described, and the problems encountered and overcome during the successful solution of these problems, are detailed. The operation of the complete, evolved washing machine controller is simulated using a graphical LabVIEWapplication. The aim of this research is to propose a general purpose system for the automatic creation of control software for use in a range of problems from the target problem class -without requiring any system tuning: In order to assess the system search performance sensitivity, experiments were performed using various population and LGP string sizes; the experimental data collected was also used to examine the utility of abandoning stalled searches and restarting. This work is significant because it identifies a realistic application of GP that can ease the burden of finite human software design resources, whilst capitalising on accelerating computing potential

Measurements, Models, Systems and Design

531 s.

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Evolutionary computing driven search based software testing and correction

For a given program, testing, locating the errors identified, and correcting those errors is a critical, yet expensive process. The field of Search Based Software Engineering (SBSE) addresses these phases by formulating them as search problems. This dissertation addresses these challenging problems through the use of two complimentary evolutionary computing based systems. The first one is the Fitness Guided Fault Localization (FGFL) system, which novelly uses a specification based fitness function to perform fault localization. The second is the Coevolutionary Automated Software Correction (CASC) system, which employs a variety of evolutionary computing techniques to perform testing, correction, and verification of software. In support of the real world application of these systems, a practitioner\u27s guide to fitness function design is provided. For the FGFL system, experimental results are presented that demonstrate the applicability of fitness guided fault localization to automate this important phase of software correction in general, and the potential of the FGFL system in particular. For the fitness function design guide, the performance of a guide generated fitness function is compared to that of an expert designed fitness function demonstrating the competitiveness of the guide generated fitness function. For the CASC system, results are presented that demonstrate the system\u27s abilities on a series of problems of both increasing size as well as number of bugs present. The system presented solutions more than 90% of the time for versions of the programs containing one or two bugs. Additionally, scalability results are presented for the CASC system that indicate that success rate linearly decreases with problem size and that the estimated convergence rate scales at worst linearly with problem size --Abstract, page ii

Protection and fault location schemes suited to large-scale multi-vendor high voltage direct current grids

Recent developments in voltage source converter (VSC) technology have led to an increased interest in high voltage direct current (HVDC) transmission to support the integration of massive amounts of renewable energy sources (RES) and especially, offshore wind energy. VSC-based HVDC grids are considered to be the natural evolution of existing point-to-point links and are expected to be one of the key enabling technologies towards expediting the integration and better utilisation of offshore energy, dealing with the variable nature of RES, and driving efficient energy balance over wide areas and across countries. Despite the technological advancements and the valuable knowledge gained from the operation of the already built multi-terminal systems, there are several outstanding issues that need to be resolved in order to facilitate the deployment of large-scale meshed HVDC grids. HVDC protection is of utmost importance to ensure the necessary reliability and security of HVDC grids, yet very challenging due to the fast nature of development of DC faults and the abrupt changes they cause in currents and voltages that may damage the system components. This situation is further exacerbated in highly meshed networks, where the effects of a DC fault on a single component (e.g. DC cable) can quickly propagate across the entire HVDC grid. To mitigate the effect of DC faults in large-scale meshed HVDC grids, fast and fully selective approaches using dedicated DC circuit breaker and protection relays are required. As the speed of DC fault isolation is one order of magnitude faster than typical AC protection (i.e. less than 10 ms), there is a need for the development of innovative approaches to system protection, including the design and implementation of more advanced protection algorithms. Moreover, in a multi-vendor environment (in which different or the same type of equipment is supplied by various manufacturers), the impact of the grid elements on the DC fault signature may differ considerably from case to case, thus increasing the complexity of designing reliable protection algorithms for HVDC grids. Consequently, there is a need for a more fundamental approach to the design and development of protection algorithms that will enable their general applicability. Furthermore, following successful fault clearance, the next step is to pinpoint promptly the exact location of the fault along the transmission medium in an effort to expedite inspection and repair time, reduce power outage time and elevate the total availability of the HVDC grid. Successful fault location becomes increasingly challenging in HVDC grids due to the short time windows between fault inception and fault clearance that limit the available fault data records that may be utilised for the execution of fault location methods. This thesis works towards the development of protection and fault location solutions, designed specifically for application in large-scale multi-vendor HVDC grids. First, a methodology is developed for the design of travelling wave based non-unit protection algorithms that can be easily configured for any grid topology and parameters. Second, using this methodology, a non-unit protection algorithm based on wavelet transform is developed that ensures fast, discriminative and enhanced protection performance. Besides offline simulations, the efficacy of the wavelet transform based algorithm is also demonstrated by means of real-time simulation, thereby removing key technical barriers that have impeded the use of wavelet transform in practical protection applications. Third, in an effort to reinforce the technical and economic feasibility of future HVDC grids, a thorough fault management strategy is presented for systems that employ efficient modular multilevel converters with partial fault tolerant capability. Finally, a fault location scheme is developed for accurately estimating the fault location in HVDC grids that are characterised by short post-fault data windows due to the utilisation of fast acting protection systems.Recent developments in voltage source converter (VSC) technology have led to an increased interest in high voltage direct current (HVDC) transmission to support the integration of massive amounts of renewable energy sources (RES) and especially, offshore wind energy. VSC-based HVDC grids are considered to be the natural evolution of existing point-to-point links and are expected to be one of the key enabling technologies towards expediting the integration and better utilisation of offshore energy, dealing with the variable nature of RES, and driving efficient energy balance over wide areas and across countries. Despite the technological advancements and the valuable knowledge gained from the operation of the already built multi-terminal systems, there are several outstanding issues that need to be resolved in order to facilitate the deployment of large-scale meshed HVDC grids. HVDC protection is of utmost importance to ensure the necessary reliability and security of HVDC grids, yet very challenging due to the fast nature of development of DC faults and the abrupt changes they cause in currents and voltages that may damage the system components. This situation is further exacerbated in highly meshed networks, where the effects of a DC fault on a single component (e.g. DC cable) can quickly propagate across the entire HVDC grid. To mitigate the effect of DC faults in large-scale meshed HVDC grids, fast and fully selective approaches using dedicated DC circuit breaker and protection relays are required. As the speed of DC fault isolation is one order of magnitude faster than typical AC protection (i.e. less than 10 ms), there is a need for the development of innovative approaches to system protection, including the design and implementation of more advanced protection algorithms. Moreover, in a multi-vendor environment (in which different or the same type of equipment is supplied by various manufacturers), the impact of the grid elements on the DC fault signature may differ considerably from case to case, thus increasing the complexity of designing reliable protection algorithms for HVDC grids. Consequently, there is a need for a more fundamental approach to the design and development of protection algorithms that will enable their general applicability. Furthermore, following successful fault clearance, the next step is to pinpoint promptly the exact location of the fault along the transmission medium in an effort to expedite inspection and repair time, reduce power outage time and elevate the total availability of the HVDC grid. Successful fault location becomes increasingly challenging in HVDC grids due to the short time windows between fault inception and fault clearance that limit the available fault data records that may be utilised for the execution of fault location methods. This thesis works towards the development of protection and fault location solutions, designed specifically for application in large-scale multi-vendor HVDC grids. First, a methodology is developed for the design of travelling wave based non-unit protection algorithms that can be easily configured for any grid topology and parameters. Second, using this methodology, a non-unit protection algorithm based on wavelet transform is developed that ensures fast, discriminative and enhanced protection performance. Besides offline simulations, the efficacy of the wavelet transform based algorithm is also demonstrated by means of real-time simulation, thereby removing key technical barriers that have impeded the use of wavelet transform in practical protection applications. Third, in an effort to reinforce the technical and economic feasibility of future HVDC grids, a thorough fault management strategy is presented for systems that employ efficient modular multilevel converters with partial fault tolerant capability. Finally, a fault location scheme is developed for accurately estimating the fault location in HVDC grids that are characterised by short post-fault data windows due to the utilisation of fast acting protection systems

Hexarray: A Novel Self-Reconfigurable Hardware System

Evolvable hardware (EHW) is a powerful autonomous system for adapting and finding solutions within a changing environment. EHW consists of two main components: a reconfigurable hardware core and an evolutionary algorithm. The majority of prior research focuses on improving either the reconfigurable hardware or the evolutionary algorithm in place, but not both. Thus, current implementations suffer from being application oriented and having slow reconfiguration times, low efficiencies, and less routing flexibility. In this work, a novel evolvable hardware platform is proposed that combines a novel reconfigurable hardware core and a novel evolutionary algorithm. The proposed reconfigurable hardware core is a systolic array, which is called HexArray. HexArray was constructed using processing elements with a redesigned architecture, called HexCells, which provide routing flexibility and support for hybrid reconfiguration schemes. The improved evolutionary algorithm is a genome-aware genetic algorithm (GAGA) that accelerates evolution. Guided by a fitness function the GAGA utilizes context-aware genetic operators to evolve solutions. The operators are genome-aware constrained (GAC) selection, genome-aware mutation (GAM), and genome-aware crossover (GAX). The GAC selection operator improves parallelism and reduces the redundant evaluations. The GAM operator restricts the mutation to the part of the genome that affects the selected output. The GAX operator cascades, interleaves, or parallel-recombines genomes at the cell level to generate better genomes. These operators improve evolution while not limiting the algorithm from exploring all areas of a solution space. The system was implemented on a SoC that includes a programmable logic (i.e., field-programmable gate array) to realize the HexArray and a processing system to execute the GAGA. A computationally intensive application that evolves adaptive filters for image processing was chosen as a case study and used to conduct a set of experiments to prove the developed system robustness. Through an iterative process using the genetic operators and a fitness function, the EHW system configures and adapts itself to evolve fitter solutions. In a relatively short time (e.g., seconds), HexArray is able to evolve autonomously to the desired filter. By exploiting the routing flexibility in the HexArray architecture, the EHW has a simple yet effective mechanism to detect and tolerate faulty cells, which improves system reliability. Finally, a mechanism that accelerates the evolution process by hiding the reconfiguration time in an “evolve-while-reconfigure” process is presented. In this process, the GAGA utilizes the array routing flexibility to bypass cells that are being configured and evaluates several genomes in parallel