bachelor-thesis/Expose/thesis.bib

@misc{dehaye_inferring_2020,
	title = {Inferring distance from {Bluetooth} signal strength: a deep dive},
	shorttitle = {Inferring distance from {Bluetooth} signal strength},
	url = {https://medium.com/personaldata-io/inferring-distance-from-bluetooth-signal-strength-a-deep-dive-fe7badc2bb6d},
	abstract = {Contact tracing apps intend to predict exposure to a COVID-19 infection, where exposure is computed as some function of time and distance…},
	language = {en},
	urldate = {2021-08-11},
	journal = {PersonalData.IO},
	author = {Dehaye, Paul-Olivier},
	month = oct,
	year = {2020},
	file = {Snapshot:/home/sebastian/Zotero/storage/8ZDHFM27/inferring-distance-from-bluetooth-signal-strength-a-deep-dive-fe7badc2bb6d.html:text/html},
}

@article{larsson_distance_nodate,
	title = {Distance estimation and positioning based on {Bluetooth} {Low} energy technology},
	language = {en},
	author = {Larsson, Johan},
	pages = {37},
	file = {Larsson - Distance estimation and positioning based on Bluet.pdf:/home/sebastian/Zotero/storage/T8XSSULJ/Larsson - Distance estimation and positioning based on Bluet.pdf:application/pdf},
}

@misc{noauthor_distance_2020,
	title = {Distance {Measuring} {Solution} for {COVID}-19 using {Bluetooth} {Low} {Energy}},
	url = {https://smartsensordevices.com/distance-measuring-solution-for-covid-19-using-bluetooth-low-energy/},
	abstract = {As the coronavirus pandemic continues, we’ve seen several social distancing, and contact tracing solution came in the market to stop the spread. Employers and businesses will want new devices to help keep social distancing when people return to work. In this case, Bluetooth technologies can be very useful. When a Bluetooth device connects to another …},
	language = {en-US},
	urldate = {2021-08-11},
	journal = {SMART SENSOR DEVICES AB},
	month = oct,
	year = {2020},
	note = {Section: BleuIO},
	file = {Snapshot:/home/sebastian/Zotero/storage/46GKWDQQ/distance-measuring-solution-for-covid-19-using-bluetooth-low-energy.html:text/html},
}

@article{cho_measuring_2015,
	series = {The 6th {International} {Conference} on {Ambient} {Systems}, {Networks} and {Technologies} ({ANT}-2015), the 5th {International} {Conference} on {Sustainable} {Energy} {Information} {Technology} ({SEIT}-2015)},
	title = {Measuring a {Distance} between {Things} with {Improved} {Accuracy}},
	volume = {52},
	issn = {1877-0509},
	url = {https://www.sciencedirect.com/science/article/pii/S1877050915009199},
	doi = {10.1016/j.procs.2015.05.119},
	abstract = {This paper suggests a method to measure the physical distance between an IoT device and a mobile device (also a Thing) using BLE(Bluetooth Low-Energy profile) interfaces with smaller distance errors. BLE is a well-known technology for the low-power connectivity suitable for IoT devices and also for the proximity with the range of several meters. Apple has already adopted the technic and enhanced it to provide subdivided proximity range levels. But as it is also a variation of RSS-based distance estimation, iBeacon could only provide immediate, near or far status but not a real and accurate distance. To provide the distance using BLE, this paper introduces additional self-correcting beacon to calibrate the reference distance and mitigate errors from environmental factors. By adopting self-correcting beacon while measuring the distance, the average distance error showed less than 10\% within the range of 1.5 meters. Some considerations are presented to extend the range to be able to get accurate distances.},
	language = {en},
	urldate = {2021-08-11},
	journal = {Procedia Computer Science},
	author = {Cho, Hosik and Ji, Jianxun and Chen, Zili and Park, Hyuncheol and Lee, Wonsuk},
	month = jan,
	year = {2015},
	keywords = {accurate distance, distance error mitigation, RSS-based estimation, self-correcting beacon},
	pages = {1083--1088},
	file = {ScienceDirect Full Text PDF:/home/sebastian/Zotero/storage/8WXPG4RL/Cho et al. - 2015 - Measuring a Distance between Things with Improved .pdf:application/pdf;ScienceDirect Snapshot:/home/sebastian/Zotero/storage/G5CKZT29/S1877050915009199.html:text/html},
}

@misc{admin_can_2020,
	title = {Can {Bluetooth} {Be} {Used} {To} {Measure} {Distance}?},
	url = {https://bluetoothtechworld.com/can-bluetooth-measure-distance/},
	abstract = {"Bluetooth should be able to measure distance if it can connect two devices", I said to myself. But is that really simple? To answer that, I did my},
	language = {en-GB},
	urldate = {2021-08-11},
	journal = {Bluetooth Tech World},
	author = {{Admin}},
	month = jun,
	year = {2020},
	file = {Snapshot:/home/sebastian/Zotero/storage/PXPTAR3D/can-bluetooth-measure-distance.html:text/html},
}

@misc{admin_can_2020-1,
	title = {Can {Bluetooth} {Go} {Through} {Walls}?},
	url = {https://bluetoothtechworld.com/can-bluetooth-go-through-walls/},
	abstract = {The first thought that came to mind when I got Britz Bluetooth speaker was to check whether the Bluetooth signal can go through walls. Guess what I did. I},
	language = {en-GB},
	urldate = {2021-08-11},
	journal = {Bluetooth Tech World},
	author = {{Admin}},
	month = jun,
	year = {2020},
	file = {Snapshot:/home/sebastian/Zotero/storage/MM4CEDMS/can-bluetooth-go-through-walls.html:text/html},
}

@misc{noauthor_exposure_nodate,
	title = {Exposure {Notifications} {BLE} calibration calculation},
	url = {https://developers.google.com/android/exposure-notifications/ble-attenuation-computation?hl=de},
	language = {en},
	urldate = {2021-08-11},
	journal = {Google Developers},
}

@article{cho_accurate_2015,
	title = {Accurate {Distance} {Estimation} between {Things}: {A} {Self}-correcting {Approach}},
	volume = {1},
	issn = {2364-7108},
	abstract = {This paper suggests a method to measure the physical distance between an IoT device (a Thing) and a mobile device (also a Thing) using BLE (Bluetooth Low-Energy profile) interfaces with smaller distance errors. BLE is a well-known technology for the low-power connectivity and suitable for IoT devices as well as for the proximity with the range of several meters. Apple has already adopted the technique and enhanced it to provide subdivided proximity range levels. However, as it is also a variation of RSS-based distance estimation, Apple’s iBeacon could only provide immediate, near or far status but not a real and accurate distance. To provide more accurate distance using BLE, this paper introduces additional self-correcting beacon to calibrate the reference distance and mitigate errors from environmental factors. By adopting self-correcting beacon for measuring the distance, the average distance error shows less than 10\% within the range of 1.5 meters. Some considerations are presented to extend the range to be able to get more accurate distances.},
	language = {en},
	number = {2},
	author = {Cho, Hosik and Ji, Jianxun and Chen, Zili and Park, Hyuncheol and Lee, Wonsuk},
	year = {2015},
	pages = {9},
	file = {Cho et al. - 2015 - Accurate Distance Estimation between Things A Sel.pdf:/home/sebastian/Zotero/storage/TV2W2LHI/Cho et al. - 2015 - Accurate Distance Estimation between Things A Sel.pdf:application/pdf},
}

@misc{noauthor_tracing_nodate,
	title = {Tracing {App} - distance measurement},
	url = {https://metirionic.com/tracing-measurement-tech-bluetooth/},
	abstract = {Bluetooth distance measuring for finding lost people, surveillance of parking garages also in combination with radar / in covered environments},
	language = {en},
	urldate = {2021-08-11},
	journal = {Metirionic},
	file = {Snapshot:/home/sebastian/Zotero/storage/DZJUG6UQ/tracing-measurement-tech-bluetooth.html:text/html},
}

@incollection{park_accuracy_2020,
	address = {Cham},
	title = {On the {Accuracy} of {Measured} {Proximity} of {Bluetooth}-{Based} {Contact} {Tracing} {Apps}},
	volume = {335},
	isbn = {978-3-030-63085-0 978-3-030-63086-7},
	url = {https://link.springer.com/10.1007/978-3-030-63086-7_4},
	abstract = {A large number of Bluetooth-based mobile apps have been developed recently to help tracing close contacts of contagious COVID19 individuals. These apps make decisions based on whether two users are in close proximity (e.g., within 6 ft) according to the distance measured from the received signal strength (RSSI ) of Bluetooth. This paper provides a detailed study of the current practice of RSSI -based distance measurements among contact tracing apps by analyzing various factors that can affect the RSSI value and how each app has responded to them. Our analysis shows that configurations for the signal transmission power (TxPower ) and broadcasting intervals that affect RSSI vary significantly across different apps and a large portion of apps do not consider these affecting factors at all, or with quite limited tuning.},
	language = {en},
	urldate = {2021-08-11},
	booktitle = {Security and {Privacy} in {Communication} {Networks}},
	publisher = {Springer International Publishing},
	author = {Zhao, Qingchuan and Wen, Haohuang and Lin, Zhiqiang and Xuan, Dong and Shroff, Ness},
	editor = {Park, Noseong and Sun, Kun and Foresti, Sara and Butler, Kevin and Saxena, Nitesh},
	year = {2020},
	doi = {10.1007/978-3-030-63086-7_4},
	note = {Series Title: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},
	pages = {49--60},
	file = {Zhao et al. - 2020 - On the Accuracy of Measured Proximity of Bluetooth.pdf:/home/sebastian/Zotero/storage/FNI6SSXL/Zhao et al. - 2020 - On the Accuracy of Measured Proximity of Bluetooth.pdf:application/pdf},
}

@inproceedings{thaljaoui_ble_2015,
	title = {{BLE} localization using {RSSI} measurements and {iRingLA}},
	doi = {10.1109/ICIT.2015.7125418},
	abstract = {Over the last few years, indoor localization has been a very dynamic research area that has drawn great attention. Many methods have been proposed for indoor positioning as well as navigation services. A big number of them were based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI) for their simplicity of use. The main issues of the studies conducted in this field are related to the improvement of localization factors like accuracy, computational complexity, easiness of deployment and cost. In our study, we used Bluetooth Low Energy (BLE) technology for indoor localization in the context of a smart home where an elderly person can be located using an hybrid system that combines radio, light and sound information. In this paper, we propose a model that averages the received signal strength indication (RSSI) at any distance domain which offered accuracy down to 0.4 meters, depending on the deployment configuration.},
	booktitle = {2015 {IEEE} {International} {Conference} on {Industrial} {Technology} ({ICIT})},
	author = {Thaljaoui, Adel and Val, Thierry and Nasri, Nejah and Brulin, Damien},
	month = mar,
	year = {2015},
	keywords = {Accuracy, BLE, Bluetooth, Distance measurement, Estimation, Indexes, Localization, Position measurement, Receivers, RSSI, Smarthome},
	pages = {2178--2183},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/HHYVIFFQ/7125418.html:text/html;Akzeptierte Version:/home/sebastian/Zotero/storage/APV7RIA4/Thaljaoui et al. - 2015 - BLE localization using RSSI measurements and iRing.pdf:application/pdf},
}

@inproceedings{al_qathrady_improving_2017,
	address = {New York, NY, USA},
	series = {{MSWiM} '17},
	title = {Improving {BLE} {Distance} {Estimation} and {Classification} {Using} {TX} {Power} and {Machine} {Learning}: {A} {Comparative} {Analysis}},
	isbn = {978-1-4503-5162-1},
	shorttitle = {Improving {BLE} {Distance} {Estimation} and {Classification} {Using} {TX} {Power} and {Machine} {Learning}},
	url = {https://doi.org/10.1145/3127540.3127577},
	doi = {10.1145/3127540.3127577},
	abstract = {Distance estimation and proximity classification techniques are essential for numerous IoT applications and in providing efficient services in smart cities. Bluetooth Low Energy (BLE) is designed for IoT devices, and its received signal strength indicator (RSSI) has been used in distance and proximity estimation, though they are noisy and unreliable. In this study, we leverage the BLE TX power level in BLE models.We adopt a comparative analysis framework that utilizes our extensive data library of measurements. It considers commonly used state-of-the-art model, in addition to our data-driven proposed approach. The RSSI and TX power are integrated into several parametric models such as log shadowing and Android Beacon library models, and machine learning models such as linear regression, decision trees, random forests and neural networks. Specific mobile apps are developed for the study experiment. We have collected more than 1.8 millions of BLE records between encounters with various distances that range from 0.5 to 22 meters in an indoor environment. Interestingly, considering TX power when estimating the distance reduced the mean errors by up to 46\% in parametric models and by up to 35\% in machine learning models. Also, the proximity classification accuracy increased by up to 103\% and 70\% in parametric and machine learning models, respectively. This work is one of the first studies (if not the first) that analyze in depth the TX power variations in improving the distance estimation and classification.},
	urldate = {2021-08-11},
	booktitle = {Proceedings of the 20th {ACM} {International} {Conference} on {Modelling}, {Analysis} and {Simulation} of {Wireless} and {Mobile} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Al Qathrady, Mimonah and Helmy, Ahmed},
	month = nov,
	year = {2017},
	keywords = {ble, distance estimation, iot, proximity classification},
	pages = {79--83},
	file = {Full Text PDF:/home/sebastian/Zotero/storage/GY6UJ92X/Al Qathrady und Helmy - 2017 - Improving BLE Distance Estimation and Classificati.pdf:application/pdf},
}

@inproceedings{lam_distance_2019,
	title = {Distance {Estimation} on {Moving} {Object} using {BLE} {Beacon}},
	doi = {10.1109/WiMOB.2019.8923185},
	abstract = {The development of Internet of Things technology has connected the smart things to the Internet, enabling users to interact for different applications such as indoor positioning or location-based notification service. To improve the user experience, an accurate distance estimation is required to ensure the interaction can be delivered precisely. For general beacon-based application, the objects keep moving while they are interacting with the beacons. Therefore, their mobility needs to be considered for distance estimation. In this paper, comprehensive experiments are conducted to study the relationship between distance estimation accuracy and packet received rate from two angels, the beacon advertising interval and the object moving speed. Moreover, an improved distance estimation method using Kalman filter and support vector regression is proposed, which has archived at least 40\% improvement comparing to current approaches. The proposed idea is also implemented in real-world application which archive less than 100μs computation time.},
	booktitle = {2019 {International} {Conference} on {Wireless} and {Mobile} {Computing}, {Networking} and {Communications} ({WiMob})},
	author = {Lam, Ching Hong and She, James},
	month = oct,
	year = {2019},
	note = {ISSN: 2160-4894},
	keywords = {Estimation, Advertising, Advertising Interval, BLE Beacon, Computational modeling, Distance Estimation, Internet of Things, Kalman filters, Mobile applications, Moving Speed, Noise measurement, Packet Receiving Rate, Support vector machines},
	pages = {1--6},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/JYYP9JX5/8923185.html:text/html;Lam und She - 2019 - Distance Estimation on Moving Object using BLE Bea.pdf:/home/sebastian/Zotero/storage/LU882993/Lam und She - 2019 - Distance Estimation on Moving Object using BLE Bea.pdf:application/pdf},
}

@inproceedings{kaczmarek_accuracy_2016,
	title = {Accuracy analysis of the {RSSI} {BLE} {SensorTag} signal for indoor localization purposes},
	abstract = {In this paper we describe possibility of use the RSSI signal (Radio Signal Strength Indication) from Texas Instruments SensorTag CC2650 for indoor positioning purposes. This idea is not a new but in our opinion it is possible to use SensorTags with Bluetooth LE wireless interface for positioning inside buildings in such applications as people findings in hospitals, senior come care, etc. RSSI is mostly selected as the sensor localization method in the indoor circumstances. In this paper, we aim to analyze accuracy, calibrate and map RSSI to distance by doing a series of the experiments. Obtained results are very promising and shows possibility of use this technique for position estimation.},
	booktitle = {2016 {Federated} {Conference} on {Computer} {Science} and {Information} {Systems} ({FedCSIS})},
	author = {Kaczmarek, Mariusz and Ruminski, Jacek and Bujnowski, Adam},
	month = sep,
	year = {2016},
	keywords = {Bluetooth, IEEE 802.11 Standard, Mathematical model, Mobile handsets, Wireless communication, Wireless sensor networks},
	pages = {1413--1416},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/9EM68JQN/7733434.html:text/html},
}

@inproceedings{maratea_non_2018,
	title = {Non parametric and robust statistics for indoor distance estimation through {BLE}},
	doi = {10.1109/EE1.2018.8385266},
	abstract = {Indoor positioning through Smart Bluetooth (Bluetooth Low Energy or BLE) sensors is a promising new field, where noisy data and outliers make challenging even the simplest distance estimates. The power of the BLE signal is known to be highly unstable even when measurement conditions remain unchanged and statistics on repeated measurements are required in order to have a good confidence in the obtained short-range distance estimates. This work proposes a stack of corrections based on non-parametric and robust statistics as a preprocessing step on the measured data, such that both the calibration and the range estimation processes improve their accuracy. According to experiments, robust and non-parametric statistics are able to handle effectively the severe noise involved in RSSI measurements, reaching most of the times a sub-meter precision.},
	booktitle = {2018 {IEEE} {International} {Conference} on {Environmental} {Engineering} ({EE})},
	author = {Maratea, Antonio and Gaglione, Salvatore and Angrisano, Antonio and Salvi, Giuseppe and Nunziata, Alessandro},
	month = mar,
	year = {2018},
	keywords = {Estimation, Wireless sensor networks, Calibration, Kernel, Meters, Pollution measurement, Power measurement},
	pages = {1--6},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/2JMAYNC8/8385266.html:text/html;Maratea et al. - 2018 - Non parametric and robust statistics for indoor di.pdf:/home/sebastian/Zotero/storage/E222IV3S/Maratea et al. - 2018 - Non parametric and robust statistics for indoor di.pdf:application/pdf},
}

@inproceedings{nguyen_optimized_2017,
	title = {Optimized indoor positioning for static mode smart devices using {BLE}},
	doi = {10.1109/PIMRC.2017.8292666},
	abstract = {Bluetooth Low Energy (BLE) technology and BLE-based devices such as iBeacons have become popular recently. In this work, an optimized indoor positioning approach using BLE for detecting a smart device's location in an indoor environment is proposed. The first stage of the proposed approach is a calibration stage for initialization. The Received Signal Strength Indicator (RSSI) is collected and pre-processed for a stable outcome, in the second stage. Then the distance is estimated by using the processed RSSI and calibrated factors in the third stage. The final stage is the position estimation using the outputs from the previous steps. The positioning technique, which is an improved Least Square estimation is evaluated against the other well-known techniques such as, Trilateration-Centroid, classic Least Square Estimation in estimating the user's location in the 2D plane. Experimental results show that our proposed approach has promising results by achieving an accuracy of positioning within 0.2 to 0.35m.},
	booktitle = {2017 {IEEE} 28th {Annual} {International} {Symposium} on {Personal}, {Indoor}, and {Mobile} {Radio} {Communications} ({PIMRC})},
	author = {Nguyen, Quang Huy and Johnson, Princy and Nguyen, Trung Thanh and Randles, Martin},
	month = oct,
	year = {2017},
	note = {ISSN: 2166-9589},
	keywords = {Bluetooth, Estimation, Localization, RSSI, Kalman filters, Mathematical model, Calibration, Bluetooth Low Energy, iBeacon, Indoor Location, Received signal strength indicator, Transmitters},
	pages = {1--6},
	file = {Akzeptierte Version:/home/sebastian/Zotero/storage/M56RCWSN/Nguyen et al. - 2017 - Optimized indoor positioning for static mode smart.pdf:application/pdf;IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/K465UVYD/8292666.html:text/html},
}

@article{ye_low-cost_2019,
	title = {A {Low}-{Cost} {Single}-{Anchor} {Solution} for {Indoor} {Positioning} {Using} {BLE} and {Inertial} {Sensor} {Data}},
	volume = {7},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2019.2951281},
	abstract = {Indoor positioning services have become necessary in many situations. Radio frequency (RF) signals are suitable for being used for positioning because of their ubiquity and imperceptibility. This paper utilizes the information from the baseband of a Bluetooth low energy (BLE) transceiver for angle estimation and signal strength for distance estimation. The scheme constitutes a single-anchor based solution to calculate the position of a client. It significantly reduces the cost of installation by avoiding traditional methods like multilateration or triangulation that require three or more anchors, even in a small space. To improve the performance, we design a fusion algorithm based on a Kalman filter to integrate measurements of the anchor station and simplified pedestrian dead reckoning (PDR) results from the client. Experiments show that the proposed solution estimates positions in high precision without initial user location or heading information. The mean error of the implementation is less than 1 m and can be improved to less than 0.5 m with a precise ranging measurement.},
	journal = {IEEE Access},
	author = {Ye, Feng and Chen, Ruizhi and Guo, Guangyi and Peng, Xuesheng and Liu, Zuoya and Huang, Lixiong},
	year = {2019},
	note = {Conference Name: IEEE Access},
	keywords = {BLE, Distance measurement, Estimation, Meters, Antenna arrays, Antenna measurements, data fusion, Fingerprint recognition, indoor positioning, Karman filter, pedestrian dead reckoning (PDR), single-anchor, Wireless fidelity},
	pages = {162439--162453},
	file = {IEEE Xplore Full Text PDF:/home/sebastian/Zotero/storage/F7SH4JDR/Ye et al. - 2019 - A Low-Cost Single-Anchor Solution for Indoor Posit.pdf:application/pdf;IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/7HZYJPTN/8890682.html:text/html},
}

@article{chen_collecting_2019,
	title = {Collecting building occupancy data of high resolution based on {WiFi} and {BLE} network},
	volume = {102},
	issn = {0926-5805},
	url = {https://www.sciencedirect.com/science/article/pii/S0926580518308744},
	doi = {10.1016/j.autcon.2019.02.016},
	abstract = {Building occupancy information is the premise of modern building service systems' control and energy conservation. Inaccurate occupancy information could result in a low comfort level and an energy waste. Existing occupancy detecting system relies on indirect and low-resolution environmental sensors, which potentially mislead facility managers and result in inefficiency in building energy use. In this study, the authors proposed a novel occupancy detection approach through a coupled indoor positioning system. The system integrates conventional k-nearest neighbor positioning algorithm and stochastic random walk algorithm to collect high-resolution occupancy data through Wi-Fi and Bluetooth Low Energy (BLE) networks. The proposed system is able to identify the meshed geospatial distribution of occupants, and to future track their movements in a network covered space. The detected occupancy meshes are suitable for direct implementation in building facility management since their operation is based on thermal zones rather than occupants' coordinates. To validate the feasibility and accuracy of the proposed system, the authors conducted a preliminary experiment in an institutional building. By comparing the positioning distance measurement metrics and matching parameters, the authors found the occupancy information detected by the proposed model is highly precise, accurate and reliable for the application in the building energy management.},
	language = {en},
	urldate = {2021-08-11},
	journal = {Automation in Construction},
	author = {Chen, Jiayu and Chen, Hainan and Luo, Xiaowei},
	month = jun,
	year = {2019},
	keywords = {Building energy system, Building occupant localization, kNN, Stochastic random walk, Zone-based},
	pages = {183--194},
	file = {ScienceDirect Snapshot:/home/sebastian/Zotero/storage/R9IHW2CV/S0926580518308744.html:text/html;Chen et al. - 2019 - Collecting building occupancy data of high resolut.pdf:/home/sebastian/Zotero/storage/BLAX7QUA/Chen et al. - 2019 - Collecting building occupancy data of high resolut.pdf:application/pdf},
}

@article{fermani_new_2013,
	title = {A new calibration for the {Blue} {Horizontal} {Branch}},
	volume = {430},
	issn = {0035-8711},
	url = {https://doi.org/10.1093/mnras/sts703},
	doi = {10.1093/mnras/sts703},
	abstract = {We suggest a simple analytic approximation for magnitudes and hence distances of Blue Horizontal Branch (BHB) stars in sloan colours. Precedent formulations do not offer a simple closed formula, nor do they cover the full dependences, e.g., on metallicity. Furthermore, using BHB star samples from the Sloan Digital Sky Survey, we validate our distance calibration directly on field stars instead of globular clusters and assess the performance of other available distance calibrations. Our method to statistically measure distances is sufficiently accurate to measure the colour and metallicity dependence on our sample and can be applied to other sets of stars or filters.},
	number = {2},
	urldate = {2021-08-11},
	journal = {Monthly Notices of the Royal Astronomical Society},
	author = {Fermani, Francesco and Schönrich, Ralph},
	month = apr,
	year = {2013},
	pages = {1294--1301},
	file = {Full Text PDF:/home/sebastian/Zotero/storage/G74NXFZM/Fermani und Schönrich - 2013 - A new calibration for the Blue Horizontal Branch.pdf:application/pdf},
}

@inproceedings{shchekotov_semi-automatic_2018,
	title = {Semi-{Automatic} {Self}-{Calibrating} {Indoor} {Localization} {Using} {BLE} {Beacon} {Multilateration}},
	doi = {10.23919/FRUCT.2018.8588081},
	abstract = {The indoor localization within public environments remains a complex and challenging task due to a number of issues related to the sensor infrastructure, space geometry and mobile device restrictions. This paper describes a hybrid indoor localization method based on received signal strength multilateration and pedestrian dead reckoning using internal smartphone sensors and relies on Bluetooth Low Energy beacons. Taking into account the beacon's zone of proximity and internal sensor data, the proposed method includes semi-automatic online calibration procedure of log-distance path loss propagation model. The proposed procedure takes into account smartphone heading angle and beacon signal obstructions due to user's body and moving people bodies.},
	booktitle = {2018 23rd {Conference} of {Open} {Innovations} {Association} ({FRUCT})},
	author = {Shchekotov, Maksim and Shilov, Nikolay},
	month = nov,
	year = {2018},
	note = {ISSN: 2305-7254},
	keywords = {Bluetooth, Estimation, Computational modeling, Mathematical model, Calibration, Fingerprint recognition, Radio transmitters},
	pages = {346--355},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/HB957T59/8588081.html:text/html},
}

@inproceedings{akeila_bluetooth-based_2010,
	title = {Bluetooth-based indoor positioning with fuzzy based dynamic calibration},
	doi = {10.1109/TENCON.2010.5686114},
	abstract = {Indoor positioning using readily available Bluetooth (BT) technology and Received Signal Strength Indication (RSSI) values on a BT node has been a goal of researchers for some time. However, the fluctuations of the RSSI values due to the nature of RF signals and the environmental changes lead to significant errors in the accuracy of the indoor positioning applications. This paper proposes a new approach based on monitoring variations of the received RSSI and dynamic calibration of the BT nodes to adapt to these variations. In order to further enhance the accuracy, a fuzzy logic unit has been designed to perceive the situations when the BT node is in proximity to one of the reference nodes available in an environment. Testing results show that the proposed scheme can significantly improve the performance of the indoor positioning compared to other known methods. Results show that the new approach is able to locate objects in the indoor environment with an average error of 1.27 m, which is sufficient for many indoor positioning applications.},
	booktitle = {{TENCON} 2010 - 2010 {IEEE} {Region} 10 {Conference}},
	author = {Akeila, Ehad and Salcic, Zoran and Swain, Akshya and Croft, Aaron and Stott, Jeremy},
	month = nov,
	year = {2010},
	note = {ISSN: 2159-3450},
	keywords = {Accuracy, Bluetooth, Mathematical model, Calibration, dynamic calibration, Equations, Fitting, fuzzy logic, Fuzzy logic, localisation/positioning},
	pages = {1415--1420},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/R7DJIQY9/5686114.html:text/html;Akeila et al. - 2010 - Bluetooth-based indoor positioning with fuzzy base.pdf:/home/sebastian/Zotero/storage/DW8LTSXG/Akeila et al. - 2010 - Bluetooth-based indoor positioning with fuzzy base.pdf:application/pdf},
}

@inproceedings{cabarkapa_comparative_2015,
	title = {Comparative analysis of the {Bluetooth} {Low}-{Energy} indoor positioning systems},
	doi = {10.1109/℡SKS.2015.7357741},
	abstract = {A key requirements in the Internet of Things (IoT) concept are context-aware computation, smart connectivity with existing networks and cost efficient low-power wireless solutions. Bluetooth Low Energy (BLE) is one of the latest developments of IoT and especially well-suited for ultra-low power sensors running on small batteries. BLE is successful alternative for indoor positioning systems (IPS) which offers reasonable accuracy and low cost deployment. This paper presents a comparative analysis of contemporary BLE indoor positioning solutions, taking into account the classification, comparison and variety considerations of parameters that are required for designing a new indoor positioning approaches.},
	booktitle = {2015 12th {International} {Conference} on {Telecommunication} in {Modern} {Satellite}, {Cable} and {Broadcasting} {Services} (℡{SIKS})},
	author = {Čabarkapa, Danijel and Grujić, Ivana and Pavlović, Petar},
	month = oct,
	year = {2015},
	keywords = {BLE, Bluetooth, Advertising, Calibration, iBeacon, Fingerprint recognition, indoor positioning, fingerprinting, IoT, Protocols, Radio frequency, RF signals},
	pages = {76--79},
	file = {Comparative analysis of the Bluetooth Low-Energy i.pdf:/home/sebastian/Zotero/storage/EA7635XI/Comparative analysis of the Bluetooth Low-Energy i.pdf:application/pdf;IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/5I2UQNNN/7357741.html:text/html},
}

@inproceedings{maratea_bagging_2019,
	title = {Bagging to {Improve} the {Calibration} of {RSSI} {Signals} in {Bluetooth} {Low} {Energy} ({BLE}) {Indoor} {Distance} {Estimation}},
	doi = {10.1109/SITIS.2019.00107},
	abstract = {Originally conceived as proximity sensors, smart Bluetooth (Bluetooth Low Energy or BLE) beacons have been quickly adopted as inexpensive means to estimate distance of the transmitter from the receiver. Unfortunately the Received Signal Strength in unstable and produces such oscillations that right beyond a couple of meters the accurate estimation of distances becomes extremely challenging. In this paper, starting from a preprocessed RSSI vector of measurements, a Bootstrap Aggregating procedure is proposed to improve the calibration of RSSI signals. The proposed method, in combination with robust and non parametric statistics, reaches a sub-meter precision up to 6 meters of distance.},
	booktitle = {2019 15th {International} {Conference} on {Signal}-{Image} {Technology} {Internet}-{Based} {Systems} ({SITIS})},
	author = {Maratea, Antonio and Salvi, Giuseppe and Gaglione, Salvatore},
	month = nov,
	year = {2019},
	keywords = {BLE, Estimation, Wireless sensor networks, Calibration, Meters, Pollution measurement, Transmitters, Bagging, Bootstrap, Robust, WSN},
	pages = {657--662},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/XILTTW6P/9067942.html:text/html;Maratea et al. - 2019 - Bagging to Improve the Calibration of RSSI Signals.pdf:/home/sebastian/Zotero/storage/CS2KLZU8/Maratea et al. - 2019 - Bagging to Improve the Calibration of RSSI Signals.pdf:application/pdf},
}

@misc{noauthor_evaluation_nodate,
	title = {Evaluation und {Anwendung} aktueller {Entwickunglen} im {Bereich} {Bluetooth} {Low} {Energy} am {Beispiel} von {iBeacon}},
	url = {https://core.ac.uk/download/pdf/33993179.pdf},
	urldate = {2021-08-11},
	file = {33993179.pdf:/home/sebastian/Zotero/storage/PGDX4ZJW/33993179.pdf:application/pdf},
}

@article{jahnel_contact-tracing-apps_2020,
	title = {Contact-{Tracing}-{Apps} als unterstützende {Maßnahme} bei der {Kontaktpersonennachverfolgung} von {COVID}-19},
	volume = {82},
	copyright = {© Georg Thieme Verlag KG Stuttgart · New York},
	issn = {0941-3790, 1439-4421},
	url = {http://www.thieme-connect.de/DOI/DOI?10.1055/a-1195-2474},
	doi = {10.1055/a-1195-2474},
	abstract = {Die Kontaktpersonennachverfolgung ist derzeit eine der wirksamsten Maßnahmen zur Eindämmung der COVID-19 Pandemie. Digitales Contact Tracing mittels Smartphones scheint eine sinnvolle zusätzliche Maßnahme zur manuellen Kontaktpersonennachverfolgung zu sein, um Personen zu identifizieren, die nicht bekannt oder nicht erinnerlich sind und um den zeitlichen Verzug beim Melden eines Infektionsfalles und beim Benachrichtigen von Kontaktpersonen so gering wie möglich zu halten. Obwohl erste Modellierungsstudien eine positive Wirkung in Bezug auf eine zeitnahe Kontaktpersonennachverfolgung nahelegen, gibt es bislang keine empirisch belastbaren Daten, weder zum bevölkerungsweiten Nutzen noch zum potenziellen Schaden von Contact-Tracing-Apps. Die Beurteilung der Zweckerfüllung und eine wissenschaftliche interdisziplinäre Begleitforschung sowohl zur Wirksamkeit, Risiken und Nebenwirkungen als auch zu Implementierungsprozessen (z. B. Planung und Einbezug verschiedener Beteiligter) sind wesentliche Bestandteile einer Nutzen-Risiko Bewertung. Dieser Beitrag betrachtet daher den möglichen Public-Health-Nutzen sowie technische, soziale, rechtliche und ethische Aspekte einer Contact-Tracing-App zur Kontaktpersonennachverfolgung im Rahmen der COVID-19-Pandemie. Weiterhin werden Bedingungen für eine möglichst breite Nutzung der App aufgezeigt.},
	language = {de},
	number = {8/9},
	urldate = {2021-08-11},
	journal = {Das Gesundheitswesen},
	author = {Jahnel, Tina and Kernebeck, Sven and Böbel, Simone and Buchner, Benedikt and Grill, Eva and Hinck, Sebastian and Ranisch, Robert and Rothenbacher, Dietrich and Schüz, Benjamin and Starke, Dagmar and Wienert, Julian and Zeeb, Hajo and Gerhardus, Ansgar},
	month = sep,
	year = {2020},
	note = {Publisher: © Georg Thieme Verlag KG},
	keywords = {19, 2, Contact tracing, CoV, COVID, digital, SARS},
	pages = {664--669},
	file = {Full Text PDF:/home/sebastian/Zotero/storage/Q69Z9AEB/Jahnel et al. - 2020 - Contact-Tracing-Apps als unterstützende Maßnahme b.pdf:application/pdf;Snapshot:/home/sebastian/Zotero/storage/RF8639M3/a-1195-2474.html:text/html},
}

@misc{matthias_rosenthaler_near_2016,
	title = {Near {Field} {Communication} mit {Bluetooth} {Low} {Energy}},
	url = {https://epub.jku.at/obvulihs/content/titleinfo/1654483/full.pdf},
	language = {Deutsch},
	urldate = {2021-08-11},
	author = {{Matthias Rosenthaler}},
	month = nov,
	year = {2016},
	file = {Matthias Rosenthaler - 2016 - Near Field Communication mit Bluetooth Low Energy.pdf:/home/sebastian/Zotero/storage/TFZ8L3PH/Matthias Rosenthaler - 2016 - Near Field Communication mit Bluetooth Low Energy.pdf:application/pdf},
}

@article{franzel_drahtlosen_nodate,
	title = {{DRAHTLOSEN} {SENSORNETZWERKEN}},
	language = {de},
	author = {Fränzel, Norbert and Greifzu, Norbert and Schneider, Manuel and Wenzel, Andreas},
	pages = {6},
	file = {Fränzel et al. - DRAHTLOSEN SENSORNETZWERKEN.pdf:/home/sebastian/Zotero/storage/WU35GVXD/Fränzel et al. - DRAHTLOSEN SENSORNETZWERKEN.pdf:application/pdf},
}

@article{truong_autonom_2021,
	title = {Autonom fahrender {Aufklärungsroboter}},
	volume = {4},
	copyright = {Copyright (c) 2021 LEGO-Praktikum. Entwickeln, programmieren, optimieren : Berichte der Studierenden zum Projektseminar Elektrotechnik/Informationstechnik},
	issn = {2629-6160},
	url = {https://journals.ub.uni-magdeburg.de/index.php/LEGO/article/view/2020},
	doi = {10.24352/UB.OVGU-2021-034},
	language = {de},
	urldate = {2021-08-11},
	journal = {LEGO-Praktikum. Entwickeln, programmieren, optimieren : Berichte der Studierenden zum Projektseminar Elektrotechnik/Informationstechnik},
	author = {Truong, Gia Bao},
	month = may,
	year = {2021},
	pages = {13--16},
	file = {Truong - 2021 - Autonom fahrender Aufklärungsroboter.pdf:/home/sebastian/Zotero/storage/VTM3R2RW/Truong - 2021 - Autonom fahrender Aufklärungsroboter.pdf:application/pdf},
}

@article{haake_schnelle_2014,
	title = {Schnelle {Simulation} von {Funkkoexistenz}-{Verhalten} im 2,45-{GHz}-{ISM}-{Band} in großflächigen intralogistischen {Szenarien}},
	copyright = {CC BY 3.0 DE},
	url = {https://www.repo.uni-hannover.de/handle/123456789/5455},
	doi = {10.15488/5408},
	abstract = {Für eine extrem schnelle Simulation des Koexistenz-Verhaltens von Funkteilnehmern im 2,45-GHz-ISM-Band im Zusammenspiel mit einem Funkortungssystem in großflächigen intralogistischen Szenarien ist eine spezielle Simulations-Software entwickelt worden. Die Ergebnisse bieten Aussagen zu Zeitverzögerungen der WLAN-Datenübertragung und der Ortungsgenauigkeit von nanoLOC. Es kann den Funkteilnehmern des Ortungssystems ein Koexistenz-Verhalten über spezielle Algorithmen aufgeprägt werden. Diese Algorithmen können dann durch Analyse der Simulationsdaten bewertet und optimiert werden.},
	language = {ger},
	urldate = {2021-08-11},
	author = {Haake, Kai and Gisch, Alexander},
	year = {2014},
	note = {Publisher: Berlin : VDE-Verlag},
	pages = {252--259},
	file = {Full Text PDF:/home/sebastian/Zotero/storage/NP44NZJ5/Haake und Gisch - 2014 - Schnelle Simulation von Funkkoexistenz-Verhalten i.pdf:application/pdf;Snapshot:/home/sebastian/Zotero/storage/AV5JBGQZ/5455.html:text/html},
}

@misc{noauthor_standards_nodate,
	title = {Standards and conventions},
	url = {https://opentracing.io/docs/best-practices/standards-and-conventions/},
	language = {en-us},
	urldate = {2021-08-11},
}

@misc{noauthor_ibeacon_nodate,
	title = {ibeacon - {Impoving} {ContactTracing} {Api} efficiency with bluetooth signal strength},
	url = {https://stackoverflow.com/questions/61212162/impoving-contacttracing-api-efficiency-with-bluetooth-signal-strength},
	urldate = {2021-08-11},
	journal = {Stack Overflow},
	annote = {
Kommentar davidgyoung:
It is very difficult to accurately determine distance by Bluetooth RSSI measured between two phones because there is a huge variation in the way different phone models measure bluetooth signals. Check out this graph produced by the Open Trace folks behind the effort in Singapore:

Those variations are consistent with my work in this area for the Android Beacon Library open source project. The fragmentation of Android devices has made it impossible to keep up with all the variations in signal strength response.
One point that the Open Trace team did not address in their work, is that there are a number of different bluetooth channels, and RSSI varies greatly on a given phone depending on which channel is being used. Mobile phones give you no indication of what channel the radio was on when a measurement was taken. The channel difference probably accounts for much of the "height" of the blue bars in the graph.
Unfortunately, there is no way to know if a device is approaching or stationary by reading RSSI updates. The changes could be because of natural variation, motion, or changes in obstacles. I do not believe self-calibration in a contact tracing app is viable.
This does not mean that RSSI is worthless for distance estimates, but it does mean that the margin of error is very high in what you can measure. If you see a device at all, there is a very good chance it is within 50 meters. And if you see that the RSSI is stronger than -70 dBm, there is a good chance you are within 2 meters. But there will always be false positives and false negatives.
},
	file = {Snapshot:/home/sebastian/Zotero/storage/PUT4H2WA/impoving-contacttracing-api-efficiency-with-bluetooth-signal-strength.html:text/html},
}

@misc{noauthor_reference_nodate,
	title = {Reference {RSSI} {Value} {Calibration}},
	url = {https://support.kontakt.io/hc/en-gb/articles/115000115610-Reference-RSSI-Value-Calibration},
	abstract = {The only way to estimate distance to a beacon is based on measuring it's signal strength. However, the Received Signal Strength Indicator (RSSI) value alone does not mean anything, because beacons ...},
	language = {en-GB},
	urldate = {2021-08-11},
	journal = {Support Center},
}

@article{paek_measurement_2016,
	title = {A {Measurement} {Study} of {BLE} {iBeacon} and {Geometric} {Adjustment} {Scheme} for {Indoor} {Location}-{Based} {Mobile} {Applications}},
	volume = {2016},
	issn = {1574-017X},
	url = {https://www.hindawi.com/journals/misy/2016/8367638/},
	doi = {10.1155/2016/8367638},
	abstract = {Bluetooth Low Energy (BLE) and the iBeacons have recently gained large interest for enabling various proximity-based application services. Given the ubiquitously deployed nature of Bluetooth devices including mobile smartphones, using BLE and iBeacon technologies seemed to be a promising future to come. This work started off with the belief that this was true: iBeacons could provide us with the accuracy in proximity and distance estimation to enable and simplify the development of many previously difficult applications. However, our empirical studies with three different iBeacon devices from various vendors and two types of smartphone platforms prove that this is not the case. Signal strength readings vary significantly over different iBeacon vendors, mobile platforms, environmental or deployment factors, and usage scenarios. This variability in signal strength naturally complicates the process of extracting an accurate location/proximity estimation in real environments. Our lessons on the limitations of iBeacon technique lead us to design a simple class attendance checking application by performing a simple form of geometric adjustments to compensate for the natural variations in beacon signal strength readings. We believe that the negative observations made in this work can provide future researchers with a reference on how well of a performance to expect from iBeacon devices as they enter their system design phases.},
	language = {en},
	urldate = {2021-08-11},
	journal = {Mobile Information Systems},
	author = {Paek, Jeongyeup and Ko, JeongGil and Shin, Hyungsik},
	month = oct,
	year = {2016},
	note = {Publisher: Hindawi},
	pages = {e8367638},
	file = {Full Text PDF:/home/sebastian/Zotero/storage/8V8CAENW/Paek et al. - 2016 - A Measurement Study of BLE iBeacon and Geometric A.pdf:application/pdf;Snapshot:/home/sebastian/Zotero/storage/93UUJEHZ/8367638.html:text/html},
}

@misc{noauthor_micro-location_nodate,
	title = {Micro-{Location} {Part} 1: {State} of the {Art}},
	shorttitle = {Micro-{Location} {Part} 1},
	url = {https://www.abaltatech.com/blog/microlocation1},
	abstract = {This is the first part of a three-part blog series that explores micro-location technologies. Over the past few years, Micro-location has gone through extensive development. There has been a recent surge of interest in micro-location due to its applicability in COVID-19 contact tracing. It also has},
	language = {en-US},
	urldate = {2021-08-11},
	journal = {Abalta Technologies},
	file = {Snapshot:/home/sebastian/Zotero/storage/8XRS6QKR/microlocation1.html:text/html},
}

@misc{noauthor_micro-location_nodate-1,
	title = {Micro-{Location} {Part} 2: {BLE} and {RSSI}},
	shorttitle = {Micro-{Location} {Part} 2},
	url = {https://www.abaltatech.com/blog/microlocation2},
	abstract = {This is the second part of a three-part blog series that explores micro-location technologies. This blog goes deeper into Bluetooth Low Energy (BLE) technology for micro-location and more specifically how Received Signal Strength Indicator (RSSI) values can be used to estimate distance and provide p},
	language = {en-US},
	urldate = {2021-08-11},
	journal = {Abalta Technologies},
	file = {Snapshot:/home/sebastian/Zotero/storage/XL5YW9TE/microlocation2.html:text/html},
}

@misc{noauthor_micro-location_nodate-2,
	title = {Micro-{Location} {Part} 3: {BLE} 5.1 and {Direction} {Finding}},
	shorttitle = {Micro-{Location} {Part} 3},
	url = {https://www.abaltatech.com/blog/microlocation3},
	abstract = {This is the last part of the three-part blog series that explores micro-location technologies. Part 2 described the current most popular way of doing micro-location using Bluetooth Low Energy (BLE) and the received signal strength indicator (RSSI) as an approximation for distance. As explained, ther},
	language = {en-US},
	urldate = {2021-08-11},
	journal = {Abalta Technologies},
	file = {Snapshot:/home/sebastian/Zotero/storage/7KC28BHN/microlocation3.html:text/html},
}

@misc{noauthor_android_nodate,
	title = {Android {Beacon} {Library}},
	url = {https://altbeacon.github.io/android-beacon-library/distance-calculations.html},
	urldate = {2021-08-11},
	file = {Android Beacon Library:/home/sebastian/Zotero/storage/N8P2G5NT/distance-calculations.html:text/html},
}

@article{narzt_is_nodate,
	title = {{IS} {BLUETOOTH} {LOW} {ENERGY} {AN} {ALTERNATIVE} {TO} {NEAR} {FIELD} {COMMUNICATION}?},
	abstract = {While the Bluetooth Low Energy (BLE) standard is commonly being used for energy-efficient mid-range data transmission and localization where distances of several meters are to be covered, its signal characteristics also reveals stable and deterministic behavior in the ultra-short range with significant higher signal strengths compared to distant placements, which potentially qualifies BLE as a substitute technology for Near Field Communication (NFC) for the purpose of identifying objects at very short distances. This paper investigates the signal strength behavior of BLE at a few centimeters distance between transmitter and receiver, points out strengths and weaknesses in terms of antenna alignments, shielding issues and interfering signals and presents potential application areas for ultra-short range object identification with a transmission technology that is not designed for that purpose.},
	language = {en},
	author = {Narzt, Wolfgang and Furtmüller, Lukas and Rosenthaler, Matthias},
	pages = {15},
	file = {Narzt et al. - IS BLUETOOTH LOW ENERGY AN ALTERNATIVE TO NEAR FIE.pdf:/home/sebastian/Zotero/storage/4DC43W4W/Narzt et al. - IS BLUETOOTH LOW ENERGY AN ALTERNATIVE TO NEAR FIE.pdf:application/pdf},
}

@book{nur_fig_2015,
	title = {Fig. 1. {Distance} between two {NFC} devices to execute an activity},
	url = {https://www.researchgate.net/figure/Distance-between-two-NFC-devices-to-execute-an-activity_fig1_278459134},
	abstract = {Download scientific diagram {\textbar} Distance between two NFC devices to execute an activity  from publication: User Friendliness of Near-Field Communication (NFC) {\textbar} Near-Field Communication or NFC is a new technology that was introduced in recent years. However, even with the simplicity and security that the technology provide, the adoption of this technology is not wide spread. In this paper, we describe the user-friendliness criteria... {\textbar} NFC, Radio Frequency Identification and Radio Frequency Identification Device {\textbar} ResearchGate, the professional network for scientists.},
	language = {en},
	urldate = {2021-08-11},
	author = {Nur, Osman and Nur, , Samsudin and Nur, Shahirah and {Husin, Heikal} and {Malim, Nurul} and {Mahinderjit Singh, Manmeet (Mandy)}},
	month = jun,
	year = {2015},
	file = {Snapshot:/home/sebastian/Zotero/storage/BI3H4QRR/Distance-between-two-NFC-devices-to-execute-an-activity_fig1_278459134.html:text/html;Fig. 1. Distance between two NFC devices to execut.pdf:/home/sebastian/Zotero/storage/HP6EJTSN/Fig. 1. Distance between two NFC devices to execut.pdf:application/pdf},
}

@article{ramirez_practice_2021,
	title = {A {Practice} of {BLE} {RSSI} {Measurement} for {Indoor} {Positioning}},
	volume = {21},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	url = {https://www.mdpi.com/1424-8220/21/15/5181},
	doi = {10.3390/s21155181},
	abstract = {Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI–distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.},
	language = {en},
	number = {15},
	urldate = {2021-08-11},
	journal = {Sensors},
	author = {Ramirez, Ramiro and Huang, Chien-Yi and Liao, Che-An and Lin, Po-Ting and Lin, Hsin-Wei and Liang, Shu-Hao},
	month = jan,
	year = {2021},
	note = {Number: 15
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {BLE, RSSI, IPS, Kalman filter, modification coefficient, trilateration},
	pages = {5181},
	file = {Full Text PDF:/home/sebastian/Zotero/storage/4AVMSHUQ/Ramirez et al. - 2021 - A Practice of BLE RSSI Measurement for Indoor Posi.pdf:application/pdf;Snapshot:/home/sebastian/Zotero/storage/3GMNTND7/htm.html:text/html},
}

@article{abboud_received_nodate,
	title = {Received {Signal} {Strength} {Based} {Indoor} {Positioning} {Using} {Bluetooth} {Low} {Energy}},
	language = {en},
	author = {Abboud, Mansour},
	pages = {86},
	file = {Abboud - Received Signal Strength Based Indoor Positioning .pdf:/home/sebastian/Zotero/storage/XBD25QT4/Abboud - Received Signal Strength Based Indoor Positioning .pdf:application/pdf},
}

@inproceedings{giovanelli_rssi_2018,
	address = {Prague},
	title = {{RSSI} or {Time}-of-flight for {Bluetooth} {Low} {Energy} based localization? {An} experimental evaluation},
	isbn = {978-3-903176-04-1},
	shorttitle = {{RSSI} or {Time}-of-flight for {Bluetooth} {Low} {Energy} based localization?},
	url = {https://ieeexplore.ieee.org/document/8480847/},
	doi = {10.23919/WMNC.2018.8480847},
	abstract = {In this paper, we focus on Bluetooth Low Energy (BLE) and in particular on its use for ranging, starting from the observation that data on RSSI in BLE comes nearly for free. The SDK typically provides to developers an easy way to extract this data and use it to implement their algorithms. However, RSSI based localization techniques have known limits. An alternative information to be used in ranging for localization purposes is Time-of-Flight (ToF). Still, this data is not provided by the BLE API, therefore we propose a practical approach for ToF extraction on top of BLE to be used as alternative to or in combination with RSSI. Furthermore, with the paper, we release the sources of the library used to perform the ToF measurement on BLE, that can be used per se or as input for a localization algorithm. We tested the measurements indoor and outdoor at different distances, both considering Line-of-Sight free or occluded by user body. We conclude evaluating ranging performance, test repeatability and comparing the obtained results with the popular RSSI based approach.},
	language = {en},
	urldate = {2021-08-11},
	booktitle = {2018 11th {IFIP} {Wireless} and {Mobile} {Networking} {Conference} ({WMNC})},
	publisher = {IEEE},
	author = {Giovanelli, Davide and Farella, Elisabetta},
	month = sep,
	year = {2018},
	pages = {1--8},
	file = {Giovanelli und Farella - 2018 - RSSI or Time-of-flight for Bluetooth Low Energy ba.pdf:/home/sebastian/Zotero/storage/F3MWANXI/Giovanelli und Farella - 2018 - RSSI or Time-of-flight for Bluetooth Low Energy ba.pdf:application/pdf},
}

@misc{sponas_things_nodate,
	title = {Things {You} {Should} {Know} {About} {Bluetooth} {Range}},
	url = {https://blog.nordicsemi.com/getconnected/things-you-should-know-about-bluetooth-range},
	abstract = {Many factors affect the range of Bluetooth Low Energy (BLE) and Bluetooth 5. Learn about the distance limiting factors and get all the relevant numbers here.},
	language = {en-gb},
	urldate = {2021-08-11},
	author = {Sponås, Jon Gunnar},
	file = {Snapshot:/home/sebastian/Zotero/storage/47KTWVKL/things-you-should-know-about-bluetooth-range.html:text/html},
}

@misc{noauthor_kingcamp_nodate,
	title = {Kingcamp {Multifunktionaler} {Spiel}- \& {Campingtisch} silber/bambus ab 199,95 € {\textbar} {Preisvergleich} bei idealo.de},
	url = {https://www.idealo.de/preisvergleich/OffersOfProduct/200489052_-multifunktionaler-spiel-campingtisch-silber-bambus-kingcamp.html},
	urldate = {2021-08-16},
	file = {Kingcamp Multifunktionaler Spiel- & Campingtisch silber/bambus ab 199,95 € | Preisvergleich bei idealo.de:/home/sebastian/Zotero/storage/W4HGBI34/200489052_-multifunktionaler-spiel-campingtisch-silber-bambus-kingcamp.html:text/html},
}

@article{thein_degree_nodate,
	title = {Degree of {Bachelor} of {Science} in {Electrical} and {Computer} {Engineering}},
	language = {en},
	author = {Thein, Myo Min},
	pages = {86},
	file = {Thein - Degree of Bachelor of Science in Electrical and Co.pdf:/home/sebastian/Zotero/storage/US3F3KA4/Thein - Degree of Bachelor of Science in Electrical and Co.pdf:application/pdf},
}

@misc{noauthor_xiaomi_nodate,
	title = {Xiaomi {Introduces} groundbreaking {UWB} {Technology} – {Mi} {Blog}},
	url = {https://blog.mi.com/en/2020/10/13/xiaomi-introduces-groundbreaking-uwb-technology/},
	language = {en-US},
	urldate = {2021-08-18},
	file = {Snapshot:/home/sebastian/Zotero/storage/TRHJKN43/xiaomi-introduces-groundbreaking-uwb-technology.html:text/html},
}

@article{miura_gps_2015,
	title = {{GPS} {Error} {Correction} by {Multipath} {Adaptation}},
	volume = {13},
	issn = {1868-8659},
	url = {https://doi.org/10.1007/s13177-013-0073-9},
	doi = {10.1007/s13177-013-0073-9},
	abstract = {From the point of view of safety applications, accurate and reliable positioning system for road users is the most important part. Although GPSs are the most widely utilized technology today, they still have the problems of performance degradation caused by multipath propagation in urban canyons. This study proposes an approach to estimate position by searching around the result of GPS. The proposed algorithm evaluates the pseudoranges of the possible multipath signals by referring to the building geometry. The assumed position is estimated by using received pseudoranges and is evaluated by the likelihood of the possible positioning error and filtering algorithm. The proposed method was verified through field experiments in urban canyons in Tokyo.},
	language = {en},
	number = {1},
	urldate = {2021-08-20},
	journal = {International Journal of Intelligent Transportation Systems Research},
	author = {Miura, Shunsuke and Kamijo, Shunsuke},
	month = jan,
	year = {2015},
	pages = {1--8},
}

@article{lalitha_distance_2020,
	title = {{DISTANCE} {MEASUREMENT} {USING} {MOBILE} {APPLICATION}},
	volume = {6},
	abstract = {This paper describes an ultrasonic sensor that is able to measure the distance from the ground of selected points of a motor vehicle. The sensor is based on the measurement of the time of flight of an ultrasonic pulse, which is reflected by the ground. A constrained optimization technique is employed to obtain reflected pulses that are easily detectable by means of a threshold comparator. Such a technique, which takes the frequency response of the ultrasonic transducers into account, allows a sub-wavelength detection to be obtained.},
	language = {en},
	number = {2},
	author = {Lalitha, A and Sooriya, P and Sunilayyappan, E and Suryanarayanan, K},
	year = {2020},
	pages = {5},
	file = {Lalitha et al. - 2020 - DISTANCE MEASUREMENT USING MOBILE APPLICATION.pdf:/home/sebastian/Zotero/storage/HNV5DV64/Lalitha et al. - 2020 - DISTANCE MEASUREMENT USING MOBILE APPLICATION.pdf:application/pdf},
}

@article{graham_software-based_2015,
	title = {A {Software}-{Based} {Sonar} {Ranging} {Sensor} for {Smart} {Phones}},
	volume = {2},
	issn = {2327-4662},
	url = {http://ieeexplore.ieee.org/document/7054431/},
	doi = {10.1109/JIOT.2015.2408451},
	abstract = {We live in a three dimensional world. However, the smart phones that we use every day are incapable of sensing depth, without the use of custom hardware. By creating new depth sensors, we can provide developers with the tools that they need to create immersive mobile applications that take advantage of the 3D nature of our world. In this paper, we propose a new sonar sensor for smart phones. This sonar sensor does not require any additional hardware, and utilizes the phone’s microphone and rear speaker. The sonar sensor calculates distances by measuring the elapsed time between the initial pulse and its reflection. We evaluate the accuracy of the sonar sensor by using it to measure the distance from the phone to an object. We found that we were able to measure the distances of objects accurately with an error bound of 12 centimeters.},
	language = {en},
	number = {6},
	urldate = {2021-08-20},
	journal = {IEEE Internet of Things Journal},
	author = {Graham, Daniel and Simmons, George and Nguyen, David T. and Zhou, Gang},
	month = dec,
	year = {2015},
	pages = {479--489},
	file = {Graham et al. - 2015 - A Software-Based Sonar Ranging Sensor for Smart Ph.pdf:/home/sebastian/Zotero/storage/4N6CVGWA/Graham et al. - 2015 - A Software-Based Sonar Ranging Sensor for Smart Ph.pdf:application/pdf},
}

@incollection{bianchi_cosero_2015,
	address = {Cham},
	title = {Cosero, {Find} {My} {Keys}! {Object} {Localization} and {Retrieval} {Using} {Bluetooth} {Low} {Energy} {Tags}},
	volume = {8992},
	isbn = {978-3-319-18614-6 978-3-319-18615-3},
	url = {http://link.springer.com/10.1007/978-3-319-18615-3_16},
	abstract = {Personal robots will contribute mobile manipulation capabilities to our future smart homes. In this paper, we propose a low-cost object localization system that uses static devices with Bluetooth capabilities, which are distributed in an environment, to detect and localize active Bluetooth beacons and mobile devices. This system can be used by a robot to coarsely localize objects in retrieval tasks. We attach small Bluetooth low energy tags to objects and require at least four static Bluetooth receivers. While commodity Bluetooth devices could be used, we have built low-cost receivers from Raspberry Pi computers. The location of a tag is estimated by lateration of its received signal strengths. In experiments, we evaluate accuracy and timing of our approach, and report on the successful demonstration at the RoboCup German Open 2014 competition in Magdeburg.},
	language = {en},
	urldate = {2021-08-20},
	booktitle = {{RoboCup} 2014: {Robot} {World} {Cup} {XVIII}},
	publisher = {Springer International Publishing},
	author = {Schwarz, David and Schwarz, Max and Stückler, Jörg and Behnke, Sven},
	editor = {Bianchi, Reinaldo A. C. and Akin, H. Levent and Ramamoorthy, Subramanian and Sugiura, Komei},
	year = {2015},
	doi = {10.1007/978-3-319-18615-3_16},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {195--206},
	file = {Schwarz et al. - 2015 - Cosero, Find My Keys! Object Localization and Retr.pdf:/home/sebastian/Zotero/storage/HVABH2LA/Schwarz et al. - 2015 - Cosero, Find My Keys! Object Localization and Retr.pdf:application/pdf},
}

@inproceedings{kanatani_triangulation_2008,
	address = {Leeds},
	title = {Triangulation from {Two} {Views} {Revisited}: {Hartley}-{Sturm} vs. {Optimal} {Correction}},
	isbn = {978-1-901725-36-0},
	shorttitle = {Triangulation from {Two} {Views} {Revisited}},
	url = {http://www.bmva.org/bmvc/2008/papers/55.html},
	doi = {10.5244/C.22.18},
	abstract = {A higher order scheme is presented for the optimal correction method of Kanatani [5] for triangulation from two views and is compared with the method of Hartley and Sturm [3]. It is pointed out that the epipole is a singularity of the Hartley-Sturm method, while the proposed method has no singularity. Numerical simulation confirms that both compute identical solutions at other points. However, the proposed method is significantly faster.},
	language = {en},
	urldate = {2021-08-20},
	booktitle = {Procedings of the {British} {Machine} {Vision} {Conference} 2008},
	publisher = {British Machine Vision Association},
	author = {Kanatani, K. and Sugaya, Y. and Niitsuma, H.},
	year = {2008},
	pages = {18.1--18.10},
	file = {Kanatani et al. - 2008 - Triangulation from Two Views Revisited Hartley-St.pdf:/home/sebastian/Zotero/storage/VHVYPE52/Kanatani et al. - 2008 - Triangulation from Two Views Revisited Hartley-St.pdf:application/pdf},
}

@inproceedings{yapeng_wang_bluetooth_2013,
	address = {Las Vegas, NV},
	title = {Bluetooth positioning using {RSSI} and triangulation methods},
	isbn = {978-1-4673-3133-3 978-1-4673-3131-9 978-1-4673-3132-6},
	url = {http://ieeexplore.ieee.org/document/6488558/},
	doi = {10.1109/CCNC.2013.6488558},
	urldate = {2021-08-20},
	booktitle = {2013 {IEEE} 10th {Consumer} {Communications} and {Networking} {Conference} ({CCNC})},
	publisher = {IEEE},
	author = {{Yapeng Wang} and {Xu Yang} and {Yutian Zhao} and {Yue Liu} and Cuthbert, L.},
	month = jan,
	year = {2013},
	pages = {837--842},
	file = {Yapeng Wang et al. - 2013 - Bluetooth positioning using RSSI and triangulation.pdf:/home/sebastian/Zotero/storage/NXXR29XW/Yapeng Wang et al. - 2013 - Bluetooth positioning using RSSI and triangulation.pdf:application/pdf},
}

@article{abbas_bluetooth_2021,
	title = {Bluetooth {Situating} {Utilizing} {Triangulation} {Strategies} and {RSSI}},
	volume = {9},
	copyright = {Copyright (c) 2021 Author},
	url = {http://advancesinmech.com/index.php/am/article/view/131},
	abstract = {Area-based administrations are the most common applications on smartphones right now, and the trend is expected to continue. Indoor remote positioning is a key innovation that allows local agencies to work successfully indoors, where the Global Positioning System (GPS) has historically failed. Bluetooth has been commonly used in handheld devices such as tablets and PDAs. The economic potential of Bluetooth -based indoor positioning is immense. Received Signal Strength Indication (RSSI) may be used to remotely locate a computer. RSS will now be discovered without pre-association thanks to the latest Bluetooth protocol (as of version 2.1). This article initially discussed the general wireless positioning innovations field unit. At that stage, the RSS-based Bluetooth position with the most recent component is being considered. The numerical model is designed to investigate the interaction between RSS feeds as well as the differences between two Bluetooth systems. Bluetooth systems are located using three distance-based measurements: the Three-Boundary and Centroid Methods, as well as the Least Squares Estimation (LSE). The examination results are dissected, and techniques for improving position accuracy are examined.},
	language = {en},
	number = {3},
	urldate = {2021-08-20},
	journal = {Advances in Mechanics},
	author = {Abbas, Zahraa and Mosleh, Mahmood F.},
	month = jul,
	year = {2021},
	note = {Number: 3},
	keywords = {LSE},
	pages = {376--386},
	file = {Abbas und Mosleh - 2021 - Bluetooth Situating Utilizing Triangulation Strate.pdf:/home/sebastian/Zotero/storage/JTPN7MCN/Abbas und Mosleh - 2021 - Bluetooth Situating Utilizing Triangulation Strate.pdf:application/pdf},
}

@inproceedings{hou_indoor_2016,
	title = {Indoor {Localization} for {Bluetooth} {Low} {Energy} {Devices} {Using} {Weighted} {Off}-set {Triangulation} {Algorithm}},
	url = {http://www.ion.org/publications/abstract.cfm?jp=p&articleID=14720},
	doi = {10.33012/2016.14720},
	abstract = {This paper proposes a new indoor positioning algorithm for Bluetooth Low Energy (BLE) devices, such as mobile phones and tablets. The algorithm integrates an Off-set triangulation algorithm and a multi-stage weighted framework. An off-set triangulation algorithm was used to provide a general method to locate the user’s position. Results are integrated into a weighted framework in order to increase the adaptability of the proposed localization algorithm to different complex environments and increase the accuracy of the localization. Three experiments in different environments have been examined in order to test the performance and the limitations of this algorithm. Results show that the proposed algorithm demonstrated strong adaptability to complex non-Line-of-Sight (LOS) environments.},
	language = {en},
	urldate = {2021-08-20},
	author = {Hou, Xiaoyue and Arslan, Tughrul and Juri, Arief and Wang, Fengzhou},
	month = sep,
	year = {2016},
	note = {ISSN: 2331-5954},
	pages = {2286--2292},
	file = {Snapshot:/home/sebastian/Zotero/storage/K3HYEHYT/abstract.html:text/html},
}

@misc{noauthor_indoor_nodate,
	title = {Indoor {Positioning} {Techniques} using {RSSI} from {Wireless} {Devices}},
	url = {https://ieeexplore.ieee.org/abstract/document/9038591/},
	abstract = {The whole world is familiar with the Global Positioning System or GPS which can determine the exact location of any object with the help of satellite. But GPS signals are not available in indoors. To overcome this, Indoor Positioning System (IPS) is used which enables us to locate objects inside an indoor environment. Our goal is to build an Indoor Positioning System by estimating the location using Received Signal Strength Indication (RSSI) through wireless networks. The proposed model will determine the position of wireless devices in a room. We took the RSSI values as coordinates and specific reference points at every two meters making the room into a grid. The RSSI values on the reference point is measured. The position of the wireless devices will be estimated from the reference points using trilateration method and ITU indoor path loss model. With the aforementioned process we calculated the position of the wireless device using ITU indoor path loss model and trilateration. Using ITU indoor path loss model our mean error was 1.01166m, while using trilateration, it was 1.22m.},
	language = {en-US},
	urldate = {2021-08-20},
	file = {Snapshot:/home/sebastian/Zotero/storage/29RB6FMP/9038591.html:text/html},
}

@inproceedings{sohan_indoor_2019,
	title = {Indoor {Positioning} {Techniques} using {RSSI} from {Wireless} {Devices}},
	doi = {10.1109/ICCIT48885.2019.9038591},
	author = {Sohan, Asif and Ali, Mohammed and Fairooz, Fabiha and Rahman, Adham and Chakrabarty, Amitabha and Kabir, Md Rayhan},
	month = dec,
	year = {2019},
	pages = {1--6},
	file = {Sohan et al. - 2019 - Indoor Positioning Techniques using RSSI from Wire.pdf:/home/sebastian/Zotero/storage/JG5W69DD/Sohan et al. - 2019 - Indoor Positioning Techniques using RSSI from Wire.pdf:application/pdf},
}

@inproceedings{naveed_smart_2019,
	title = {Smart indoor {Positioning} {Model} for {Deterministic} {Environment}},
	doi = {10.1109/ITT48889.2019.9075118},
	abstract = {Location based services are very popular nowadays. However, indoor location identification is a big challenge. Many different solutions are proposed for indoor mapping however, most of these require additional hardware are also not very accurate. One of these solutions is use of received signal strength indication (RSSI) values for location mapping. Theoretically, it is possible to triangulate the location by tagging specific location point with different signal strengths received from multiple access points however this technique has many issues. It is affected by many factors like layout of the building, furniture, number of people in the area. Moreover, reflection of signals from the walls also add huge noise to the signals received. Since, many machines learning algorithms are successfully being applied in many real-world scenarios for predicting and inferring information, in this paper, we propose use of machine learning techniques on the Wi-Fi received signal strength indication (RSSI) for indoor localization. This study belongs to range-free and fingerprinting of localization. The research is divided into off-line training and on-line predicting stages. In the off-line training stage, a signal map with two-dimensional array structure is established by capturing the Wi-Fi RSSI of different reference locations, and different access points (Aps), Each output node of the neural network (NN) represents the probability that a signal vector occurs at the corresponding reference location. In the on-line predicting stage, instantaneous RSSI is recorded at an unknown location. The trained NN can accurately predict indoor positions even far from network devices.},
	booktitle = {2019 {Sixth} {HCT} {Information} {Technology} {Trends} ({ITT})},
	author = {Naveed, Munir and Javed, Yasir and Bhatti, Ghulam M and Asif, Sayed},
	month = nov,
	year = {2019},
	keywords = {Wireless sensor networks, Wireless fidelity, Convolutional Neural Networks, ensemble models, Forestry, Indoor positioning, Predictive models, Random Forest, Sensors, Solid modeling},
	pages = {288--291},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/BTBZKKUE/9075118.html:text/html;Naveed et al. - 2019 - Smart indoor Positioning Model for Deterministic E.pdf:/home/sebastian/Zotero/storage/NNFRQ32T/Naveed et al. - 2019 - Smart indoor Positioning Model for Deterministic E.pdf:application/pdf},
}

@inproceedings{naik_comparison_2012,
	title = {Comparison of {RSSI} techniques in {Wireless} {Indoor} {Geolocation}},
	doi = {10.1109/NCCCS.2012.6413008},
	abstract = {Geolocation is the identification of the geographic location of any object or device. Indoor Geolocation can be seen as an alternative for Global Positioning System as, GPS fails in an indoor area. As an emerging technology there are two main classes of infrastructure based on which these systems are built. There are basic challenges which these systems have to face and they include the signaling systems, cost of deployment, and accuracy of prediction, pattern recognition and use of signature databases. The use of appropriate methods with proper estimation values gives better accuracy. There are various methods depending on which these systems are implemented. Indoor Geolocation has various techniques to determine the position of the object. This paper has a comparative study of the two main techniques of RSSI. Also a design for one of the applications of Indoor Geolocation i.e. a system that can be implemented for a medical store has been proposed. The basic aim is to find the RSSI value based on which the mobile station can be located. The RSSI values can be modified for any changes in the environment. An algorithm that combines the best features of the calibration procedures, fingerprinting techniques and pattern recognition helps to implement the system with best accuracy in results. For different applications, the values the various methods of determination can be selected as per requirements.},
	booktitle = {2012 {NATIONAL} {CONFERENCE} {ON} {COMPUTING} {AND} {COMMUNICATION} {SYSTEMS}},
	author = {Naik, Gauri A. and Khedekar, Madhavi P. and Krishnamoorthy, Mahalakshmy and Patil, Sayali D. and Deshmukh, Rupali N.},
	month = nov,
	year = {2012},
	keywords = {Accuracy, Wireless communication, Wireless sensor networks, Fingerprint recognition, Databases, Geology, Global Positioning System, Global Positioning System (GPS), IEEE 802.11, Indoor Localization, Received signal strength indicator (RSSI), Wireless Sensor Network (WSN)},
	pages = {1--5},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/BD4CV459/6413008.html:text/html;Naik et al. - 2012 - Comparison of RSSI techniques in Wireless Indoor G.pdf:/home/sebastian/Zotero/storage/BCQDJ672/Naik et al. - 2012 - Comparison of RSSI techniques in Wireless Indoor G.pdf:application/pdf},
}

@inproceedings{sohan_indoor_2019-1,
	title = {Indoor {Positioning} {Techniques} using {RSSI} from {Wireless} {Devices}},
	doi = {10.1109/ICCIT48885.2019.9038591},
	abstract = {The whole world is familiar with the Global Positioning System or GPS which can determine the exact location of any object with the help of satellite. But GPS signals are not available in indoors. To overcome this, Indoor Positioning System (IPS) is used which enables us to locate objects inside an indoor environment. Our goal is to build an Indoor Positioning System by estimating the location using Received Signal Strength Indication (RSSI) through wireless networks. The proposed model will determine the position of wireless devices in a room. We took the RSSI values as coordinates and specific reference points at every two meters making the room into a grid. The RSSI values on the reference point is measured. The position of the wireless devices will be estimated from the reference points using trilateration method and ITU indoor path loss model. With the aforementioned process we calculated the position of the wireless device using ITU indoor path loss model and trilateration. Using ITU indoor path loss model our mean error was 1.01166m, while using trilateration, it was 1.22m.},
	booktitle = {2019 22nd {International} {Conference} on {Computer} and {Information} {Technology} ({ICCIT})},
	author = {Sohan, Asif Ahmed and Ali, Mohammad and Fairooz, Fabiha and Rahman, Adham Ibrahim and Chakrabarty, Amitabha and Kabir, Md. Rayhan},
	month = dec,
	year = {2019},
	keywords = {RSSI, Indoor Positioning, ITU, Trilateration, WiFi},
	pages = {1--6},
}

@article{konings_springloc_2019,
	title = {{SpringLoc}: {A} {Device}-{Free} {Localization} {Technique} for {Indoor} {Positioning} and {Tracking} {Using} {Adaptive} {RSSI} {Spring} {Relaxation}},
	volume = {7},
	issn = {2169-3536},
	shorttitle = {{SpringLoc}},
	doi = {10.1109/ACCESS.2019.2913910},
	abstract = {Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density.},
	journal = {IEEE Access},
	author = {Konings, Daniel and Alam, Fakhrul and Noble, Frazer and Lai, Edmund M-K.},
	year = {2019},
	note = {Conference Name: IEEE Access},
	keywords = {Calibration, Fingerprint recognition, Device-free localization (DFL), histogram distance, Histograms, indoor positioning systems (IPS), Particle filters, smart homes, Smart homes, spring-relaxation, Springs},
	pages = {56960--56973},
	file = {Konings et al. - 2019 - SpringLoc A Device-Free Localization Technique fo.pdf:/home/sebastian/Zotero/storage/Q3KT4YHR/Konings et al. - 2019 - SpringLoc A Device-Free Localization Technique fo.pdf:application/pdf;IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/V2HP3VVA/8703051.html:text/html},
}

@inproceedings{noertjahyana_development_2017,
	title = {Development of {Mobile} {Indoor} {Positioning} {System} {Application} {Using} {Android} and {Bluetooth} {Low} {Energy} with {Trilateration} {Method}},
	doi = {10.1109/ICSIIT.2017.64},
	abstract = {The development of information technology and the concept of smart city began to grow. Information technology needed by the public that is looking for information position and location of destination in the building (indoor positionting). Indoor positioning using WiFi has limits on location placement. To overcome these shortcomings are used sensor beacon bluetooth low energy with the advantages of having low power consumption and relatively small dimensions that can be placed in various places that are difficult to reach by WiFi. Indoor Positioning System (IPS) is a system for knowing the position of objects or people in a room by using radio waves, magnetic fields, or other sensors obtained by mobile devices. The indoor positioning method is divided into deterministic and probabilistic. Deterministic can determine the position faster by using measurement techniques such as Trilateration and Triangulation. Trilateration is a method for determining location with known three location information and device distance to each access point. This research used Trilateration method with measurement technique based on RSSI value. Based on the results of the test, RSSI of the beacon is strongly affected by objects that have thickness and density. In line of sight conditions, Android devices are able to receive signals properly and determine the location of the device using trilateration with quite accurate.},
	booktitle = {2017 {International} {Conference} on {Soft} {Computing}, {Intelligent} {System} and {Information} {Technology} ({ICSIIT})},
	author = {Noertjahyana, Agustinus and Wijayanto, Ignatius Alex and Andjarwirawan, Justinus},
	month = sep,
	year = {2017},
	keywords = {Bluetooth, Calibration, Meters, trilateration, Android, Androids, bluetooth low energy, Humanoid robots, Indoor positioning system, IP networks, Testing},
	pages = {185--189},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/CB4CRVWS/8262565.html:text/html;Noertjahyana et al. - 2017 - Development of Mobile Indoor Positioning System Ap.pdf:/home/sebastian/Zotero/storage/6FG3SSR4/Noertjahyana et al. - 2017 - Development of Mobile Indoor Positioning System Ap.pdf:application/pdf},
}

@inproceedings{sosa-sesma_fusion_2016,
	title = {Fusion system based on {WiFi} and ultrasounds for in-home positioning systems: {The} {UTOPIA} experiment},
	shorttitle = {Fusion system based on {WiFi} and ultrasounds for in-home positioning systems},
	doi = {10.1109/IPIN.2016.7743622},
	abstract = {The research presented in this paper is focused on In-home positioning systems. These indoor environments are characterized by a reduced number of AP, narrow spaces and very important radio signal attenuation. Existing indoor techniques seems to be not totally suitable for these scenarios; this paper proposes an add-on for helping these techniques by the use of wearable ultrasound sensors (distance measurements). One important step for indoor techniques using RF signals is the off-line phase and the generation of Radio Maps. In order to considerably reduce the time needed for the off-line phase, this paper uses an existing tool for generating maps automatically. Therefore, a considerable gain of time is obtained. On the other hand, the combination of WiFi and range techniques allows us to do coarse and fine location. An IT artifact named UTOPIA (UlTrasOund Positioning Indoor App) has been developed to track all data needed for computing indoor position like RSSI, distances to walls, number of steps, heading and more. The project uses two maps for positioning: a WiFi map automatically generated; and an ultrasound map, that takes into account what should be the ultrasound value at every single point of the building and for several directions of the sensor. To get position, the values obtained by smartphone are compared with WiFi map. A particle filter technique is used to propagate position and ultrasound values are used to get the weight in the particle filter. Results obtained show that the methodology is coherent with real scenario and can be used for helping existing indoor position techniques in these specific scenarios (In-home environments). The main contributions of this paper are: 1) offering an alternative system suitable for in-home features that allows to calculate positioning by using WiFi and ultrasound; 2) avoiding the off-line mapping phase of WiFi fingerprinting by comparing with an automatically calculated WiFi map; and 3) using the map for improving positioning by generating an ultrasound reference map with the value of ultrasound signal at every single point of the building, and for several directions.},
	booktitle = {2016 {International} {Conference} on {Indoor} {Positioning} and {Indoor} {Navigation} ({IPIN})},
	author = {Sosa-Sesma, Sergio and Perez-Navarro, Antoni},
	month = oct,
	year = {2016},
	note = {ISSN: 2471-917X},
	keywords = {IEEE 802.11 Standard, Mobile handsets, Sensors, Indoor Positioning, Android, IP networks, Arduino, Buildings, Inertial Sensors, Mobile device, Ultrasonic imaging, Ultrasonic variables measurement, Ultrasound Distance Sensors},
	pages = {1--8},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/F8N7FWAC/7743622.html:text/html;Sosa-Sesma und Perez-Navarro - 2016 - Fusion system based on WiFi and ultrasounds for in.pdf:/home/sebastian/Zotero/storage/AFR96JU4/Sosa-Sesma und Perez-Navarro - 2016 - Fusion system based on WiFi and ultrasounds for in.pdf:application/pdf},
}

@article{forghani_cellular_2020,
	title = {From cellular positioning data to trajectories: {Steps} towards a more accurate mobility exploration},
	volume = {117},
	issn = {0968090X},
	shorttitle = {From cellular positioning data to trajectories},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0968090X20305817},
	doi = {10.1016/j.trc.2020.102666},
	abstract = {The recent years have witnessed a greater demand for understanding how people move in urban environments. Due to the widespread usage of mobile phones, there have been several trajectorybased studies focusing on extracting the characteristics of human mobility from georeferenced mobile phone data. Mobile positioning data is generally generated as scattered points in CDRs (Call Detail Records). Even though CDR data can be regarded as an inexpensive scalable source of information on human mobility, mobility studies in urban settings based on such data sources still prove to be a research challenge due to the coarseness of CDR spatial granularity. Motivated by the need for transforming large-scale CDRs to movement trajectories, the present study offers a new solution which is made of two principal building blocks: (1) Developing a Bayesian-based induction method through adopting a GIS-based wave propagation model to solve the GSM-based localization problem when methods such as triangulation are not applicable due to the lack of measurements from more than one base station; (2) Reconstruction of movement trajectories from cellular location information using overlapping relations existing between observed cells as well as detection of ping-pong phenomena as auxiliary information. A case study employing CDR and GPS records obtained from an experimental survey on one of the central urban zones of Tehran was conducted, which showed the effectiveness of the proposed methodology in comparison to current approaches with respect to three perspectives, including movement path exploration, individual-oriented movement features extraction, and crowd-movement modelling.},
	language = {en},
	urldate = {2021-08-20},
	journal = {Transportation Research Part C: Emerging Technologies},
	author = {Forghani, Mohammad and Karimipour, Farid and Claramunt, Christophe},
	month = aug,
	year = {2020},
	pages = {102666},
	file = {Forghani et al. - 2020 - From cellular positioning data to trajectories St.pdf:/home/sebastian/Zotero/storage/KST5DHFF/Forghani et al. - 2020 - From cellular positioning data to trajectories St.pdf:application/pdf},
}

@incollection{toyama_implementation_2021,
	title = {Implementation of an {Indoor} {Position} {Detecting} {System} {Using} {Mean} {BLE} {RSSI} for {Moving} {Omnidirectional} {Access} {Point} {Robot}},
	isbn = {978-3-030-79724-9},
	abstract = {Recently, various communication technologies have been developed in order to satisfy the requirements of many users. Especially, mobile communication technology continues to develop rapidly and Wireless Mesh Networks (WMNs) are attracting attention from many researchers in order to provide cost efficient broadband wireless connectivity. The main issue of WMNs is to improve network connectivity and stability in terms of user coverage. In this paper, we introduce a moving omnidirectional access point robot (called MOAP robot) and propose an indoor position detecting system using mean BLE RSSI for MOAP Robot. In order to realize a moving Access Point (AP), the MOAP robot should move omni directionally in 2 dimensional space. It is important that the MOAP robot moves to an accurate position in order to have a good connectivity. Thus, MOAP robot can provide good communication and stability for WMNs.},
	author = {Toyama, Atushi and Mitsugi, Kenshiro and Matsuo, Keita and Kulla, Elis and Barolli, Leonard},
	month = jun,
	year = {2021},
	doi = {10.1007/978-3-030-79725-6_22},
	pages = {225--234},
}

@article{jeong_smartphone_2019,
	title = {A {Smartphone} {Magnetometer}-{Based} {Diagnostic} {Test} for {Automatic} {Contact} {Tracing} in {Infectious} {Disease} {Epidemics}},
	volume = {7},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2019.2895075},
	abstract = {Smartphone magnetometer readings exhibit high linear correlation when two phones coexist within a short distance. Thus, the detected coexistence can serve as a proxy for close human contact events, and one can conceive using it as a possible automatic tool to modernize the contact tracing in infectious disease epidemics. This paper investigates how good a diagnostic test it would be, by evaluating the discriminative and predictive power of the smartphone magnetometer-based contact detection in multiple measures. Based on the sensitivity, specificity, likelihood ratios, and diagnostic odds ratios, we find that the decision made by the smartphone magnetometer-based test can be accurate in telling contacts from no contacts. Furthermore, through the evaluation process, we determine the appropriate range of compared trace segment sizes and the correlation cutoff values that we should use in such diagnostic tests.},
	journal = {IEEE Access},
	author = {Jeong, Seungyeon and Kuk, Seungho and Kim, Hyogon},
	year = {2019},
	note = {Conference Name: IEEE Access},
	keywords = {Meters, Sensors, Global Positioning System, Correlation, diagnostic test, human contact tracing, infectious disease epidemic, Infectious diseases, Magnetometers, Mobile sensing, smartphone magnetometer},
	pages = {20734--20747},
	file = {IEEE Xplore Full Text PDF:/home/sebastian/Zotero/storage/3XTVU8VA/Jeong et al. - 2019 - A Smartphone Magnetometer-Based Diagnostic Test fo.pdf:application/pdf;IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/XNFKLV95/8626091.html:text/html},
}

@article{kothari_robust_2012,
	series = {{ANT} 2012 and {MobiWIS} 2012},
	title = {Robust {Indoor} {Localization} on a {Commercial} {Smart} {Phone}},
	volume = {10},
	issn = {1877-0509},
	url = {https://www.sciencedirect.com/science/article/pii/S1877050912005157},
	doi = {10.1016/j.procs.2012.06.158},
	abstract = {Low-cost localization solutions for indoor environments have a variety of real-world applications ranging from emergency evacuation to mobility aids for people with disabilities. In this paper, we introduce a methodology for indoor localization using a commercial smart-phone combining dead reckoning and Wifi signal strength fingerprinting. Additionally, we outline an automated procedure for collecting Wifi calibration data that uses a robot equipped with a laser rangefinder and fiber optic gyroscope. These measurements along with a generated robot map of the environment are combined using a particle filter towards robust pose estimation. The uniqueness of our approach lies in the implementation of the complementary nature of the solution as well as in the efficient adaptation to the smart-phone platform. The system was tested using multiple participants in two different indoor environments, and achieved localization accuracies on the order of 5 meters; sufficient for a variety of navigation and context-aware applications.},
	language = {en},
	urldate = {2021-08-20},
	journal = {Procedia Computer Science},
	author = {Kothari, Nisarg and Kannan, Balajee and Glasgwow, Evan D. and Dias, M. Bernardine},
	month = jan,
	year = {2012},
	keywords = {inertial navigation solution, laser-baed map-representation, robot navigation, RSSI fingerprinting},
	pages = {1114--1120},
	file = {ScienceDirect Full Text PDF:/home/sebastian/Zotero/storage/PSUBWWSL/Kothari et al. - 2012 - Robust Indoor Localization on a Commercial Smart P.pdf:application/pdf;ScienceDirect Snapshot:/home/sebastian/Zotero/storage/YINKU2PK/S1877050912005157.html:text/html},
}

@inproceedings{bauer_-accuracy_2013,
	address = {New York, NY, USA},
	series = {{MoMM} '13},
	title = {On the ({In}-){Accuracy} of {GPS} {Measures} of {Smartphones}: {A} {Study} of {Running} {Tracking} {Applications}},
	isbn = {978-1-4503-2106-8},
	shorttitle = {On the ({In}-){Accuracy} of {GPS} {Measures} of {Smartphones}},
	url = {https://doi.org/10.1145/2536853.2536893},
	doi = {10.1145/2536853.2536893},
	abstract = {Sports tracking applications are increasingly available on the market, and research has recently picked up this topic. Tracking a user's running track and providing feedback on the performance are among the key features of such applications. However, little attention has been paid to the accuracy of the applications' localization measurements. In evaluating the nine currently most popular running applications, we found tremendous differences in the GPS measurements. Besides this finding, our study contributes to the scientific knowledge base by qualifying the findings of previous studies concerning accuracy with smartphones' GPS components.},
	urldate = {2021-08-20},
	booktitle = {Proceedings of {International} {Conference} on {Advances} in {Mobile} {Computing} \& {Multimedia}},
	publisher = {Association for Computing Machinery},
	author = {Bauer, Christine},
	month = dec,
	year = {2013},
	keywords = {Accuracy, Localization, Global Positioning System, GPS, Location-based System, Positioning, Running Tracking, Smartphone, Sports Tracking},
	pages = {335--341},
	file = {Bauer - 2013 - On the (In-)Accuracy of GPS Measures of Smartphone.pdf:/home/sebastian/Zotero/storage/SLVNMW9X/Bauer - 2013 - On the (In-)Accuracy of GPS Measures of Smartphone.pdf:application/pdf},
}

@article{kuhn_smartphones_2013,
	title = {Smartphones as {Experimental} {Tools}: {Different} {Methods} to {Determine} the {Gravitational} {Acceleration} in {Classroom} {Physics} by {Using} {Everyday} {Devices}},
	volume = {4},
	issn = {1309-7202},
	shorttitle = {Smartphones as {Experimental} {Tools}},
	url = {https://eric.ed.gov/?id=EJ1052301},
	abstract = {New media technology becomes more and more important for our daily life as well as for teaching physics. Within the scope of our N.E.T. research project we develop experiments using New Media Experimental Tools (N.E.T.) in physics education and study their influence on students learning abilities. We want to present the possibilities e.g. of smartphones as special new media devices serving as tools for conducting experiments in classroom physics and in daily life as well. In this paper, we give an overview about different methods for determining the gravitational acceleration as one of the most fundamental parameter in physics by using these easy-to-have and easy-to-use everyday tools. The theoretically backgrounds of the experiments range from the simple use of the law of gravitation to the coefficient of restitution and refer to different physical concepts (mechanics and acoustics). So each of these experiments requires different pre-conditions and it's possible to conduct these experiments and determine this most fundamental parameter in classroom physics by completely different types of learners (high-school as well as college level).},
	language = {en},
	number = {1},
	urldate = {2021-08-20},
	journal = {European Journal of Physics Education},
	author = {Kuhn, Jochen and Vogt, Patrik},
	year = {2013},
	note = {Publisher: Erciyes University},
	keywords = {Acoustics, College Science, Energy, Handheld Devices, High Schools, Measurement, Mechanics (Physics), Motion, Physics, Science Experiments, Science Instruction, Scientific Concepts, Scientific Principles, Secondary School Science, Telecommunications},
	pages = {16--27},
	file = {Kuhn und Vogt - 2013 - Smartphones as Experimental Tools Different Metho.pdf:/home/sebastian/Zotero/storage/GMIME458/Kuhn und Vogt - 2013 - Smartphones as Experimental Tools Different Metho.pdf:application/pdf;Snapshot:/home/sebastian/Zotero/storage/72AEUIVD/eric.ed.gov.html:text/html},
}

@inproceedings{qian_improved_2013,
	title = {An improved indoor localization method using smartphone inertial sensors},
	doi = {10.1109/IPIN.2013.6817854},
	abstract = {In this paper, an improved indoor localization method based on smartphone inertial sensors is presented. Pedestrian dead reckoning (PDR), which determines the relative location change of a pedestrian without additional infrastructure supports, is combined with a floor plan for a pedestrian positioning in our work. To address the challenges of low sampling frequency and limited processing power in smartphones, reliable and efficient PDR algorithms have been proposed. A robust step detection technique leaves out the preprocessing of raw signal and reduces complex computation. Given the fact that the precision of the stride length estimation is influenced by different pedestrians and motion modes, an adaptive stride length estimation algorithm based on the motion mode classification is developed. Heading estimation is carried out by applying the principal component analysis (PCA) to acceleration measurements projected to the global horizontal plane, which is independent of the orientation of a smartphone. In addition, to eliminate the sensor drift due to the inaccurate distance and direction estimations, a particle filter is introduced to correct the drift and guarantee the localization accuracy. Extensive field tests have been conducted in a laboratory building to verify the performance of proposed algorithm. A pedestrian held a smartphone with arbitrary orientation in the tests. Test results show that the proposed algorithm can achieve significant performance improvements in terms of efficiency, accuracy and reliability.},
	booktitle = {International {Conference} on {Indoor} {Positioning} and {Indoor} {Navigation}},
	author = {Qian, Jiuchao and Ma, Jiabin and Ying, Rendong and Liu, Peilin and Pei, Ling},
	month = oct,
	year = {2013},
	keywords = {Estimation, Sensors, Particle filters, Acceleration, indoor localization, Legged locomotion, Navigation, particle filter, PCA, PDR, Principal component analysis},
	pages = {1--7},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/FFCF2SZK/6817854.html:text/html;Qian et al. - 2013 - An improved indoor localization method using smart.pdf:/home/sebastian/Zotero/storage/E3X6XDFK/Qian et al. - 2013 - An improved indoor localization method using smart.pdf:application/pdf},
}

@inproceedings{li_reliable_2012,
	address = {New York, NY, USA},
	series = {{UbiComp} '12},
	title = {A reliable and accurate indoor localization method using phone inertial sensors},
	isbn = {978-1-4503-1224-0},
	url = {https://doi.org/10.1145/2370216.2370280},
	doi = {10.1145/2370216.2370280},
	abstract = {This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meter-level indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area.},
	urldate = {2021-08-20},
	booktitle = {Proceedings of the 2012 {ACM} {Conference} on {Ubiquitous} {Computing}},
	publisher = {Association for Computing Machinery},
	author = {Li, Fan and Zhao, Chunshui and Ding, Guanzhong and Gong, Jian and Liu, Chenxing and Zhao, Feng},
	month = sep,
	year = {2012},
	keywords = {indoor localization, inertial tracking, pedestrian model},
	pages = {421--430},
	file = {Li et al. - 2012 - A reliable and accurate indoor localization method.pdf:/home/sebastian/Zotero/storage/UJCJERD8/Li et al. - 2012 - A reliable and accurate indoor localization method.pdf:application/pdf},
}

@article{yang_mobility_2015,
	title = {Mobility {Increases} {Localizability}: {A} {Survey} on {Wireless} {Indoor} {Localization} using {Inertial} {Sensors}},
	volume = {47},
	issn = {0360-0300},
	shorttitle = {Mobility {Increases} {Localizability}},
	url = {https://doi.org/10.1145/2676430},
	doi = {10.1145/2676430},
	abstract = {Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.},
	number = {3},
	urldate = {2021-08-20},
	journal = {ACM Computing Surveys},
	author = {Yang, Zheng and Wu, Chenshu and Zhou, Zimu and Zhang, Xinglin and Wang, Xu and Liu, Yunhao},
	month = apr,
	year = {2015},
	keywords = {Mobility, smartphones, wireless indoor localization},
	pages = {54:1--54:34},
	file = {Yang et al. - 2015 - Mobility Increases Localizability A Survey on Wir.pdf:/home/sebastian/Zotero/storage/BRXUFR9M/Yang et al. - 2015 - Mobility Increases Localizability A Survey on Wir.pdf:application/pdf},
}

@inproceedings{nickel_authentication_2012,
	title = {Authentication of {Smartphone} {Users} {Based} on the {Way} {They} {Walk} {Using} k-{NN} {Algorithm}},
	doi = {10.1109/IIH-MSP.2012.11},
	abstract = {Accelerometer-based biometric gait recognition offers a convenient way to authenticate users on their mobile devices. Modern smartphones contain in-built accelerometers which can be used as sensors to acquire the necessary data while the subjects are walking. Hence, no additional costs for special sensors are imposed to the user. In this publication we extract several features from the gait data and use the k-Nearest Neighbour algorithm for classification. We show that this algorithm yields a better biometric performance than the machine learning algorithms we previously used for classification, namely Hidden Markov Models and Support Vector Machines. We implemented the presented method on a smartphone and demonstrate that it is efficient enough to be applied in practice.},
	booktitle = {2012 {Eighth} {International} {Conference} on {Intelligent} {Information} {Hiding} and {Multimedia} {Signal} {Processing}},
	author = {Nickel, Claudia and Wirtl, Tobias and Busch, Christoph},
	month = jul,
	year = {2012},
	keywords = {Legged locomotion, accelerometer, Authentication, biometrics, Error analysis, Feature extraction, gait recognition, Magnetic resonance, Mel frequency cepstral coefficient, smartphone, Vectors},
	pages = {16--20},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/4WZMP8NA/6274118.html:text/html;Nickel et al. - 2012 - Authentication of Smartphone Users Based on the Wa.pdf:/home/sebastian/Zotero/storage/XQ9ZQWUS/Nickel et al. - 2012 - Authentication of Smartphone Users Based on the Wa.pdf:application/pdf},
}

@inproceedings{hennecke_towards_2011,
	title = {Towards acoustic self-localization of ad hoc smartphone arrays},
	doi = {10.1109/HSCMA.2011.5942378},
	abstract = {The advent of the smartphone in recent years opened new possibilities for the concept of ubiquitous computing. We propose to use multiple smartphones spontaneously assembled into an ad hoc microphone array as part of a teleconferencing system. The unknown spatial positions, the asynchronous sampling and the unknown time offsets between clocks of smartphones in the ad hoc array are the main problems for such an application as well as for almost all other acoustic signal processing algorithms. A maximum likelihood approach using time of arrival measurements of short calibration pulses is proposed to solve this self-localization problem. The global orientation of each phone, obtained by the means of nowadays common built-in geomagnetic compasses, in combination with the constant microphone-loudspeaker distance lead to a nonlinear optimization problem with a reduced dimensionality in contrast to former methods. The applicability of the proposed self-localization is shown in simulation and via recordings in a typical reverberant and noisy conference room.},
	booktitle = {2011 {Joint} {Workshop} on {Hands}-free {Speech} {Communication} and {Microphone} {Arrays}},
	author = {Hennecke, Marius H. and Fink, Gernot A.},
	month = may,
	year = {2011},
	keywords = {Calibration, Acoustics, Loudspeakers, maximum likelihood estimation, Microphone arrays, Noise, Optimization, self-localization, smartphone array},
	pages = {127--132},
	file = {IEEE Xplore Abstract Record:/home/sebastian/Zotero/storage/3R7SVYF9/5942378.html:text/html;Hennecke und Fink - 2011 - Towards acoustic self-localization of ad hoc smart.pdf:/home/sebastian/Zotero/storage/F7JC3RME/Hennecke und Fink - 2011 - Towards acoustic self-localization of ad hoc smart.pdf:application/pdf},
}

@article{subbu_locateme_2013,
	title = {{LocateMe}: {Magnetic}-fields-based indoor localization using smartphones},
	volume = {4},
	issn = {2157-6904},
	shorttitle = {{LocateMe}},
	url = {https://doi.org/10.1145/2508037.2508054},
	doi = {10.1145/2508037.2508054},
	abstract = {Fine-grained localization is extremely important to accurately locate a user indoors. Although innovative solutions have already been proposed, there is no solution that is universally accepted, easily implemented, user centric, and, most importantly, works in the absence of GSM coverage or WiFi availability. The advent of sensor rich smartphones has paved a way to develop a solution that can cater to these requirements. By employing a smartphone's built-in magnetic field sensor, magnetic signatures were collected inside buildings. These signatures displayed a uniqueness in their patterns due to the presence of different kinds of pillars, doors, elevators, etc., that consist of ferromagnetic materials like steel or iron. We theoretically analyze the cause of this uniqueness and then present an indoor localization solution by classifying signatures based on their patterns. However, to account for user walking speed variations so as to provide an application usable to a variety of users, we follow a dynamic time-warping-based approach that is known to work on similar signals irrespective of their variations in the time axis. Our approach resulted in localization distances of approximately 2m--6m with accuracies between 80--100\% implying that it is sufficient to walk short distances across hallways to be located by the smartphone. The implementation of the application on different smartphones yielded response times of less than five secs, thereby validating the feasibility of our approach and making it a viable solution.},
	number = {4},
	urldate = {2021-08-20},
	journal = {ACM Transactions on Intelligent Systems and Technology},
	author = {Subbu, Kalyan Pathapati and Gozick, Brandon and Dantu, Ram},
	month = oct,
	year = {2013},
	keywords = {smartphones, Indoor localization, magnetic fields, ubiquitous},
	pages = {73:1--73:27},
	file = {Subbu et al. - 2013 - LocateMe Magnetic-fields-based indoor localizatio.pdf:/home/sebastian/Zotero/storage/7DIFJ4LC/Subbu et al. - 2013 - LocateMe Magnetic-fields-based indoor localizatio.pdf:application/pdf},
}

@article{basri_review_2020,
	title = {A {REVIEW} {ON} {INDOOR} {LOCALIZATION} {WITH} {INTERNET} {OF} {THINGS}},
	volume = {XLIV-4/W3-2020},
	doi = {10.5194/isprs-archives-XLIV-4-W3-2020-121-2020},
	abstract = {The advancement of Internet of things (IoT) has revolutionized the field of telecommunication opening the door for interesting applications such as smart cities, resources management, logistics and transportation, wearables and connected healthcare. The emergence of IoT in multiple sectors has enabled the requirement for an accurate real time location information. Location-based services are actually, due to development of networks, sensors, wireless communications and machine learning algorithms, able to collect and transmit data in order to determine the target positions, and support the needs imposed by several applications and use cases. The performance of an indoor positioning system in IoT networks depends on the technical implementation, network architecture, the deployed technology, techniques and algorithms of positioning. This paper highlights the importance of indoor localization in internet of things applications, gives a comprehensive review of indoor positioning techniques and methods implemented in IoT networks, and provides a detailed analysis on recent advances in this field.},
	journal = {ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
	author = {Basri, C. and Elkhadimi, A.},
	month = nov,
	year = {2020},
	pages = {121--128},
	file = {Volltext:/home/sebastian/Zotero/storage/3I4RSIE6/Basri und Elkhadimi - 2020 - A REVIEW ON INDOOR LOCALIZATION WITH INTERNET OF T.pdf:application/pdf},
}

@article{maghdid_comprehensive_2021,
	title = {A {Comprehensive} {Review} of {Indoor}/{Outdoor} {Localization} {Solutions} in {IoT} era: {Research} {Challenges} and {Future} {Perspectives}},
	shorttitle = {A {Comprehensive} {Review} of {Indoor}/{Outdoor} {Localization} {Solutions} in {IoT} era},
	url = {https://www.techrxiv.org/articles/preprint/A_Comprehensive_Review_of_Indoor_Outdoor_Localization_Solutions_in_IoT_era_Research_Challenges_and_Future_Perspectives/15138609/1},
	doi = {10.36227/techrxiv.15138609.v1},
	abstract = {The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since, most of the people daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT enabled technologies in health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated.},
	language = {en},
	urldate = {2021-08-23},
	author = {Maghdid, Safar M. and Maghdid, Halgurd},
	month = aug,
	year = {2021},
	note = {Publisher: TechRxiv},
	file = {Maghdid und Maghdid - 2021 - A Comprehensive Review of IndoorOutdoor Localizat.pdf:/home/sebastian/Zotero/storage/EVSSNU7Y/Maghdid und Maghdid - 2021 - A Comprehensive Review of IndoorOutdoor Localizat.pdf:application/pdf;Snapshot:/home/sebastian/Zotero/storage/FMM4F93D/15138609.html:text/html},
}

@article{welch_introduction_1997,
	title = {An {Introduction} to the {Kalman} {Filter}},
	abstract = {In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation.},
	language = {en},
	author = {Welch, Greg},
	year = {1997},
	pages = {16},
	file = {Welch - 1997 - An Introduction to the Kalman Filter.pdf:/home/sebastian/Zotero/storage/VPI7SDML/Welch - 1997 - An Introduction to the Kalman Filter.pdf:application/pdf},
}