The Internet of Things (IoT) plays an important role in the coming era of the Internet development. In addition to the convenience and opportunities it brings to us, there comes with the security issues, which could lead to the privacy leakage, it’s a threaten to the whole IoT system. To deal with the potential dangerous element hidden behind this technology, monitoring on the network would be indispensable. To develop and implements the digital network watchdog system that monitors the local network and the connected device, firstly, I surveyed the area related to the IoT attacks. The network monitor system provides basic network monitoring function, connected device tracking and monitoring function, reliable device operating function. I used the packages provided by Raspberry Pi to realize the general monitoring and transferred the captured result for further analysis. Also, I made use of SNMP and drawing tool to create graphs of different parameters in the monitoring of both network and connected devices. Then I implemented database with web service on Raspberry Pi to realize device operating. In evaluation, the system works well in general monitoring with all information provided and low lost package percentage, the graphs can provide situation of different parameters, and the respond time in the operation time of database is short. I discussed the ethical thinking and proposed the ethical thinking and future work.

The area of computer vision has been making big improvements in the latest decades, equally so has the area of electronics and small computers improved. These areas together have made it more available to build small, standalone systems for object detection in live video. This project's main objective is to examine whether a small device, e.g. Raspberry Pi 3, can manage an implementation of an object detection algorithm, called Viola-Jones, to count the occupancy of sitting people in a room with a camera. This study is done by creating an application with the library OpenCV, together with the language C+ +, and then test if the application can run on the small device. Whether or not the application will detect people depends on the models used, therefore three are tested: Haar Face, Haar Upper body and Haar Upper body MCS. The library's object detection function takes some parameters that works like settings for the detection algorithm. With that, the parameters needs to be tailored for each model and use case, for an optimal performance. A function was created to find the accuracy of different parameters by brute-force. The test showed that the Haar Face model was the most accurate. All the models, with their most optimal parameters, are then speed-tested with a FPS test on the raspberry pi. The result shows whether or not the raspberry pi can manage the application with the models. All models could be run and the Haar face model was fastest. As the system uses cameras, some ethical aspects are discussed about what people might think of top-corner cameras.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This paper presents the obtaining of electric power consumption curves, from the responses generated by an embedded low-cost Raspberry Pi 2 Model B system running the Linux operating system Raspbian working as a remote access server SSH, which is assessed through different types of access and brute force attacks dictionaries through specialized tools Medusa and Hydra, as well as the tool Metasploit unspecialized. The energy behavior is interpreted by a current consumption measurement system developed by low embedded platform cost Arduino Uno that runs a current sensor based on ACS721ELC-5A Hall effect chip, which has the ability to collect the variations generated by the platform test in response to events produced by the proposed test scenarios, the data is processed by the framework Matlab that collects, parses and normalizes using the Welch method, the current signal which is interpreted by Arduino Uno subsequently presents a standard curve It features a particular event based on scenarios of evidence. The results show the different curves standard patterns, and contextualized on the types of scenarios evaluated subsequently presents a theoretical mathematical model of the proposed power consumption, as well as rules or signatures proposed to identify an attack using the detection method of standards used IDS Snort. These current curves facilitate understanding and obtaining a pattern of current consumption for each access and attack the embedded platform. / Este trabalho apresenta a obtenção de curvas de consumo de corrente elétrica, a partir das respostas geradas por um sistema embarcado de baixo custo Raspberry Pi 2 Model B executando o sistema operacional Linux Raspbian trabalhando como um servidor de acesso remoto SSH, que é avaliado através de diferentes tipos de acessos e ataques de força bruta com dicionários através das ferramentas especializadas Medusa e Hydra, como também a ferramenta não especializada Metasploit. O comportamento energético é interpretado por um sistema de medição de consumo de corrente desenvolvido pela plataforma embarcada de baixo custo Arduino Uno que administra um sensor de corrente baseado no chip ACS721ELC- 5A de efeito Hall, que possui a capacidade de coletar as variações geradas pela plataforma de teste em resposta aos eventos produzidos pelos cenários de provas propostos, os dados são processados pelo Framework Matlab que coleta, analisa e normaliza por meio do método de Welch o sinal de corrente que é interpretado pelo Arduino Uno, posteriormente apresentase uma curva padrão que caracteriza um determinado evento baseado nos cenários de provas. Os resultados apresentam as diferentes curvas padrões normalizadas, e contextualizadas nos tipos de cenários avaliados, seguidamente apresenta-se um modelo matemático teórico do consumo de corrente proposto, como também as regras ou assinaturas propostas para identificar um ataque através do método de detecção por padrões que utilizada o IDS Snort. Essas curvas de corrente facilitam o entendimento e obtenção de um padrão de consumo de corrente para cada acesso e ataque na plataforma embarcada.

The Internet of Things (IoT) is a concept that revolves around ordinary devices that are connected to the internet for extended control and ease of use. Altran, a company dealing in high technology and innovation consultancy, predicts a large growth in business opportunities in the IoT area in the coming years, and therefore wants to invest in knowledge about the Internet of Things. Altran wanted a report that described popular wireless IoT communication technologies along with a proposal for a general IoT communication platform or base that could be used to implement many of these technologies. Additionally, an audio streaming application were to be implemented on the proposed platform to validate its credibility. The project resulted in a report on 6 different wireless IoT technologies: Z-wave, ZigBee, Thread, Bluetooth, 802.11n, and 802.11ah. A hardware and software base was proposed that could implement 4 of 6 of these technologies. This base was the Raspberry Pi 2 along with the Raspbian Jessie operating system. Finally an audio streaming system that could stream data to a set of smart Speaker nodes over wireless links based on IEEE 802.11n was implemented on the proposed base.

Termisk energi som solfångare producerar kan lagras i Borehole Thermal Energy Storage, BTES, när efterfrågan är låg, för att sedan användas när efterfrågan är hög. Målet med detta examensarbete är att utveckla en skalbar systemarkitektur för styrning och monitorering av en BTES prototyp, där ringarna som borrhålen utgör är indelade i olika temperaturhierarkier. De ringar som ligger närmare centrum av borrhålen har högre temperaturhierarki än de som ligger längre ut. Driftinformation från systemet ska kunna följas på en webbplats och temperaturdata från systemet ska sparas undan för lagring. Datakommunikationen består av en One-Wire buss som innehåller temperatursensorer och ett CAN-buss system för datakommunikation mellan sensor/aktuator-noder och server-nod. Utifrån sensordata bestäms reglering av ventiler. Driftinformation lagras i en databas och från denna databas presenteras informationen på en hemsida. Hemsidan innehåller en överblick av brunnparken där temperaturen i varje brunn kan utläsas. Regleralgoritmen uppfyller den sökta temperaturhierarkin där de högsta temperaturerna ska vara i centrum av brunnparken. Prototypen fungerar som en utvecklingsplattform och demonstrerande prototyp. / Thermal energy produced from solar collectors can be stored in Borehole Thermal Energy Storage, BTES, when demand is low for later usage when demand is high. The aim of this thesis is to develop a scalable system architecture for control and monitoring of a BTES prototype.The BTES prototype consist of 13 boreholes configured in a hierarchically manner in two circles and one core. The core is of the highest priority. The operational information is displayed on a website and stored in a database.The data communication consist of two One-wire buses and one CAN bus. The temperature sensors are connected to the One-Wire buses. The CAN bus consist of sensor/actuator nodes and a server node. Based on sensor data, a control loop configures the actuators. Operational data is stored in a database and visually presented on a website. The website displays an overview of all the boreholes where all of the sensors data can be read. The control algorithm runs successfully according to its hierarchically priorities. The prototype works as a developement platform and a demonstrating prototype.

This is a report on the possibility of using a Raspberry Pi as an intrusion detection system in a home environment to increase network security. The focus of this study was on how well two different generations of Raspberry Pi would be able to  handle network traffic while acting as an intrusion detection system. To examine this a testing environment was set up containing two workstation computers connected to a Raspberry Pi, each computer hosting a virtual machine. Tests measuring the network throughput as well as the CPU and memory usage were performed on each of the Raspberry Pi devices. Two models of Raspberry Pis were used; Raspberry Pi model B+ and Raspberry Pi 2 model B; each of them running the operating system Arch Linux ARM. The results of these tests were that both of the Raspberry Pis could be used as an intrusion detection system but has some limitations that could impede usage depending on the requirements of the user. Raspberry Pi 2 model B show benefits of its updated hardware by suffering lower throughput degradation than Raspberry Pi model B+, while using less of it's total CPU and memory capacity. / Den här rapporten behandlar möjligheten att använda en Raspberry Pi som ett intrångdetekteringssystem i en hemma miljö för att öka nätverkssäkerheten. Fokusen i den här studien ligger på hur väl de två senaste generationerna av Raspberry Pi skulle kunna hantera nätverkstrafik samtidigt som den undersöker nätverkstrafiken och söker efter hot. För att kontrollera hur väl en Raspberry Pi kan fungera som ett intrångdetekteringssystem har en laborationsmiljö upprättats bestående av två fysiska maskiner som vardera används för att virtualisera en virtuell maskin. Tester för att mäta datagenomströmning, processor och minnesbelastning utfördes på var och en av Raspberry Pi. Två modeller av Raspberry Pi användes; Raspberry Pi model b+ och Raspberry Pi 2 model b, både körde operativsystemet Arch Linux ARM. Resultatet av testerna visade att det går att använda båda enheterna för att upprätta ett intrångdetekteringssystem, men det finns vissa begränsningar i enheterna vilket kan begränsa implementationsmöjligheterna. Raspberry Pi 2 model B uppvisade bättre resultat i form av att den är lägre belastad och har en högre datagenomströmning till skillnad från Raspberry Pi model B+. Raspberry Pi 2 model B har nyare och snabbare hårdvara vilket är den troliga orsaken till att den presterar bättre.

I detta projekt byggdes en fjärrstyrd robot som styrs över Wi-Fi, ochstreamar video till användaren av vad den ser. Som komplement körsäven en temperatursensor för att utöka robotens möjlighet att kännaav sin omgivning. Huvudkomponenten är en Raspberry PI 3 modell B, därall kod för styrning och hemsidekontrollerna körs. Resultatet är enrobot som kan styras utanför synhåll och kan sända tillbaka en stabilkameraström så länge den är kopplad till ett stabilt nätverk.Dessvärre sjunker resultatet i takt med nätverkskvalitén. Vid ettsvagare nätverk blir det långa laddningstider för kameraströmmenvilket leder till att roboten blir mindre responsiv. Det tillsammansmed den förlorade kameraströmmen resulterar i att roboten ärobrukbar. Vid tillräckligt svaga nätverk, eller vid nätverksproblemkommer roboten repetera sin sista order tills dess att kontakt äråterupprättad.

This thesis summarizes our project in building and setting up a Beowulf cluster. The idea of the project was brought forward by the company CGI in Karlstad, Sweden. CGI’s wish is that the project will serve as a starting point for future research and development of a larger Beowulf cluster. The future work can be made by both employees at CGI and student exam projects from universities. The projects main purpose was to construct a cluster by using several credit card sized single board computers, in our case the Raspberry Pi 3. The process of installing, compiling and con- figuring software for the cluster is explained. The MPICH and TensorFlow software platforms are reviewed. A performance evaluation of the cluster with TensorFlow is given. A single Raspberry Pi 3 can perform neural network training at a rate of seven times slower than an Intel system (i5-5250U at 2.7 GHz and 8 GB RAM at 1600 MHz). The performance degraded significantly when the entire cluster was training. The precise cause of the performance degradation was not found, but is ruled out to be in software, either a programming error or a bug in TensorFlow.

The report aims to investigate how well a certain type of affordable embedded single board computer can hold up against today's more expensive computers in a computer system by doing various tests on a system with the specified requirements. The system has a Raspberry Pi as the single board computer which task is to control a camera based on coordinates obtained from a server as well as capture and stream a video signal on a network. The researches were conducted to check how much network traffic a single-chip computer sent in different video formats and how much CPU utilization was required. Studies were also made to ensure the accuracy of the camera control. The researches have been experimental, where several tests have been performed and analyzed. The results show that a sufficiently good accuracy can be obtained from the camera steering unit, in which two different servos have been investigated. When the video format MJPEG and H.264 are used, the single-chip computer is able to transmit a video signal up to 1280x720 at 15 fps. The system managed to download and perform calculations on an object from the server at 42.3 ms. When the entire system was up and running at the same time the Raspberry Pi didn’t manage to deliver a video signal and obtain the coordinates from the server fast enough. Depending on the video format the performance on the single-chip computer varied, but no setup managed to keep the system stable enough to reach the requirements. / Rapportens syfte är att undersöka hur väl en viss typ av billigare enkortsdator kan stå sig mot dagens dyrare datorer i ett datorsystem genom att göra olika undersökningar på ett system med uppsatta krav. Systemet har en Raspberry Pi som enkortsdator och har till uppgift att styra en kamera utifrån koordinater som fås från en server samt fånga och strömma en videosignal ut på ett nätverk. De undersökningar som gjordes var att kontrollera hur mycket nätverkstrafik som enkortsdatorn sände vid olika format på videosignalen samt hur mycket CPU- utnyttjande som krävdes. Undersökningar gjordes också för att säkerställa precisionen på kamerastyrningen. Alla undersökningar har varit experimentella, där flera olika tester har utförts och analyserats. Resultatet från undersökningarna visar att en tillräckligt god precision kan fås från kamerastyrningen, där två olika servon har undersökts. När videoformaten MJPEG och H.264 används kan enkortsdatorn klara av att sända ut en videosignal upp till 1280x720 med 15 bildrutor per sekund. I systemet som testerna utfördes på klarade enkortsdatorn av att hämta och utföra beräkningar på ett objekt från servern på 42,3 ms. När hela systemet var igång samtidigt klarade dock inte Raspberry Pi av att leverera en videosignal och hämta koordinater från servern tillräckligt snabbt. Beroende på vilket videoformat som användes presterade enkortsdatorn olika bra, men det var ingen inställning som stabilt klarade av att nå kraven.

The diploma thesis focuses on programming in the Matlab for the Raspberry Pi 3 platform. For the purpose of the presentation, there are two applications designed for Raspberry Pi that are using available hardware, camera and servos. The first application serves as colour object detecting and accurate tracking by using camera calibration. The second application serves as a face detection and recognition. These applications are implemented by modern methods and knowledge of computer vision. Tracking of the objects and face recognition are verified by an experiment that reveals the accuracy of the used methods.