No description available.

A model to predict air temperatures inside a residential home was created, which used local and remote environmental sensor data. Space conditioning experiments were carried out in the residential home and models were created to describe the observed temperature data. Results from laboratory space conditioning experiments were used to create a new experimental prediction model. Static inputs of the experimental model were replaced with dynamic inputs and an improved model was created, capable of general prediction application in the residential home. A novel system for space conditioning control was designed, which applied the improved model for air temperature prediction. Initial prediction models showed prediction error margins of ±1°C. Residential home and laboratory space conditioning experiments were utilized to create non-linear temperature prediction models capable of application outside of the experimental scope. Improved prediction models were based on the experimental models and showed average error margins of ±0:12°C compared to observed temperature data. A novel system design was proposed, combining the improved model with a traditional heating control to create a new optimal start-stop heating application for residential homes.

Published Article / South African cities are in the process of transition in the changing scenario and need a change in the planning perspective for their sustainable development. The concept of smart city offers opportunities for such development to many middle sized cities of South Africa. Therefore, in this paper conceptual modelling for development of smart cities in South Africa is attempted based on systems concept. The conceptual models are built by using the principles of system dynamics methodology and based on causal feedback relationships among the various factors under different smart characteristics of a city such as, smart economy, smart people, smart governance, smart mobility, smart environment and smart living. The causal feedback loops and interrelationship among various parameters illustrate the dynamicity and influence of parameters on one another, which would able to assist in evolving plausible policy interventions for developing smart cities in South Africa. It is concluded that the modelling approaches presented here could guide the policy makers and city planners to evolve robust and responsive policy interventions for developing smart cities in the changing scenario.

Published Article / In this paper an evaluation of smart socioeconomic, physical and environmental characteristics of Bloemfontein city of South Africa was done to understand the current scenario of the city and evolve perspective indicative planning guidelines for transforming the city into a smart city. The evaluation was done based on 74 smart indicators, and 30 factors under six characteristics, such as, smart economy, smart people, smart governance, smart mobility, smart environment and smart living. For this purpose, survey research methodology with analysis of primary and secondary data and review of the current Integrated Development Plan of the city was followed. The investigation of the various indicators revealed that although the city is lagging behind in most of the characteristics, yet provides ample opportunity to develop it as a smart city, if smart city concept and smart growth principles are employed in city development process.

Smart environments are increasingly common. By utilizing sensor data from the indoor environment and applying methods like machine learning, they can autonomously control and increase productivity, comfort, and well-being of occupants. The aim of this thesis was to model indoor climate in conference rooms and use K-means clustering to determine quality levels. Together, they enable categorization of conference room quality level during meetings. Theoretically, by alerts to the user, this may enhance occupant productivity, comfort, and well-being. Moreover, the objective was to determine which features and which k would produce the highest quality clusters given chosen evaluation measures. To do this, a quasi-experiment was used. CO2, temperature, and humidity sensors were placed in four conference rooms and were sampled continuously. K-means clustering was then used to generate clusters with 10 days of sensor data. To evaluate which feature combination and which k created optimal clusters, we used Silhouette, Davis Bouldin, and the Elbow method. The resulting model, using three clusters to represent quality levels, enabled categorization of the quality of specific meetings. Additionally, all three methods indicated that a feature combination of CO2 and humidity, with k = 2 or k = 3, was suitable.

Agent-based simulation of smart environment finds its application in studying people’s movement to help the design of a variety of applications such as energy utilization, HAVC control and egress strategy in emergency situation. Traditionally, agent-based simulation is not dynamic data driven, they run offline and do not assimilate real sensor data about the environment. As more and more buildings are equipped with various sensors, it is possible to utilize real time sensor data to inform the simulation. To incorporate the real sensor data into the simulation, we introduce the method of data assimilation. The goal of data assimilation is to provide inference about system state based on the incomplete, ambiguous and uncertain sensor data using a computer model. A typical data assimilation framework consists of a computer model, a series of sensors and a melding scheme. The purpose of this dissertation is to develop a data assimilation framework for agent-based simulation of smart environment. With the developed data assimilation framework, we demonstrate an application of building occupancy estimation which focuses on position estimation using the framework. We build an agent based model to simulate the occupants’ movement s in the building and use this model in the data assimilation framework. The melding scheme we use to incorporate sensor data into the built model is particle filter algorithm. It is a set of statistical method aiming at compute the posterior distribution of the underlying system using a set of samples. It has the benefit that it does not have any assumption about the target distribution and does not require the target system to be written in analytic form .To overcome the high dimensional state space problem as the number of agents increases, we develop a new resampling method named as the component set resampling and evaluate its effectiveness in data assimilation. We also developed a graph-based model for simulating building occupancy. The developed model will be used for carrying out building occupancy estimation with extremely large number of agents in the future.

<p>The accelerated development of mobile computational platforms and artificial intelligence (AI) has led to increase in interconnected products with sensors that are creating smart environments. The smart environment expands the interactive spaces from limited digital screens, such as desktops and phones, to a much broader category that includes everyday objects, smart things, surrounding contexts, robots, and humans. The improvement of personal computing devices including smartphones, watches, and AR glasses further broadens the communication bandwidth between us and the ambient intelligence from the surrounding environment. Additionally in this smart environment people want to pursue personalization and are motivated to design and build their own smart environments and author customized functions.</p> <p> </p> <p>My work in this thesis focuses on investigating workflows and methods to support end-users to create personalized interactive experiences within smart environments. In particular, I designed the authoring systems by inspecting different interaction modalities, namely the direct input, spatial movement, in-situ activity and embodied interactions between users and everyday objects, smart things, robots and virtual mid-air contents. To this end, we explored 1) the software tools, hardware modules, and machines that support users to augment non-smart environments with digital interfaces and functions, and 2) the intelligence and context-awareness powered by the smart environments that deliver automatic and assistance during living and entertaining experiences. In this thesis, I mainly studied the following authoring workflows and systems: 1) customizing interactive interfaces on ordinary objects by surface painted circuits, 2) constructing a spatially aware environment for service robots with IoT modules, 3) authoring robot and IoT applications that can be driven by body actions and daily activities and 4) creating interactive and responsive augmented reality applications and games that can be played through natural input modalities.</p> <p> </p> <p>Takeaways from the main body of the research indicate that the authoring systems greatly lower the barrier for end-users to understand, control, and modify the smart environments. We conclude that seamless, fluent, and intuitive authoring experiences are crucial for building harmonious human-AI symbiotic living environments.</p>

Submitted by Kamila Costa (kamilavasconceloscosta@gmail.com) on 2015-06-11T20:40:36Z No. of bitstreams: 1 Dissertação-Wesllen S Lima.pdf: 2617450 bytes, checksum: 12694bdd075a47f1778193c5500476be (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-15T18:09:11Z (GMT) No. of bitstreams: 1 Dissertação-Wesllen S Lima.pdf: 2617450 bytes, checksum: 12694bdd075a47f1778193c5500476be (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-15T18:10:18Z (GMT) No. of bitstreams: 1 Dissertação-Wesllen S Lima.pdf: 2617450 bytes, checksum: 12694bdd075a47f1778193c5500476be (MD5) / Made available in DSpace on 2015-06-15T18:10:18Z (GMT). No. of bitstreams: 1 Dissertação-Wesllen S Lima.pdf: 2617450 bytes, checksum: 12694bdd075a47f1778193c5500476be (MD5) Previous issue date: 2015-03-05 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In recent years, power consumption has gradually increased in all sectors, especially in residential areas. This increase is mainly due to the emergence of new electrical appliances, for this reason, several solutions have been proposed by government and industry in order to minimize the energy consumption in homes. Among the proposed approaches, people's awareness, use of renewable energy sources and the creation of intelligent devices are highlighted . In addition, the use of Information and Communication Technologies (ICTs) in smart environments has been seen as an interesting alternative to deal with this problem. The idea is that the residences are instrumented with sensors and actuators in order to monitor people activities and, thereby, manage the power consumption based on their habits. In this context, this work proposes and validates a method to save energy through user activities in an intelligent environment using artificial intelligence techniques. The goal is to identify the devices related to user activities and make recommendations during their execution, avoiding waste. The proposed method, called AAEC (Activity-Appliance-Energy Consumption), is able to analyze a set of data collected from sensors available in the environment, after it recognizes user activities and recommends actions aimed at cost containment. Tests on a real database shown that the proposed method is able to save up to 35% of electricity. In general, the inclusion of AAEC method was a good solution to help people save energy without effort on individual behavior changes, contributing to the conscious use of energy and to the development of a sustainable society. / Nos últimos anos, o consumo de energia elétrica tem aumentado gradativamente em todos os setores, especialmente em ambientes residenciais. Esse aumento ocorre, principalmente, devido ao surgimento de novos aparelhos elétricos. Por este motivo, várias soluções têm sido propostas pelo governo e pela indústria na tentativa de minimizar o consumo de energia elétrica em residências. Dentre as abordagens propostas, destacam-se a conscientização das pessoas, uso de fontes de energia renováveis e a criação de aparelhos inteligentes. Além disso, o uso de Tecnologias da Informação e Comunicação (TICs) em ambientes inteligentes tem sido visto como uma alternativa interessante para lidar com este problema. A ideia é que as residências sejam instrumentadas com sensores e atuadores com objetivo de monitorar as atividades das pessoas e, por meio disso, gerenciar o consumo de energia elétrica com base nos seus hábitos. Nesse contexto, este trabalho propõe e valida um método capaz de economizar energia com base nas atividades dos usuários em um ambiente inteligente utilizando técnicas de inteligência artificial. O objetivo é identificar os aparelhos relacionados às atividades dos usuários e fazer recomendações ao longo da execução dessas atividades, evitando tais desperdícios. O método proposto, denominado de AAEC (Activity-Appliance-Energy Consumption), é capaz de analisar um conjunto de dados coletados pelos sensores disponíveis no ambiente, reconhecer automaticamente as atividades dos usuários e recomendar ações que visam a contenção de gastos. Testes feitos com uma base de dados real mostram que o método proposto é capaz de economizar até 35% de energia elétrica. De maneira geral, a inclusão do método AAEC se mostrou uma boa solução para auxiliar as pessoas a poupar energia sem que haja esforço na mudança de comportamento do indivíduo, contribuindo para o uso consciente de energia e no desenvolvimento de uma sociedade sustentável.

Contemporary cities challenge with complex problems. Different aspects of problems can be listed like ecological, demographical, economical or spatial. These problems require smart solutions. Increasing population and urbanization also force us to develop intelligent approaches to create economically, socially and environmentally sustainable cities. It&rsquo / s important that local authorities and planners have to have precise data to take actions against day to day urban problems. Collecting data and turning them in a knowledge are valuable for taking decisions and formulating urban policies. ICT technologies help spatial planners and decision-makers for collecting data and using them for taking agile and smart decisions. These smart solutions can be applied to different domains of cities. This thesis aims at elaborating the smart city concept and its potentials for solving complex urban challenges. A general analysis of world examples and a more profound analysis of a case study, namely Singapore Intelligent Island, have helped understanding the principles and criteria for attaining smartness in cities. The inferences from these analyses can provide a basis for the Turkish context. There are certain policies driven by Ministry of Environment and Urbanism in Turkey. Strategic action plans and legislative regulations can be a base for smart solutions for Turkish context.

The prevalent visions of ambient intelligence leverage natural interactions between users and available services in a smart space. In recent years, we have seen a huge interest from industry and academia in using handheld devices to interact with things, places and people in the real world. To facilitate such interactions, things are usually annotated with RFID tags or visual markers. These tags or markers are read by a handheld device equipped with an integrated RFID reader or a camera, in order to fetch related information and initiate further actions. Interacting with the Internet of Things (IoT) in a real environment has become increasingly desirable and feasible. This thesis contributes to the domain of physical interactions with IoT; however, we use a spatial-geometric approach instead of RFID or marker based solutions. Using this approach, for example, a user can point his/her handheld device to an annotated thing, from a distance, for the purpose of interaction. The pointing direction and location is determined based on the fusion of the mobile position and of the accelerometer data of the handheld device. To annotate things, their geometric coordinates are specified and related information or services are associated to them. In this thesis, we present a comprehensive and extensible framework to integrate various physical interactions with IoT into multimedia applications. The framework supports the implementations of pointMe, touchMe, and context-aware based interactions with geometrically annotated IoT. We define specific methods and practices that can be incorporated in order to build the interactions. We realize smart home, atlas learning, presentation interaction, smart haptic interaction, and learning based video interaction game prototypes in order to perform experiments and demonstrate the applicability and potential of the proposed geometric based annotation approach. In the analysis of the interaction techniques of the prototypes, we present the advantages and disadvantages of the geometric based annotation of IoT as seen by potential users, in comparison to RFID tags or visual markers based approaches.