@Article{Purssell_2020, author = {Edward Purssell and Dinah Gould and Jane Chudleigh}, journal = {{BMJ} Open}, title = {Impact of isolation on hospitalised patients who are infectious: systematic review with meta-analysis}, year = {2020}, month = {feb}, number = {2}, pages = {e030371}, volume = {10}, doi = {10.1136/bmjopen-2019-030371}, file = {:/home/sebastian/Zotero/storage/2VN5N2U6/Purssell et al. - 2020 - Impact of isolation on hospitalised patients who a.pdf:PDF}, publisher = {{BMJ}}, } @Misc{Dehaye_2020, author = {Dehaye, Paul-Olivier}, title = {Inferring distance from Bluetooth signal strength: a deep dive}, year = {2020}, abstract = {Contact tracing apps intend to predict exposure to a COVID-19 infection, where exposure is computed as some function of time and distance…}, journal = {PersonalData.IO}, language = {en}, shorttitle = {Inferring distance from Bluetooth signal strength}, url = {files/390/inferring-distance-from-bluetooth-signal-strength-a-deep-dive-fe7badc2bb6d.html}, } @MastersThesis{Larsson_2015, author = {Larsson, Johan}, school = {KTH Vetenskap Och Konst}, title = {Distance estimation and positioning based on {B}luetooth {L}ow energy technology}, year = {2015}, type = {mathesis}, abstract = {This thesis deals with location determining, also known as positioning, using Bluetooth Low energy radio. The goal is to implement a low power low cost indoor positioning system which utilize existing hardware. Two main methods are investigated and their viability assessed. Previous efforts with indoor positioning systems concentrate on statistical fingerprinting methods, mainly using 802.11 (WLAN) as the platform. Some efforts have been made with purely signal strength based positioning, but indoor environments have shown to work unfavorably for these kinds of methods.}, file = {:files/391/Larsson - Distance estimation and positioning based on Bluet.pdf:PDF}, groups = {Thesis von anderen}, language = {en}, pages = {37}, priority = {prio3}, qualityassured = {qualityAssured}, } @Misc{_2020, title = {Distance Measuring Solution for COVID-19 using Bluetooth Low Energy}, year = {2020}, 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 …}, journal = {SMART SENSOR DEVICES AB}, language = {en-US}, url = {files/394/distance-measuring-solution-for-covid-19-using-bluetooth-low-energy.html}, } @Misc{Admin_2020, author = {Admin}, title = {Can Bluetooth Be Used To Measure Distance?}, year = {2020}, 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}, file = {:files/399/can-bluetooth-measure-distance.html:URL}, journal = {Bluetooth Tech World}, language = {en-GB}, url = {files/399/can-bluetooth-measure-distance.html}, } @Misc{Admin_2020a, author = {Admin}, title = {Can Bluetooth Go Through Walls?}, year = {2020}, 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}, journal = {Bluetooth Tech World}, language = {en-GB}, url = {files/401/can-bluetooth-go-through-walls.html}, } @Misc{_2021, month = aug, title = {Exposure Notifications BLE calibration calculation}, year = {2021}, journal = {Google Developers}, language = {en}, url = {https://developers.google.com/android/exposure-notifications/ble-attenuation-computation?hl=de}, } @Misc{_2021a, month = aug, title = {Tracing App - distance measurement}, year = {2021}, abstract = {Bluetooth distance measuring for finding lost people, surveillance of parking garages also in combination with radar / in covered environments}, journal = {Metirionic}, language = {en}, url = {files/406/tracing-measurement-tech-bluetooth.html}, } @InProceedings{Thaljaoui_2015, author = {Thaljaoui, Adel and Val, Thierry and Nasri, Nejah and Brulin, Damien}, title = {{BLE} localization using {RSSI} measurements and i{R}ing{LA}}, year = {2015}, month = {mar}, pages = {2178--2183}, publisher = {{IEEE}}, 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.}, doi = {10.1109/icit.2015.7125418}, eventtitle = {2015 IEEE International Conference on Industrial Technology (ICIT)}, file = {:files/Thaljaoui et al. - 2015 - BLE localization using RSSI measurements and iRing.pdf:PDF}, groups = {Thesis}, journal = {2015 IEEE International Conference on Industrial Technology (ICIT)}, keywords = {Accuracy, BLE, Bluetooth, Distance measurement, Estimation, Indexes, Localization, Position measurement, Receivers, RSSI, Smarthome}, priority = {prio2}, qualityassured = {qualityAssured}, readstatus = {read}, url = {files/410/7125418.html}, } @InProceedings{Lam_2019, author = {Lam, Ching Hong and She, James}, title = {Distance Estimation on Moving Object using BLE Beacon}, year = {2019}, pages = {1--6}, 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.}, doi = {10.1109/WiMOB.2019.8923185}, eventtitle = {2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)}, groups = {Methoden}, journal = {2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)}, 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}, url = {files/415/8923185.html}, } @InProceedings{Kaczmarek_2016, author = {Kaczmarek, Mariusz and Ruminski, Jacek and Bujnowski, Adam}, booktitle = {Proceedings of the 2016 Federated Conference on Computer Science and Information Systems}, title = {{A}ccuracy analysis of the {RSSI} {BLE} {S}ensor{T}ag signal for indoor localization purposes}, year = {2016}, month = {oct}, pages = {1413--1416}, publisher = {{IEEE}}, 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.}, doi = {10.15439/2016f501}, eventtitle = {2016 Federated Conference on Computer Science and Information Systems (FedCSIS)}, groups = {Thesis}, journal = {2016 Federated Conference on Computer Science and Information Systems (FedCSIS)}, keywords = {Bluetooth, IEEE 802.11 Standard, Mathematical model, Mobile handsets, Wireless communication, Wireless sensor networks}, priority = {prio1}, qualityassured = {qualityAssured}, url = {files/418/7733434.html}, } @InProceedings{Nguyen_2017, author = {Nguyen, Quang Huy and Johnson, Princy and Nguyen, Trung Thanh and Randles, Martin}, title = {Optimized indoor positioning for static mode smart devices using BLE}, year = {2017}, pages = {1--6}, 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.}, doi = {10.1109/PIMRC.2017.8292666}, eventtitle = {2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)}, groups = {Methoden}, journal = {2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)}, keywords = {Bluetooth, Estimation, Localization, RSSI, Kalman filters, Mathematical model, Calibration, Bluetooth Low Energy, iBeacon, Indoor Location, Received signal strength indicator, Transmitters}, url = {files/424/8292666.html}, } @InProceedings{Shchekotov_2018, author = {Shchekotov, Maksim and Shilov, Nikolay}, title = {Semi-Automatic Self-Calibrating Indoor Localization Using BLE Beacon Multilateration}, year = {2018}, pages = {346--355}, 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.}, doi = {10.23919/FRUCT.2018.8588081}, eventtitle = {2018 23rd Conference of Open Innovations Association (FRUCT)}, journal = {2018 23rd Conference of Open Innovations Association (FRUCT)}, keywords = {Bluetooth, Estimation, Computational modeling, Mathematical model, Calibration, Fingerprint recognition, Radio transmitters}, url = {files/434/8588081.html}, } @InProceedings{Akeila_2010, author = {Akeila, Ehad and Salcic, Zoran and Swain, Akshya and Croft, Aaron and Stott, Jeremy}, title = {Bluetooth-based indoor positioning with fuzzy based dynamic calibration}, year = {2010}, pages = {1415--1420}, 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.}, doi = {10.1109/TENCON.2010.5686114}, eventtitle = {TENCON 2010 - 2010 IEEE Region 10 Conference}, journal = {TENCON 2010 - 2010 IEEE Region 10 Conference}, keywords = {Accuracy, Bluetooth, Mathematical model, Calibration, dynamic calibration, Equations, Fitting, fuzzy logic, Fuzzy logic, localisation/positioning}, url = {files/436/5686114.html}, } @InProceedings{Cabarkapa_2015, author = {Čabarkapa, Danijel and Grujić, Ivana and Pavlović, Petar}, title = {Comparative analysis of the Bluetooth Low-Energy indoor positioning systems}, year = {2015}, pages = {76--79}, 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.}, doi = {10.1109/℡SKS.2015.7357741}, eventtitle = {2015 12th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (℡SIKS)}, journal = {2015 12th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (℡SIKS)}, keywords = {BLE, Bluetooth, Advertising, Calibration, iBeacon, Fingerprint recognition, indoor positioning, fingerprinting, IoT, Protocols, Radio frequency, RF signals}, url = {files/442/7357741.html}, } @MastersThesis{Kudak_2021, author = {Andreas Kudak}, school = {Universität Heidelberg, Hochschule Heilbronn}, title = {{E}valuation und {A}nwendung aktueller {E}ntwickunglen im {B}ereich {B}luetooth {L}ow {E}nergy am {B}eispiel von i{B}eacon}, year = {2021}, month = aug, type = {mathesis}, access = {2021.08.11}, file = {:files/Evaluation_Masterarbeit_Andreas-Kudak.pdf:PDF}, groups = {Thesis von anderen}, keywords = {Bluetooth, BLE, iBeacon}, priority = {prio1}, qualityassured = {qualityAssured}, url = {https://core.ac.uk/download/pdf/33993179.pdf}, } @Article{Jahnel_2020, 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}, journal = {Das Gesundheitswesen}, title = {Contact-Tracing-Apps als unterstützende Maßnahme bei der Kontaktpersonennachverfolgung von COVID-19}, year = {2020}, issn = {0941-3790, 1439-4421}, number = {8/9}, pages = {664--669}, volume = {82}, 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.}, doi = {10.1055/a-1195-2474}, keywords = {19, 2, Contact tracing, CoV, COVID, digital, SARS}, language = {de}, url = {files/450/a-1195-2474.html}, } @Misc{Rosenthaler_2016, author = {Rosenthaler, Matthias}, title = {Near Field Communication mit Bluetooth Low Energy}, year = {2016}, language = {Deutsch}, url = {https://epub.jku.at/obvulihs/content/titleinfo/1654483/full.pdf}, } @Article{Fraenzel_, author = {Fränzel, Norbert and Greifzu, Norbert and Schneider, Manuel and Wenzel, Andreas}, title = {DRAHTLOSEN SENSORNETZWERKEN}, pages = {6}, language = {de}, } @Article{Truong_2021, author = {Truong, Gia Bao}, journal = {LEGO-Praktikum. Entwickeln, programmieren, optimieren : Berichte der Studierenden zum Projektseminar Elektrotechnik/Informationstechnik}, title = {Autonom fahrender Aufklärungsroboter}, year = {2021}, issn = {2629-6160}, pages = {13--16}, volume = {4}, doi = {10.24352/UB.OVGU-2021-034}, language = {de}, url = {https://journals.ub.uni-magdeburg.de/index.php/LEGO/article/view/2020}, } @Article{Haake_2014, author = {Haake, Kai and Gisch, Alexander}, title = {Schnelle Simulation von Funkkoexistenz-Verhalten im 2,45-GHz-ISM-Band in großflächigen intralogistischen Szenarien}, year = {2014}, pages = {252--259}, 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.}, doi = {10.15488/5408}, language = {ger}, url = {files/462/5455.html}, } @Misc{_2021c, month = aug, title = {Standards and conventions}, year = {2021}, language = {en-us}, url = {https://opentracing.io/docs/best-practices/standards-and-conventions/}, } @Misc{_2021d, month = aug, title = {ibeacon - Impoving ContactTracing Api efficiency with bluetooth signal strength}, year = {2021}, comment = {

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:

Transmitter RSSI by Device

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.

}, journal = {Stack Overflow}, url = {files/465/impoving-contacttracing-api-efficiency-with-bluetooth-signal-strength.html}, } @Misc{_2021e, month = aug, title = {Reference RSSI Value Calibration}, year = {2021}, 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 ...}, journal = {Support Center}, language = {en-GB}, url = {https://support.kontakt.io/hc/en-gb/articles/115000115610-Reference-RSSI-Value-Calibration}, } @Article{Paek_2016, author = {Paek, Jeongyeup and Ko, JeongGil and Shin, Hyungsik}, journal = {Mobile Information Systems}, title = {{A} {M}easurement {S}tudy of {BLE} i{B}eacon and {G}eometric {A}djustment {S}cheme for {I}ndoor {L}ocation-{B}ased {M}obile {A}pplications}, year = {2016}, issn = {1574-017X}, pages = {e8367638}, volume = {2016}, 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.}, doi = {10.1155/2016/8367638}, file = {:files/Paek et al. - 2016 - A Measurement Study of BLE iBeacon and Geometric A.pdf:PDF}, groups = {Thesis}, language = {en}, priority = {prio2}, publisher = {Hindawi Limited}, qualityassured = {qualityAssured}, readstatus = {read}, } @Misc{_2021f, month = aug, title = {Micro-Location Part 1: State of the Art}, year = {2021}, 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}, journal = {Abalta Technologies}, language = {en-US}, shorttitle = {Micro-Location Part 1}, url = {files/472/microlocation1.html}, } @Misc{_2021g, month = aug, title = {Micro-Location Part 2: BLE and RSSI}, year = {2021}, 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}, journal = {Abalta Technologies}, language = {en-US}, shorttitle = {Micro-Location Part 2}, url = {files/474/microlocation2.html}, } @Misc{_2021h, month = aug, title = {Micro-Location Part 3: BLE 5.1 and Direction Finding}, year = {2021}, 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}, journal = {Abalta Technologies}, language = {en-US}, shorttitle = {Micro-Location Part 3}, url = {files/476/microlocation3.html}, } @Article{Narzt_, author = {Narzt, Wolfgang and Furtmüller, Lukas and Rosenthaler, Matthias}, title = {IS BLUETOOTH LOW ENERGY AN ALTERNATIVE TO NEAR FIELD COMMUNICATION?}, pages = {15}, 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}, } @Book{Nur_2015_BOOK, author = {Nur, Osman and Nur, Samsudin and Nur, Shahirah and Husin, Heikal and Malim, Nurul and Mahinderjit Singh, Manmeet (Mandy)}, title = {Fig. 1. Distance between two NFC devices to execute an activity}, year = {2015}, abstract = {Download scientific diagram | Distance between two NFC devices to execute an activity  from publication: User Friendliness of Near-Field Communication (NFC) | 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... | NFC, Radio Frequency Identification and Radio Frequency Identification Device | ResearchGate, the professional network for scientists.}, groups = {Books}, language = {en}, url = {files/482/Distance-between-two-NFC-devices-to-execute-an-activity_fig1_278459134.html}, } @Article{Ramirez_2021, author = {Ramirez, Ramiro and Huang, Chien-Yi and Liao, Che-An and Lin, Po-Ting and Lin, Hsin-Wei and Liang, Shu-Hao}, journal = {Sensors}, title = {{A} {P}ractice of {BLE} {RSSI} {M}easurement for {I}ndoor {P}ositioning}, year = {2021}, number = {15}, pages = {5181}, volume = {21}, 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.}, doi = {10.3390/s21155181}, file = {:files/Ramirez et al. - 2021 - A Practice of BLE RSSI Measurement for Indoor Posi.pdf:PDF}, groups = {Methoden, Thesis}, keywords = {BLE, RSSI, IPS, Kalman filter, modification coefficient, trilateration}, language = {en}, priority = {prio1}, qualityassured = {qualityAssured}, readstatus = {read}, } @Article{Abboud_, author = {Abboud, Mansour}, title = {Received Signal Strength Based Indoor Positioning Using Bluetooth Low Energy}, pages = {86}, language = {en}, } @InProceedings{Giovanelli_2018, author = {Giovanelli, Davide and Farella, Elisabetta}, title = {RSSI or Time-of-flight for Bluetooth Low Energy based localization? An experimental evaluation}, year = {2018}, pages = {1--8}, publisher = {IEEE}, 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.}, doi = {10.23919/WMNC.2018.8480847}, eventtitle = {2018 11th IFIP Wireless and Mobile Networking Conference (WMNC)}, issn = {978-3-903176-04-1}, journal = {2018 11th IFIP Wireless and Mobile Networking Conference (WMNC)}, language = {en}, shorttitle = {RSSI or Time-of-flight for Bluetooth Low Energy based localization?}, url = {https://ieeexplore.ieee.org/document/8480847/}, } @Misc{Sponaas_2021, author = {Sponås, Jon Gunnar}, month = aug, title = {Things You Should Know About Bluetooth Range}, year = {2021}, 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}, url = {files/492/things-you-should-know-about-bluetooth-range.html}, } @Misc{_2021j, month = aug, title = {Kingcamp Multifunktionaler Spiel- & Campingtisch silber/bambus ab 199,95 € | Preisvergleich bei idealo.de}, year = {2021}, url = {files/494/200489052_-multifunktionaler-spiel-campingtisch-silber-bambus-kingcamp.html}, } @Article{Thein_, author = {Thein, Myo Min}, title = {Degree of Bachelor of Science in Electrical and Computer Engineering}, pages = {86}, file = {:files/Thein - Degree of Bachelor of Science in Electrical and Co.pdf:PDF}, groups = {Thesis von anderen}, language = {en}, priority = {prio1}, } @Misc{_2021k, month = aug, title = {Xiaomi Introduces groundbreaking UWB Technology - Mi Blog}, year = {2021}, language = {en-US}, url = {files/499/xiaomi-introduces-groundbreaking-uwb-technology.html}, } @Article{Miura_2015, author = {Miura, Shunsuke and Kamijo, Shunsuke}, journal = {International Journal of Intelligent Transportation Systems Research}, title = {{GPS} {E}rror {C}orrection by {M}ultipath {A}daptation}, year = {2015}, issn = {1868-8659}, number = {1}, pages = {1--8}, volume = {13}, 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.}, doi = {10.1007/s13177-013-0073-9}, groups = {Methoden}, language = {en}, qualityassured = {qualityAssured}, url = {https://doi.org/10.1007/s13177-013-0073-9}, } @Article{Lalitha_2020, author = {Lalitha, A. and Sooriya, P. and Sunilayyappan, E. and Suryanarayanan, K.}, title = {DISTANCE MEASUREMENT USING MOBILE APPLICATION}, year = {2020}, number = {2}, pages = {5}, 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}, } @Article{Graham_2015, author = {Graham, Daniel and Simmons, George and Nguyen, David T. and Zhou, Gang}, journal = {IEEE Internet of Things Journal}, title = {{A} {S}oftware-{B}ased {S}onar {R}anging {S}ensor for {S}mart {P}hones}, year = {2015}, issn = {2327-4662}, month = {dec}, number = {6}, pages = {479--489}, volume = {2}, 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.}, comment = {http://ieeexplore.ieee.org/document/7054431/}, doi = {10.1109/JIOT.2015.2408451}, file = {:files/Graham et al. - 2015 - A Software-Based Sonar Ranging Sensor for Smart Ph.pdf:PDF}, groups = {Thesis, Verwendet}, language = {en}, priority = {prio3}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, qualityassured = {qualityAssured}, readstatus = {read}, } @InCollection{Schwarz_2015, author = {Schwarz, David and Schwarz, Max and Stückler, Jörg and Behnke, Sven and Bianchi, Reinaldo A. C. and Akin, H. Levent and Ramamoorthy, Subramanian and Sugiura, Komei}, publisher = {Springer International Publishing}, title = {Cosero, Find My Keys! Object Localization and Retrieval Using Bluetooth Low Energy Tags}, year = {2015}, address = {Cham}, pages = {195--206}, volume = {8992}, 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.}, issn = {978-3-319-18614-6 978-3-319-18615-3}, journal = {RoboCup 2014: Robot World Cup XVIII}, language = {en}, url = {http://link.springer.com/10.1007/978-3-319-18615-3_16}, } @InProceedings{Kanatani_2008, author = {Kanatani, K. and Sugaya, Y. and Niitsuma, H.}, title = {Triangulation from Two Views Revisited: Hartley-Sturm vs. Optimal Correction}, year = {2008}, pages = {18.1--18.10}, publisher = {British Machine Vision Association}, 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.}, doi = {10.5244/C.22.18}, eventtitle = {Procedings of the British Machine Vision Conference 2008}, issn = {978-1-901725-36-0}, journal = {British Machine Vision Conference 2008}, language = {en}, shorttitle = {Triangulation from Two Views Revisited}, url = {http://www.bmva.org/bmvc/2008/papers/55.html}, } @InProceedings{Wang_2013, author = {Wang, Yapeng and Yang, Xu and Zhao, Yutian and Liu, Yue and Cuthbert, L.}, title = {Bluetooth positioning using RSSI and triangulation methods}, year = {2013}, pages = {837--842}, publisher = {IEEE}, doi = {10.1109/CCNC.2013.6488558}, eventtitle = {2013 IEEE 10th Consumer Communications and Networking Conference (CCNC)}, issn = {978-1-4673-3133-3 978-1-4673-3131-9 978-1-4673-3132-6}, journal = {2013 IEEE 10th Consumer Communications and Networking Conference (CCNC)}, priority = {prio1}, url = {http://ieeexplore.ieee.org/document/6488558/}, } @Article{Abbas_2021, author = {Abbas, Zahraa and Mosleh, Mahmood F.}, journal = {Advances in Mechanics}, title = {{B}luetooth {S}ituating {U}tilizing {T}riangulation {S}trategies and {RSSI}}, year = {2021}, month = jul, number = {3}, pages = {376--386}, volume = {9}, 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.}, file = {:files/Abbas und Mosleh - 2021 - Bluetooth Situating Utilizing Triangulation Strate.pdf:PDF}, keywords = {LSE, RSSI, Bluetooth situating, Triangulation}, language = {en}, priority = {prio3}, qualityassured = {qualityAssured}, readstatus = {read}, url = {http://advancesinmech.com/index.php/am/article/view/131}, } @InProceedings{Hou_2016, author = {Hou, Xiaoyue and Arslan, Tughrul and Juri, Arief and Wang, Fengzhou}, title = {Indoor Localization for Bluetooth Low Energy Devices Using Weighted Off-set Triangulation Algorithm}, year = {2016}, pages = {2286--2292}, 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.}, doi = {10.33012/2016.14720}, journal = {Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016)}, language = {en}, url = {files/520/abstract.html}, } @InProceedings{Naveed_2019, author = {Naveed, Munir and Javed, Yasir and Bhatti, Ghulam M. and Asif, Sayed}, title = {Smart indoor Positioning Model for Deterministic Environment}, year = {2019}, pages = {288--291}, 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.}, doi = {10.1109/ITT48889.2019.9075118}, eventtitle = {2019 Sixth HCT Information Technology Trends (ITT)}, journal = {2019 Sixth HCT Information Technology Trends (ITT)}, keywords = {Wireless sensor networks, Wireless fidelity, Convolutional Neural Networks, ensemble models, Forestry, Indoor positioning, Predictive models, Random Forest, Sensors, Solid modeling}, url = {files/527/9075118.html}, } @InProceedings{Naik_2012, author = {Naik, Gauri A. and Khedekar, Madhavi P. and Krishnamoorthy, Mahalakshmy and Patil, Sayali D. and Deshmukh, Rupali N.}, title = {Comparison of RSSI techniques in Wireless Indoor Geolocation}, year = {2012}, pages = {1--5}, 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.}, doi = {10.1109/NCCCS.2012.6413008}, eventtitle = {2012 NATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION SYSTEMS}, journal = {2012 NATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION SYSTEMS}, 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)}, url = {files/530/6413008.html}, } @Article{Konings_2019, author = {Konings, Daniel and Alam, Fakhrul and Noble, Frazer and Lai, Edmund M.-K.}, journal = {IEEE Access}, title = {SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation}, year = {2019}, issn = {2169-3536}, pages = {56960--56973}, volume = {7}, 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.}, doi = {10.1109/ACCESS.2019.2913910}, groups = {Methoden}, keywords = {Calibration, Fingerprint recognition, Device-free localization (DFL), histogram distance, Histograms, indoor positioning systems (IPS), Particle filters, smart homes, Smart homes, spring-relaxation, Springs}, shorttitle = {SpringLoc}, url = {files/535/8703051.html}, } @InProceedings{Noertjahyana_2017, author = {Noertjahyana, Agustinus and Wijayanto, Ignatius Alex and Andjarwirawan, Justinus}, booktitle = {2017 International Conference on Soft Computing, Intelligent System and Information Technology ({ICSIIT})}, title = {{D}evelopment of {M}obile {I}ndoor {P}ositioning {S}ystem {A}pplication {U}sing {A}ndroid and {B}luetooth {L}ow {E}nergy with {T}rilateration {M}ethod}, year = {2017}, month = {sep}, pages = {185--189}, publisher = {{IEEE}}, 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.}, doi = {10.1109/ICSIIT.2017.64}, eventtitle = {2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT)}, file = {:files/Noertjahyana et al. - 2017 - Development of Mobile Indoor Positioning System Ap.pdf:PDF}, groups = {Verwendet}, journal = {2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT)}, keywords = {Bluetooth, Calibration, Meters, trilateration, Android, Androids, bluetooth low energy, Humanoid robots, Indoor positioning system, IP networks, Testing}, priority = {prio2}, qualityassured = {qualityAssured}, readstatus = {skimmed}, } @InProceedings{SosaSesma_2016, author = {Sosa-Sesma, Sergio and Perez-Navarro, Antoni}, booktitle = {2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, title = {{F}usion system based on {W}i{F}i and ultrasounds for in-home positioning systems: {T}he {UTOPIA} experiment}, year = {2016}, month = {oct}, pages = {1--8}, publisher = {{IEEE}}, 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.}, doi = {10.1109/IPIN.2016.7743622}, eventtitle = {2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, file = {:files/Sosa-Sesma und Perez-Navarro - 2016 - Fusion system based on WiFi and ultrasounds for in.pdf:PDF}, groups = {Methoden, Verwendet}, 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}, qualityassured = {qualityAssured}, shorttitle = {Fusion system based on WiFi and ultrasounds for in-home positioning systems}, } @Article{Forghani_2020, author = {Forghani, Mohammad and Karimipour, Farid and Claramunt, Christophe}, journal = {Transportation Research Part C: Emerging Technologies}, title = {From cellular positioning data to trajectories: Steps towards a more accurate mobility exploration}, year = {2020}, issn = {0968090X}, pages = {102666}, volume = {117}, 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.}, doi = {10.1016/j.trc.2020.102666}, language = {en}, shorttitle = {From cellular positioning data to trajectories}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0968090X20305817}, } @InCollection{Toyama_2021, author = {Toyama, Atushi and Mitsugi, Kenshiro and Matsuo, Keita and Kulla, Elis and Barolli, Leonard}, title = {Implementation of an Indoor Position Detecting System Using Mean BLE RSSI for Moving Omnidirectional Access Point Robot}, year = {2021}, pages = {225--234}, 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.}, issn = {978-3-030-79724-9}, } @Article{Jeong_2019, author = {Jeong, Seungyeon and Kuk, Seungho and Kim, Hyogon}, journal = {IEEE Access}, title = {A Smartphone Magnetometer-Based Diagnostic Test for Automatic Contact Tracing in Infectious Disease Epidemics}, year = {2019}, issn = {2169-3536}, pages = {20734--20747}, volume = {7}, 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.}, doi = {10.1109/ACCESS.2019.2895075}, keywords = {Meters, Sensors, Global Positioning System, Correlation, diagnostic test, human contact tracing, infectious disease epidemic, Infectious diseases, Magnetometers, Mobile sensing, smartphone magnetometer}, priority = {prio3}, url = {files/550/8626091.html}, } @Article{Kothari_2012, author = {Kothari, Nisarg and Kannan, Balajee and Glasgwow, Evan D. and Dias, M. Bernardine}, journal = {Procedia Computer Science}, title = {Robust Indoor Localization on a Commercial Smart Phone}, year = {2012}, issn = {1877-0509}, pages = {1114--1120}, volume = {10}, 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.}, doi = {10.1016/j.procs.2012.06.158}, groups = {Methoden}, keywords = {inertial navigation solution, laser-baed map-representation, robot navigation, RSSI fingerprinting}, language = {en}, series = {ANT 2012 and MobiWIS 2012}, url = {files/553/S1877050912005157.html}, } @InProceedings{Bauer_2013, author = {Bauer, Christine}, title = {On the (In-)Accuracy of GPS Measures of Smartphones: A Study of Running Tracking Applications}, year = {2013}, pages = {335--341}, publisher = {Association for Computing Machinery}, series = {MoMM '13}, 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.}, doi = {10.1145/2536853.2536893}, eventtitle = {Proceedings of International Conference on Advances in Mobile Computing & Multimedia}, issn = {978-1-4503-2106-8}, keywords = {Accuracy, Localization, Global Positioning System, GPS, Location-based System, Positioning, Running Tracking, Smartphone, Sports Tracking}, shorttitle = {On the (In-)Accuracy of GPS Measures of Smartphones}, url = {https://doi.org/10.1145/2536853.2536893}, } @Article{Kuhn_2013, author = {Kuhn, Jochen and Vogt, Patrik}, journal = {European Journal of Physics Education}, title = {Smartphones as Experimental Tools: Different Methods to Determine the Gravitational Acceleration in Classroom Physics by Using Everyday Devices}, year = {2013}, issn = {1309-7202}, number = {1}, pages = {16--27}, volume = {4}, 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).}, 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}, language = {en}, shorttitle = {Smartphones as Experimental Tools}, url = {files/558/eric.ed.gov.html}, } @InProceedings{Qian_2013, author = {Qian, Jiuchao and Ma, Jiabin and Ying, Rendong and Liu, Peilin and Pei, Ling}, title = {An improved indoor localization method using smartphone inertial sensors}, year = {2013}, pages = {1--7}, 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.}, doi = {10.1109/IPIN.2013.6817854}, eventtitle = {International Conference on Indoor Positioning and Indoor Navigation}, groups = {Methoden}, journal = {International Conference on Indoor Positioning and Indoor Navigation}, keywords = {Estimation, Sensors, Particle filters, Acceleration, indoor localization, Legged locomotion, Navigation, particle filter, PCA, PDR, Principal component analysis}, priority = {prio2}, url = {files/560/6817854.html}, } @InProceedings{Li_2012, author = {Li, Fan and Zhao, Chunshui and Ding, Guanzhong and Gong, Jian and Liu, Chenxing and Zhao, Feng}, booktitle = {Proceedings of the 2012 {ACM} Conference on Ubiquitous Computing - {UbiComp} {\textquotesingle}12}, title = {{A} reliable and accurate indoor localization method using phone inertial sensors}, year = {2012}, pages = {421--430}, publisher = {{ACM} Press}, series = {UbiComp '12}, 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.}, doi = {10.1145/2370216.2370280}, eventtitle = {Proceedings of the 2012 ACM Conference on Ubiquitous Computing}, file = {:files/li2012.pdf:PDF}, groups = {Verwendet}, issn = {978-1-4503-1224-0}, keywords = {indoor localization, inertial tracking, pedestrian model}, priority = {prio2}, qualityassured = {qualityAssured}, url = {https://doi.org/10.1145/2370216.2370280}, } @Article{Yang_2015, author = {Yang, Zheng and Wu, Chenshu and Zhou, Zimu and Zhang, Xinglin and Wang, Xu and Liu, Yunhao}, journal = {ACM Computing Surveys}, title = {Mobility Increases Localizability: A Survey on Wireless Indoor Localization using Inertial Sensors}, year = {2015}, issn = {0360-0300}, number = {3}, pages = {54:1--54:34}, volume = {47}, 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.}, doi = {10.1145/2676430}, keywords = {Mobility, smartphones, wireless indoor localization}, shorttitle = {Mobility Increases Localizability}, url = {https://doi.org/10.1145/2676430}, } @InProceedings{Nickel_2012, author = {Nickel, Claudia and Wirtl, Tobias and Busch, Christoph}, title = {Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm}, year = {2012}, pages = {16--20}, 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.}, doi = {10.1109/IIH-MSP.2012.11}, eventtitle = {2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing}, file = {:files/567/6274118.html:URL}, journal = {2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing}, keywords = {Legged locomotion, accelerometer, Authentication, biometrics, Error analysis, Feature extraction, gait recognition, Magnetic resonance, Mel frequency cepstral coefficient, smartphone, Vectors}, priority = {prio3}, } @InProceedings{Hennecke_2011, author = {Hennecke, Marius H. and Fink, Gernot A.}, title = {Towards acoustic self-localization of ad hoc smartphone arrays}, year = {2011}, pages = {127--132}, 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.}, doi = {10.1109/HSCMA.2011.5942378}, eventtitle = {2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays}, journal = {2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays}, keywords = {Calibration, Acoustics, Loudspeakers, maximum likelihood estimation, Microphone arrays, Noise, Optimization, self-localization, smartphone array}, url = {files/570/5942378.html}, } @Article{Subbu_2013, author = {Subbu, Kalyan Pathapati and Gozick, Brandon and Dantu, Ram}, journal = {ACM Transactions on Intelligent Systems and Technology}, title = {{L}ocate{M}e: {M}agnetic-fields-based indoor localization using smartphones}, year = {2013}, issn = {2157-6904}, month = {sep}, number = {4}, pages = {73:1--73:27}, volume = {4}, 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.}, doi = {10.1145/2508037.2508054}, groups = {Verwendet}, keywords = {smartphones, Indoor localization, magnetic fields, ubiquitous}, publisher = {Association for Computing Machinery ({ACM})}, qualityassured = {qualityAssured}, shorttitle = {LocateMe}, url = {https://doi.org/10.1145/2508037.2508054}, } @Article{Basri_2020, author = {C. Basri and A. Elkhadimi}, journal = {ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, title = {A {REVIEW} {ON} {INDOOR} {LOCALIZATION} {WITH} {INTERNET} {OF} {THINGS}}, year = {2020}, month = {nov}, pages = {121--128}, volume = {{XLIV}-4/W3-2020}, 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.}, doi = {10.5194/isprs-archives-xliv-4-w3-2020-121-2020}, file = {:files/isprs-archives-XLIV-4-W3-2020-121-2020.pdf:PDF}, priority = {prio3}, publisher = {Copernicus {GmbH}}, qualityassured = {qualityAssured}, } @Article{Maghdid_2021, author = {Maghdid, Safar M. and Maghdid, Halgurd}, title = {A {C}omprehensive {R}eview of {I}ndoor/{O}utdoor {L}ocalization {S}olutions in {I}o{T} era: {R}esearch {C}hallenges and {F}uture {P}erspectives}, year = {2021}, 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.}, doi = {10.36227/techrxiv.15138609.v1}, file = {:files/579/15138609.html:URL}, groups = {Methoden}, language = {en}, priority = {prio3}, qualityassured = {qualityAssured}, 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}, } @InProceedings{Wu_2008, author = {Wu, Rong-Hou and Lee, Yang-Han and Tseng, Hsien-Wei and Jan, Yih-Guang and Chuang, Ming-Hsueh}, booktitle = {2008 IEEE International Conference on Industrial Technology}, title = {Study of characteristics of RSSI signal}, year = {2008}, pages = {1-3}, doi = {10.1109/ICIT.2008.4608603}, file = {:Files/rong-houwu2008.pdf:PDF}, } @InProceedings{Welch_1997a, author = {Greg Welch and Gary Bishop}, title = {{A}n {I}ntroduction to the {K}alman {F}ilter}, year = {1997}, organization = {Department of Computer ScienceUniversity of North Carolina at Chapel Hill}, 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.}, file = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/580/Welch - 1997 - An Introduction to the Kalman Filter.pdf:PDF}, groups = {Distanzmessung, Algorithmen, Methoden}, priority = {prio1}, qualityassured = {qualityAssured}, } @Article{Cho_2015, author = {Hosik Cho and Jianxun Ji and Zili Chen and Hyuncheol Park and Wonsuk Lee}, journal = {Procedia Computer Science}, title = {{M}easuring a {D}istance between {T}hings with {I}mproved {A}ccuracy}, year = {2015}, pages = {1083--1088}, volume = {52}, 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.}, doi = {10.1016/j.procs.2015.05.119}, file = {:files/396/Cho et al. - 2015 - Measuring a Distance between Things with Improved .pdf:PDF}, groups = {Distanzmessung}, publisher = {Elsevier {BV}}, qualityassured = {qualityAssured}, } @InProceedings{Khazraj_2016, author = {Hesam Khazraj and F. Faria da Silva and Claus Leth Bak}, title = {{A} performance comparison between extended {K}alman {F}ilter and unscented {K}alman {F}ilter in power system dynamic state estimation}, year = {2016}, month = {sep}, publisher = {{IEEE}}, doi = {10.1109/upec.2016.8114125}, eprint = {https://core.ac.uk/display/102700436}, groups = {Methoden}, priority = {prio2}, qualityassured = {qualityAssured}, } @Article{Suhas_2014, author = {Suhas A.R}, journal = {International Journal of Innovations in Engineering and Technolog ({IJIET})}, title = {{A} {C}omparative {S}tudy between {E}xtrended {K}alman {F}ilter and {U}nscented {K}alman {F}ilter for {T}raffic {S}tate {E}stimation}, year = {2014}, issn = {2319-1058}, month = jun, number = {1}, pages = {188--197}, volume = {4}, abstract = {This paper presents a freeway traffic state estimation and METANET traffic flow model. Since the environmental conditions on a freeway may change over time (e.g., changing weather conditions), parameter estimation is also considered. This paper also provides the introduction to Extended Kalman filter and Unscented Kalman filter. We compare the performance of the extended Kalman filter and the unscented Kalman filter for state estimation, parameter estimation, joint estimation and dual estimation. Furthermore, the performance is evaluated for different detector configurations.}, file = {:files/27.pdf:PDF}, groups = {Methoden}, priority = {prio1}, qualityassured = {qualityAssured}, timestamp = {2021-10-28}, url = {https://core.ac.uk/display/102700436}, } @Article{Ye_2019, author = {Ye, Feng and Chen, Ruizhi and Guo, Guangyi and Peng, Xuesheng and Liu, Zuoya and Huang, Lixiong}, journal = {{IEEE} Access}, title = {{A} {L}ow-{C}ost {S}ingle-{A}nchor {S}olution for {I}ndoor {P}ositioning {U}sing {BLE} and {I}nertial {S}ensor {D}ata}, year = {2019}, issn = {2169-3536}, pages = {162439--162453}, volume = {7}, 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.}, doi = {10.1109/ACCESS.2019.2951281}, file = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/426/Ye et al. - 2019 - A Low-Cost Single-Anchor Solution for Indoor Posit.pdf:PDF}, groups = {Methoden, Verwendet}, 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}, priority = {prio2}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, qualityassured = {qualityAssured}, } @Article{Fermani_2013, author = {Fermani, Francesco and Schönrich, Ralph}, journal = {Monthly Notices of the Royal Astronomical Society}, title = {A new calibration for the Blue Horizontal Branch}, year = {2013}, issn = {0035-8711}, month = {jan}, number = {2}, pages = {1294--1301}, volume = {430}, 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.}, doi = {10.1093/mnras/sts703}, eprint = {https://academic.oup.com/mnras/article/430/2/1294/2892440}, file = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/432/Fermani und Schönrich - 2013 - A new calibration for the Blue Horizontal Branch.pdf:PDF}, groups = {Methoden}, publisher = {Oxford University Press ({OUP})}, qualityassured = {qualityAssured}, timestamp = {2021-08-11}, url = {https://doi.org/10.1093/mnras/sts703}, } @InProceedings{Maratea_2019, author = {Maratea, Antonio and Salvi, Giuseppe and Gaglione, Salvatore}, title = {Bagging to Improve the Calibration of RSSI Signals in Bluetooth Low Energy (BLE) Indoor Distance Estimation}, year = {2019}, month = {nov}, organization = {Department of Science and TechnologiesUniversity of Naples “Parthenope”}, pages = {657--662}, publisher = {{IEEE}}, 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.}, doi = {10.1109/SITIS.2019.00107}, eventtitle = {2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)}, file = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/445/Maratea et al. - 2019 - Bagging to Improve the Calibration of RSSI Signals.pdf:PDF}, groups = {Bluetooth, Distanzmessung, Algorithmen, Methoden}, journal = {2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)}, keywords = {BLE, Estimation, Wireless sensor networks, Calibration, Meters, Pollution measurement, Transmitters, Bagging, Bootstrap, Robust, WSN}, qualityassured = {qualityAssured}, url = {files/444/9067942.html}, } @Article{Chen_2019, author = {Chen, Jiayu and Chen, Hainan and Luo, Xiaowei}, journal = {Automation in Construction}, title = {{C}ollecting building occupancy data of high resolution based on {W}i{F}i and {BLE} network}, year = {2019}, issn = {0926-5805}, month = {jun}, pages = {183--194}, volume = {102}, 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.}, doi = {10.1016/j.autcon.2019.02.016}, file = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/430/Chen et al. - 2019 - Collecting building occupancy data of high resolut.pdf:PDF}, groups = {Verwendet}, keywords = {Building energy system, Building occupant localization, kNN, Stochastic random walk, Zone-based}, language = {en}, publisher = {Elsevier {BV}}, qualityassured = {qualityAssured}, } @InProceedings{AlQathrady_2017, author = {Al Qathrady, Mimonah and Helmy, Ahmed}, booktitle = {Proceedings of the 20th {ACM} International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems}, title = {Improving BLE Distance Estimation and Classification Using TX Power and Machine Learning: A Comparative Analysis}, year = {2017}, month = {nov}, pages = {79--83}, publisher = {Association for Computing Machinery}, series = {MSWiM '17}, 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.}, doi = {10.1145/3127540.3127577}, eventtitle = {Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems}, file = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/413/Al Qathrady und Helmy - 2017 - Improving BLE Distance Estimation and Classificati.pdf:PDF}, groups = {Bluetooth}, issn = {978-1-4503-5162-1}, keywords = {ble, distance estimation, iot, proximity classification}, shorttitle = {Improving BLE Distance Estimation and Classification Using TX Power and Machine Learning}, url = {https://doi.org/10.1145/3127540.3127577}, } @InCollection{Zhao_2020, author = {Zhao, Qingchuan and Wen, Haohuang and Lin, Zhiqiang and Xuan, Dong and Shroff, Ness and Park, Noseong and Sun, Kun and Foresti, Sara and Butler, Kevin and Saxena, Nitesh}, booktitle = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering}, publisher = {Springer International Publishing}, title = {On the Accuracy of Measured Proximity of Bluetooth-Based Contact Tracing Apps}, year = {2020}, address = {Cham}, pages = {49--60}, volume = {335}, 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.}, doi = {10.1007/978-3-030-63086-7_4}, file = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/407/Zhao et al. - 2020 - On the Accuracy of Measured Proximity of Bluetooth.pdf:PDF}, issn = {978-3-030-63085-0 978-3-030-63086-7}, journal = {Security and Privacy in Communication Networks}, language = {en}, url = {https://link.springer.com/10.1007/978-3-030-63086-7_4}, } @InProceedings{Maratea_2018, author = {Maratea, Antonio and Gaglione, Salvatore and Angrisano, Antonio and Salvi, Giuseppe and Nunziata, Alessandro}, title = {Non parametric and robust statistics for indoor distance estimation through BLE}, year = {2018}, pages = {1--6}, 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.}, doi = {10.1109/EE1.2018.8385266}, eventtitle = {2018 IEEE International Conference on Environmental Engineering (EE)}, file = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/421/Maratea et al. - 2018 - Non parametric and robust statistics for indoor di.pdf:PDF}, journal = {2018 IEEE International Conference on Environmental Engineering (EE)}, keywords = {Estimation, Wireless sensor networks, Calibration, Kernel, Meters, Pollution measurement, Power measurement}, url = {files/420/8385266.html}, } @Misc{_2021l, author = {Sohan, Asif Ahmed and Ali, Mohammad and Fairooz, Fabiha and Rahman, Adham Ibrahim and Chakrabarty, Amitabha and Kabir, Md. Rayhan}, month = aug, title = {Indoor Positioning Techniques using RSSI from Wireless Devices}, year = {2021}, 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.}, doi = {10.1109/ICCIT48885.2019.9038591}, eventtitle = {2019 22nd International Conference on Computer and Information Technology (ICCIT)}, journal = {2019 22nd International Conference on Computer and Information Technology (ICCIT)}, keywords = {RSSI, Indoor Positioning, ITU, Trilateration, WiFi}, language = {en-US}, pages = {1--6}, url = {files/522/9038591.html}, } @Book{BergerGrabner_2016_BOOK, author = {Berger-Grabner, Doris}, publisher = {Springer-Verlag GmbH}, title = {{W}issenschaftliches {A}rbeiten in den {W}irtschafts- und {S}ozialwissenschaften}, year = {2016}, isbn = {9783658130787}, month = sep, ean = {9783658130787}, file = {:files/balzert.pdf:PDF}, groups = {Books}, pagetotal = {246}, priority = {prio1}, qualityassured = {qualityAssured}, url = {https://www.ebook.de/de/product/27954104/doris_berger_grabner_wissenschaftliches_arbeiten_in_den_wirtschafts_und_sozialwissenschaften.html}, } @Article{Cho_2015a, author = {Ho-sik Cho and Jianxun Ji and Zili Chen and Hyuncheol Park and Wonsuk Lee}, journal = {{O}pen {J}ournal of {I}nternet {O}f {T}hings ({OJIOT})}, title = {{A}ccurate {D}istance {E}stimation between {T}hings: {A} {S}elf-correcting {A}pproach}, year = {2015}, issn = {2364-7108}, number = {2}, pages = {19--27}, volume = {1}, 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.}, file = {:files/Cho et al. - 2015 - Accurate Distance Estimation between Things A Sel.pdf:PDF}, groups = {Thesis, Verwendet}, priority = {prio1}, qualityassured = {qualityAssured}, ranking = {rank4}, readstatus = {read}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-201704244959}, urldate = {2021-08-11}, } @InProceedings{Akcan_2006a, author = {Hüseyin Akcan and Vassil Kriakov and Herv{\'{e}} Brönnimann and Alex Delis}, booktitle = {Proceedings of the 5th {ACM} international workshop on Data engineering for wireless and mobile access - {MobiDE} {\textquotesingle}06}, title = {{GPS}-{F}ree node localization in mobile wireless sensor networks}, year = {2006}, publisher = {{ACM} Press}, doi = {10.1145/1140104.1140113}, groups = {Thesis}, priority = {prio2}, qualityassured = {qualityAssured}, } @Article{Yu_2015a, author = {Minghe Yu and Guoliang Li and Ting Wang and Jianhua Feng and Zhiguo Gong}, journal = {{IEEE} Transactions on Knowledge and Data Engineering}, title = {{E}fficient {F}iltering {A}lgorithms for {L}ocation-{A}ware {P}ublish/{S}ubscribe}, year = {2015}, issn = {1041-4347}, month = apr, number = {4}, pages = {950--963}, volume = {27}, abstract = {Location-based services have been widely adopted in many systems. Existing works employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers' messages to relevant subscribers. In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an R-tree based index by integrating textual descriptions into R-tree nodes. We devise efficient filtering algorithms and effective pruning techniques to achieve high performance. Our method can support both conjunctive queries and ranking queries. We discuss how to support dynamic updates efficiently. Experimental results show our method achieves high performance which can filter 500 messages in a second for 10 million subscriptions on a commodity computer.}, doi = {10.1109/tkde.2014.2349906}, file = {:files/yu2015.pdf:PDF}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, qualityassured = {qualityAssured}, } @Article{Bajaj_2002a, author = {Bajaj, R. and Ranaweera, S.L. and Agrawal, D.P.}, journal = {Computer}, title = {{GPS}: location-tracking technology}, year = {2002}, number = {4}, pages = {92--94}, volume = {35}, doi = {10.1109/mc.2002.993780}, file = {:files/bajaj2002.pdf:PDF}, groups = {Thesis, Verwendet}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, qualityassured = {qualityAssured}, readstatus = {skimmed}, } @Article{Davidson_2017a, author = {Pavel Davidson and Robert Piche}, journal = {{IEEE} Communications Surveys {\&} Tutorials}, title = {{A} {S}urvey of {S}elected {I}ndoor {P}ositioning {M}ethods for {S}martphones}, year = {2017}, issn = {1553-877X}, number = {2}, pages = {1347--1370}, volume = {19}, doi = {10.1109/comst.2016.2637663}, file = {:files/davidson2016.pdf:PDF}, groups = {Thesis, Verwendet}, priority = {prio1}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, qualityassured = {qualityAssured}, url = {https://ieeexplore.ieee.org/abstract/document/7782316}, } @InCollection{Cho_2014a, author = {Seong Yun Cho}, booktitle = {Lecture Notes in Geoinformation and Cartography}, publisher = {Springer International Publishing}, title = {{R}ange {D}omain {IMM} {F}iltering with {A}dditional {S}ignal {A}ttenuation {E}rror {M}itigation of {I}ndividual {C}hannels for {WLAN} {RSSI}-{B}ased {P}osition-{T}racking}, year = {2014}, isbn = {978-3-319-11878-9}, month = {nov}, pages = {91--104}, doi = {10.1007/978-3-319-11879-6_7}, ean = {9783319118789}, groups = {Thesis}, pagetotal = {296}, priority = {prio1}, qualityassured = {qualityAssured}, url = {https://www.ebook.de/de/product/22762371/progress_in_location_based_services_2014.html}, } @Article{Cheng_2014a, author = {Jiantong Cheng and Ling Yang and Yong Li and Weihua Zhang}, journal = {Physical Communication}, title = {{S}eamless outdoor/indoor navigation with {WIFI}/{GPS} aided low cost {I}nertial {N}avigation {S}ystem}, year = {2014}, month = {dec}, pages = {31--43}, volume = {13}, abstract = {This paper describes an integrated navigation system that can be used for pedestrian navigation in both outdoor and indoor environments. With the aid of Global Positioning System (GPS) positioning solutions, an Inertial Navigation System (INS) can provide stable and continuous outdoor navigation. When moving indoors, WIFI positioning can replace the GPS in order to maintain the integrated system’s long-term reliability and stability. On the other hand, the position from an INS can also provide apriori information to aid WIFI positioning. Signal strength-based WIFI positioning is widely used for indoor navigation. A new fingerprinting method is proposed so as to improve the performance of WIFI stand-alone positioning. For pedestrian navigation applications, a step detection method is implemented to constrain the growth of the INS error using an Extend Kalman Filter (EKF). Experiments have been conducted to test this system and the results have demonstrated the feasibility of this seamless outdoor/indoor navigation system.}, comment = {Artikel zum Zeigen das man die Bluetoothnavigation durch Inertial Sensoren verbessern könnte.}, doi = {10.1016/j.phycom.2013.12.003}, file = {:files/cheng2014.pdf:PDF}, keywords = {Indoor Navigation, WIFI Positioning, Fingerprinting, Step Detection, Pedestrian Navigation}, priority = {prio1}, publisher = {Elsevier {BV}}, qualityassured = {qualityAssured}, } @InProceedings{10.1007/978-3-642-12654-3_3, author = {Kj{\ae}rgaard, Mikkel Baun and Blunck, Henrik and Godsk, Torben and Toftkj{\ae}r, Thomas and Christensen, Dan Lund and Gr{\o}nb{\ae}k, Kaj}, booktitle = {Pervasive Computing}, title = {{I}ndoor {P}ositioning {U}sing {GPS} {R}evisited}, year = {2010}, address = {Berlin, Heidelberg}, editor = {Flor{\'e}en, Patrik and Kr{\"u}ger, Antonio and Spasojevic, Mirjana}, pages = {38--56}, publisher = {Springer Berlin Heidelberg}, abstract = {It has been considered a fact that GPS performs too poorly inside buildings to provide usable indoor positioning. We analyze results of a measurement campaign to improve on the understanding of indoor GPS reception characteristics. The results show that using state-of-the-art receivers GPS availability is good in many buildings with standard material walls and roofs. The measured root mean squared 2D positioning error was below five meters in wooden buildings and below ten meters in most of the investigated brick and concrete buildings. Lower accuracies, where observed, can be linked to either low signal-to-noise ratios, multipath phenomena or bad satellite constellation geometry. We have also measured the indoor performance of embedded GPS receivers in mobile phones which provided lower availability and accuracy than state-of-the-art ones. Finally, we consider how the GPS performance within a given building is dependent on local properties like close-by building elements and materials, number of walls, number of overlaying stories and surrounding buildings.}, isbn = {978-3-642-12654-3}, priority = {prio2}, } @Article{Molina_2018a, author = {Benjamin Molina and Eneko Olivares and Carlos Enrique Palau and Manuel Esteve}, journal = {{IEEE} Access}, title = {{A} {M}ultimodal {F}ingerprint-{B}ased {I}ndoor {P}ositioning {S}ystem for {A}irports}, year = {2018}, pages = {10092--10106}, volume = {6}, doi = {10.1109/access.2018.2798918}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, qualityassured = {qualityAssured}, } @Misc{BluetoothSIG_2021, author = {Bluetooth {SIG}, Inc}, title = {2021 {M}arket {U}pdate}, year = {2021}, access = {2021-12-15}, file = {:files/2021-Bluetooth_Market_Update.pdf:PDF}, groups = {Verwendet}, url = {https://www.bluetooth.com/wp-content/uploads/2021/01/2021-Bluetooth_Market_Update.pdf}, urldate = {2021-12-15}, } @Book{Grote_2007, author = {Karl-Heinrich Grote and Jörg Feldhusen}, editor = {Karl-Heinrich Grote and Jörg Feldhusen}, publisher = {Springer Berlin Heidelberg}, title = {Dubbel}, year = {2007}, address = {Berlin New York}, edition = {22}, isbn = {978-3-540-49714-1}, month = {2007}, doi = {10.1007/978-3-540-68191-5}, } @Misc{BluetoothSIG_2021a, author = {Bluetooth {SIG}, Inc.}, note = {Abgerufen: 2021-11-29}, title = {{B}luetooth {T}echnologie-{Ü}bersicht}, year = {2021}, url = {https://www.bluetooth.com/de/learn-about-bluetooth/tech-overview/}, } @Book{Lerch_2006_BOOK, author = {Lerch, Reinhard}, publisher = {Springer}, title = {{E}lektrische {M}esstechnik}, year = {2006}, address = {Berlin Heidelberg}, edition = {3}, isbn = {9783540340553}, series = {Springer-Lehrbuch}, doi = {10.1007/3-540-34057-2}, file = {:files/Lerch2006_Book_ElektrischeMesstechnik.pdf:PDF}, groups = {Verwendet, Books}, priority = {prio2}, qualityassured = {qualityAssured}, } @Misc{Duden_Distanze, author = {Dudenredaktion}, title = {Duden - Die deutsche Rechtschreibung}, keywords = {duden}, publisher = {Bibliographisches Institut}, url = {https://www.duden.de/node/33536/revision/617769}, } @Book{Strang_2008_BOOK, author = {Thomas Strang and Frank Schubert and Steffen Thölert and Rainer Oberweis}, publisher = {Shaker}, title = {Lokalisierungsverfahren}, year = {2008}, address = {Aachen}, isbn = {9783832274924}, series = {Geoinformatik}, comment = {Deutsches Zentrum für Luft- und Raumfahrt, (DLR) URL: https://elib.dlr.de/54309/1/Lokalisierungsverfahren_v22.pdf}, file = {:files/Lokalisierungsverfahren_v22.pdf:PDF}, groups = {Verwendet, Books}, keywords = {GPS, Galileo, Indoor, Intertial, GBAS, SBAS, Ubisense, Kreisel, Navigation, Position}, priority = {prio2}, qualityassured = {qualityAssured}, readstatus = {skimmed}, } @Article{Oguejiofor_2013, author = {Oguejiofor, OS and Aniedu, AN and Ejiofor, HC and Okolibe, AU}, journal = {International Journal of Science and Modern Engineering ({IJISME})}, title = {{T}rilateration based localization algorithm for wireless sensor network}, year = {2013}, issn = {2319-6386}, month = sep, number = {10}, pages = {21--27}, volume = {1}, file = {:files/J04470911013.pdf:PDF}, keywords = {Trilateration, localisation, algorithm, wireless sensor}, qualityassured = {qualityAssured}, } @Misc{BluetoothSIG_2016, author = {{Bluetooth SIG, Inc.}}, howpublished = {\url{https://www.bluetooth.com/de/specifications/specs/core-specification-5/}}, month = dec, title = {{B}luetooth {C}ore {S}pecification 5.0}, year = {2016}, access = {2021-12-01}, file = {:files/Core_v5.0.pdf:PDF}, keywords = {Bluetooth, Core Specification}, timestamp = {2021-12-09}, url = {https://www.bluetooth.com/de/specifications/specs/core-specification-5/}, urldate = {2021-12-09}, } @Misc{BluetoothSIG_2014, author = {{Bluetooth SIG, Inc.}}, howpublished = {\url{https://www.bluetooth.com/de/specifications/specs/core-specification-4-2/}}, month = dec, title = {{B}luetooth {C}ore {S}pecification 4.2}, year = {2014}, access = {2021-12-01}, file = {:files/Core_v4.2.pdf:PDF}, groups = {Verwendet, Normen}, keywords = {Bluetooth, Core Specification}, priority = {prio1}, qualityassured = {qualityAssured}, timestamp = {2021-12-09}, url = {https://www.bluetooth.com/de/specifications/specs/core-specification-4-2/}, urldate = {2021-12-09}, } @Book{Harten_2012_BOOK, author = {Ulrich Harten}, publisher = {Springer Berlin Heidelberg}, title = {Physik}, year = {2012}, address = {Heidelberg}, edition = {5}, isbn = {978-3-642-19978-3}, doi = {10.1007/978-3-642-19979-0}, file = {:files/Harten2012_Book_Physik.pdf:PDF}, groups = {Verwendet, Books}, qualityassured = {qualityAssured}, } @Article{BSIG_2019, author = {{Bluetooth Spezial Interest Group Inc.}}, title = {{B}luetooth {M}arket {U}pdate 2019}, year = {2019}, } @Book{Gupta_2016_BOOK, author = {Gupta, Naresh}, publisher = {Artech House}, title = {{I}nside {B}luetooth low energy}, year = {2016}, address = {London}, isbn = {9781630813703}, groups = {Books}, qualityassured = {qualityAssured}, } @Misc{BluetoothSIG_2019, author = {{Bluetooth SIG, Inc.}}, howpublished = {\url{https://www.bluetooth.com/de/specifications/specs/core-specification-5-2/}}, month = dec, title = {{B}luetooth {C}ore {S}pecification 5.2}, year = {2019}, access = {2021-12-01}, file = {:files/Core_v5.2.pdf:PDF}, keywords = {Bluetooth, Core Specification}, timestamp = {2021-12-09}, url = {https://www.bluetooth.com/de/specifications/specs/core-specification-5-2/}, urldate = {2021-12-15}, } @Misc{BluetoothSIG_2019a, author = {Bluetooth {SIG}, Inc}, title = {{I}nfografik - {B}luetooth {L}ocation {S}ervices}, year = {2019}, access = {2021-12-15}, file = {:files/1907_Location_Services_Infographic_FINAL-compressed-1.pdf:PDF}, groups = {Verwendet}, qualityassured = {qualityAssured}, url = {https://www.bluetooth.com/wp-content/uploads/2019/09/1907_Location_Services_Infographic_FINAL-compressed-1.pdf}, urldate = {2021-12-15}, } @Misc{BluetoothSIG_2014, author = {{Bluetooth SIG, Inc.}}, howpublished = {\url{https://www.bluetooth.com/de/specifications/specs/core-specification-4-2/}}, month = dec, title = {{A}rchitecture \& {T}erminology {O}verview}, year = {2014}, access = {2021-12-01}, file = {:files/Core_v4.2.pdf:PDF}, groups = {Verwendet}, keywords = {Bluetooth, Core Specification}, timestamp = {2021-12-09}, url = {https://www.bluetooth.com/de/specifications/specs/core-specification-4-2/}, urldate = {2021-12-09}, } @Misc{ATL_2021, author = {{Argenox Technologies LLC}}, month = dec, title = {{BLE} {A}dvertising {P}rimer}, year = {2021}, comment = {Bluetooth Low Energy shares some similarities with Classic Bluetooth. Both use the 2.4GHz spectrum. Basic Rate (BR) and BLE both use GFSK modulation at 1Mbps, but their modulation index is different. Enhanced Data Rate (EDR) uses a completely different modulation than GFSK. Classic Bluetooth has 79 channels compared to LE’s 40 channels. The channels are also spaced differently. Both of these differences make LE and Classic different and incompatible, so they can’t communicate. Dual Mode Radios, like the Texas Instrument's CC256x or Corvo, support LE and Classic by switching their modulation parameters and the channels on which they’re running.}, file = {:files/ble-advertising-primer.html:URL}, groups = {Verwendet}, keywords = {Bluetooth, Advertising}, qualityassured = {qualityAssured}, url = {https://www.argenox.com/library/bluetooth-low-energy/ble-advertising-primer/}, urldate = {2021-12-16}, } @InProceedings{Kajita_2016, author = {Kajita, Shugo and Amano, Tatsuya and Yamaguchi, Hirozumi and Higashino, Teruo and Takai, Mineo}, booktitle = {Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services}, title = {{W}i-{F}i {C}hannel {S}election {B}ased on {U}rban {I}nterference {M}easurement}, year = {2016}, address = {New York, NY, USA}, pages = {143--150}, publisher = {Association for Computing Machinery}, series = {MOBIQUITOUS 2016}, abstract = {Increasing availability and usability enhancement of Wi-Fi in public areas has become more active. However, due to the dense deployment of Wi-Fi access points (APs), there is a chaotic and disorderly environment in urban areas. In our previous work, we have designed a function that predicts the network performance at each Wi-Fi AP according to the measurement of IEEE802.11 MAC frames sensed in each Wi-Fi channel. However, it was not examined in such scenarios assuming urban environment. We should understand the situations of current Wi-Fi AP deployment and traffic conditions, and should confirm the effectiveness of channel migration in such realistic environment. In this study, we proposed urban Wi-Fi channel utilization model based on real urban Wi-Fi measurement. We show that our method can predict the best channels and APs can migrate to them in the urban scenario.}, doi = {10.1145/2994374.2994402}, file = {:files/kajita2016.pdf:PDF}, groups = {Verwendet}, isbn = {9781450347501}, keywords = {Channel Selection, 2.4GHz Wi-Fi, Machine Learning, Interference}, location = {Hiroshima, Japan}, numpages = {8}, qualityassured = {qualityAssured}, url = {https://doi.org/10.1145/2994374.2994402}, } @Book{Seybold_2005_BOOK, author = {Seybold}, publisher = {John Wiley & Sons}, title = {{I}ntroduction to {RF} {P}ropagation}, year = {2005}, isbn = {0471655961}, month = sep, comment = {https://www.ebook.de/de/product/4435643/seybold_intro_rf_propagation.html}, ean = {9780471655961}, groups = {Verwendet}, pagetotal = {348}, priority = {prio1}, qualityassured = {qualityAssured}, } @Misc{RadiusNetworks_2021, author = {{Radius Networks}}, title = {{C}alculating {F}ormula {C}onstants}, year = {2021}, file = {:distance-calculations2.html:URL}, groups = {Verwendet}, qualityassured = {qualityAssured}, url = {https://altbeacon.github.io/android-beacon-library/distance-calculations2.html}, urldate = {2021-12-21}, } @Article{Paterna_2017, author = {Vicente Cant{\'{o}}n Paterna and Anna Calveras Aug{\'{e}} and Josep Paradells Aspas and Mar{\'{i}}a P{\'{e}}rez Bullones}, journal = {Sensors}, title = {{A} {B}luetooth {L}ow {E}nergy {I}ndoor {P}ositioning {S}ystem with {C}hannel {D}iversity, {W}eighted {T}rilateration and {K}alman {F}iltering}, year = {2017}, month = {dec}, number = {12}, pages = {2927}, volume = {17}, doi = {10.3390/s17122927}, file = {:files/sensors-17-02927-v2.pdf:PDF}, groups = {Verwendet}, keywords = {Filter, Kalman-Filter, Bluetooth, Location, Positioning, BLE}, priority = {prio2}, publisher = {{MDPI} {AG}}, qualityassured = {qualityAssured}, readstatus = {skimmed}, } @Misc{Ltd_2017, author = {{Pur3 Ltd}}, title = {Espruino {Software} {Reference}}, year = {2017}, file = {Espruino Hardware Reference:files/598/Reference.html:text/html}, groups = {Verwendet}, priority = {prio1}, qualityassured = {qualityAssured}, url = {http://www.espruino.com/Reference#software}, urldate = {2022-01-04}, }  @PhdThesis{Lategahn_2018, author = {Lategahn, Julian}, school = {FernUniversität Hagen}, title = {{K}artenbasierte {L}okalisierung von {P}ersonen innerhalb von {G}eb{\"a}uden}, year = {2018}, type = {phdthesis}, abstract = {In der vorliegenden Arbeit wird ein neuartiges Lokalisierungssystem entwickelt, das mit Hilfe von Karteninformationen Personen das Auffinden von R{\"a}umen innerhalb eines Geb{\"a}udes erleichtert. Ein solches System kann zum Beispiel in Hochschulen, Einkaufszentren, auf Messegel{\"a}nden oder auch in Krankenh{\"a}usern zum Einsatz kommen. Prinzipiell ist der Aufbau einer solchen Anwendung bislang mit einem hohen Aufwand in der Implementierung, Installation undWartung verbunden, da keine Standardl{\"o}sungen, wie die Nutzung eines GNSS im Au{\ss}enbereich, verf{\"u}gbar sind.}, comment = {https://ub-deposit.fernuni-hagen.de/receive/mir_mods_00001418}, doi = {10.18445/20180824-220948-0}, file = {:Diss_Lategahn_Kartenbasierte_Lokalisierung_2018.pdf:PDF}, groups = {Algorithmen, Thesis von anderen}, keywords = {Ortsbestimmung; Positionierung; Geb{\"a}udekarten; Topologische Karten; Kalman-Filter; Smartphone; Bluetooth Low Energy; Pedestrian Dead Reckoning; Sequenzielle Monte-Carlo-Methode}, priority = {prio1}, publisher = {FernUniversit{\"a}t Hagen}, qualityassured = {qualityAssured}, } @Misc{Raytac_2021, author = {{Raytac Corporation}}, month = jul, title = {{A}pproval {S}heet}, year = {2021}, comment = {Chip der auf dem Beacon verbaut ist. Zitat: Antennen abstrahlcharakteristik!}, file = {:[nRF52832] MDBT42Q-512KV2 & MDBT42Q-P512KV2_Ver.M spec.pdf:PDF}, groups = {Verwendet}, keywords = {MDBT42Q-512KV2}, priority = {prio1}, qualityassured = {qualityAssured}, readstatus = {skimmed}, url = {https://www.raytac.com/download/index.php?index_id=27}, urldate = {2022-01-20}, } @Book{Traenkler_2014_BOOK, editor = {Hans-Rolf Tränkler and Leo Reindl}, publisher = {Springer Berlin Heidelberg}, title = {Sensortechnik}, year = {2014}, isbn = {978-3-642-29941-4}, month = feb, series = {VDI-Buch}, doi = {10.1007/978-3-642-29942-1}, ean = {9783642299414}, file = {:2014_Book_Sensortechnik.pdf:PDF}, groups = {Verwendet}, qualityassured = {qualityAssured}, subtitle = {Handbuch für Praxis und Wissenschaft}, } @Misc{beacon_library_2021, title = {{A}ndroid {B}eacon {L}ibrary}, year = {2021}, comment = {Calculating Formula Constants: https://altbeacon.github.io/android-beacon-library/distance-calculations2.html https://docs.google.com/spreadsheets/d/1ymREowDj40tYuA5CXd4IfC4WYPXxlx5hq1x8tQcWWCI/edit#gid=0}, groups = {Verwendet}, url = {https://github.com/AltBeacon/android-beacon-library}, urldate = {2021-12-21}, } @Misc{jcgm_2008, title = {{E}valuation of measurementdata — {S}upplement 1 to the \“{G}uide to the expression ofuncertainty in measurement\” —{P}ropagation of distributionsusing a {M}onte {C}arlo method}, year = {2008}, comment = {https://www.bipm.org/en/committees/jc/jcgm/publications}, file = {:JCGM_101_2008_E.pdf:PDF}, groups = {Verwendet, Normen}, key = {JCGM 101:2008}, number = {JCGM 101}, qualityassured = {qualityAssured}, ranking = {rank5}, type = {Norm}, url = {https://www.bipm.org/en/committees/jc/jcgm/publications}, } @Misc{jcgm_2012, note = {Erstveröffentlichung 2008}, title = {{I}nternational vocabulary ofmetrology – {B}asic and generalconcepts and associated terms ({VIM})}, year = {2012}, comment = {https://www.bipm.org/en/committees/jc/jcgm/publications}, file = {:JCGM_200_2012.pdf:PDF}, groups = {Verwendet, Normen}, key = {JCGM 200:2012}, number = {JCGM 200:2012}, priority = {prio1}, qualityassured = {qualityAssured}, ranking = {rank5}, type = {Norm}, url = {https://www.bipm.org/en/committees/jc/jcgm/publications}, volume = {3}, } @Book{Heilmann_2020_BOOK, author = {Rolf Heilmann}, publisher = {Springer Fachmedien Wiesbaden}, title = {{R}auschen in der {S}ensorik}, year = {2020}, isbn = {978-3-658-29213-3}, month = mar, comment = {https://www.ebook.de/de/product/38468474/rolf_heilmann_rauschen_in_der_sensorik.html}, doi = {10.1007/978-3-658-29214-0}, ean = {9783658292133}, file = {:Heilmann2020_Book_RauschenInDerSensorik.epub:ePUB;:Heilmann2020_Book_RauschenInDerSensorik.pdf:PDF}, groups = {Verwendet}, qualityassured = {qualityAssured}, } @Misc{DIN_13312_2005, title = {{N}avigation –{B}egriffe, {A}bkürzungen, {F}ormelzeichen, graphische {S}ymbole}, year = {2005}, comment = {https://www.bipm.org/en/committees/jc/jcgm/publications}, file = {:files/DIN 13312_2005 (9601621).PDF:PDF}, groups = {Normen}, key = {DIN 13312:2005-02}, number = {DIN 13312}, priority = {prio1}, qualityassured = {qualityAssured}, ranking = {rank3}, type = {Norm}, } @Article{Cheveigne_2019, author = {Alain de Cheveign{\'{e}} and Israel Nelken}, journal = {Neuron}, title = {{F}ilters: {W}hen, {W}hy, and {H}ow ({N}ot) to {U}se {T}hem}, year = {2019}, month = {apr}, number = {2}, pages = {280--293}, volume = {102}, doi = {10.1016/j.neuron.2019.02.039}, file = {:files/Filter-when-why-and-how-to-use.pdf:PDF}, groups = {Normen}, publisher = {Elsevier {BV}}, qualityassured = {qualityAssured}, } @InProceedings{MI191_2021, author = {Reinhard Remfort, Nicolas Wöhrl}, booktitle = {Methodisch Inkorrekt}, title = {{M}191 - {F}remdkork}, year = {2021}, month = may, number = {191}, series = {Audio-Podcast}, collection-number = {191}, collection-title = {Methodisch Inkorrekt}, date = {18.05.2021}, entrysubtype = {podcastepisode}, groups = {Verwendet}, journal = {Methodisch Inkorrekt}, qualityassured = {qualityAssured}, url = {https://minkorrekt.de/mi191-fremdkork/}, urldate = {2021-08-05}, } @Comment{jabref-meta: databaseType:bibtex;} @Comment{jabref-meta: fileDirectoryLatex-sebastian-anarchy:/home/sebastian/Dokumente/Privat/Studium/WBH/Thesis;} @Comment{jabref-meta: grouping: 0 AllEntriesGroup:; 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