This thesis analysis a theoretical basis of the home automatization. The thesis contains the overview of commercial realizations of an intelligent house. The thesis also contains design of device, which could distinguish low and high tariff. Design of device is focused on wireless control of aquarium. In the following chapters a overview of components, design of circuits, practical realization and test of whole system.

This master's thesis compares wireless technologies which they are used at current time. The most of information in this thesis are obtained from my practical experiences with design and realization of wireless network during last four years. The thesis briefly describes standards and security of WiFi. More time is devoted to the description of new standard 802.11n and the E-Band. The use of E-Band is allowed in Czech Republic from a year 2008. The thesis describes a practical evaluation of wireless network 802.11a/b/g/n and E-Band. Examples of the use are described in case studies of high-speed point-to-point wireless bridge, coverage of a logistic warehouse and wireless network with location in a hospital.

This thesis deals with the investigation and assessment of communication standards LTE-Advance and WiFi (IEEE 802.11n/ac). The EVM error parameter is examined for each standard. To work with different standards a universal workplace have been proposed (testbed). This universal workplace serves for adjusting transmitting and receiving means and for processing signals transmitted and interpreted. Matlab was chosen for this work, through which only control devices such as generators and analyzers from Rohde & Schwarz. This workplace also measures the coexistence of these standards. At the conclusion of this thesis, different coexistence scenarios were designed and evaluated through testbed results.

A common method of connecting to the internet is a wireless network. These networks can be monitored to discover the area of their coverage, but commercial receivers don't always provide the most accurate results. A software-defined radio was programmed to sniff wireless signals and tested against a commercial receiver and the results were compared. The results suggest that the software-defined radio performs at least as well as the commercial receiver in distance measurements and significantly better in samples taken per minute. It was determined that the software-defined radio is a viable replacement for a commercial receiver in 3D heat mapping.

In the investigation of crimes committed by wireless users, one of the key goals is to determine the location of the mobile device at the time of the crime. Since this happens during the investigative phase after the crime is committed, we term this the post hoc geographical localization estimation problem. In this thesis, we introduce the post hoc geographical localization estimation problem and present approaches for its solution based on radio frequency (RF) fingerprinting. Motivated by the goal of establishing a crime's location with enough accuracy to obtain a search warrant, our focus is on locating a criminal mobile device in indoor environments with roughly the granularity to distiguish between two adjacent rooms, without having the ability to enter those rooms or the building to gather input data for the RF fingerprinting algorithm. While empirical performance studies of instantaneous indoor positioning systems based radio frequency (RF) fingerprinting have been presented in the literature, the core of this thesis is the first empirical study focused on the post hoc version of problem from the viewpoint of digital forensics. In this study, we set up experiments in a residential area and collect a large set of raw data in order to analyze and evaluate the algorithms, the best of which provides a mean error distance of roughly 1.4 meters. In addition, we consider enhancements to the baseline algorithms if knowledge of the blueprint of the building is available. In particular, we consider whether compensating the raw data for the attenuation caused by walls can improve algorithm performance.

This thesis focuses on the design and implementation of a new, inexpensive, and less complex system for a Do-It-Yourself (DIY) Internet of Things (IoT) platform. The hardware aspects focus on a new chip called the ESP8266 which contains both microcontroller and WiFi connectivity capabilities in an extremely affordable package. The system uses the Arduino IDE to program the ESP8266, which is known to be an extremely user-friendly environment. All other software is both free and easy to use. Past methods of creating IoT projects involved either expensive hardware, often ranging from $50-$100 per node, or complicated programming requiring a full computer, or a constant connection to an immobile power source. This method costs as little as $2.50, can last for months or even years off of batteries, can be smaller than a quarter, and only requires a few lines of code to get data moving, making this platform much more attractive for ubiquitous use.

Smart agriculture is an increasingly popular field in which the technology of wireless sensor networks (WSN) has played a large role. Significant research has been done at Cal Poly and elsewhere to develop a computer vision (CV) and machine learning (ML) pipeline to monitor crops and accurately predict crop yield numbers. By autonomously providing farmers with this data, both time and money are saved. During the past development of a prediction pipeline, the primary focuses were CV and ML processing while a lack of attention was given to the collection of quality image data. This lack of focus in previous research presented itself as incomplete and inefficient processing models. This thesis work attempts to solve this image acquisition problem through the initial development and design of an Internet of Things (IoT) prototype network to collect consistent image data with no human interaction. The system is developed with the goals of being low-power, low-cost, autonomous, and scalable. The proposed IoT network nodes are based on the ESP32 SoC and communicate over-the-air with the gateway node via Bluetooth Low Energy (BLE). In addition to BLE, the gateway node periodically uplinks image data via Wi-Fi to a cloud server to ensure the accessibility of collected data. This research develops all functionality of the network, comprehensively characterizes the power consumption of IoT nodes, and provides battery life estimates for sensor nodes. The sensor node developed consumes a peak current of 150mA in its active state and sleeps at 162µA in its standby state. Node-to-node BLE data transmission throughput of 220kbps and node-tocloud Wi-Fi data transmission throughput of 709.5kbps is achieved. Sensor node device lifetime is estimated to be 682 days on a 6600mAh LiPo battery while acquiring five images per day. This network can be utilized by any application that requires a wireless sensor network (WSN), high data rates, low power consumption, short range communication, and large amounts of data to be transmitted at low frequency intervals.

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No / Continuously identifying a user’s location context provides new opportunities to understand daily life and human behavior. Indoor location systems have been mainly based on WiFi infrastructures which consume a great deal of energy mostly due to keeping the user’s WiFi device connected to the infrastructure and network communication, limiting the overall time when a user can be tracked. Particularly such tracking systems on battery-limited mobile devices must be energy-efficient to limit the impact on the experience of using a phone. Recently, there have been a lot of studies of energy-efficient positioning systems, but these have focused on outdoor positioning technologies. In this paper, we propose a novel indoor tracking framework that intelligently determines the location sampling rate and the frequency of network communication, to optimize the accuracy of the location data while being energy-efficient at the same time. This framework leverages an accelerometer, widely available on everyday smartphones, to reduce the duty cycle and the network communication frequency when a tracked user is moving slowly or not at all. Our framework can work for 14 h without charging, supporting applications that require this location information without affecting user experience.

Made available in DSpace on 2016-04-04T18:31:21Z (GMT). No. of bitstreams: 1 Fernando Lino.pdf: 1954006 bytes, checksum: a697088d7a7477d92d2183541ef1e1c6 (MD5) Previous issue date: 2008-02-08 / The literature describes the fast flat fading propagation model in mobile environment as a Rayleigh distribution with different standard deviations which considers only one parameter to describe the channel fading. In the Weibull distribution there are two parameters to be modeled: shape factor and scale factor. The Rayleigh distribution is a particular case of the Weibull distribution where the shape factor has a firm value equals to two. This work has the objective to evaluate, through practical measurements, both parameters in the Weibull distribution at an indoor environment. The results will show the real value of the shape factor parameter and the scale factor as well, indicating that the Weibull distribution is more flexible to describe a fast flat fading channel at an indoor propagation. / A literatura descreve a propaga??o fast flat fading em sistemas m?veis como o modelo de distribui??o Rayleigh com diferentes desvios padr?es, o qual considera apenas um par?metro para descrever o canal de desvanecimento. Na distribui??o de Weibull existem dois par?metros a serem modelados: fator de forma e fator de escala. A distribui??o Rayleigh ? um caso particular da distribui??o de Weibull onde o fator de forma tem um valor igual a dois. Este trabalho tem o objetivo de avaliar, atrav?s de medidas pr?ticas, ambos os par?metros da distribui??o de Weibull em um ambiente indoor. Os resultados ir?o mostrar os reais valores dos par?metros de forma e de escala, indicando que a distribui??o de Weibull ? mais flex?vel para descrever um canal flat fast fading em uma propaga??o indoor.