scholarly journals FUSION OF LOCATION FINGERPRINTING AND TRILATERATION BASED ON THE EXAMPLE OF DIFFERENTIAL WI-FI POSITIONING

2017 ◽  
Vol IV-2/W4 ◽  
pp. 377-384 ◽  
Author(s):  
G. Retscher
Keyword(s):  
Low Cost ◽  
Mobile User ◽  
Reference Station ◽  
Raspberry Pi ◽  
Indoor Environments ◽  
Differential Gps ◽  
Area Of Interest ◽  
Location Fingerprinting ◽  
Novel Approach ◽  
Interest Differential

Positioning of mobile users in indoor environments with Wireless Fidelity (Wi-Fi) has become very popular whereby location fingerprinting and trilateration are the most commonly employed methods. In both the received signal strength (RSS) of the surrounding access points (APs) are scanned and used to estimate the user’s position. Within the scope of this study the advantageous qualities of both methods are identified and selected to benefit their combination. By a fusion of these technologies a higher performance for Wi-Fi positioning is achievable. For that purpose, a novel approach based on the well-known Differential GPS (DGPS) principle of operation is developed and applied. This approach for user localization and tracking is termed Differential Wi-Fi (DWi-Fi) by analogy with DGPS. From reference stations deployed in the area of interest differential measurement corrections are derived and applied at the mobile user side. Hence, range or coordinate corrections can be estimated from a network of reference station observations as it is done in common CORS GNSS networks. A low-cost realization with Raspberry Pi units is employed for these reference stations. These units serve at the same time as APs broadcasting Wi-Fi signals as well as reference stations scanning the receivable Wi-Fi signals of the surrounding APs. As the RSS measurements are carried out continuously at the reference stations dynamically changing maps of RSS distributions, so-called radio maps, are derived. Similar as in location fingerprinting this radio maps represent the RSS fingerprints at certain locations. From the areal modelling of the correction parameters in combination with the dynamically updated radio maps the location of the user can be estimated in real-time. The novel approach is presented and its performance demonstrated in this paper.

Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning

2019 ◽  
Vol 13 (1) ◽  
pp. 47-61
Author(s):  
Guenther Retscher ◽  
Jonathan Kleine ◽  
Lisa Whitemore
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Ground Truth ◽  
Mobile User ◽  
Location Based Services ◽  
Raspberry Pi ◽  
Indoor Environments ◽  
Point Location ◽  
Point Determination ◽  
The Relationship

Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

scholarly journals Low-Cost Conversion of Single-Zone HVAC Systems to Multi-Zone Control Systems Using Low-Power Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3611
Author(s):  
Julio Antonio Jornet-Monteverde ◽  
Juan José Galiana-Merino
Keyword(s):  
Low Power ◽  
Energy Savings ◽  
Low Cost ◽  
Transport Layer ◽  
Raspberry Pi ◽  
Air Conditioner ◽  
Efficient System ◽  
Ultra Low Power ◽  
Zone Control ◽  
Novel Approach

This paper presents a novel approach to convert a conventional house air conditioning installation into a more efficient system that individually controls the temperature of each zone of the house through Wi-Fi technology. Each zone regulates the air flow depending on the detected temperature, providing energy savings and increasing the machine performance. Therefore, the first step was to examine the communication bus of the air conditioner and obtain the different signal codes. Thus, an alternative Controller module has been designed and developed to control and manage the requests on the communication bus (Bus–Wi-Fi gateway). A specific circuit has been designed to adapt the signal of the serial port of the Controller with the communication bus. For the acquisition of the temperature and humidity data in each zone, a Node module has been developed, which communicates with the Controller through the Wi-Fi interface using the Message Queuing Telemetry Transport (MQTT) protocol with Secure Sockets Layer / Transport Layer Security (SSL/TLS) certificates. It has been equipped with an LCD touch screen as a human-machine interface. The Controller and the Node modules have been developed with the ultra-low power consumption CC3200 microController of Texas Instruments and the code has been implemented under the TI-RTOS real-time operating system. An additional module based on the Raspberry Pi computer has been designed to create the Wi-Fi network and implement the required network functionalities. The developed system not only ensures that the temperature in each zone is the desired one, but also controls the fan velocity of the indoor unit and the opening area of the vent registers, which considerably improves the efficiency of the system. Compared with the single-zone system, the experiments carried out show energy savings between 75% and 94% when only one of the zones is selected, and 44% when the whole house is air-conditioned, in addition to considerably improving user comfort.

Indoor positioning using differential Wi-Fi lateration

2017 ◽  
Vol 11 (4) ◽  
Author(s):  
Guenther Retscher ◽  
Thomas Tatschl
Keyword(s):  
Mobile User ◽  
Office Building ◽  
Wall Model ◽  
Position Errors ◽  
Area Of Interest ◽  
Location Fingerprinting ◽  
Propagation Effects ◽  
The Common ◽  
Temporal And Spatial ◽  
The One

AbstractFor Wi-Fi positioning usually location fingerprinting or (tri)lateration are employed whereby the received signal strengths (RSSs) of the surrounding Wi-Fi Access Points (APs) are scanned on the mobile devices and used to perform localization. Within the scope of this study, the position of a mobile user is determined on the basis of lateration. Two new differential approaches are developed and compared to two common models, i.e., the one-slope and multi-wall model, for the conversion of the measured RSS of the Wi-Fi signals into ranges. The two novel methods are termed DWi-Fi as they are derived either from the well-known DGPS or VLBI positioning principles. They make use of a network of reference stations deployed in the area of interest. From continuous RSS observations on these reference stations correction parameters are derived and applied by the user in real-time. This approach leads to a reduced influence of temporal and spatial variations and various propagation effects on the positioning result. In practical use cases conducted in a multi-storey office building with three different smartphones, it is proven that the two DWi-Fi approaches outperform the common models as static positioning yielded to position errors of about 5 m in average under good spatial conditions.

scholarly journals Towards human distance estimation using a thermal sensor array

2021 ◽  
Author(s):  
Abdallah Naser ◽  
Ahmad Lotfi ◽  
Joni Zhong
Keyword(s):  
High Resolution ◽  
Independent Living ◽  
Sensor Array ◽  
Distance Estimation ◽  
Thermal Sensor ◽  
Indoor Environments ◽  
Contagious Diseases ◽  
Novel Approach ◽  
Thermal Cameras ◽  
Distance Estimator

AbstractHuman distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of $$\pm 0.2$$ ± 0.2  m in continuous-based estimation and $$96.8\%$$ 96.8 % achieved-accuracy in discrete distance estimation.

scholarly journals Designing of an Advanced Compression Bioreactor with an Implementation of a Low-Cost Controlling System Connected to a Mobile Application

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 915
Author(s):  
Gözde Dursun ◽  
Muhammad Umer ◽  
Bernd Markert ◽  
Marcus Stoffel
Keyword(s):  
Mobile Application ◽  
Low Cost ◽  
Experimental Information ◽  
Raspberry Pi ◽  
Tissue Constructs ◽  
Cost Controlling ◽  
Controlling System ◽  
And Control ◽  
Tissue Models

(1) Background: Bioreactors mimic the natural environment of cells and tissues by providing a controlled micro-environment. However, their design is often expensive and complex. Herein, we have introduced the development of a low-cost compression bioreactor which enables the application of different mechanical stimulation regimes to in vitro tissue models and provides the information of applied stress and strain in real-time. (2) Methods: The compression bioreactor is designed using a mini-computer called Raspberry Pi, which is programmed to apply compressive deformation at various strains and frequencies, as well as to measure the force applied to the tissue constructs. Besides this, we have developed a mobile application connected to the bioreactor software to monitor, command, and control experiments via mobile devices. (3) Results: Cell viability results indicate that the newly designed compression bioreactor supports cell cultivation in a sterile environment without any contamination. The developed bioreactor software plots the experimental data of dynamic mechanical loading in a long-term manner, as well as stores them for further data processing. Following in vitro uniaxial compression conditioning of 3D in vitro cartilage models, chondrocyte cell migration was altered positively compared to static cultures. (4) Conclusion: The developed compression bioreactor can support the in vitro tissue model cultivation and monitor the experimental information with a low-cost controlling system and via mobile application. The highly customizable mold inside the cultivation chamber is a significant approach to solve the limited customization capability of the traditional bioreactors. Most importantly, the compression bioreactor prevents operator- and system-dependent variability between experiments by enabling a dynamic culture in a large volume for multiple numbers of in vitro tissue constructs.

scholarly journals Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 432
Author(s):  
Guenther Retscher ◽  
Alexander Leb
Keyword(s):  
Performance Improvement ◽  
Low Cost ◽  
Information Service ◽  
Raspberry Pi ◽  
University Library ◽  
Stop And Go ◽  
Promising Solution ◽  
Localization Performance ◽  
The University ◽  
Suitable Location

A guidance and information service for a University library based on Wi-Fi signals using fingerprinting as chosen localization method is under development at TU Wien. After a thorough survey of suitable location technologies for the application it was decided to employ mainly Wi-Fi for localization. For that purpose, the availability, performance, and usability of Wi-Fi in selected areas of the library are analyzed in a first step. These tasks include the measurement of Wi-Fi received signal strengths (RSS) of the visible access points (APs) in different areas. The measurements were carried out in different modes, such as static, kinematic and in stop-and-go mode, with six different smartphones. A dependence on the positioning and tracking modes is seen in the tests. Kinematic measurements pose much greater challenges and depend significantly on the duration of a single Wi-Fi scan. For the smartphones, the scan durations differed in the range of 2.4 to 4.1 s resulting in different accuracies for kinematic positioning, as fewer measurements along the trajectories are available for a device with longer scan duration. The investigations indicated also that the achievable localization performance is only on the few meter level due to the small number of APs of the University own Wi-Fi network deployed in the library. A promising solution for performance improvement is the foreseen usage of low-cost Raspberry Pi units serving as Wi-Fi transmitter and receiver.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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