Wireless sensor networks (WSN) increasingly permeate modern societies nowadays. But in spite of their plethora of successful applications, WSN are often unable to surmount many operational challenges that unexpectedly arise during their lifetime. Fortunately, robotic agents can now assist a WSN in various ways. This thesis illustrates how mobile robots which are able to carry a limited number of sensors can help the network react to sensor faults, either during or after its deployment in the monitoring region. Two scenarios are envisioned. In the first one, carrier robots surround a point of interest with multiple sensor layers (focused coverage formation). We put forward the first known algorithm of its kind in literature. It is energy-efficient, fault-reactive and aware of the bounded robot cargo capacity. The second one is that of replacing damaged sensing units with spare, functional ones (coverage repair), which gives rise to the formulation of two novel combinatorial optimization problems. Three nature-inspired metaheuristic approaches that run at a centralized location are proposed. They are able to find good-quality solutions in a short time. Two frameworks for the identification of the damaged nodes are considered. The first one leans upon diagnosable systems, i.e. existing distributed detection models in which individual units perform tests upon each other. Two swarm intelligence algorithms are designed to quickly and reliably spot faulty sensors in this context. The second one is an evolving risk management framework for WSNs that is entirely formulated in this thesis.

Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: In the current economic climate where credit crises, fluctuating commodity prices, poor governance, rising unemployment and declining consumer spending exist, risk management is of utmost importance. Proclaiming the existence of a risk management strategy is not enough to ensure that an enterprise achieves its objectives. The implementation of a holistic enterprise-wide risk management framework is required in order to execute strategies and achieve objectives effectively and efficiently Two types of risk management have emerged in industry, namely quantitative and qualitative risk management. On the one hand, qualitative analysis of risk can be done quickly and with minimal effort. However, these methods rely on the opinion of an individual or group of individuals to analyse the risks. The process may be highly subjective and does not fully consider the characteristics of the enterprise. This renders qualitative risk analysis as an ineffective singular strategy although it has been shown to be effective when the risks are well understood. Quantitative analysis, on the other hand, is particularly effective when the risks are not well understood. These methods have been shown to provide substantially more information regarding risks compared to qualitative analysis. However, many quantitative risk management methods presented in literature are studied in isolation and not within the context of a holistic risk management process. Furthermore, quantitative methods tend to be complex in nature and require a reasonable understanding of mathematical and statistical concepts in order to be used effectively. In view of this, there is a need for an enterprise risk management framework that emphasises the use of qualitative methods when the risks are well understood and quantitative methods when in-depth analyses of the risks are required. In this study, a systematic enterprise-wide risk management framework that incorporates both quantitative and qualitative methods was developed. The framework integrates these methods in a logical and holistic manner. The quantitative methods were found be to be largely practical while the qualitative methods presented are simple and easy to understand. / AFRIKAANSE OPSOMMING: In die huidige ekonomiese klimaat waar krediet krisisse, wisselende kommoditeitspryse, swak bestuur, stygende werkloosheid en dalende verbruikersbesteding bestaan, is risikobestuur van die uiterste belang. Die verkondiging van die bestaan van 'n risiko bestuurstrategie is nie genoeg om te verseker dat 'n onderneming sy doelwitte bereik nie. Die implementering van 'n holistiese ondernemings- breë risikobestuursraamwerk is nodig om strategieë en doelwitte doeltreffend en effektief te bereik. Twee tipe risikobestuur het na vore gekom in die bedryf, naamlik kwantitatiewe en kwalitatiewe risikobestuur. Aan die een kant , kan kwalitatiewe ontleding van risiko vinnig en met minimale inspanning gedoen word. Hierdie metode is gewoontlik die mening van 'n individu of 'n groep individue wat die risiko ontleed. Die proses kan hoogs subjektief wees en nie ten volle die eienskappe van die onderneming in ag neem nie. Kwalitatiewe risiko-analise kan dan gesien word as 'n ondoeltreffende enkelvoud strategie maar dit is wel doeltreffend wanneer daar verstaan word wat die onderneming se risiko is. Kwantitatiewe analise, aan die ander kant, is veral effektief wanneer die risiko's nie goed verstaanbaar is nie. Hierdie metode het getoon dat daar aansienlik meer inligting oor die risiko's, in vergelyking met kwalitatiewe ontleding, verskaf word. Daar is egter baie kwantitatiewe risikobestuur metodes wat in literatuur verskaf word, wat in isolasie bestudeer word en nie binne die konteks van 'n holistiese risikobestuur proses nie. Verder is, kwantitatiewe metodes geneig om kompleks van aard te wees en vereis 'n redelike begrip van wiskundige en statistiese konsepte sodat kwantitatiewe analise effektief kan wees. In lig hiervan, is daar 'n sterk behoefte vir 'n onderneming om 'n risikobestuursraamwerk in plek te het. Die risikobestuursraamwerk sal beide die gebruik van kwalitatiewe metodes, wanneer die risiko goed verstaan word, en kwantitatiewe metodes, wanneer daar in diepte-ontledings van die risiko is, beklemtoon. In hierdie studie was 'n sistematiese onderneming-breë risikobestuursraamwerk ontwikkel wat beide kwantitatiewe en kwalitatiewe metodes insluit. Die raamwerk integreer hierdie metodes in 'n logiese en holistiese wyse. Die kwantitatiewe metodes is gevind om grootliks prakties te wees, terwyl die kwalitatiewe metodes wat aangebied word, eenvoudig en maklik is om te verstaan.

Wireless sensor networks (WSN) increasingly permeate modern societies nowadays. But in spite of their plethora of successful applications, WSN are often unable to surmount many operational challenges that unexpectedly arise during their lifetime. Fortunately, robotic agents can now assist a WSN in various ways. This thesis illustrates how mobile robots which are able to carry a limited number of sensors can help the network react to sensor faults, either during or after its deployment in the monitoring region. Two scenarios are envisioned. In the first one, carrier robots surround a point of interest with multiple sensor layers (focused coverage formation). We put forward the first known algorithm of its kind in literature. It is energy-efficient, fault-reactive and aware of the bounded robot cargo capacity. The second one is that of replacing damaged sensing units with spare, functional ones (coverage repair), which gives rise to the formulation of two novel combinatorial optimization problems. Three nature-inspired metaheuristic approaches that run at a centralized location are proposed. They are able to find good-quality solutions in a short time. Two frameworks for the identification of the damaged nodes are considered. The first one leans upon diagnosable systems, i.e. existing distributed detection models in which individual units perform tests upon each other. Two swarm intelligence algorithms are designed to quickly and reliably spot faulty sensors in this context. The second one is an evolving risk management framework for WSNs that is entirely formulated in this thesis.

In a seemingly ever faster moving world where global competition is rising, companies has to find their competitive advantage. This advantage could be by offering a lower price on similar products or for instance by offering a superior product. What makes a product superior in comparison with the competitor’s products and how can the company maintain its competitive advantage. One of the main solutions for this dilemma is to be more innovative than the competitors and thereby gaining the competitive advantage. Becoming innovative doesn’t mean relying on sheer luck; instead the company has to adapt the proper innovation management. This thesis aims to research and suggest how a conceptual innovation management framework could be modelled for a large technology heavy organization. The findings of applicable theories together with the empirical study clearly show that in order for a large technology heavy company to improve its innovativeness it has to act under an innovation management framework, where innovation strategy and designated roles are clearly stated.

The traditional workplace evolved over time, moving through different waves or phases of change. The phases, although prevalent in different stages in different countries, have been characterised by various technological developments. The virtual workplace evolved as part of the Information Age or Fifth Wave, bringing with it its own unique characteristics and requirements. These unique characteristics, such as the speed at which information is communicated, crossing of time and space boundaries, social networks and requirements, such as sustainable high speed internet connectivity, are evident in the relationship between people, processes and the virtual workplace. This study explored the manner in which virtual workers executed their activities through the use of technology, the type of business processes supporting them and the challenges experienced by them. The proposed framework for the management of people, processes and places in the virtual workplace was subsequently derived. The framework has as aim to represent the relationship between people, processes and places components in the virtual workplace and in doing so providing a management framework supporting the virtual workplace. The people, processes and places components have been incorporated in the management, technology and practice sections of the framework, thereby providing a framework based on the relationship between people, processes and places. This study also developed the Extended Hermeneutic Circle of Learning which was used as guideline for the research conducted as part of this thesis. The deeper understanding created through the use of this research guideline assisted in providing structure to the research, thus enabling the researcher to derive the proposed framework for the management of people, processes and placed in the virtual workplace. / Thesis (PhD)--University of Pretoria, 2010. / Informatics / unrestricted

Next Generation Networks (NGNs) aim to provide a unified network infrastructure to offer multimedia data and telecommunication services through IP convergence. NGNs utilize multiple broadband, QoS-enabled transport technologies, creating a converged packet-switched network infrastructure, where service-related functions are separated from the transport functions. This requires significant changes in the way how networks are managed to handle the complexity and heterogeneity of NGNs. This thesis proposes a Service Oriented Architecture (SOA) based management framework that integrates heterogeneous management systems in a loose coupling manner. The key benefit of the proposed management architecture is the reduction of the complexity through service and data integration. A network management middleware layer that merges low level management functionality with higher level management operations to resolve the problem of heterogeneity was proposed. A prototype was implemented using Web Services and a testbed was developed using trouble ticket systems as the management application to demonstrate the functionality of the proposed framework. Test results show the correcting functioning of the system. It also concludes that the proposed framework fulfils the principles behind the SOA philosophy.

The project aims to propose MAPE based hybrid elasticity management framework on the basis of valuable insights accrued during systematic analysis of relevant literature. Each stage of MAPE process acts independently as a black box in proposed framework, while dealing with neighbouring stages. Thus, being modular in nature; underlying algorithms in any of the stage can be replaced with more suitable ones, without affecting any other stage. The hybrid framework enables proactive and reactive autoscaling approaches to be implemented simultaneously within same system. Proactive approach is incorporated as a core decision making logic on the basis of forecast data, while reactive approach being based upon actual data would act as a damage control measure; activated only in case of any problem with proactive approach. Thus, benefits of both the worlds; pre-emption as well as reliability can be achieved through proposed framework. It uses time series analysis (moving average method / exponential smoothing) and threshold based static rules (with multiple monitoring intervals and dual threshold settings) during analysis and planning phases of MAPE loop, respectively. Mathematical illustration of the framework incorporates multiple parameters namely VM initiation delay / release criterion, network latency, system oscillations, threshold values, smart kill etc. The research concludes that recommended parameter settings primarily depend upon certain autoscaling objective and are often conflicting in nature. Thus, no single autoscaling system with similar values can possibly meet all objectives simultaneously, irrespective of reliability of an underlying framework. The project successfully implements complete cloud infrastructure and autoscaling environment over experimental platforms i-e OpenStack and CloudSim Plus. In nutshell, the research provides solid understanding of autoscaling phenomenon, devises MAPE based hybrid elasticity management framework and explores its implementation potential over OpenStack and CloudSim Plus.

Think tanks are identified as policy research institutions that conduct interdisciplinary research on social or policy issues and provide consultation with the government, enterprises and the general public. Civil think tanks place social value prior to profits and uphold research independence. Through producing research content on social issues, civil think tanks make contribution with publications, advocacy, and action promotion. Civil think tanks are facing challenging complexity and obstacles in development due to their independence from governments and universities. To resolve these difficulties, civil think tanks require an innovative business model and a matching management mechanism. This thesis aims to understand the business model performed by civil think tanks and explore a practical management model to support this business. Specific recommendations will be made to the case organization. The case study was performed in collaboration with Youthink Center, one of China’s leading civil think tanks which provides young people with a platform to learn, take advocacy, and action on global frontier issues about sustainable development. A systematic literature review was conducted to elaborate concepts connected to think tanks and understand existing management frameworks. Data regarding this research project was collected through internal and external interviews and documentations.The case-study resulted in an evaluation on the business model of Youthink Center and a proposed management framework in accordance with the business. Key aspects of succeeding in a think tank are to insist on the value proposition of social responsibility and to enhance content creation and influence expansion ability with digital tools. To make this business model operate effectively, think tanks are expected to focus on project management processes, talent gathering, evaluation mechanism, and deeper collaborations on knowledge and technology. Understanding these areas will guide the social think tanks to increase working efficiency and enhance social influence. / Tankesmedjor (think tanks) identifieras som politiska forskningsinstitutioner som bedriver tvärvetenskaplig forskning om sociala eller politiska frågor och erbjuder samråd med regeringen, företag och allmänheten. Civila tankesmedjor lägger socialt värde före vinst och upprätthåller forskningsoberoende. Genom att producera forskningsinnehåll om sociala frågor bidrar civila tankesmedjor med publikationer, förespråkande och handlingsfrämjande. Civila tankesmedjor står inför utmanande komplexitet och hinder i utvecklingen på grund av deras oberoende från regeringar och universitet. För att lösa dessa svårigheter kräver civila tankesmedjor en innovativ affärsmodell och en matchande hanteringsmekanism. Denna uppsats syftar till att förstå den affärsmodell som utförs av civila tankesmedjor och utforska en praktisk ledningsmodell för att stödja denna verksamhet. Specifika rekommendationer kommer att ges till fallorganisationen. Fallstudien utfördes i samarbete med Youthink Center, en av Kinas ledande civila tankesmedjor som ger ungdomar en plattform för att lära sig, ta förtal och agera i globala gränsfrågor om hållbar utveckling. En systematisk litteraturgranskning genomfördes för att utarbeta begrepp kopplade till tankesmedjor och förstå befintliga ledningsramar. Data om detta forskningsprojekt samlades in genom interna och externa intervjuer och dokument. Fallstudien resulterade i en utvärdering av affärsmodellen för Youthink Center och en föreslagen ledningsram i enlighet med verksamheten. Nyckelaspekter för att lyckas med en tankesmedja är att insistera på värdet av socialt ansvar och att förbättra innehållsskapandet och påverka expansionsförmågan med digitala verktyg. För att få denna affärsmodell att fungera effektivt förväntas tankesmedjor att fokusera på projektledningsprocesser, talangsamling, utvärderingsmekanismer och djupare samarbete om kunskap och teknik. Att förstå dessa områden kommer att vägleda de sociala tankesmedjorna för att öka arbetseffektiviteten och förbättra det sociala inflytandet.

Software-Defined Networking (SDN) objetiva aliviar as limitações impostas por redes IP tradicionais dissociando tarefas de rede executadas em cada dispositivo em planos específicos. Esta abordagem oferece vários benefícios, tais como a possibilidade de uso de protocolos de comunicação padrão, funções de rede centralizadas, e elementos de rede mais específicos e modulares, tais como controladores de rede. Apesar destes benefícios, ainda há uma falta de apoio adequado para a realização de tarefas relacionadas à classificação de tráfego, pois (i) as características de fluxo nativas disponíveis no protocolo OpenFlow, tais como contadores de bytes e pacotes, não oferecem informação suficiente para distinguir de forma precisa fluxos específicos; (ii) existe uma falta de suporte para determinar qual é o conjunto ótimo de características de fluxo para caracterizar um dado perfil de tráfego; (iii) existe uma necessidade de estratégias flexíveis para compor diferentes mecanismos relacionados à detecção, classificação e mitigação de anomalias de rede usando abstrações de software; (iv) existe uma necessidade de monitoramento de tráfego em tempo real usando técnicas leves e de baixo custo; (v) não existe um framework capaz de gerenciar detecção, classificação e mitigação de anomalias de uma forma coordenada considerando todas as demandas acima. Adicionalmente, é sabido que mecanismos de detecção e classificação de anomalias de tráfego precisam ser flexíveis e fáceis de administrar, a fim de detectar o crescente espectro de anomalias. Detecção e classificação são tarefas difíceis por causa de várias razões, incluindo a necessidade de obter uma visão precisa e abrangente da rede, a capacidade de detectar a ocorrência de novos tipos de ataque, e a necessidade de lidar com erros de classificação. Nesta dissertação, argumentamos que SDN oferece ambientes propícios para a concepção e implementação de esquemas mais robustos e extensíveis para detecção e classificação de anomalias. Diferentemente de outras abordagens na literatura relacionada, que abordam individualmente detecção ou classificação ou mitigação de anomalias, apresentamos um framework para o gerenciamento e orquestração dessas tarefas em conjunto. O framework proposto é denominado ATLANTIC e combina o uso de técnicas com baixo custo computacional para monitorar tráfego e técnicas mais computacionalmente intensivas, porém precisas, para classificar os fluxos de tráfego. Como resultado, ATLANTIC é um framework flexível capaz de categorizar anomalias de tráfego utilizando informações coletadas da rede para lidar com cada perfil de tráfego de um modo específico, como por exemplo, bloqueando fluxos maliciosos. / Software-Defined Networking (SDN) aims to alleviate the limitations imposed by traditional IP networks by decoupling network tasks performed on each device in particular planes. This approach offers several benefits, such as standard communication protocols, centralized network functions, and specific network elements, such as controller devices. Despite these benefits, there is still a lack of adequate support for performing tasks related to traffic classification, because (i) the native flow features available in OpenFlow, such as packet and byte counts, do not convey sufficient information to accurately distinguish between some types of flows; (ii) there is a lack of support to determine what is the optimal set of flow features to characterize different types of traffic profiles; (iii) there is a need for a flexible way of composing different mechanisms to detect, classify and mitigate network anomalies using software abstractions; (iv) there is a need of online traffic monitoring using lightweight/low-cost techniques; (v) there is no framework capable of managing anomaly detection, classification and mitigation in a coordinated manner and considering all these demands. Additionally, it is well-known that anomaly traffic detection and classification mechanisms need to be flexible and easy to manage in order to detect the ever growing spectrum of anomalies. Detection and classification are difficult tasks because of several reasons, including the need to obtain an accurate and comprehensive view of the network, the ability to detect the occurrence of new attack types, and the need to deal with misclassification. In this dissertation, we argue that Software-Defined Networking (SDN) form propitious environments for the design and implementation of more robust and extensible anomaly classification schemes. Different from other approaches from the literature, which individually tackle either anomaly detection or classification or mitigation, we present a management framework to perform these tasks jointly. Our proposed framework is called ATLANTIC and it combines the use of lightweight techniques for traffic monitoring and heavyweight, but accurate, techniques to classify traffic flows. As a result, ATLANTIC is a flexible framework capable of categorizing traffic anomalies and using the information collected to handle each traffic profile in a specific manner, e.g., blocking malicious flows.

Software-Defined Networking (SDN) objetiva aliviar as limitações impostas por redes IP tradicionais dissociando tarefas de rede executadas em cada dispositivo em planos específicos. Esta abordagem oferece vários benefícios, tais como a possibilidade de uso de protocolos de comunicação padrão, funções de rede centralizadas, e elementos de rede mais específicos e modulares, tais como controladores de rede. Apesar destes benefícios, ainda há uma falta de apoio adequado para a realização de tarefas relacionadas à classificação de tráfego, pois (i) as características de fluxo nativas disponíveis no protocolo OpenFlow, tais como contadores de bytes e pacotes, não oferecem informação suficiente para distinguir de forma precisa fluxos específicos; (ii) existe uma falta de suporte para determinar qual é o conjunto ótimo de características de fluxo para caracterizar um dado perfil de tráfego; (iii) existe uma necessidade de estratégias flexíveis para compor diferentes mecanismos relacionados à detecção, classificação e mitigação de anomalias de rede usando abstrações de software; (iv) existe uma necessidade de monitoramento de tráfego em tempo real usando técnicas leves e de baixo custo; (v) não existe um framework capaz de gerenciar detecção, classificação e mitigação de anomalias de uma forma coordenada considerando todas as demandas acima. Adicionalmente, é sabido que mecanismos de detecção e classificação de anomalias de tráfego precisam ser flexíveis e fáceis de administrar, a fim de detectar o crescente espectro de anomalias. Detecção e classificação são tarefas difíceis por causa de várias razões, incluindo a necessidade de obter uma visão precisa e abrangente da rede, a capacidade de detectar a ocorrência de novos tipos de ataque, e a necessidade de lidar com erros de classificação. Nesta dissertação, argumentamos que SDN oferece ambientes propícios para a concepção e implementação de esquemas mais robustos e extensíveis para detecção e classificação de anomalias. Diferentemente de outras abordagens na literatura relacionada, que abordam individualmente detecção ou classificação ou mitigação de anomalias, apresentamos um framework para o gerenciamento e orquestração dessas tarefas em conjunto. O framework proposto é denominado ATLANTIC e combina o uso de técnicas com baixo custo computacional para monitorar tráfego e técnicas mais computacionalmente intensivas, porém precisas, para classificar os fluxos de tráfego. Como resultado, ATLANTIC é um framework flexível capaz de categorizar anomalias de tráfego utilizando informações coletadas da rede para lidar com cada perfil de tráfego de um modo específico, como por exemplo, bloqueando fluxos maliciosos. / Software-Defined Networking (SDN) aims to alleviate the limitations imposed by traditional IP networks by decoupling network tasks performed on each device in particular planes. This approach offers several benefits, such as standard communication protocols, centralized network functions, and specific network elements, such as controller devices. Despite these benefits, there is still a lack of adequate support for performing tasks related to traffic classification, because (i) the native flow features available in OpenFlow, such as packet and byte counts, do not convey sufficient information to accurately distinguish between some types of flows; (ii) there is a lack of support to determine what is the optimal set of flow features to characterize different types of traffic profiles; (iii) there is a need for a flexible way of composing different mechanisms to detect, classify and mitigate network anomalies using software abstractions; (iv) there is a need of online traffic monitoring using lightweight/low-cost techniques; (v) there is no framework capable of managing anomaly detection, classification and mitigation in a coordinated manner and considering all these demands. Additionally, it is well-known that anomaly traffic detection and classification mechanisms need to be flexible and easy to manage in order to detect the ever growing spectrum of anomalies. Detection and classification are difficult tasks because of several reasons, including the need to obtain an accurate and comprehensive view of the network, the ability to detect the occurrence of new attack types, and the need to deal with misclassification. In this dissertation, we argue that Software-Defined Networking (SDN) form propitious environments for the design and implementation of more robust and extensible anomaly classification schemes. Different from other approaches from the literature, which individually tackle either anomaly detection or classification or mitigation, we present a management framework to perform these tasks jointly. Our proposed framework is called ATLANTIC and it combines the use of lightweight techniques for traffic monitoring and heavyweight, but accurate, techniques to classify traffic flows. As a result, ATLANTIC is a flexible framework capable of categorizing traffic anomalies and using the information collected to handle each traffic profile in a specific manner, e.g., blocking malicious flows.