The aim of the thesis was to ensure simple and clear operation while providing users the ability to monitor and analyze the current historical data from sensors in the smart home. Beyond the task I have extended the work on the Windows Phone platform. Designed user interface based on the requirements for intelligent home control in the project IoT (Internet of Things), which are written in this work. Tests were conducted using the available auto-mated testing and continuous testing at alpha and beta. Finally, the quality of the interface and user friendliness assessed using questionnaires.

This thesis explores new posibilities of using mobile devices, specificaly cell phones and tablets which support the USB OTG feature, to read data from smart electricity meters. To achieve this goal, an application for OS Android was developed in practical part of this thesis. This mobile application communicates with electricity meters via optical sensor serving as serial line and using the DLMS/COSEM standard. Acquired data are saved to a remote database for further use.

The master’s thesis deals with the design of the Smart Home System (SHS), which takes advantage of the 'Raspberry Pi' single-board computer. Background research about the theoretical concept of SHS is carried out, which reveals weaknesses in that field. The aim of the thesis is elimination these weak points and takes into account reliability, extensibility and low acquisition price. The practical part is introduced by design of particular modules, which include both hardware design and software. The project is concluded with integration all components into the single functional universal system together with the extensibility presentation.

This thesis is devoted to the control of intelligent buildings and a smart home. It presents the possibilities of building/home automation and changes the subject to a particular subsystem: door speaker. It describes a realization of communication between door speaker and rooms of the home and among rooms as well.

This master thesis presents the design and implementation of a framework for monitoringdata related to activities of daily living (ADL) in a smart home environment, conducted for theHuman Health and Activity Laboratory (H2Al) at Luleå University of Technology. The generalaim of such environments is to increase the quality of life by enabling elderly to live longer athome while reducing the consumption of resources necessary. The complexity of collection,filtering and storing of data in smart home environments is however inherent due to oftenmany interworking sensor-systems, which allmay have different APIs and communicationpathways. This means that knowing whether ‘all systems are go’ when for example doing astudy is not easy, especially for persons not trained in data science.This work therefore aim to design and implement a framework for datamonitoring thattargets smart home environments in which activities of daily living are important for analysisof health-related conditions and for the personalised tailoring of interventions. The frameworkprimarily collects data from four selected systems, that for example track the position andmovements of a person. The data is stored in a database and visualised on a website toallow for monitoring of individual sensor data being collected. The framework was validatedtogether with a occupational therapist through a proof-of-concept trial in the Human Healthand Activity Laboratory, for which healthy subjects conducted a typical test (making a salad)used when assessing human performance.In conclusion, the developed framework works as expected, collecting data frommanysensor systems and storing the data in a common format, while the visualisation on a websiteis perceived as giving an easy overview of monitored data. Additional data can easily be addedto the framework and other processes beyond monitoring can be linked to the data, suchas further data refinement and algorithms for activity recognition (possibly using machinelearning techniques). Future work include to better distinguish data from multiple occupants,develop themanagement of synchronous and asynchronous data, and refine the web interfacefor additional simplicity

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A smart home is an apartment or a house in which smart devices communicate with each other to improve key areas of daily life, such as comfort, security or energy consumption. Therefore, the smart home domain is user-centric and exhibits characteristics that distinguish it from other application domains of the Internet of Things. Specifically, this concerns the existence of different 'regular' and 'smart' devices, but also the basic arrangement of each household, which is highly individual. As a result, the realization of analytics scenarios in the smart home domain is influenced by household-specific requirements regarding the configuration, composition and execution of analytics tasks. Existing approaches in scientific literature cover the resulting architectural challenges only insufficiently. With the emergence of new computing paradigms, architectural concepts and technologies, new opportunities for analytics approaches, which enable individual household insights, become evident. For this reason, the objective of this work is the design of an Internet of Things analytics architecture for smart home applications, which supports the flexible deployment of analytics pipelines, therefore enabling the generation of individual household insights. In order to achieve this goal, challenges for Internet of Things analytics architectures are identified and analyzed by conducting a literature review. Based on the resulting challenges catalog, an architectural model is designed that facilitates the processing and analysis of streaming data from smart devices of different kinds. The developed architecture utilizes the fog computing paradigm, therefore allowing the deployment and execution of analytics pipelines in the cloud as well as at edge of the network. The architectural model is the foundation for a prototype, which is implemented to evaluate the proposed solution. The evaluation is performed by conducting several experiments, which are designed in order to validate the prototypes feasibility to address the found challenges. The main contributions of this work are a challenges catalog for Internet of Things analytics architectures, an architectural model for analytics in smart home applications as well as a prototype, which is based on it.

Recent advances in smartphones and affordable open-source hardware platforms have enabled the development of low-cost architectures for Internet-of-Things (IoT)-enabled home automation and security systems. These systems usually consist of sensing and actuating layer that is made up of sensors such as passive infrared sensors, also known as motion sensors; temperature sensors; smoke sensors, and web cameras for security surveillance. These sensors, smart electrical appliances, and other IoT devices connect to the Internet through a home gateway. This paper lays out an architecture for a cost-effective smart door sensor that will inform a user through an Android application, of door open events in a house or office environment. The proposed architecture uses an Arduino-compatible Elegoo Mega 2560 microcontroller board along with the Raspberry Pi 2 board for communicating with a web server that implements a RESTful API. Several programming languages are used in the implementation and further applications of the door sensor are discussed as well as some of its shortcomings such as possible interference from other radio frequency devices.

The use of the Internet of Things (IoT) devices has surged in recent years. However, due to the lack of substantial security, IoT devices are vulnerable to cyber-attacks like Denial-of-Service (DoS) attacks. Most of the current security solutions are either computationally expensive or unscalable as they require known attack signatures or full packet inspection. In this paper, we introduce a novel Graph-based Outlier Detection in Internet of Things (GODIT) approach that (i) represents smart home IoT traffic as a real-time graph stream, (ii) efficiently processes graph data, and (iii) detects DoS attack in real-time. The experimental results on real-world data collected from IoT-equipped smart home show that GODIT is more effective than the traditional machine learning approaches, and is able to outperform current graph-stream anomaly detection approaches.

Recent advances in smartphones and affordable open-source hardware platforms have enabled the development of low-cost architectures for IoT-enabled home automation and security systems. These systems usually consist of a sensing and actuating layer that is made up of sensors such as PIR (Passive Infra-red) sensors, also known as motion sensors; temperature sensors; smoke sensors, and web cameras for security surveillance. These sensors, smart electrical appliances and other IoT devices connect to the Internet through a home gateway. This paper lays out architecture for a cost effective “smart” door sensor that will inform a user through an Android application, of door open events in a house or office environment. The proposed architecture uses an Arduino-compatible Elegoo Mega 2560 microcontroller (MCU) board along with the Raspberry Pi 2 board for communicating with a web server that implements a RESTful API. Several programming languages are used in the implementation and further applications of the door sensor are discussed as well as some of its shortcomings such as possible interference from other RF (Radio Frequency) devices.