DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Ambient intelligence in buildings : design and development of an interoperable Internet of Things platform

During many years, people and governments have been warned about the increasing levels of pollution and greenhouse gases (GHG) emissions that are endangering our lives on this planet. The Information and Communication Technology sector, usually known as the ICT sector, responsible for the computerization of the society, has been pinpointed as one of the most important sectors contributing to such a problem. Many efforts, however, have been put to shift the trend towards the utilization of renewable resources, such as wind or solar power. Even though governments have agreed to follow this path and avoid the usage of non-renewable energies, it is not enough. Although the ICT sector might seem an added problem due to the number of connected devices, technology improvements and hardware optimization enable new ways of fighting against global warming and GHG emissions. The aforementioned computerization has forced companies to evolve their work into a computer-assisted one. Due to this, companies are now forced to establish their main headquarters inside buildings for work coordination, connection and management. Due to this, buildings are becoming one of the most important issues regarding energy consumption. In order to cope with such problem, the Internet of Things (IoT) offers new paradigms and alternatives for leading the change. IoT is commonly defined as the network of physical and virtual objects that are capable of collecting surrounding data and exchanging it between them or through the Internet. Thanks to these networks, it is possible to monitor any thinkable metric inside buildings, and, then, utilize this information to build efficient automated systems, commonly known as Building Energy Management Systems (BEMS), capable of extracting conclusions on how to optimally and efficiently manage the resources of the building. ICT companies have foreseen this market opportunity that, paired with the appearance of smaller, efficient and more durable sensors, allows the development of efficient IoT systems. However, the lack of agreement and standardization creates chaos inside IoT, and the horizontal connectivity between such systems is still a challenge. Moreover, the vast amount of data to process requires the utilization of Big Data techniques to guarantee close to real-time responses. This thesis initially presents a standard Cloud-based IoT architecture that tries to cope with the aforementioned problems by employing a Cloud middleware that obfuscates the underlying hardware architecture and permits the aggregation of data from multiple heterogeneous sources. Also, sensor information is exposed to any third-party client after authentication. The utilization of automated IoT systems for managing building resources requires high reliability, resilience, and availability. The loss of sensor data is not permitted due to the negative consequences it might have, such as disruptive resource management. For this, it is mandatory to grant backup options to sensor networks in order to guarantee correct functioning in case of partial network disconnections. Additionally, the placement of the sensors inside the building must guarantee minimal energy consumption while fulfilling sensing requirements. Finally, a building resource management use case is presented by means of a simulation tool. The tool draws on occupants' probabilistic models and environmental condition models for actuating upon building elements to ensure optimal and efficient functioning. Occupants' comfort is also taken into consideration and the trade-off between the two metrics is studied. All the presented work is meant to deliver insights and tools for current and future IoT system implementations by setting the basis for standardization agreements yet to happen.Durant molts anys, s'ha alertat a la població i als governs sobre l'increment en els nivells de pol·lució i d'emissió de gasos d'efecte hivernacle, que estan posant en perill la nostra vida a la Terra. El sector de les Tecnologies de la Informació i Comunicació, normalment conegut com les TIC, responsable de la informatització de la societat, ha estat senyalat com un dels sectors més importants encarregat d'agreujar tal problema. Però, molt esforç s'està posant per revertir aquesta situació mitjançant l'ús de recursos renovables, com l'energia eòlica o solar. Tot i que els governs han acordat seguir dit camí i evitar l'ús d'energia no renovable tant com sigui possible, no és suficient per erradicar el problema. Encara que el sector de les TIC pugui semblar un problema afegit donada la gran quantitat i l'increment de dispositius connectats, les millores en tecnologia i en hardware estan habilitant noves maneres de lluitar contra l'escalfament global i l'emissió de gasos d'efecte hivernacle. La informatització, anteriorment mencionada, ha forçat a les empreses a evolucionar el seu model de negoci cap a un més enfocat a la utilització de xarxes d'ordinadors per gestionar els seus recursos. Per això, dites companyies s'estan veient forçades a establir les seves seus centrals dintre d'edificis, per tenir un major control sobre la coordinació, connexió i maneig dels seus recursos. Això està provocant un augment en el consum energètic dels edificis, que s'estan convertint en un dels principals problemes. Per poder fer front al problema, la Internet de les Coses o Internet of Things (IoT) ofereix nous paradigmes i alternatives per liderar el canvi. IoT es defineix com la xarxa d'objectes físics i virtuals, capaços de recol·lectar la informació per construir sistemes automatitzats, coneguts com a Sistemes de Gestió Energètica per Edificis, capaços d'extreure conclusions sobre com utilitzar de manera eficient i òptima els recursos de l'edifici. Companyies pertanyents a les TIC han previst aquesta oportunitat de mercat que, en sincronia amb l'aparició de sensors més petits, eficients i duradors, permeten el desenvolupament de sistemes IoT eficients. Però, la falta d'acord en quant a l'estandardització de dits sistemes està creant un escenari caòtic, ja que s'està fent impossible la connectivitat horitzontal entre dits sistemes. A més, la gran quantitat de dades a processar requereix la utilització de tècniques de Big Data per poder garantir respostes en temps acceptables. Aquesta tesi presenta, inicialment, una arquitectura IoT estàndard basada en la Neu, que tracta de fer front als problemes anteriorment presentats mitjançant l'ús d'un middleware allotjat a la Neu que ofusca l'arquitectura hardware subjacent i permet l'agregació de la informació originada des de múltiples fonts heterogènies. A més, la informació dels sensors s'exposa perquè qualsevol client de tercers pugui consultar-la, després d'haver-se autenticat. La utilització de sistemes IoT automatitzats per gestionar els recursos dels edificis requereix un alt nivell de fiabilitat, resistència i disponibilitat. La perduda d'informació no està permesa degut a les conseqüències negatives que podría suposar, com una mala presa de decisions. Per això, és obligatori atorgar opcions de backup a les xarxes de sensors per garantir un correcte funcionament inclús quan es produeixen desconnexions parcials de la xarxa. Addicionalment, la col·locació dels sensors dintre de l'edifici ha de garantir un consum energètic mínim dintre de les restriccions de desplegament imposades. Finalment, presentem un cas d'ús d'un Sistema de Gestió Energètica per Edificis mitjançant una eina de simulació. Dita eina utilitza com informació d'entrada models probabilístics sobre les accions dels ocupants i models sobre la condició ambiental per actuar sobre els elements de l'edifici i garantir un funcionament òptim i eficient. A més, el confort dels ocupants també es considera com mètrica a optimitzar. Donada la impossibilitat d’optimitzar les dues mètriques de manera conjunta, aquesta tesi també presenta un estudi sobre el trade-off que existeix entre elles. Tot el treball presentat està pensat per atorgar idees i eines pels sistemes IoT actuals i futurs, i assentar les bases per l’estandardització que encara està per arribar.Durante muchos años, se ha alertado a la población y a los gobiernos acerca del incremento en los niveles de polución y de emisión de gases de efecto invernadero, que están poniendo en peligro nuestra vida en la Tierra. El sector de las Tecnologías de la Información y Comunicación, normalmente conocido como las TIC, responsable de la informatización de la sociedad, ha sido señalada como uno de los sectores más importantes encargado de agravar tal problema. Sin embargo, mucho esfuerzo se está poniendo para revertir esta situación mediante el uso de recursos renovables, como la energía eólica o solar. A pesar de que los gobiernos han acordado seguir dicho camino y evitar el uso de energía no renovable tanto como sea posible, no es suficiente para erradicar el problema. Aunque el sector de las TIC pueda parecer un problema añadido dada la gran cantidad y el incremento de dispositivos conectados, las mejoras en tecnología y en hardware están habilitando nuevas maneras de luchar contra el calentamiento global y la emisión de gases de efecto invernadero. Durante las últimas décadas, compañías del sector público y privado conscientes del problema han centrado sus esfuerzos en la creación de soluciones orientadas a la eficiencia energética tanto a nivel de hardware como de software. Las nuevas redes troncales están siendo creadas con dispositivos eficientes y los proveedores de servicios de Internet tienden a crear sistemas conscientes de la energía para su optimización dentro de su dominio. Siguiendo esta tendencia, cualquier nuevo sistema creado y añadido a la red debe garantizar un cierto nivel de conciencia y un manejo óptimo de los recursos que utiliza. La informatización, anteriormente mencionada, ha forzado a las empresas a evolucionar su modelo de negocio hacia uno más enfocado en la utilización de redes de ordenadores para gestionar sus recursos. Por eso, dichas compañías se están viendo forzadas a establecer sus sedes centrales dentro de edificios, para tener un mayor control sobre la coordinación, conexión y manejo de sus recursos. Esto está provocando un aumento en el consumo energético de los edificios, que se están convirtiendo en uno de los principales problemas. Para poder hacer frente al problema, el Internet de las Cosas o Internet of Things (IoT) ofrece nuevos paradigmas y alternativas para liderar el cambio. IoT se define como la red de objetos físicos y virtuales, capaces de recolectar la información del entorno e intercambiarla entre los propios objetos o a través de Internet. Gracias a estas redes, es posible monitorizar cualquier métrica que podamos imaginar dentro de un edificio, y, después, utilizar dicha información para construir sistemas automatizados, conocidos como Sistemas de Gestión Energética para Edificios, capaces de extraer conclusiones sobre cómo utilizar de manera eficiente y óptima los recursos del edificio. Compañías pertenecientes a las TIC han previsto esta oportunidad de mercado que, en sincronía con la aparición de sensores más pequeños, eficientes y duraderos, permite el desarrollo de sistemas IoT eficientes. Sin embargo, la falta de acuerdo en cuanto a la estandarización de dichos sistemas está creando un escenario caótico, ya que se hace imposible la conectividad horizontal entre dichos sistemas. Además, la gran cantidad de datos a procesar requiere la utilización de técnicas de Big Data para poder garantizar respuestas en tiempos aceptables. Esta tesis presenta, inicialmente, una arquitectura IoT estándar basada en la Nube que trata de hacer frente a los problemas anteriormente presentados mediante el uso de un middleware alojado en la Nube que ofusca la arquitectura hardware subyacente y permite la agregación de la información originada des de múltiples fuentes heterogéneas. Además, la información de los sensores se expone para que cualquier cliente de terceros pueda consultarla, después de haberse autenticado. La utilización de sistemas IoT automatizados para manejar los recursos de los edificios requiere un alto nivel de fiabilidad, resistencia y disponibilidad. La pérdida de información no está permitida debido a las consecuencias negativas que podría suponer, como una mala toma de decisiones. Por eso, es obligatorio otorgar opciones de backup a las redes de sensores para garantizar su correcto funcionamiento incluso cuando se producen desconexiones parciales de la red. Adicionalmente, la colocación de los sensores dentro del edificio debe garantizar un consumo energético mínimo dentro de las restricciones de despliegue impuestas. En esta tesis, mejoramos el problema de colocación de los sensores para redes heterogéneas de sensores inalámbricos añadiendo restricciones de clustering o agrupamiento, para asegurar que cada tipo de sensor es capaz de obtener su métrica correspondiente, y restricciones de protección mediante la habilitación de rutas de transmisión secundarias. En cuanto a grandes redes homogéneas de sensores inalámbricos, esta tesis estudia aumentar su resiliencia mediante la identificación de los sensores más críticos. Finalmente, presentamos un caso de uso de un Sistema de Gestión Energética para Edificios mediante una herramienta de simulación. Dicha herramienta utiliza como información de entrada modelos probabilísticos sobre las acciones de los ocupantes y modelos sobre la condición ambiental para actuar sobre los elementos del edificio y garantizar un funcionamiento óptimo y eficiente. Además, el comfort de los ocupantes también se considera como métrica a optimizar. Dada la imposibilidad de optimizar las dos métricas de manera conjunta, esta tesis también presenta un estudio sobre el trade-off que existe entre ellas. Todo el trabajo presentado está pensado para otorgar ideas y herramientas para los sistemas IoT actuales y futuros, y asentar las bases para la estandarización que todavía está por llegar.Postprint (published version

Natural Disaster Detection Using Wavelet and Artificial Neural Network

Indonesia, by the location of its geographic and geologic, it have more potential encounters for natural disasters. This nation is traversed by three tectonic plates, namely: IndoAustralian, the Eurasian and the Pacific plates. One of the tools employed to detect danger and send an early disaster warning is sensor device for ocean waves, but it has drawbacks related to the very limited time gap between information/warnings obtained and the real disaster event, which is only less than 30 minutes. Natural disaster early detection information system is essential to prevent potential danger. The system can make use of the pattern recognition of satellite imagery sequences that take place before and during the natural disaster. This study is conducted to determine the right wavelet to compress the satellite image sequences and to perform the pattern recognition process of a natural disaster employing an artificial neural network. This study makes use of satellite imagery sequences of tornadoes and hurricanes

Anthropology Resources For Librarians

As of 1989, no one had compiled a bibliography of resources of use to librarians who had a role supporting anthropologists in their institutions. The present lengthy bibliography began as a print list of 25 entries in the November 1989 issue of ANSS Currents. Over the years the list grew and it was published on ANSSWeb when the site was created in 1995. The bibliography has now migrated to a pdf version and contains more than 500 entries in 25 categories. It may be updated periodically as new resources are discovered

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations