Indoor Localization Using FM Signals

Yin, Chen; Lymberopoulos, Dimitrios; Liu, Jie; Priyantha, Bodhi

doi:10.1109/tmc.2013.58

Cited by 79 publications

(9 citation statements)

References 19 publications

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“…With IoT applications in place, users of the current campus will be enabled to use the campus more effectively, and campus managers will be better positioned to assess the effects of these demands on space usage and adapt their campus strategies accordingly. Location-based user applications Castro et al (2001), Chapre et al (2013), Chen et al (2013), D'Souza et al (2013, Dave et al (2018), Deak et al (2010), Furey et al (2012), Jiang et al (2012), Kosba et al (2012), Lim et al (2008), Liu et al (2008) Figure A1. Activity diagram TU Eindhoven.…”

Section: Resultsmentioning

confidence: 99%

Towards a smart campus: supporting campus decisions with Internet of Things applications

Valks

Arkesteijn

Koutamanis

et al. 2020

Building Research & Information

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At universities worldwide, creating a 'smart campus' is gaining significance. This is a response to the increasingly dynamic use of the campus and the pressure on resources: energy, financial and human resources. The university community has become more mobile, student numbers more unpredictable and funding more uncertain. Consequently, campus strategies focus on resource efficiency and sharing space, requiring investment in management information to improve decision making. The Internet of Things (IoT) can help to provide big data about use patterns: by collecting real-time data on space utilization, users can make better use of current spaces and real estate managers can make better decisions about long-term demand. Furthermore, space utilisation data can be complemented with user feedback data and environmental variables, e.g. noise levels, luminance. In this research we identify the capabilities of existing IoT applications through a literature study. Literature also suggests that information from IoT applications is not utilized in organisational decision-making processes. Through four case studies we analyse these decision-making processes and identify the process-level requirements to make strategic decisions in campus management. Then we show how information from the IoT can be directly connected to these processes, thus providing a valuable addition of real-time data as input.

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Section: Resultsmentioning

confidence: 99%

Towards a smart campus: supporting campus decisions with Internet of Things applications

Valks

Arkesteijn

Koutamanis

et al. 2020

Building Research & Information

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“…However, quantization only partially reduces the complexity of fingerprinting as the number of APs and reference fingerprints are not altered. Other relevant works that have focused on reducing the radio map include a focus on removing useless APs from the radio map [53], applying unsupervised learning to cluster/split the radio map [33,38,54,55], using radio-signal propagation knowledge to filter fingerprints out the radio-map [34,[56][57][58], combining clustering with RSSI-based rules [59], and, even, reducing on-the-fly the reference samples and APs of the radio map [60]. In this study, we focused on the nominal case where no other optimizations have been performed.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Received Signal Strength Quantization in Fingerprinting Localization

Khandker

Torres-Sospedra

Ristaniemi

2020

Sensors

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In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic.

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“…Sin embargo, hay zonas que no cuentan con este tipo de señales, además, debido a su longitud de onda, las señales emitidas por los transmisores son atenuadas fácilmente ante obstáculos como las paredes (Ali-Rantala et al, 2003) y otros tipos de objetos de diferentes materiales comúnmente encontrados en interiores. Para resolver esta dificultad algunos autores han propuesto utilizar señales de una menor frecuencia como GSM (Anastasijevi and Neškovi, 2012;Machaj and Brida, 2014;Tian et al, 2014;Varshavsky et al, 2007) y FM (Popleteev et al, 2012;Interaction, 2010;Moghtadaiee et al, 2011;Chen et al, 2013;Moghtadaiee and Dempster, 2014;Matic et al, 2010). Los trabajos mencionados anteriormente que son basados en FM requieren de la instalación de algún tipo de infraestructura como antenas para transmitir sus propias señales FM y facilitar la estimación de la posición.…”

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“…La técnica propuesta, utiliza fingerprinting y algoritmos de aprendizaje profundo (Deep Learning) para estimar la posición de la estación base. El desempeño de la técnica se evalúa respecto a técnicas previamente utilizadas en la literatura (Moghtadaiee et al, 2011;Chen et al, 2013) que estiman la posición con algoritmos KNN regresivo (k-vecinos mas cercanos) y fingerprinting. A diferencia de otros trabajos donde los experimentos se realizan en lugares vacíos, nuestros experimentos se realizaron en un espacio residencial amoblado y habitado.…”

Section: Introductionunclassified

Sistema de posicionamiento en interiores utilizando señales de radio estaciones FM comerciales y Deep Learning

Roman

Márquez-Viloria

Velásquez

et al. 2020

Rev. iberoam. autom. inform. ind.

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<p>Para dar solución al problema de posicionamiento en interiores, muchos autores han propuesto el uso de diversas técnicas. Desde la simulación de un Sistema de Posicionamiento Global (GPS) a través de antenas Pseudolites, la construcción de campos magnéticos artificiales, el uso de diversos sensores como visión, ultrasonido, unidades de medida inercial, entre otros. Hasta el uso de transmisores y receptores en el rango de la Radio Frecuencia como los presentes en una oficina. Muchos de los sistemas anteriormente mencionados dependen para su correcto funcionamiento de una instalación y configuración de equipos en el lugar de posicionamiento, lo cual puede llevar en ocasiones a costosas implementaciones. Es por esto que para realizar este trabajo hemos propuesto una metodología de posicionamiento en interiores que no requiere de adecuaciones e instalaciones en el lugar de aplicación. La metodología propuesta hace uso de las radio estaciones FM existentes y utiliza algoritmos de Deep Learning como algoritmo de posicionamiento.</p>

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Indoor Localization Using FM Signals

Cited by 79 publications

References 19 publications

Towards a smart campus: supporting campus decisions with Internet of Things applications

Towards a smart campus: supporting campus decisions with Internet of Things applications

Analysis of Received Signal Strength Quantization in Fingerprinting Localization

Sistema de posicionamiento en interiores utilizando señales de radio estaciones FM comerciales y Deep Learning

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