Relevant bibliographies by topics / Wi-Fi Sensors

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Wi-Fi Sensors'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 September 2024

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Journal articles on the topic "Wi-Fi Sensors"

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Choi, Woo-Yong. "Efficient Node Insertion Algorithm for Connectivity-Based Multipolling MAC Protocol in Wi-Fi Sensor Networks." Applied Sciences 13, no. 21 (November 2, 2023): 11974. http://dx.doi.org/10.3390/app132111974.

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Since low-power Wi-Fi sensors are connected to the Internet, effective radio spectrum use is crucial for developing an efficient Medium Access Control (MAC) protocol for Wi-Fi sensor networks. A connectivity-based multipolling mechanism was employed for Access Points to grant uplink transmission opportunities to Wi-Fi nodes with a reduced number of multipolling frame transmissions. The existing connectivity-based multipolling mechanism in IEEE 802.11 wireless LANs with many nodes may require excessive time to derive the optimal number of serially connected sequences due to the backtracking algorithm based on the Traveling Salesman Problem model. This limitation hinders the real-time implementation of the connectivity-based multipolling mechanism in Wi-Fi sensor networks. In this study, an efficient node insertion algorithm is proposed, by which the number of derived serially connected multipolling sequences that cover nodes in Wi-Fi sensor networks converges to only one as the number of Wi-Fi sensors increases in Wi-Fi sensor networks. As verified by simulation experiments for Wi-Fi sensor networks, the proposed node insertion algorithm produces a near-optimal number of multipolling sequences that cover the nodes in Wi-Fi sensor networks. This study proposes a node insertion algorithm for the real-time implementation of the connectivity-based multipolling mechanism in MAC protocol for Wi-Fi sensor networks.
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Yu, Yue, Ruizhi Chen, Liang Chen, Guangyi Guo, Feng Ye, and Zuoya Liu. "A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors." Remote Sensing 11, no. 5 (March 1, 2019): 504. http://dx.doi.org/10.3390/rs11050504.

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More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.
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Lin, Jen-Yung, Huan-Liang Tsai, and Wei-Hong Lyu. "An Integrated Wireless Multi-Sensor System for Monitoring the Water Quality of Aquaculture." Sensors 21, no. 24 (December 7, 2021): 8179. http://dx.doi.org/10.3390/s21248179.

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Water temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), and salinity levels are the critical cultivation factors for freshwater aquaculture. This paper proposes a novel wireless multi-sensor system by integrating the temperature, pH, DO, and EC sensors with an ESP 32 Wi-Fi module for monitoring the water quality of freshwater aquaculture, which acquires the sensing data and salinity information directly derived from the EC level. The information of water temperature, pH, DO, EC, and salinity levels was displayed in the ThingSpeak IoT platform and was visualized in a user-friendly manner by ThingView APP. Firstly, these sensors were integrated with an ESP32 Wi-Fi platform. The observations of sensors and the estimated salinity from the EC level were then transmitted by a Wi-Fi network to an on-site Wi-Fi access point (AP). The acquired information was further transmitted to the ThingSpeak IoT and displayed in the form of a web-based monitoring system which can be directly visualized by online browsing or the ThingView APP. Through the complete processes of pre-calibration, in situ measurement, and post-calibration, the results illustrate that the proposed wireless multi-sensor IoT system has sufficient accuracy, reliable confidence, and a good tolerance for monitoring the water quality of freshwater aquaculture.
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Duives, Dorine C., Tim van Oijen, and Serge P. Hoogendoorn. "Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data." Sensors 20, no. 21 (October 23, 2020): 6032. http://dx.doi.org/10.3390/s20216032.

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Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best.
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Sun, Chao, Junhao Zhou, Kyongseok Jang, and Youngok Kim. "Indoor Localization Based on Integration of Wi-Fi with Geomagnetic and Light Sensors on an Android Device Using a DFF Network." Electronics 12, no. 24 (December 16, 2023): 5032. http://dx.doi.org/10.3390/electronics12245032.

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Sensor-related indoor localization has attracted considerable attention in recent years. The accuracy of conventional fingerprint solutions based on a single sensor, such as a Wi-Fi sensor, is affected by multipath interferences from other electronic devices that are produced as a result of complex indoor environments. Light sensors and magnetic (i.e., geomagnetic) field sensors can be used to enhance the accuracy of a system since they are less vulnerable to disturbances. In this paper, we propose a deep feedforward (DFF)-neural-network-based method, termed DFF-WGL, which integrates the data from the embedded Wi-Fi sensor, geomagnetic field sensor, and light sensor (WGL) in a smart device to localize the device in an indoor environment. DFF-WGL does not require complex and expensive auxiliary equipment, except for basic fluorescent lamps and low-density Wi-Fi signal coverage, conditions that are easily satisfied in modern offices or educational buildings. The proposed system was implemented on a commercial off-the-shelf android device, and performance was evaluated through an experimental analysis conducted in two different indoor testbeds, one measuring 60.5 m2 and the other measuring 38 m2, with 242 and 60 reference points, respectively. The results indicate that the model prediction with an input consisting of the combination of light, a magnetic field sensor, and two Wi-Fi RSS signals achieved mean localization errors of 0.01 m and 0.04 m in the two testbeds, respectively, compared with any subset of combination of sensors, verifying the effectiveness of the proposed DFF-WGL method.
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Silva , Ivo, Cristiano Pendão, Joaquín Torres-Sospedra, and Adriano Moreira. "Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data." Data 8, no. 10 (October 23, 2023): 157. http://dx.doi.org/10.3390/data8100157.

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This paper describes a dataset collected in an industrial setting using a mobile unit resembling an industrial vehicle equipped with several sensors. Wi-Fi interfaces collect signals from available Access Points (APs), while motion sensors collect data regarding the mobile unit’s movement (orientation and displacement). The distinctive features of this dataset include synchronous data collection from multiple sensors, such as Wi-Fi data acquired from multiple interfaces (including a radio map), orientation provided by two low-cost Inertial Measurement Unit (IMU) sensors, and displacement (travelled distance) measured by an absolute encoder attached to the mobile unit’s wheel. Accurate ground-truth information was determined using a computer vision approach that recorded timestamps as the mobile unit passed through reference locations. We assessed the quality of the proposed dataset by applying baseline methods for dead reckoning and Wi-Fi fingerprinting. The average positioning error for simple dead reckoning, without using any other absolute positioning technique, is 8.25 m and 11.66 m for IMU1 and IMU2, respectively. The average positioning error for simple Wi-Fi fingerprinting is 2.19 m when combining the RSSI information from five Wi-Fi interfaces. This dataset contributes to the fields of Industry 4.0 and mobile sensing, providing researchers with a resource to develop, test, and evaluate indoor tracking solutions for industrial vehicles.
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Jiang, Xinlong, Yiqiang Chen, Junfa Liu, Dingjun Liu, Yang Gu, and Zhenyu Chen. "Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/545792.

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As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method.
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Milani, Ileana, Carlo Bongioanni, Fabiola Colone, and Pierfrancesco Lombardo. "Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance." Remote Sensing 13, no. 18 (September 7, 2021): 3556. http://dx.doi.org/10.3390/rs13183556.

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In this work, we consider the joint use of different passive sensors for the localization and tracking of human targets and small drones at short ranges, based on the parasitic exploitation of Wi-Fi signals. Two different sensors are considered in this paper: (i) Passive Bistatic Radar (PBR) that exploits the Wi-Fi Access Point (AP) as an illuminator of opportunity to perform uncooperative target detection and localization and (ii) Passive Source Location (PSL) that uses radio frequency (RF) transmissions from the target to passively localize it, assuming that it is equipped with Wi-Fi devices. First, we show that these techniques have complementary characteristics with respect to the considered surveillance applications that typically include targets with highly variable motion parameters. Therefore, an appropriate sensor fusion strategy is proposed, based on a modified version of the Interacting Multiple Model (IMM) tracking algorithm, in order to benefit from the information diversity provided by the two sensors. The performance of the proposed strategy is evaluated against both simulated and experimental data and compared to the performance of the single sensors. The results confirm that the joint exploitation of the considered sensors based on the proposed strategy largely improves the positioning accuracy, target motion recognition capability and continuity in target tracking.
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Naik, M. Renubabu. "Greenhouse Environment Monitoring and Controlling Through IoT." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2412–17. http://dx.doi.org/10.22214/ijraset.2022.44318.

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Abstract: Our project is based on IOT (Internet of things) which is very useful for monitoring and controlling the greenhouse environment, Agriculture under the greenhouse environment has more benefit of getting more crops by making proper climatic conditions for plants, fruits and vegetables. This greenhouse monitoring environment system have the transparent paper on the top and it contains the five main sensors they are temperature, humidity, rain, soil, LDR sensors. Most of the farmers are fail to get good crops by various reasons such as diseases due to temperature and humidity, if farmers really concerned about suitable temperature and humidity then they can get good crops and this can possible by providing greenhouse environment. The Arduino Nano is the heart of this project, and the five sensors are senses of their respective value and send to the Arduino Nano, through Wi-Fi module the respective detected value is monitored on the smart mobile where Wi-Fi controller app is there. Temperature sensor detects temperature, if temperature exceeds the threshold value then the fan is automatically on, there by temperature are decreases in the greenhouse environment. If LDR detects the sunlight then light will be off and when the sunlight not fall on the LDR then the light will be on in the greenhouse environment. If Rain sensor detects Rain then through the Wi-Fi controller we can open the top of the Greenhouse environment. The top is to be closed after the rain stop, by the Wi-Fi controller. If Soil sensor detects soil is to be dry then automatically the water pump is ON, and water pump is OFF automatically when soil becomes wet.
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Faydhe, Fatima, Majida Saud Ibrahim, and Kamal Y. Kamal. "HaLow Wi-Fi performance in multiusers and channels environment with MATLAB Simulink." International Journal of Communication Networks and Information Security (IJCNIS) 15, no. 1 (May 26, 2023): 01–11. http://dx.doi.org/10.17762/ijcnis.v15i1.5487.

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HaLow Wi-Fi (IEEE 802.11ah) wireless networking standard. As opposed to 2.4 GHz and 5 GHz-based conventional Wi-Fi networks, it leverages 900 MHz frequencies license-exempt for enabling networks Wi-Fi with a longer range. Lower energy usage makes it possible to build extensive networks of sensors or stations that work together to communicate signals, which is another advantage. In this paper IEEE 802.11ah Wi-Fi system design and implemented using MATLAB Simulink and tested under multiusers and channels environment in terms of Spectrum analyzer and constellation Diagram where 4 users, 2 MHz and 4 MHz channels bandwidth used to perfume the test also power of coarse synchronization, fine synchronization and initial channel estimation, to make Wi-Fi networks with a greater range possible were illustrated in space time stream.
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Dissertations / Theses on the topic "Wi-Fi Sensors"

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Ta, Viet-Cuong. "Smartphone-based indoor positioning using Wi-Fi, inertial sensors and Bluetooth." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM092/document.

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Grâce à l’émergence dans la vie quotidienne des appareils de plus en plus populaires que sont les smartphones et les tablettes, la tâche de postionner l'utilisateur par le biais de son téléphone est une problématique fortement étudiée dans les domaines non seulement de la recherche mais également des communautés industrielles. Parmi ces technologies, les approches GPS sont devenues une norme et ont beaucoup de succès pour une localisation en environnement extérieur. Par contre, le Wi-Fi, les capteurs inertiels et le Bluetooth sont plutôt préférés pour les tâches de positionnement dans un environnement intérieur.Pour ce qui concerne le positionnement des smartphones, les approches basées sur les « empreintes digitales » (fingerprint) Wi-Fi sont bien établies. D'une manière générale, ces approches tentent d'apprendre la fonction de correspondance (cartographie) des caractéristiques du signal Wi-Fi par rapport à la position de l’appareil dans le monde réel. Elles nécessitent généralement une grande quantité de données pour obtenir une bonne cartographie. Lorsque ces données d'entraînement disponibles sont limitées, l'approche basée sur les empreintes digitales montre alors des taux d’erreurs élevés et devient moins stable. Dans nos travaux, nous explorons d’autres approches, différentes, pour faire face à cette problématique du manque de données d'entraînement. Toutes ces méthodes sont testées sur un ensemble de données public qui est utilisé lors d’une compétition internationale à la Conférence IPIN 2016.En plus du système de positionnement basé sur la technologie Wi-Fi, les capteurs inertiels du smartphone sont également utiles pour la tâche de suivi. Les trois types de capteurs, qui sont les accéléromètres, le gyroscope et la boussole magnétique, peuvent être utilisés pour suivre l'étape et la direction de l'utilisateur (méthode SHS). Le nombre d'étapes et la distance de déplacement de l'utilisateur sont calculés en utilisant les données de l'accéléromètre. La position de l'utilisateur est calculée par trois types de données avec trois méthodes comprenant la matrice de rotation, le filtre complémentaire et le filtre de Madgwick. Il est raisonnable de combiner les sorties SHS avec les sorties de Wi-Fi, car les deux technologies sont présentes dans les smartphones et se complètent. Deux approches combinées sont testées. La première approche consiste à utiliser directement les sorties Wi-Fi comme points de pivot pour la fixation de la partie de suivi SHS. Dans la deuxième approche, nous comptons sur le signal Wi-Fi pour construire un modèle d'observation, qui est ensuite intégré à l'étape d'approximation du filtre à particules. Ces combinaisons montrent une amélioration significative par rapport au suivi SHS ou au suivi Wi-Fi uniquement.Dans un contexte multiutilisateur, la technologie Bluetooth du smartphone pourrait fournir une distance approximative entre les utilisateurs. La distance relative est calculée à partir du processus de numérisation du périphérique Bluetooth. Elle est ensuite utilisée pour améliorer la sortie des modèles de positionnement Wi-Fi. Nous étudions deux méthodes. La première vise à créer une fonction d'erreur qui permet de modéliser le bruit dans la sortie Wi-Fi et la distance approximative produite par le Bluetooth pour chaque intervalle de temps spécifié. La seconde méthode considère par contre cette relation temporelle et la contrainte de mouvement lorsque l'utilisateur se déplace. Le modèle d'observation du filtre à particules est une combinaison entre les données Wi-Fi et les données Bluetooth. Les deux approches sont testées en fonction de données réelles, qui incluent jusqu'à quatre utilisateurs différents qui se déplacent dans un bureau. Alors que la première approche n'est applicable que dans certains scénarios spécifiques, la deuxième approche montre une amélioration significative par rapport aux résultats de position basés uniquement sur le modèle d'empreintes digitales Wi-Fi
With the popularity of smartphones and tablets in daily life, the task of finding user’s position through their phone gains much attention from both the research and industry communities. Technologies integrated in smartphones such as GPS, Wi-Fi, Bluetooth and camera are all capable for building a positioning system. Among those technologies, GPS has approaches have become a standard and achieved much success for the outdoor environment. Meanwhile, Wi-Fi, inertial sensors and Bluetooth are more preferred for positioning task in indoor environment.For smartphone positioning, Wi-Fi fingerprinting based approaches are well established within the field. Generally speaking, the approaches attempt to learn the mapping function from Wi-Fi signal characteristics to the real world position. They usually require a good amount of data for finding a good mapping. When the available training data is limited, the fingerprinting-based approach has high errors and becomes less stable. In our works, we want to explore different approaches of Wi-Fi fingerprinting methods for dealing with a lacking in training data. Based on the performance of the individual approaches, several ensemble strategies are proposed to improve the overall positioning performance. All the proposed methods are tested against a published dataset, which is used as the competition data of the IPIN 2016 Conference with offsite track (track 3).Besides the positioning system based on Wi-Fi technology, the smartphone’s inertial sensors are also useful for the tracking task. The three types of sensors, which are accelerate, gyroscope and magnetic, can be employed to create a Step-And-Heading (SHS) system. Several methods are tested in our approaches. The number of steps and user’s moving distance are calculated from the accelerometer data. The user’s heading is calculated from the three types of data with three methods, including rotation matrix, Complimentary Filter and Madgwick Filter. It is reasonable to combine SHS outputs with the outputs from Wi-Fi due to both technologies are present in the smartphone. Two combination approaches are tested. The first approach is to use directly the Wi-Fi outputs as pivot points for fixing the SHS tracking part. In the second approach, we rely on the Wi-Fi signal to build an observation model, which is then integrated into the particle filter approximation step. The combining paths have a significant improvement from the SHS tracking only and the Wi-Fi only. Although, SHS tracking with Wi-Fi fingerprinting improvement achieves promising results, it has a number of limitations such as requiring additional sensors calibration efforts and restriction on smartphone handling positions.In the context of multiple users, Bluetooth technology on the smartphone could provide the approximated distance between users. The relative distance is calculated from the Bluetooth inquiry process. It is then used to improve the output from Wi-Fi positioning models. We study two different combination methods. The first method aims to build an error function which is possible to model the noise in the Wi-Fi output and Bluetooth approximated distance for each specific time interval. It ignores the temporal relationship between successive Wi-Fi outputs. Position adjustments are then computed by minimizing the error function. The second method considers the temporal relationship and the movement constraint when the user moves around the area. The tracking step are carried out by using particle filter. The observation model of the particle filter are a combination between the Wi-Fi data and Bluetooth data. Both approaches are tested against real data, which include up to four different users moving in an office environment. While the first approach is only applicable in some specific scenarios, the second approach has a significant improvement from the position output based on Wi-Fi fingerprinting model only
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Aaro, Gustav. "Smartphone Based Indoor Positioning Using Wi-Fi Round Trip Time and IMU Sensors." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166340.

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While GPS long has been an industry standard for localization of an entity or person anywhere in the world, it loses much of its accuracy and value when used indoors. To enable services such as indoor navigation, other methods must be used. A new standard of the Wi-Fi protocol, IEEE 802.11mc (Wi-Fi RTT), enables distance estimation between the transmitter and the receiver based on the Round-Trip Time (RTT) delay of the signal. Using these distance estimations and the known locations of the transmitting Access Points (APs), an estimation of the receiver’s location can be determined. In this thesis, a smartphone Wi-Fi RTT based Indoor Positioning System (IPS) is presented using an Unscented Kalman Filter (UKF). The UKF using only RTT based distance estimations as input, is established as a baseline implementation. Two extensions are then presented to improve the positioning performance; 1) a dead reckoning algorithm using smartphone sensors part of the Inertial Measurement Unit (IMU) as an additional input to the UKF, and 2) a method to detect and adjust distance measurements that have been made in Non-Line-of-Sight (NLoS) conditions. The implemented IPS is evaluated in an office environment in both favorable situations (plenty of Line-of-Sight conditions) and sub-optimal situations (dominant NLoS conditions). Using both extensions, meter level accuracy is achieved in both cases as well as a 90th percentile error of less than 2 meters.
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Fabre, Léa. "Contributions and Opportunities of Wi-Fi Data to Improve Transport Demand Knowledge / Utilisation de données Wi-Fi, quels apports pour la connaissance de la demande de transport?" Electronic Thesis or Diss., Lyon 2, 2024. http://www.theses.fr/2024LYO20011.

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La mobilité joue un rôle clé dans les paysages urbains, en particulier, les transports en commun sont essentiels au bon fonctionnement des villes. Par conséquent, il est nécessaire de planifier les systèmes de transport en commun afin de leur garantir un fonctionnement efficace. Pour cela, il est important d'avoir une bonne connaissance de la demande de transport en commun, d'autant plus dans un monde en constante évolution.Actuellement, nous observons une forte croissance démographique mondiale ainsi qu'un important étalement urbain, deux facteurs qui sont les principales causes de l'augmentation de la demande de transport urbain. À cela il faut ajouter une forte tendance à la diversification des comportements de mobilité, principalement due à l'émergence de nouveaux modes de transport. Les données traditionnellement utilisées pour prévoir cette demande, et pour la planification des transports de manière générale, ne sont plus à même de refléter ces changements dans les comportements de mobilité. Le développement des technologies de l'information, la digitalisation et le boom de la science des données sont autant de nouvelles opportunités pour la prévision de la demande de transport. Le développement de nouveaux outils et algorithmes, notamment issus de l'intelligence artificielle, contribuent à la diversification et participent à complexifier les modèles pour améliorer la prévision des comportements de mobilité. En parallèle, nous observons également une grande diversification des données utilisées dans la recherche en transport. Parmi ces nouvelles sources de données, les données Wi-Fi sont très prometteuses. Ces données présentent des avantages significatifs lorsqu'elles sont utilisées pour la planification des transports (collectées en continue, de manière passive, elles apportent des informations sur les trajets Origine-Destination…). Cependant, les données Wi-Fi présentent aussi quelques inconvénients. Ainsi elles doivent être traitées avant d'être utilisées dans des modèles de prévision de la demande. En tant que nouvelle méthode de collecte de données sur la mobilité, des questions subsistent quant à la qualité des données, à leur contribution dans le domaine et à la manière dont elles peuvent être utilisées.L'objectif de cette thèse est de fournir une approche clé en main de l'utilisation des données Wi-Fi pour la connaissance des comportements de mobilité. Dans cette thèse, nous proposons donc des solutions pour traiter ces données au fort potentiel. Nous présentons tout d'abord une méthodologie pour filtrer les signaux parasites détectés par les capteurs Wi-Fi de manière à ne construire la matrice Origine-Destination qu'avec les signaux provenant de passagers. Le redressement des données Wi-Fi pour pallier aux erreurs de prédiction des volumes de passagers du fait de signaux non détectés est également traité. Au final, ces méthodes permettent d'obtenir des matrices Origine-Destination à la fois pertinentes pour la structure des déplacements et complètes dans les volumes de déplacements. Dans cette thèse, nous proposons également un modèle pour quantifier l'erreur entre la matrice Origine-Destination produite par les données Wi-Fi et les déplacements Origine-Destination réels, malgré la rare disponibilité de ces derniers. Quelques applications pour l'utilisation des données Wi-Fi sont également présentées. Pour conclure, les résultats de cette thèse montrent que les données Wi-Fi peuvent enrichir la connaissance des comportements de mobilité, de manière continue et à faible coût
Due to its social, environmental and economic importance, mobility plays a key role in urban landscapes. In particular, public transportation is critical to the smooth functioning of cities. Therefore, public transportation systems must be planned to operate properly and efficiently. To this end, it is of paramount importance to have a great knowledge of the mobility demand, especially in an evolving world. The world today is facing a significant demographic growth along with urban sprawl, which implies an increasing demand for transportation in the cities. In addition, travel patterns are diversifying and becoming less regular, mainly due to the emergence of new modes of transport. The data traditionally used for public transportation planning are inadequate to reflect these changes in mobility behaviors. The development of information technologies, digitization and the data science boom can bring interesting benefits to the forecasting of transport demand. The development of new tools and algorithms, such as artificial intelligence, contributes to the diversification and complexity of models to improve the prediction of mobility behaviors. In parallel, we are currently witnessing the diversification of data sources used in mobility analyses. Among them, Wi-Fi data are very promising. These data have significant advantages when used in transportation planning (they provide information on Origin-Destination trips, they are collected continuously and passively…). However, Wi-Fi data also have some drawbacks. Therefore, they require further processing to be used in demand forecasting models. As a new way of collecting mobility data, questions remain about the quality of the data, their contribution, and how they can be used. The objective of this thesis is to provide a data-driven approach to the use of Wi-Fi data for mobility behaviors. In this thesis, we therefore propose solutions to process this interesting data source. A methodology is presented to filter the parasite signals detected by Wi-Fi sensors in order to keep only the passenger signals and construct relevant Origin-Destination matrices. Scaling of the Wi-Fi data to avoid errors in the predicted total number of trips due to undetected Wi-Fi devices is also handled. In the end, we provide Origin-Destination matrices that are relevant to the structure of the trips and complete in trip volumes. In addition, we propose a modeling to quantify the error between the Origin-Destination matrix produced by Wi-Fi data and real Origin-Destination trips, despite the non-continuous availability of the latter. Some applications of the use of Wi-Fi data are also presented. In conclusion, the results of this thesis show that interesting insights into mobility behaviors can be derived from Wi-Fi data, continuously and at low cost
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Danielsson, Simon, and Jakob Flygare. "A Multi-Target Graph-Constrained HMM Localisation Approach using Sparse Wi-Fi Sensor Data." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231090.

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This thesis explored the possibilities of using a Hidden Markov Model approach for multi-target localisation in an urban environment, with observations generated from Wi-Fi sensors. The area is modelled as a network of nodes and arcs, where the arcs represent sidewalks in the area and constitutes the hidden states in the model. The output of the model is the expected amount of people at each road segment throughout the day. In addition to this, two methods for analyzing the impact of events in the area are proposed. The first method is based on a time series analysis, and the second one is based on the updated transition matrix using the Baum-Welch algorithm. Both methods reveal which road segments are most heavily affected by a surge of traffic in the area, as well as potential bottleneck areas where congestion is likely to have occurred.
I det här examensarbetet har lokalisering av gångtrafikanter med hjälp av Hidden Markov Models utförts. Lokaliseringen är byggd på data från Wi-Fi sensorer i ett område i Stockholm. Området är modellerat som ett graf-baserat nätverk där linjerna mellan noderna representerar möjliga vägar för en person att befinna sig på. Resultatet för varje individ är aggregerat för att visa förväntat antal personer på varje segment över en hel dag. Två metoder för att analysera hur event påverkar området introduceras och beskrivs. Den första är baserad på tidsserieanalys och den andra är en maskinlärningsmetod som bygger på Baum-Welch algoritmen. Båda metoderna visar vilka segment som drabbas mest av en snabb ökning av trafik i området och var trängsel är troligt att förekomma.
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Skytte, Joakim. "Feasibility Study of Indoor Positioning in a Hospital Environment Using Smartphone Sensors." Thesis, Linköpings universitet, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-145172.

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This thesis is a feasibility study of contemporary indoor positioning approaches in an hospital environment using sensor available on Android phones together with Wi-Fi fingerprintingand map information. The purpose is to determine the resolution of pedestrian indoor positioning and whether it is sufficient for room level accuracy. Accurate and robust positioning for outdoor applications based on mobile networks and satellite systems, such as the Global Positioning Service (GPS), has been around for many years. However these systems are not suitable for positioning inside buildings due to a high level of signal degradation. Through the years various pedestrian indoor positioning methods have been proposed.A simple algorithm for suppressing random movement of the mobile phone is tested. Two versions of the Extended Kalman Filter (EKF) are compared for fusing the Inertial Navigation System (INS) measurements during Pedestrian Dead Reckoning (PDR). The TRIAD algorithm is tested for suppressing the effects of large magnetic disturbances. Wi-Fi fingerprinting using two combinations of positioning algorithms and radio maps is tested. The EKF is tested for fusing PDR and Wi-Fi fingerprint position estimations. The Particle Filter (PF) is tested for combining PDR with Wi-Fi fingerprint positioning with a geometrical map. Static Received Signal Strength Indication (RSSI) measurements are carried out to detect variable Wi-Fi transmission power. The results show that adding more informations sources improves the positioning performance. Also fusion using PF outperforms the EKF in more complex indoor environments and movement patterns.
En starkt växande tillgång och kapacitet hos trådlösa nätverk i kombination med explosionen inom mobiltelefoni, i synnerhet vad gäller smartphones, har lett till ett enormt ökat intresse för och utveckling inom området inomhuspositionering. Det har under många år funnits lösningar för positionering i utomhusmiljöer, exempelvis GPS och triangulering med mobila basstationer, men inga av dessa system lämpar sig för inomhuspositionering eftersom signalerna tappar alldeles för mycket i intensitet när användaren befinner sig inomhus. Under årens lopp har flera olika lösningar för inomhuspositionering föreslagits. I denna uppsats testas olika lösningar för inomhuspositionering med smartphones i en sjukhusmiljö. Testen baserar sig på de sensorer som finns i en smartphone med operativsystemet Android i kombination med Wi-Fi triangulering och en digital planlösning över testområdet. Syftet är att undersöka om noggrannheten kan bli såpass så bra att en upplösning på rumsnivå uppnås. En enkel algoritm för att kompensera för slumpartade och oplanerade rörelser hos mobiltelefonen testas.  Två versioner av det utökade Kalmanfiltret testas för tröghetsnavigering. TRIAD algoritmen testas för att motverka magnetiska störningar. Två kombinationer av radiokartor och positioneringsalgoritmer provas för att genomföra Wi-Fi positionering. Ett utökat Kalmanfilter används för att kombinera resultaten av tröghetsnavigeringen med Wi-Fi positioneringen. Ett partikelfilter används för att utföra sensorfusionen av tröghetsnavigeringen, Wi-Fi positioneringen och den digitala planlösningen. Resultaten visar att ju mer information som tillförs under positioneringen desto större blir noggrannheten samt att partikelfiltret ger en bättre noggrannhet i en komplex inomhusmiljö i kombination med komplicerade rörelsemönster än det utökade Kalmanfiltret.
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Zewdu, Yesitla Ephrem. "Survey of microcontrollers and short-range radio transceivers for wireless sensors." Thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39640.

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A significant growth was witnessed in the field of Wireless Sensor Networks (WSNs), in the previous decade. The objective of this study has been Survey of micro controllers and short-range radio transceivers for wireless sensors and provide an extensive overview of micro controllers and RF-transceivers in the Market and compare the relevant properties for designing wireless sensor nodes. In the survey, RF-transvers from Nordic semiconductors is extensively presented for short-rang wireless protocols some of the protocols are RF-Communication Module, Bluetooth Low Energy Module, ZigBee module and Wi-Fi module.          In WSNs node design Power consumption is one the most important design issue, this thesis work present the different type of WSN protocols energy consumption efficiency and power consumption, compared and conclude graphically.        Microcontrollers are the main part of WSNs node for processing and gathering sensor data. There is different microcontroller’s products in the market however the WSN protocols presented in this thesis uses Cortex-M4 processor which is one of ARM product, the specification and comparison of this product with other products is presented.
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Waikul, Devendra Mahendra. "BLUETOOTH-ENABLED ENERGY MONITORING SYSTEM WITH WIRELESS DATA ACQUISITION USING WEB SERVER." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1596563312207117.

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Dorazil, Jan. "Bezdrátové senzorové sítě s využitím mobilních zařízení." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236372.

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This master's thesis deals with the idea of using mobile devices as sensor nodes in wireless sensor network. Focused mostly on Android platform, we explore possibilities of wireless communication, and describe various types of sensors on mobile devices. We design and implement wireless sensor network based on mobile devices running Android operating system. The network performs real-time capturing of data from all sensors available and optionally from GPS. All measurements are visualized at the base station, which is Java Standard Edition desktop application. Wi-Fi, Bluetooth or even cellular internet can be used for communication within the network. Nodes can be remotely configured via SMS messages.
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Miccoli, Roberta. "Implementation of a complete sensor data collection and edge-cloud communication workflow within the WeLight project." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22563/.

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This thesis aims at developing the full workflow of data collection from a laser sensor connected to a mobile application, working as edge device, which subsequently transmits the data to a Cloud platform for analysing and processing. The project is part of the We Light (WErable LIGHTing for smart apparels) project, in collaboration with TTLab of the INFN (National Institute of Nuclear Physics). The goal of We Light is to create an intelligent sports shirt, equipped with sensors that take information from the external environment and send it to a mobile device. The latter then sends the data via an application to an open source Cloud platform in order to create a real IoT system. The smart T-shirt is capable of emitting different levels of light depending on the perceived external light, with the aim of ensuring greater safety for road sports people. The thesis objective is to employ a prototype board provided by the CNR-IMAMOTER to collect data and send it to the specially created application via Bluetooth Low Energy connection. Furthermore, the connection between the edge device and the Thingsboard IoT platform is performed via MQTT protocol. Several device authentication techniques are implemented on TB and a special dashboard is created to display data from the IoT device; the user is also able to view data in numerical and even graphical form directly in the application without necessarily having to access TB. The app created is useful and versatile and can be adapted to be used for other IoT purposes, not only within the We Light project.
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Fernandes, Rui Miguel Félix. "Object signature in radio frequency." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13708.

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Mestrado em Engenharia Eletrónica e Telecomunicações
The RF signature can be consider as a fingerprint of an object when submitted to electromagnetic radiation. Based on this concept, the initial goal of this work was to elaborate a comparative analysis of the Radio Frequency signature of different materials. Through the design of a prototype based on an adapted Wi-Fi network was developed an innovative system capable of distinguishing materials with the analysis of their interference in the propagated channel. In order to refine this distinction was utilized a signal processing tool, the Wavelet Transform. This technique serve as support tool of the system for a better differentiation of the studied targets. The versatility of this concept was proved through the analysis of signatures of static targets like metal, wood and plastic, as well as moving targets, giving the example of a moving human. Due to the promising results obtained, the initial objective of the work was expanded being also presented in this document the concept of intruder detection through a Wi-Fi network by the analysis of the Wavelet coefficients.
A Assinatura em Rádio Frequência pode ser considerada como a impressão digital que um objeto manifesta quando submetido a radiação eletromagnética. O objetivo inicial deste trabalho era a elaboração de uma análise comparativa das assinaturas em Rádio Frequência de diferentes materiais. Tendo por base uma rede Wi-Fi adaptada, foi desenvolvido um sistema inovador capaz de distinguir materiais pela análise da interferência dos mesmos no canal de propagação. Com vista a melhorar o desempenho do protótipo inicial, o sinal recebido foi processado através da Transformada de Wavelet. Esta técnica serviu como ferramenta de suporte do sistema para a obtenção de uma diferenciação mais clara dos alvos estudados. Demonstrando a versatilidade deste conceito foram avaliadas as assinaturas de alvos estáticos como o metal, madeira e plástico bem como de alvos móveis dando, como exemplo, uma pessoa em movimento. Devido aos resultados promissores obtidos, o objetivo inicial do sistema foi alargado estando também presente neste documento o conceito de deteção de intrusos através de uma rede Wi-Fi pela análise dos coeficientes de Wavelet.
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Book chapters on the topic "Wi-Fi Sensors"

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Mendez, Gerard Rudolph, and Subhas Chandra Mukhopadhyay. "A Wi-Fi Based Smart Wireless Sensor Network for an Agricultural Environment." In Smart Sensors, Measurement and Instrumentation, 247–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36365-8_10.

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Joseph, Irene, Prasad B. Honnavalli, and B. R. Charanraj. "Detection of DoS Attacks on Wi-Fi Networks Using IoT Sensors." In Lecture Notes in Electrical Engineering, 549–58. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9012-9_44.

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Choi, Jeongsik, Yang-Seok Choi, and Shilpa Talwar. "Localization with Wi-Fi Ranging and Built-in Sensors: Self-Learning Techniques." In Machine Learning for Indoor Localization and Navigation, 101–30. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26712-3_5.

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Zbakh, Douae, Abdelouahid Lyhyaoui, and Mariam Tanana. "Localization and Tracking System Using Wi-Fi Signal Strength with Wireless Sensors Network." In Advances in Intelligent Systems and Computing, 821–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11928-7_74.

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Jiang, Xinlong, Yiqiang Chen, Junfa Liu, Yang Gu, and Zhenyu Chen. "Wi-Fi and Motion Sensors Based Indoor Localization Combining ELM and Particle Filter." In Proceedings in Adaptation, Learning and Optimization, 105–13. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14066-7_11.

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Chaturvedi, Sarthak, S. Deepak, Dhivya Bharathi, and Bhargava Rama Chilukuri. "Data Imputation for Traffic State Estimation and Pre-diction Using Wi-Fi Sensors." In Proceedings of the Sixth International Conference of Transportation Research Group of India, 385–95. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4204-4_23.

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Lu, Chi-Chang, Chung-Hsien Wu, and Hui-Kai Su. "Intelligent Infant Monitoring System Involving a Wi-Fi Wireless Sensor Network." In Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing, 269–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03748-2_33.

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Liang, Zou. "Research on Airborne Wireless Sensor Network Based on Wi-Fi Technology." In Communications and Networking, 3–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99200-2_1.

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Bernas, Marcin, and Bartłomiej Płaczek. "Energy Aware Object Localization in Wireless Sensor Network Based on Wi-Fi Fingerprinting." In Computer Networks, 33–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19419-6_4.

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Hari Krishna, Konda, Tapas Kumar, Y. Suresh Babu, R. Madan Mohan, N. Sainath, and V. Satyanarayana. "Blockage With in Wi-Fi Sensor Networks in Addition to Systems Regarding Controlling Congestion." In Lecture Notes in Networks and Systems, 19–27. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3935-5_3.

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Conference papers on the topic "Wi-Fi Sensors"

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Nowosielski, Jan, Marcin Jastrzebski, Pavel Halavach, Wojciech Wasilewski, Mateusz Mazelanik, and Michal Parniak. "Wi-Fi Detection via Room-Temperature Rydberg Atoms." In CLEO: Applications and Technology, JW2A.111. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jw2a.111.

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We present a detection scheme for Wi-Fi signals using room-temperature Rydberg atoms. We compare the Rydberg-atoms sensor with its classical counterpart. Finally, we discuss the microwave-to-optical conversion of Wi-Fi signals using the wave mixing process.
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Fernandes, Rui, Joao N. Matos, Tiago Varum, and Pedro Pinho. "Wi-Fi intruder detection." In 2014 IEEE Conference on Wireless Sensors (ICWiSe). IEEE, 2014. http://dx.doi.org/10.1109/icwise.2014.7042668.

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Liu, Changhao, Jiang Liu, and Shigeru Shimamoto. "Sign Language Estimation Scheme Employing Wi-Fi Signal." In 2021 IEEE Sensors Applications Symposium (SAS). IEEE, 2021. http://dx.doi.org/10.1109/sas51076.2021.9530132.

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Pritt, Noah. "Indoor positioning with maximum likelihood classification of Wi-Fi signals." In 2013 IEEE Sensors. IEEE, 2013. http://dx.doi.org/10.1109/icsens.2013.6688619.

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Kim, Lori, Hossain Shahriar, and Chi Zhang. "Non-Invasive Wi-Fi Sensors For Smart Healthcare." In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2019. http://dx.doi.org/10.1109/compsac.2019.10246.

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Tozlu, Serbulent, and Murat Senel. "Battery lifetime performance of Wi-Fi enabled sensors." In 2012 IEEE Consumer Communications and Networking Conference (CCNC). IEEE, 2012. http://dx.doi.org/10.1109/ccnc.2012.6181000.

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Chan, Hao-Wei, Alexander I.-Chi Lai, and Ruey-Beei Wu. "Transfer Learning of Wi-Fi FTM Responder Positioning with NLOS Identification." In 2021 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT). IEEE, 2021. http://dx.doi.org/10.1109/wisnet51848.2021.9413793.

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Awasthi, Deepak, Syed Azeemuddin, Suresh Purini, and M. Annesha. "Flow Sensor IoT Node for Wi-Fi Equipped Apartments and Gated Communities." In 2018 IEEE Sensors. IEEE, 2018. http://dx.doi.org/10.1109/icsens.2018.8589575.

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Matsumoto, Yoshinori, and Masatoshi Satoh. "Wi-Fi-connected radiation measurement system by small-scale solar energy harvesting." In 2015 IEEE Sensors. IEEE, 2015. http://dx.doi.org/10.1109/icsens.2015.7370436.

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Mahgoub, Aya, Dina Nadeem, Hadeer Ahmed, Hassan H. Halawa, Markus Rentschier, Ramez M. Daoud, and Hassanein H. Amer. "A quantitative study of Wi-Fi interference on PRP-ZigBee." In 2014 IEEE Conference on Wireless Sensors (ICWiSe). IEEE, 2014. http://dx.doi.org/10.1109/icwise.2014.7042653.

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Reports on the topic "Wi-Fi Sensors"

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Riter, Karmann, Anthony Clint Clayton, Kelley Rountree, and Prakash Doraiswamy. Solar Station for an Off-the-Grid Air Quality Sensor System. RTI Press, June 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0051.2306.

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Air quality monitoring is a rapidly growing area of citizen science, or community science (CS), thanks to the availability of low-cost sensors. Contributing to a crowdsourced data platform (e.g., http:// purpleair .com/ map) is usually easy in urban areas, where there is access to uninterrupted electricity and wireless internet (Wi-Fi). However, there are sometimes security restrictions on Wi-Fi or a lack of exterior power outlets. Also, rural regions, particularly in low- and middle-income countries, often lack electricity and Wi-Fi continuity. RTI International has designed and distributed a solar power and Wi-Fi station that can adequately power both a small air quality sensor (e.g., PurpleAir PA-II) and a Wi-Fi hotspot to overcome these challenges. The station housing can accommodate a battery, a controller, and a cell phone or another type of Wi-Fi hotspot device. This paper discusses the need for such a station; a design for the current station, including parts list; suggestions for modifications in various use cases; and design factors to consider, including amount of sunlight per day, intended number of operational days under cloudy conditions, season, and total power requirements. This method is intended to be open source and a starting point for citizen scientists and CS projects.
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