On Comparative Algorithmic Pathfinding in Complex Networks for Resource-Constrained Software Agents

Software engineering projects that utilize inappropriate pathfinding algorithms carry a significant risk of poor runtime performance for customers. Using social network theory, this experimental study examined the impact of algorithms, frameworks, and map complexity on elapsed time and computer memory consumption. The 1,800 2D map samples utilized were computer random generated and data were collected and processed using Python language scripts. Memory consumption and elapsed time results for each of the 12 experimental treatment groups were compared using factorial MANOVA to determine the impact of the 3 independent variables on elapsed time and computer memory consumption. The MANOVA indicated a significant factor interaction between algorithms, frameworks, and map complexity upon elapsed time and memory consumption, F(4, 3576) = 94.09, p \u3c .001, h2 = .095. The main effects of algorithms, F(4, 3576) = 885.68, p \u3c .001, h2 = .498; and frameworks, F(2, 1787) = 720,360.01, p .001, h2 = .999; and map complexity, F(2, 1787) = 112,736.40, p \u3c .001, h2 = .992, were also all significant. This study may contribute to positive social change by providing software engineers writing software for complex networks, such as analyzing terrorist social networks, with empirical pathfinding algorithm results. This is crucial to enabling selection of appropriately fast, memory-efficient algorithms that help analysts identify and apprehend criminal and terrorist suspects in complex networks before the next attack

Doctor of Philosophy

dissertationNetwork emulation has become an indispensable tool for the conduct of research in networking and distributed systems. It offers more realism than simulation and more control and repeatability than experimentation on a live network. However, emulation testbeds face a number of challenges, most prominently realism and scale. Because emulation allows the creation of arbitrary networks exhibiting a wide range of conditions, there is no guarantee that emulated topologies reflect real networks; the burden of selecting parameters to create a realistic environment is on the experimenter. While there are a number of techniques for measuring the end-to-end properties of real networks, directly importing such properties into an emulation has been a challenge. Similarly, while there exist numerous models for creating realistic network topologies, the lack of addresses on these generated topologies has been a barrier to using them in emulators. Once an experimenter obtains a suitable topology, that topology must be mapped onto the physical resources of the testbed so that it can be instantiated. A number of restrictions make this an interesting problem: testbeds typically have heterogeneous hardware, scarce resources which must be conserved, and bottlenecks that must not be overused. User requests for particular types of nodes or links must also be met. In light of these constraints, the network testbed mapping problem is NP-hard. Though the complexity of the problem increases rapidly with the size of the experimenter's topology and the size of the physical network, the runtime of the mapper must not; long mapping times can hinder the usability of the testbed. This dissertation makes three contributions towards improving realism and scale in emulation testbeds. First, it meets the need for realistic network conditions by creating Flexlab, a hybrid environment that couples an emulation testbed with a live-network testbed, inheriting strengths from each. Second, it attends to the need for realistic topologies by presenting a set of algorithms for automatically annotating generated topologies with realistic IP addresses. Third, it presents a mapper, assign, that is capable of assigning experimenters' requested topologies to testbeds' physical resources in a manner that scales well enough to handle large environments

Localised Routing Algorithms with Quality of Service Constraints. Development and performance evaluation by simulation of new localised Quality of Service routing algorithms for communication networks using residual bandwidth and mean end-to-end delay as metrics.

School of Computing, Informatics and MediaLocalised QoS routing is a relatively new, alternative and viable approach to solve the problems of traditional QoS routing algorithms which use global state information resulting in the imposition of a large communication overhead and route flapping. They make use of a localised view of the network QoS state in source nodes to select paths and route flows to destination nodes. Proportional Sticky Routing (PSR) and Credit Based Routing (CBR) have been proposed as localised QoS routing schemes and these can offer comparable performances. However, since network state information for a specific path is only updated when the path is used, PSR and CBR operate with decision criteria that are often stale for paths that are used infrequently. The aim of this thesis is to focus on localised QoS routing and contribute to enhancing the scalability of QoS routing algorithms. In this thesis we have developed three new localised QoS routing schemes which are called Score Based QoS Routing (SBR), Bandwidth Based QoS Routing (BBR) and Delay Based Routing (DBR). In some of these schemes, the path setup procedure is distributed and uses the current network state to make decisions thus avoiding problems of staleness. The methods also avoid any complicated calculations. Both SBR and BBR use bandwidth as the QoS metric and mean delay is used as the QoS metric in DBR. Extensive simulations are applied to compare the performance of our proposed algorithms with CBR and the global Dijkstra¿s algorithm for different update intervals of link state, different network topologies and using different flow arrival distributions under a wide range of traffic loads. It is demonstrated by simulation that the three proposed algorithms offer a superior performance under comparable conditions to the other localised and global algorithms

Data-driven flight path rerouting during adverse weather: Design and development of a passenger-centric model and framework for alternative flight path generation using nature inspired techniques

A major factor that negatively impacts flight operations globally is adverse weather. To reduce the impact of adverse weather, avoidance procedures such as finding an alternative flight path can usually be carried out. However, such procedures usually introduce extra costs such as flight delay. Hence, there exists a need for alternative flight paths that efficiently avoid adverse weather regions while minimising costs. Existing weather avoidance methods used techniques, such as Dijkstra’s and artificial potential field algorithms that do not scale adequately and have poor real time performance. They do not adequately consider the impact of weather and its avoidance on passengers. The contributions of this work include a new development of an improved integrated model for weather avoidance, that addressed the impact of weather on passengers by defining a corresponding cost metric. The model simultaneously considered other costs such as flight delay and fuel burn costs. A genetic algorithm (GA)-based rerouting technique that generates optimised alternative flight paths was proposed. The technique used a modified mutation strategy to improve global search. A discrete firefly algorithm-based rerouting method was also developed to improve rerouting efficiency. A data framework and simulation platform that integrated aeronautical, weather and flight data into the avoidance process was developed. Results show that the developed algorithms and model produced flight paths that had lower total costs compared with existing techniques. The proposed algorithms had adequate rerouting performance in complex airspace scenarios. The developed system also adequately avoided the paths of multiple aircraft in the considered airspace

Development of a decision support system through modelling of critical infrastructure interdependencies : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Critical Infrastructure (CI) networks provide functional services to support the wellbeing of a community. Although it is possible to obtain detailed information about individual CI and their components, the interdependencies between different CI networks are often implicit, hidden or not well understood by experts. In the event of a hazard, failures of one or more CI networks and their components can disrupt the functionality and consequently affect the supply of services. Understanding the extent of disruption and quantification of the resulting consequences is important to assist various stakeholders' decision-making processes to complete their tasks successfully. A comprehensive review of the literature shows that a Decision Support System (DSS) integrated with appropriate modelling and simulation techniques is a useful tool for CI network providers and relevant emergency management personnel to understand the network recovery process of a region following a hazard event. However, the majority of existing DSSs focus on risk assessment or stakeholders' involvement without addressing the overall CI interdependency modelling process. Furthermore, these DSSs are primarily developed for data visualization or CI representation but not specifically to help decision-makers by providing them with a variety of customizable decision options that are practically viable. To address these limitations, a Knowledge-centred Decision Support System (KCDSS) has been developed in this study with the following aims: 1) To develop a computer-based DSS using efficient CI network recovery modelling algorithms, 2) To create a knowledge-base of various recovery options relevant to specific CI damage scenarios so that the decision-makers can test and verify several ‘what-if’ scenarios using a variety of control variables, and 3) To bridge the gap between hazard and socio-economic modelling tools through a multidisciplinary and integrated natural hazard impact assessment. Driven by the design science research strategy, this study proposes an integrated impact assessment framework using an iterative design process as its first research outcome. This framework has been developed as a conceptual artefact using a topology network-based approach by adopting the shortest path tree method. The second research outcome, a computer-based KCDSS, provides a convenient and efficient platform for enhanced decision making through a knowledge-base consisting of real-life recovery strategies. These strategies have been identified from the respective decision-makers of the CI network providers through the Critical Decision Method (CDM), a Cognitive Task Analysis (CTA) method for requirement elicitation. The capabilities of the KCDSS are demonstrated through electricity, potable water, and road networks in the Wellington region of Aotearoa New Zealand. The network performance has been analysed independently and with interdependencies to generate outage of services spatially and temporally. The outcomes of this study provide a range of theoretical and practical contributions. Firstly, the topology network-based analysis of CI interdependencies will allow a group of users to build different models, make and test assumptions, and try out different damage scenarios for CI network components. Secondly, the step-by-step process of knowledge elicitation, knowledge representation and knowledge modelling of CI network recovery tasks will provide a guideline for improved interactions between researchers and decision-makers in this field. Thirdly, the KCDSS can be used to test the variations in outage and restoration time estimates of CI networks due to the potential uncertainty related to the damage modelling of CI network components. The outcomes of this study also have significant practical implications by utilizing the KCDSS as an interface to integrate and add additional capabilities to the hazard and socio-economic modelling tools. Finally, the variety of ‘what-if’ scenarios embedded in the KCDSS would allow the CI network providers to identify vulnerabilities in their networks and to examine various post-disaster recovery options for CI reinstatement projects

Mobi-System: towards an information system to support sustainable mobility with electric vehicle integration

Tese de doutoramento do Programa Doutoral em Líderes para as Indústrias Tecnológicas (Programa MIT-Portugal - Área EDAM)The current Thesis proposes the conceptual aspects and the preliminary prototype of a mobile information system to support information integration and manipulation towards the Electric Vehicle (EV) introduction, and the support of mobility process in urban environments, giving recommendations to drivers about EV range autonomy, charging stations, electricity market, and also as route planner taking into account public transportation, car or bike sharing systems. The main work objective is the creation of an Information and Communication Technology (ICT) platform based on successful approaches developed in the Computer Science Area, recommender systems, cooperative systems and mobile devices, to help the driver of EV by giving real time information related with EV charging process, range autonomy, electricity market participation, and also smart mobility process in cities by giving guidance towards best route options, taking into account time travel and CO2 emissions. Based on the analysis of the problem a conceptual system and a prototype application were created under the designation “Mobi-System”, designed to mobile devices, with relevant information oriented to: (1) EV charging process; (2) EV range autonomy; (3) electricity market participation; and (4) mobility process in smart cities of the future. In this work it was developed an application to store data related with EV charging/discharging process, for further intelligent analysis and remote interaction with the charging system, determining a smart charging procedure, taking into account the distribution electrical system limitations, and the creation of communities with participation in the electricity market. A range estimation and representation process is introduced as part of the help process to assist EV drivers. An Aggregator system and a collaborative broker for distributed energy sources are proposed, taking into account the electricity market. A proposal for data integration of different transportation sources and a multimodal best route path are proposed based on CO2 emissions and time travel.O presente trabalho consiste na concepção e discussão do sistema Mobi-System, que disponibiliza informação relevante para condutores de veículos elétricos (VE), tendo em conta os problemas dos carregamentos dos VE, a gestão da ansiedade de autonomia (range anxiety) dos condutores, a participação no mercado de energia elétrica, a integração das fontes de energia renováveis, bem como a integração de informação de transportes públicos e a criação de sistemas para gerir o problema da mobilidade sustentável em cidades inteligentes (smart cities). O objectivo principal do trabalho é o uso apropriado de Tecnologia da Informação e Comunicação (TIC) baseada em abordagens bem-sucedidas desenvolvidas na área da informática, como os sistemas de recomendação, sistemas cooperativos e dispositivos móveis para ajudar o condutor de VE, dando informações relevantes em tempo real, orientando o condutor para os pontos de carregamento públicos, ou para o melhor caminho tendo em conta o tempo e as políticas ambientais, nomeadamente as emissões de CO2. Com base na análise do problema, um sistema conceitual e uma aplicação protótipo foram criadas sob a designação de Mobi-System, projetada para dispositivos móveis com informações relevantes orientadas a: (1) processo de carregamento do VE feito num local público com a orientação e a reserva de slots de carregamento, ou em casa com a programação do processo de carregamento lento, tendo em conta limitações de potência; (2) gestão assistida da autonomia dos VE; (3) participação no mercado de energia, pela criação de comunidades de condutores com capacidade de participar no mercado de energia, dado o VE poder atuar como um armazenador de energia; e (4) processo de mobilidade em cidades inteligentes do futuro, com a proposta de integração de dados de diferentes tipos de transporte, com indicação do trajeto de melhor rota multimodal, proposto com base nas emissões de CO2 e no tempo das viagens

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Determining Additional Modulus of Subgarde Reaction Based on Tolerable Settlement for the Nailed-slab System Resting on Soft Clay.

Abstract—Nailed-slab System is a proposed alternative solution for rigid pavement problem on soft soils. Equivalent modulus of subgrade reaction (k’) can be used in designing of nailed-slab system. This modular is the cumulative of modulus of subgrade reaction from plate load test (k) and additional modulus of subgrade reaction due to pile installing (∆∆∆∆k). A recent method has used reduction of pile resistance approach in determining ∆∆∆∆k. The relative displacement between pile and soils, and reduction of pile resistance has been identified. In fact, determining of reduction of pile resistance is difficult. This paper proposes an approach by considering tolerable settlement of rigid pavement. Validation is carried out with respect to a loading test of nailed-slab models. The models are presented as strip section of rigid pavement. The theory of beams on elastic foundation is used to calculate the slab deflection by using k’. Proposed approach can results in deflection prediction close to observed one. In practice, the Nailed-slab System would be constructed by multiple-row piles. Designing this system based on one-pile row analysis will give more safety design and will consume less time

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Objective Validation of Airport Terminal Architecture using Agent-based Simulations

This thesis explores how airport terminal architecture is tested before it is built. The purpose of testing is to make sure an architectural layout aligns with the rest of the airport’s systems. The design of a terminal is a long and expensive process that must accommodate tens of thousands of passengers every hour, the movement of logistics, and control of security. Evaluating spaces for that many people can be difficult to measure, which can result in architects relying on their intuition and experience to judge the impact of a layout for daily operations without objective validation. It is not practical for designers to build a complete airport to see how it works and make renovations after finding aspects that have poor performance. As a result, testing airports requires using mathematical models and simulations to validate how well different systems work together. Designers try to validate architectural layouts in airport terminals by using crowd simulations to approximate passenger behaviour. Existing research in civil engineering and computer science has shown how mathematical models can predict patterns of human activity in the built environment on a large scale. However, these simulations have primarily focused on either modelling passengers as a process flow or people in emergency building evacuation. As a result, existing agent navigation does not consider how passengers use the surrounding architecture for decision-making during daily airport interactions. When passengers enter a terminal for the first time, they can be unaware of what they need to do or how to get there. Instead, passengers rely on using their perception of the environment (the architecture) to inform them what to do. However, there currently are no methods that incorporate architectural perception to validate a building layout in these conditions. This thesis develops an agent-based simulation to validate how well architectural layouts align with the daily operations of an airport terminal. It quantifies the value of a spatial arrangement as a function of people’s interactions in a given space. The model approximates human behaviour based on statistics from existing crowd simulations. It uses spatial analysis, like the isovist and graph theory, for agent navigation and measuring architectural conditions. The proposal incorporates agent perception to provide feedback between people’s decision-making and the influence of the surrounding space. The thesis calculates architectural value using normalized passenger priorities based on typical processing and non-processing airport domains. The success of a terminal layout is dependent on the agent’s ability to complete airport processing and fulfill their priorities. The final value of an architectural layout is determined using statistical methods to provide a probability distribution of likely values. The proposed agent simulation and mathematical models are built using Unity software, which is used to perform several simulation tests in this thesis. Basic functional components of the simulation are validated using existing crowd modelling standards. Tests are also performed to illustrate how different agent perception and priorities influence the value of architectural spaces. Monte Carlo simulations are created for simple terminal layouts to illustrate how changing the floor plan of a security area affects the architectural value for departing passengers. Finally, the architectural values of two real airport terminals are compared against an established passenger experience survey in a basic simulation model. The results of the testing shows that the agent simulation can differentiate between different architectural conditions, within reason, depending on the passengers’ priorities