Admission Control Optimisation for QoS and QoE Enhancement in Future Networks

Recent exponential growth in demand for traffic heterogeneity support and the number of associated devices has considerably increased demand for network resources and induced numerous challenges for the networks, such as bottleneck congestion, and inefficient admission control and resource allocation. Challenges such as these degrade network Quality of Service (QoS) and user-perceived Quality of Experience (QoE). This work studies admission control from various perspectives. For example, two novel single-objective optimisation-based admission control models, Dynamica Slice Allocation and Admission Control (DSAAC) and Signalling and Admission Control (SAC), are presented to enhance future limited-capacity network Grade of Service (GoS), and for control signalling optimisation, respectively. DSAAC is an integrated model whereby a cost-estimation function based on user demand and network capacity quantifies resource allocation among users. Moreover, to maximise resource utility, adjustable minimum and maximum slice resource bounds have also been derived. In the case of user blocking from the primary slice due to congestion or resource scarcity, a set of optimisation algorithms on inter-slice admission control and resource allocation and adaptability of slice elasticity have been proposed. A novel SAC model uses an unsupervised learning technique (i.e. Ranking-based clustering) for optimal clustering based on users’ homogeneous demand characteristics to minimise signalling redundancy in the access network. The redundant signalling reduction reduces the additional burden on the network in terms of unnecessary resource utilisation and computational time. Moreover, dynamically reconfigurable QoE-based slice performance bounds are also derived in the SAC model from multiple demand characteristics for clustered user admission to the optimal network. A set of optimisation algorithms are also proposed to attain efficient slice allocation and users’ QoE enhancement via assessing the capability of slice QoE elasticity. An enhancement of the SAC model is proposed through a novel multi-objective optimisation model named Edge Redundancy Minimisation and Admission Control (E-RMAC). A novel E-RMAC model for the first time considers the issue of redundant signalling between the edge and core networks. This model minimises redundant signalling using two classical unsupervised learning algorithms, K-mean and Ranking-based clustering, and maximises the efficiency of the link (bandwidth resources) between the edge and core networks. For multi-operator environments such as Open-RAN, a novel Forecasting and Admission Control (FAC) model for tenant-aware network selection and configuration is proposed. The model features a dynamic demand-estimation scheme embedded with fuzzy-logic-based optimisation for optimal network selection and admission control. FAC for the first time considers the coexistence of the various heterogeneous cellular technologies (2G, 3G,4G, and 5G) and their integration to enhance overall network throughput by efficient resource allocation and utilisation within a multi-operator environment. A QoS/QoE-based service monitoring feature is also presented to update the demand estimates with the support of a forecasting modifier. he provided service monitoring feature helps resource allocation to tenants, approximately closer to the actual demand of the tenants, to improve tenant-acquired QoE and overall network performance. Foremost, a novel and dynamic admission control model named Slice Congestion and Admission Control (SCAC) is also presented in this thesis. SCAC employs machine learning (i.e. unsupervised, reinforcement, and transfer learning) and multi-objective optimisation techniques (i.e. Non-dominated Sorting Genetic Algorithm II ) to minimise bottleneck and intra-slice congestion. Knowledge transfer among requests in form of coefficients has been employed for the first time for optimal slice requests queuing. A unified cost estimation function is also derived in this model for slice selection to ensure fairness among slice request admission. In view of instantaneous network circumstances and load, a reinforcement learning-based admission control policy is established for taking appropriate action on guaranteed soft and best-effort slice requests admissions. Intra-slice, as well as inter-slice resource allocation, along with the adaptability of slice elasticity, are also proposed for maximising slice acceptance ratio and resource utilisation. Extensive simulation results are obtained and compared with similar models found in the literature. The proposed E-RMAC model is 35% superior at reducing redundant signalling between the edge and core networks compared to recent work. The E-RMAC model reduces the complexity from O(U) to O(R) for service signalling and O(N) for resource signalling. This represents a significant saving in the uplink control plane signalling and link capacity compared to the results found in the existing literature. Similarly, the SCAC model reduces bottleneck congestion by approximately 56% over the entire load compared to ground truth and increases the slice acceptance ratio. Inter-slice admission and resource allocation offer admission gain of 25% and 51% over cooperative slice- and intra-slice-based admission control and resource allocation, respectively. Detailed analysis of the results obtained suggests that the proposed models can efficiently manage future heterogeneous traffic flow in terms of enhanced throughput, maximum network resources utilisation, better admission gain, and congestion control

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Development of a sustainable groundwater management strategy and sequential compliance monitoring to control saltwater intrusion in coastal aquifers

The coastal areas of the world are characterized by high population densities, an abundance of food, and increased economic activities. These increasing human settlements, subsequent increases in agricultural developments and economic activities demand an increasing amount quantity of freshwater supplies to different sectors. Groundwater in coastal aquifers is one of the most important sources of freshwater supplies. Over exploitation of this coastal groundwater resource results in seawater intrusion and subsequent deterioration of groundwater quality in coastal aquifers. In addition, climate change induced sea level rise, in combination with the effect of excessive groundwater extraction, can accelerate the seawater intrusion. Adequate supply of good quality water to different sectors in coastal areas can be ensured by adoption of a proper management strategy for groundwater extraction. Optimal use of the coastal groundwater resource is one of the best management options, which can be achieved by employing a properly developed optimal groundwater extraction strategy. Coupled simulation-optimization (S-O) approaches are essential tools to obtain the optimal groundwater extraction patterns. This study proposes approaches for developing multiple objective management of coastal aquifers with the aid of barrier extraction wells as hydraulic control measure of saltwater intrusion in multilayered coastal aquifer systems. Therefore, two conflicting objectives of management policy are considered in this research, i.e. maximizing total groundwater extraction for advantageous purposes, and minimizing the total amount of water abstraction from barrier extraction wells. The study also proposes an adaptive management strategy for coastal aquifers by developing a three-dimensional (3-D) monitoring network design. The performance of the proposed methodologies is evaluated by using both an illustrative multilayered coastal aquifer system and a real life coastal aquifer study area. Coupled S-O approach is used as the basic tool to develop a saltwater intrusion management model to obtain the optimal groundwater extraction rates from a combination of feasible solutions on the Pareto optimal front. Simulation of saltwater intrusion processes requires solution of density dependent coupled flow and solute transport numerical simulation models that are computationally intensive. Therefore, computational efficiency in the coupled S-O approach is achieved by using an approximate emulator of the accompanying physical processes of coastal aquifers. These emulators, often known as surrogate models or meta-models, can replace the computationally intensive numerical simulation model in a coupled S-O approach for achieving computational efficiency. A number of meta-models have been developed and compared in this study for integration with the optimization algorithm in order to develop saltwater intrusion management model. Fuzzy Inference System (FIS), Adaptive Neuro Fuzzy Inference System (ANFIS), Multivariate Adaptive Regression Spline (MARS), and Gaussian Process Regression (GPR) based meta-models are developed in the present study for approximating coastal aquifer responses to groundwater extraction. Properly trained and tested meta-models are integrated with a Controlled Elitist Multiple Objective Genetic Algorithm (CEMOGA) within a coupled S-O approach. In each iteration of the optimization algorithm, the meta-models are used to compute the corresponding salinity concentrations for a set of candidate pumping patterns generated by the optimization algorithm. Upon convergence, the non-dominated global optimal solutions are obtained as the Pareto optimal front, which represents a trade-off between the two conflicting objectives of the pumping management problem. It is observed from the solutions of the meta-model based coupled S-O approach that the considered meta-models are capable of producing a Pareto optimal set of solutions quite accurately. However, each meta-modelling approach has distinct advantages over the others when utilized within the integrated S-O approach. Uncertainties in estimating complex flow and solute transport processes in coastal aquifers demand incorporation of the uncertainties related to some of the model parameters. Multidimensional heterogeneity of aquifer properties such as hydraulic conductivity, compressibility, and bulk density are considered as major sources of uncertainty in groundwater modelling system. Other sources of uncertainty are associated with spatial and temporal variability of hydrologic as well as human interventions, e.g. aquifer recharge and transient groundwater extraction patterns. Different realizations of these uncertain model parameters are obtained from different statistical distributions. FIS based meta-models are advanced to a Genetic Algorithm (GA) tuned hybrid FIS model (GA-FIS), to emulate physical processes of coastal aquifers and to evaluate responses of the coastal aquifers to groundwater extraction under groundwater parameter uncertainty. GA is used to tune the FIS parameters in order to obtain the optimal FIS structure. The GA-FIS models thus obtained are linked externally to the CEMOGA in order to derive an optimal pumping management strategy using the coupled S-O approach. The evaluation results show that the proposed saltwater intrusion management model is able to derive reliable optimal groundwater extraction strategies to control saltwater intrusion for the illustrative multilayered coastal aquifer system. The optimal management strategies obtained as solutions of GA-FIS based management models are shown to be reliable and accurate within the specified ranges of values for different realizations of uncertain groundwater parameters. One of the major concerns of the meta-model based integrated S-O approach is the uncertainty associated with the meta-model predictions. These prediction uncertainties, if not addressed properly, may propagate to the optimization procedures, and may deteriorate the optimality of the solutions. A standalone meta-model, when used within an optimal management model, may result in the optimization routine producing actually suboptimal solutions that may undermine the optimality of the groundwater extraction strategies. Therefore, this study proposes an ensemble approach to address the prediction uncertainties of meta-models. Ensemble is an approach to assimilate multiple similar or different algorithms or base learners (emulators). The basic idea of ensemble lies in developing a more reliable and robust prediction tool that incorporates each individual emulator's unique characteristic in order to predict future scenarios. Each individual member of the ensemble contains different input -output mapping functions. Based on their own mapping functions, these individual emulators provide varied predictions on the response variable. Therefore, the combined prediction of the ensemble is likely to be less biased and more robust, reliable, and accurate than that of any of the individual members of the ensemble. Performance of the ensemble meta-models is evaluated using an illustrative coastal aquifer study area. The results indicate that the meta-model based ensemble modelling approach is able to provide reliable solutions for a multilayered coastal aquifer management problem. Relative sea level rise, providing an additional saline water head at the seaside, has a significant impact on an increase in the salinization process of the coastal aquifers. Although excessive groundwater withdrawal is considered as the major cause of saltwater intrusion, relative sea level rise, in combination with the effect of excessive groundwater pumping, can exacerbate the already vulnerable coastal aquifers. This study incorporates the effects of relative sea level rise on the optimized groundwater extraction values for the specified management period. Variation of water concentrations in the tidal river and seasonal fluctuation of river water stage are also incorporated. Three meta-models are developed from the solution results of the numerical simulation model that simulates the coupled flow and solute transport processes in a coastal aquifer system. The results reveal that the proposed meta-models are capable of predicting density dependent coupled flow and solute transport patterns quite accurately. Based on the comparison results, the best meta-model is selected as a computationally cheap substitute of the simulation model in the coupled S-O based saltwater intrusion management model. The performance of the proposed methodology is evaluated for an illustrative multilayered coastal aquifer system in which the effect of climate change induced sea level rise is incorporated for the specified management period. The results show that the proposed saltwater intrusion management model provides acceptable, accurate, and reliable solutions while significantly improving computational efficiency in the coupled S-O methodology. The success of the developed management strategy largely depends on how accurately the prescribed management policy is implemented in real life situations. The actual implementation of a prescribed management strategy often differs from the prescribed planned strategy due to various uncertainties in predicting the consequences, as well as practical constraints, including noncompliance with the prescribed strategy. This results in actual consequences of a management strategy differing from the intended results. To bring the management consequences closer to the intended results, adaptive management strategies can be sequentially modified at different stages of the management horizon using feedback measurements from a deigned monitoring network. This feedback information can be the actual spatial and temporal concentrations resulting from the implementation of actual management strategy. Therefore, field-scale compliance of the developed coastal aquifer management strategy is a crucial aspect of an optimally designed groundwater extraction policy. A 3-D compliance monitoring network design methodology is proposed in this study in order to develop an adaptive and sequentially modified management policy, which aims to improve optimal and justifiable use of groundwater resources in coastal aquifers. In the first step, an ensemble meta-model based multiple objective prescriptive model is developed using a coupled S-O approach in order to derive a set of Pareto optimal groundwater extraction strategies. Prediction uncertainty of meta-models is addressed by utilizing a weighted average ensemble using Set Pair Analysis. In the second step, a monitoring network is designed for evaluating the compliance of the implemented strategies with the prescribed management goals due to possible uncertainties associated with field-scale application of the proposed management policy. Optimal monitoring locations are obtained by maximizing Shannon's entropy between the saltwater concentrations at the selected potential locations. Performance of the proposed 3-D sequential compliance monitoring network design is assessed for an illustrative multilayered coastal aquifer study area. The performance evaluations show that sequential improvements of optimal management strategy are possible by utilizing saltwater concentrations measurements at the proposed optimal compliance monitoring locations. The integrated S-O approach is used to develop a saltwater intrusion management model for a real world coastal aquifer system in the Barguna district of southern Bangladesh. The aquifer processes are simulated by using a 3-D finite element based combined flow and solute transport numerical code. The modelling and management of seawater intrusion processes are performed based on very limited hydrogeological data. The model is calibrated with respect to hydraulic heads for a period of five years from April 2010 to April 2014. The calibrated model is validated for the next three-year period from April 2015 to April 2017. The calibrated and partially validated model is then used within the integrated S-O approach to develop optimal groundwater abstraction patterns to control saltwater intrusion in the study area. Computational efficiency of the management model is achieved by using a MARS based meta-model approximately emulating the combined flow and solute transport processes of the study area. This limited evaluation demonstrates that a planned transient groundwater abstraction strategy, acquired as solution results of a meta-model based integrated S-O approach, is a useful management strategy for optimized water abstraction and saltwater intrusion control. This study shows the capability of the MARS meta-model based integrated S-O approach to solve real-life complex management problems in an efficient manner

Design and Evaluation of a Traffic Safety System based on Vehicular Networks for the Next Generation of Intelligent Vehicles

La integración de las tecnologías de las telecomunicaciones en el sector del automóvil permitirá a los vehículos intercambiar información mediante Redes Vehiculares, ofreciendo numerosas posibilidades. Esta tesis se centra en la mejora de la seguridad vial y la reducción de la siniestralidad mediante Sistemas Inteligentes de Transporte (ITS). El primer paso consiste en obtener una difusión eficiente de los mensajes de advertencia sobre situaciones potencialmente peligrosas. Hemos desarrollado un marco para simular el intercambio de mensajes entre vehículos, utilizado para proponer esquemas eficientes de difusión. También demostramos que la disposición de las calles tiene gran influencia sobre la eficiencia del proceso. Nuestros algoritmos de difusión son parte de una arquitectura más amplia (e-NOTIFY) capaz de detectar accidentes de tráfico e informar a los servicios de emergencia. El desarrollo y evaluación de un prototipo demostró la viabilidad del sistema y cómo podría ayudar a reducir el número de víctimas en carretera

Elastic Build System in a Hybrid Cloud Environment

Linux-based operating systems such as MeeGo consist of thousands of modular packages. Compiling source code and packaging software is an automated but computationally heavy task. Fast and cost-efficient software building is one of the requirements for rapid software development and testing. Meanwhile, the arrival of cloud services makes it easier to buy computing infrastructure and platforms over the Internet. The difference to earlier hosting services is the agility; services are accessible within minutes from the request and the customer only pays per use. This thesis examines how cloud services could be leveraged to ensure sufficient computing capacity for a software build system. The chosen system is Open Build Service, a centrally managed distributed build system capable of building packages for MeeGo among other distributions. As the load on a build cluster can vary greatly, a local infrastructure is difficult to provision efficiently, thus virtual machines from the cloud could be acquired temporarily to accommodate the fluctuating demand. Main issues are whether cloud could be utilized safely and whether it is time-efficient to transfer computational jobs to an outside service. A MeeGo-enabled instance of Open Build Service was first set up in-house, running a management server and a server for workers which build the packages. A virtual machine template for cloud workers was created. Virtual machines created from this template would start the worker program and connect to the management server through a secured tunnel. A service manager script was then implemented to monitor jobs and the usage of workers and to make decisions whether new machines from the cloud should be requested or idle ones terminated. This elasticity is automated and is capable of scaling up in a matter of minutes. The service manager also features cost optimizations implemented with a specific cloud service (Amazon Web Services) in mind. The latency between the in-house and the cloud did not prove to be insurmountable, but as each virtual machine from the cloud has a starting delay of three minutes, the system reacts fairly slowly to increasing demand. The main advantage of the cloud usage is the seemingly infinite number of machines available, ideal for building a large number of packages that can be built in parallel. Packages may need other packages during building, which inhibits the system from building all packages in parallel. Powerful workers are needed to quickly build larger bottleneck packages. Finding the balance between the number and performance of workers is one of the issues for future research. To ensure high availability, improvements should be made to the service manager and a separate virtual infrastructure manager should be used to utilize multiple cloud providers. In addition, mechanisms are needed to keep proprietary source code on in-house workers and to ensure that malicious code cannot be injected into the system via packages originating from open development communities. /Kir1

Elastic Build System in a Hybrid Cloud Environment

Linux-based operating systems such as MeeGo consist of thousands of modular packages. Compiling source code and packaging software is an automated but computationally heavy task. Fast and cost-efficient software building is one of the requirements for rapid software development and testing. Meanwhile, the arrival of cloud services makes it easier to buy computing infrastructure and platforms over the Internet. The difference to earlier hosting services is the agility; services are accessible within minutes from the request and the customer only pays per use. This thesis examines how cloud services could be leveraged to ensure sufficient computing capacity for a software build system. The chosen system is Open Build Service, a centrally managed distributed build system capable of building packages for MeeGo among other distributions. As the load on a build cluster can vary greatly, a local infrastructure is difficult to provision efficiently, thus virtual machines from the cloud could be acquired temporarily to accommodate the fluctuating demand. Main issues are whether cloud could be utilized safely and whether it is time-efficient to transfer computational jobs to an outside service. A MeeGo-enabled instance of Open Build Service was first set up in-house, running a management server and a server for workers which build the packages. A virtual machine template for cloud workers was created. Virtual machines created from this template would start the worker program and connect to the management server through a secured tunnel. A service manager script was then implemented to monitor jobs and the usage of workers and to make decisions whether new machines from the cloud should be requested or idle ones terminated. This elasticity is automated and is capable of scaling up in a matter of minutes. The service manager also features cost optimizations implemented with a specific cloud service (Amazon Web Services) in mind. The latency between the in-house and the cloud did not prove to be insurmountable, but as each virtual machine from the cloud has a starting delay of three minutes, the system reacts fairly slowly to increasing demand. The main advantage of the cloud usage is the seemingly infinite number of machines available, ideal for building a large number of packages that can be built in parallel. Packages may need other packages during building, which inhibits the system from building all packages in parallel. Powerful workers are needed to quickly build larger bottleneck packages. Finding the balance between the number and performance of workers is one of the issues for future research. To ensure high availability, improvements should be made to the service manager and a separate virtual infrastructure manager should be used to utilize multiple cloud providers. In addition, mechanisms are needed to keep proprietary source code on in-house workers and to ensure that malicious code cannot be injected into the system via packages originating from open development communities. /Kir1

Estudio de sistemas renovables avanzados para el desarrollo energético sostenible

Tesis por compendio[ES] La energía juega un papel fundamental en el desarrollo sostenible de las comunidades. Así, proporcionar recursos energéticos fiables, económicamente aceptables, medioambientalmente respetuosos y socialmente beneficiosos, resulta esencial para el desarrollo sostenible de las mismas. A pesar de la universalidad de dicha definición, el uso de la energía está muy vinculada al nivel de desarrollo de los países. De este modo, la problemática energética de los países desarrollados contrasta enormemente con la de los países en desarrollo. En esta tesis doctoral se ha identificado la principal problemática energética de ambas realidades: grave impacto medioambiental de los modelos de generación del transporte tradicionales en los países desarrollados y pobreza energética en los países en desarrollo. A partir del compendio de artículos científicos de esta tesis doctoral se ha caracterizado el uso de sistemas renovables avanzados que permite solucionar dicha problemática de forma sostenible. En concreto, el principal problema energético en países desarrollados ha sido tratado mediante la planificación energética y el diseño óptimo de sistemas híbridos de energías renovables (HRES por sus siglas en inglés) en electrolineras, necesarios para la introducción de vehículos eléctricos como alternativa de movilidad sostenible. Por otro lado, el estudio de metodologías de diseño óptimas de HRES off grid y de las estufas para cocinar mejoradas mediante gasificación de biomasa se ha focalizado en la inaccesibilidad eléctrica y a sistemas de cocina limpia que sufren las comunidades en desarrollo. Así, esta tesis aporta una serie de metodologías para optimizar y adecuar los sistemas renovables presentados para el desarrollo energético sostenible de las comunidades. Además, no sólo demuestra la idoneidad de estos sistemas para dicho fin, sino también su versatilidad de aplicación en función del nivel de crecimiento de las comunidades.[CA] L'energia juga un paper fonamental en el desenvolupament sostenible de les comunitats. Així, proporcionar recursos energètics fiables, econòmicament acceptables, mediambientalment respectuosos i socialment beneficiosos, resulta essencial per al desenvolupament sostenibles de les mateixes. A pesar de la universalitat d'aquesta definició, l'ús de la energia està vinculada al nivell de desenvolupament dels països. D'aquesta manera, la problemàtica energètica dels països desenvolupats contrasta enormement amb la dels països en desenvolupament. A aquesta tesis doctoral s'ha identificat la principal problemàtica energètica d'ambdues realitats: greu impacte mediambiental dels models de generació del transport tradicional en els països desenvolupats i pobresa energètica en els països en desenvolupament. A partir del compendi d'articles científics d'aquesta tesis doctoral s'ha caracteritzat l'ús de sistemes renovables avançats que permet solucionar aquesta problemàtica de manera sostenible. En concret, el principal problema energètic en països desenvolupats s'ha tractat mitjançant la planificació energètica i el disseny òptim de sistemes híbrids d'energies renovables (HRES, per les seues segles en anglès) en electrolineres, necessaris per la introducció de vehicles elèctrics com alternativa de mobilitat sostenible. D'altra banda, l'estudi de metodologies de disseny òptimes de HRES off grid i d'estufes per a cuinar millorades mitjançant gasificació de biomassa s'ha focalitzat en la inaccessibilitat elèctrica i a sistemes de cuina neta que pateixen les comunitats en desenvolupament. Així, aquesta tesis aporta una sèrie de metodologies per optimitzar i adequar el sistemes renovables presentats per al desenvolupament energètic sostenible de les comunitats. A més, no tan sols demostra la idoneïtat d'aquests sistemes per a aqueix fi, sinó també la seua versatilitat d'aplicació en funció del nivell de creixement de les comunitats.[EN] Energy plays a significant role for the sustainable development of communities. Hence, supplying reliable energy resources, which result economically acceptable, environmentally friendly and socially beneficial, arises as essential for their sustainable development. Despite the universality of such definition, the energy use is highly linked to the development degree of the countries. Thus, energy problems of developed countries sharply contrast with those of developing countries. This doctoral thesis identifies the main energy issues of both realities: severe environmental impact of energy generation models for traditional transport in developed countries and energy poverty in developing countries. The compendium of scientific papers of this doctoral dissertation characterizes the use of advanced renewable energy systems to solve such problems in a sustainable way. Namely, the main energy issue in developed countries has been addressed by means of energy planning and the optimal design of Hybrid Renewable Energy Systems (HRES) in electric vehicle charging stations, which ensure the introduction of electric vehicles as a sustainable mobility alternative. Moreover, the study of methodologies for the optimal design of off grid HRES and improved cooking stoves based on biomass gasification have approached the inaccessibility to electricity and to clean cooking systems that developing communities suffer. Therefore, this thesis provides a number of methodologies to optimize and adapt the presented renewable energy systems for the sustainable energy development of communities. Furthermore, it demonstrates not only the suitability of these systems for such aim, but also their versatility of application regarding the growing degree of the communities.Bastida Molina, P. (2021). Estudio de sistemas renovables avanzados para el desarrollo energético sostenible [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172548TESISCompendi

Measuring open innovation practices through topic modelling: Revisiting their impact on firm financial performance

Despite the popularity of open innovation in recent years, studies examining the impact of open innovation upon firm performance have shown mixed results. Previous empirical work on this topic is often based on surveys or archival sources, usually done either in isolation or in aggregate through employing proxy measures. In contrast, we employ an unsupervised learning technique (i.e., topic modelling) utilizing natural language processing to extract information on companies’ open innovation practices, creating an initial keyword basket for future development. We then revisit the relationship between open innovation practices and financial performance of firms. The results show that a firm’s overall openness level is associated with improved financial performance. More granular practices developed from our approach, however, show variations. The inverted U-shaped relationships are observed in specific open innovation practices but not in all, partly supporting the existence of the openness paradox from prior literature. The complementarity between internal R&D and individual open innovation practices also varies by practice. Further, the influence of these open innovation practices also varies by sector. Our findings prompt us to conclude that open innovation’s impact on financial performance is nuanced, and that there is no uniform set of best practices to practice open innovation effectively

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Security hardened remote terminal units for SCADA networks.

Remote terminal units (RTUs) are perimeter supervisory control and data acquisition (SCADA) devices that measure and control actual physical devices. Cyber security was largely ignored in SCADA for many years, and the cyber security issues that now face SCADA and DCS, specifically RTU security, are investigated in this research. This dissertation presents a new role based access control model designed specifically for RTUs and process control. The model is developed around the process control specific data element called a point, and point operations. The model includes: assignment constraints that limit the RTU operations that a specific role can be assigned and activation constraints that allow a security administrator to specify conditions when specific RTU roles or RTU permissions cannot be used. RTU enforcement of the new access control model depends on, and is supported by, the protection provided by an RTU\u27s operating system. This dissertation investigates two approaches for using minimal kernels to reduce potential vulnerabilities in RTU protection enforcement and create a security hardened RTU capable of supporting the new RTU access control model. The first approach is to reduce a commercial OS kernel to only those components needed by the RTU, removing any known or unknown vulnerabilities contained in the eliminated code and significantly reducing the size of the kernel. The second approach proposes using a microkernel that supports partitioning as the basis for an RTU specific operating system which isolates network related RTU software, the RTU attack surface, from critical RTU operational software such as control algorithms and analog and digital input and output. In experimental analysis of a prototype hardened RTU connected to real SCADA hardware, a reduction of over 50% was obtained in reducing a 2.4 Linux kernel to run on actual RTU hardware. Functional testing demonstrated that different users were able to carryout assigned tasks with the limited set of permissions provided by the security hardened RTU and a series of simulated insider attacks were prevented by the RTU role based access control system. Analysis of communication times indicated response times would be acceptable for many SCADA and DCS application areas. Investigation of a partitioning microkernel for an RTU identified the L4 microkernel as an excellent candidate. Experimental evaluation of L4 on real hardware found the IPC overhead for simulated critical RTU operations protected by L4 partitioning to be sufficiently small to warrant continued investigation of the approach