This thesis describes processing of RSSI parameter and its subsequently use for cal- culating distance of wireless node. This thesis also describes analysis of radio model environment and calibration of key variables needed for localization. There is also sys- tem realized for localization of wireless nodes in sensor network. It uses dynamically calibrated variables for calculations, which describes radio model.

The indoor positioning system is a series of networking systems used to monitor/locate objects at indoor area as opposed that of GPS which does the same at outdoor. The increase in the popularity of the Internet of Things made the demand for Bluetooth Low Energy technology more and more essential due to their compatibility in the smartphones which makes it to access easier. The BLE’s reliable signal and accuracy in calculating the distance has a cutting edge on others in IPS. In this thesis, the Bluetooth Low Energy indoor positioning system was designed and implemented in the office area, and the positionofIoTdevicesweremonitored. OntheIoTdevices,thebeaconswereplaced. And thesebeaconswerecoveringtheofficearea. Thereceiver,smartphoneinourcase,recorded theReceivedSignalStrengthIndicationofthetransmittedsignalsfromthebeaconswithin the range of the signal and stored the collected data in a database. Two experiments have beenconducted. Oneisforbeaconsthatarestationaryandonethatismoving. Toevaluate these experiments, a few tests were performed to predict the position of beacons based on therecordedreceivedsignalstrength’s. Inthecaseofstationarybeacons, itoffersaccuracy range from 1 m to 5 m, and 3 m to 9.5 m in anticipating the position of each beacon in the case of moving beacon. This methodology was a mixture of fingerprinting and an algorithm of multilateration. Finally, the experiments show that the algorithm used provides the most accurate indoor position using BLE beacons that can be monitored through an Android-based application in real-time.

Internet of Things ger människor möjligheter att genom tjänster dra nytta av sensorer och andra enheter som tillsammans skapar ett brett utbud av lösningar som smart hem, smart transport, äldreomsorg och mycket mer. Den senaste innovationen av Bluetooth SIG är Bluetooth mesh topologi som tillåter att ansluta trådlösa enheter i ett många till många förhållanden. I denna avhandling utvecklade vi ett inomhusspårningssystem baserat på Bluetooth Low Energy teknik och mesh topologi för att undersöka de potentiella fördelar som Bluetooth Low Energy nätverk har att erbjuda för inomhusspårningssystemet. Systemet är utvecklat för att spåra Bluetooth beacon i en inomhus kontorsmiljö. Received Signal Strength används för att beräkna avståndet till beacon, medan positionen av beacon beräknas med Extended Min-Max och Trilateration algoritmer. Beräkningar utförs på servern. Resultaten analyseras genom jämförelse av Root Mean Square Error av båda algoritmerna. I denna avhandling utvärderas inomhusspårning som en del av ett uppdrag som ges av u-blox. / The Internet of Things brings connectivity of people, services, sensors and other devicesenabling a wide range of applications like smart home, smart transport, elder care andmuch more. The latest innovation of Bluetooth SIG is the Bluetooth mesh topology thatallows us to connect wireless devices in a many-to-many relationship. To investigate thepotential benefit that the BLE mesh network has to offer for the indoor tracking system.In this thesis, we developed an indoor tracking system based on Bluetooth Low Energytechnology and mesh topology. The system is developed to track Bluetooth beacon inan indoor office environment. Received Signal Strength is used to calculate the distance tothe beacon, while the position of the beacon is calculated using Extended Min-Max andTrilateration algorithms. Calculations are performed on the server. The results are analysed through comparison of Root Mean Square Error of both algorithms. In this thesis, indoor tracking is evaluated as part of an assignment given by u-blox.

The demand for accurate localization under indoor environments has increased dramatically in recent years. To be cost-effective, most of the localization solutions are based on the WiFi signals, utilizing the pervasive deployment of WiFi infrastructure and availability of the WiFi enabled mobile devices. In this thesis, we develop completed indoor localization solutions based on WiFi fingerprinting and machine learning approaches with two types of WiFi fingerprints including received signal strength indicator (RSSI) and channel state information (CSI). Starting from the low complexity algorithm, we propose a soft range limited K nearest neighbours (SRL-KNN) to address spatial ambiguity and the fluctuation of WiFi signals. SRL-KNN exploits RSSI and scales the fingerprint distance by a range factor related to the physical distance between the user’s previous position and the reference location in the database. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Besides, the idea of the soft range limiting factor can be applied to all of the existed probabilistic methods, i.e., parametric and nonparametric methods, to improve their performances. A semi-sequential short term memory step is proposed to add to the existed probabilistic methods to reduce their spatial ambiguity of fingerprints and boost significantly their localization accuracy. In the following research phase, instead of locating user's position one at a time as in the cases of conventional algorithms, our recurrent neuron networks (RNNs) solution aims at trajectory positioning and takes into account of the relation among RSSI measurements in a trajectory. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. Next, the problem of localization using only one single router is analysed. CSI information will be adopted along with RSSI to enhance the localization accuracy. Each of the reference point (RP) is presented by a group of CSI measurements from several WiFi subcarriers which we call CSI images. The combination of convolutional neural network (CNN) and LSTM model is proposed. CNN extracts the useful information from several CSI values (CSI images), and then LSTM will exploit this information in sequential timesteps to determine the user's location. Finally, a fully practical passive indoor localization is proposed. Most of the conventional methods rely on the collected WiFi signal on the mobile devices (active information), which requires a dedicated software to be installed. Different from them, we leverage the received data of the routers (passive information) to locate the position of the user. The localization accuracy is investigated through experiments with several phones, e.g., Nexus 5, Samsung, Iphone and HTC, in hundreds of testing locations. The experimental results demonstrate that our proposed localization scheme achieves an average localization error of around 1.5 m when the phone is in idle mode, and approximately 1 m when it actively transmits data. / Graduate

The smartphone is becoming a common device carried by hundreds of millions of individual humans worldwide, and is used to accomplish a multitude of different tasks like basic communication, internet browsing, online shopping and fitness tracking. Limited by its small size and tight energy storage, the human-smartphone interface is largely bound to the smartphones small screens and simple keypads. This prohibits introducing new rich ways of interaction with smartphones.   The industry and research community are working extensively to find ways to enrich the human-smartphone interface by either seizing the existing smartphones resources like microphones, cameras and inertia sensors, or by introducing new specialized sensing capabilities into the smartphones like compact gesture sensing radar devices.   The prevalence of Radio Frequency (RF) signals and their limited power needs, led us towards investigating using RF signals received by smartphones to recognize gestures and activities around smartphones. This thesis introduces a solution for recognizing touch-less dynamic hand gestures from the Wi-Fi Received Signal Strength (RSS) received by the smartphone using a recurrent neural network (RNN) based probabilistic model. Unlike other Wi-Fi based gesture recognition solutions, the one introduced in this thesis does not require a change to the smartphone hardware or operating system, and performs the hand gesture recognition without interfering with the normal operation of other smartphone applications.   The developed hand gesture recognition solution achieved a mean accuracy of 78% detecting and classifying three hand gestures in an online setting involving different spatial and traffic scenarios between the smartphone and Wi-Fi access points (AP). Furthermore the characteristics of the developed solution were studied, and a set of improvements have been suggested for further future work. / Smarta telefoner bärs idag av hundratals miljoner människor runt om i världen, och används för att utföra en mängd olika uppgifter, så som grundläggande kommunikation, internetsökning och online-inköp. På grund av begränsningar i storlek och energilagring är människa-telefon-gränssnitten dock i hög grad begränsade till de förhållandevis små skärmarna och enkla knappsatser.   Industrin och forskarsamhället arbetar för att hitta vägar för att förbättra och bredda gränssnitten genom att antingen använda befintliga resurser såsom mikrofoner, kameror och tröghetssensorer, eller genom att införa nya specialiserade sensorer i telefonerna, som t.ex. kompakta radarenheter för gestigenkänning.   Det begränsade strömbehovet hos radiofrekvenssignaler (RF) inspirerade oss till att undersöka om dessa kunde användas för att känna igen gester och aktiviteter i närheten av telefoner. Denna rapport presenterar en lösning för att känna igen gester med hjälp av ett s.k. recurrent neural network (RNN). Till skillnad från andra Wi-Fi-baserade lösningar kräver denna lösning inte en förändring av vare sig hårvara eller operativsystem, och ingenkänningen genomförs utan att inverka på den normala driften av andra applikationer på telefonen.   Den utvecklade lösningen når en genomsnittlig noggranhet på 78% för detektering och klassificering av tre olika handgester, i ett antal olika konfigurationer vad gäller telefon och Wi-Fi-sändare. Rapporten innehåller även en analys av flera olika egenskaper hos den föreslagna lösningen, samt förslag till vidare arbete.

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This thesis compares and evaluates the accuracy of two RSSI-based tri-lateration methods in an indoor setting, implementing either Wi-Fi andBluetooth Low Energy (BLE) while using commercially available hardware.The purpose of evaluation is part of the long-term vision of improving thesafety of workers in adverse environments such as factories, by providing awearable Indoor Positioning System where other systems like GPS are notsuitable due to signal obstruction. Within a confined space replicating in-tended real-world conditions in terms of signal attenuation and adversity,30 consecutive measurements of signal strength readings (RSSI) to threereference nodes were collected at 10 randomized sample positions, andwas repeated across 5 tests. The accuracy of trilateration was evaluatedusing an averaged Root Mean Square Error (RMSE) over the five tests. Itwas observed that RSSI using Wi-Fi achieved better accuracy of predictingthe actual position within the testing environment than signal-strength us-ing BLE, with Wi-Fi and BLE achieving an accuracy of 0.88 and 1.85 metersrespectively. However, because of the power efficiency of BLE it is a viablecandidate for a future low-cost and device-based Indoor Localization Sys-tem to potentially be used and worn by workers. The results while alignedwith similar existing literature, infer what a low-cost indoor positioningsystem might achieve. Future research with the goal of developing suchsolutions could benefit from implementing both Wi-Fi and BLE as the basisof signal strength trilateration.

Modern indoor positioning systems encounter challenges inherent to indoor environments. Signal changes can stem from various factors like object movement, signal propagation, or obstructed line of sight. This thesis explores a supervised machine learning approach that integrates Bluetooth Low Energy (BLE) and inertial sensor data to achieve consistent angle and distance estimations. The method relies on BLE angle estimations and signal strength alongside additional sensor data from an Inertial Measurement Unit (IMU). Relevant features are extracted and a supervised learning model is trained and then validated on familiar environment tests. The model is then gradually introduced to more unfamiliar test environments, and its performance is evaluated and compared accordingly. This thesis project was conducted at the u-blox office and presents a comprehensive methodology utilizing their existing hardware. Several extensive experiments were conducted, refining both data collection procedures and experimental setups. This iterative approach facilitated the improvement of the supervised learning model, resulting in a proposed model architecture based on transformers and convolutional layers. The provided methodology encompasses the entire process, from data collection to the evaluation of the proposed supervised learning model, enabling direct comparisons with existing angle estimation solutions employed at u-blox. The results of these comparisons demonstrate more accurate outcomes compared to existing solutions when validated in familiar environments. However, performance gradually declines when introduced to a new environment, encountering a wider range of signal conditions than the supervised model had trained on. Distance estimations are then compared with the path loss propagation equation, showing an overall improvement. / Moderna inomhuspositioneringssystem möter utmaningar som förekommer i inomhusmiljöer. Signalförändringar kan bero på olika faktorer som objektets rörelse, signalutbredning eller blockerad siktlinje. Denna kandidat avhandling undersöker ett övervakat maskininlärningssätt som integrerar Bluetooth Low Energy (BLE) och tröghetssensorer för att uppnå konsekventa vinkel- och avståndsberäkningar. Metoden bygger på BLE-vinkelberäkningar och signalstyrka tillsammans med ytterligare sensordata från en Inertial Measurment Unit (IMU). Relevanta funktioner extraheras och en övervakad inlärningsmodell tränas och valideras sedan på tester i bekanta miljöer. Modellen introduceras sedan gradvis till mer obekanta testmiljöer, och dess prestanda utvärderas och jämförs därefter. Detta examensarbete genomfördes på u-blox kontor och presenterar en omfattande metodik som utnyttjar deras befintliga hårdvara. Flera omfattande experiment genomfördes, vilket förfinade både datainsamlingsprocedurer och experimentuppsättningar. Detta iterativa tillvägagångssätt underlättade förbättringen av den övervakade inlärningsmodellen, vilket resulterade i en föreslagen modellarkitektur baserad på transformatorer och konvolutionella lager. Den tillhandahållna metodiken omfattar hela processen, från datainsamling till utvärdering av den föreslagna övervakade inlärningsmodellen, vilket möjliggör direkta jämförelser med befintliga vinkelberäkningslösningar som används på u-blox. Resultaten av dessa jämförelser visar mer exakta resultat jämfört med befintliga lösningar när de valideras i bekanta miljöer. Dock minskar prestandan gradvis när den introduceras till en ny miljö, där den möter ett bredare spektrum av signalförhållanden än vad inlärningsmodellen har tränats på. Avståndsberäkningar jämförs sedan med en matematisk formel, kallat path loss propagation ekvationen, som ger distans som en funktion av uppmätt signalstyrka.

Made available in DSpace on 2016-04-04T18:31:33Z (GMT). No. of bitstreams: 1 Gerson Roberto Luqueta.pdf: 6509270 bytes, checksum: ca46642c2f88e2b7c6526f32ab1af762 (MD5) Previous issue date: 2012-12-06 / This present dissertation aims to propose the use of a wireless sensor network for monitoring the lethality of sterilization process in biologically contaminated materials that uses heat as a sterilizing agent, to replace a wired system. The survey also assesses if sensors could be used as monitors of the parameter in theoretical thermic lethality of the process, and therefore serves as an assistance element in decision making for releasing the materials sterilized in hospitls and laboratories. For this purpose, a platform with sensor nodes was mounted for temperature measurement process, lethality calculation and quality determination of the radio signal and is results were compared with wired commercial equipment. / A presente disserta??o tem por objetivo propor o uso de uma rede de sensores sem fio para monitora??o da letalidade de processo de esteriliza??o de materiais biologicamente contaminados que utiliza o calor como agente esterilizante, em substitui??o a um sistema com fio. A pesquisa avalia tamb?m se os sensores poder?o ser utilizados como monitores do par?metro da letalidade t?rmica te?rica do processo, e assim servirem como elemento de aux?lio na tomada de decis?o para a libera??o dos materiais esterilizados em hospitais e laborat?rios. Para tanto, uma plataforma com n?s sensores foi montada para medi??o da temperatura de processo, c?lculo da letalidade e determina??o da qualidade do sinal de r?dio e os seus resultados foram comparados com um equipamento comercial com fio.