@inproceedings{Din2024a,

title = {Feasibility Analysis of Self-oriented Antennas for Indoor Positioning based on Direction-of-Arrival and Bluetooth-Low-Energy},

author = {Teodor Constatin Din and Joaquín Huerta-Guijarro and Sergio Trilles-Oliver and Joaquín Torres-Sospedra},

doi = {https://doi.org/10.1109/IPIN62893.2024.10786160},

isbn = {979-8-3503-6641-9},

year  = {2024},

date = {2024-10-17},

booktitle = {2024 IEEE 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN)},

pages = {1-6},

publisher = {IEEE},

abstract = {Indoor positioning systems have typically relied on static anchors, such as beacons or WiFi routers, and static environmental conditions, such as magnetic fields. On the other way around, it is also common to have fixed sensing devices, such as cameras, monitoring the environment, or passive receivers collecting relevant measurements from devices being tracked (signal strength, time/direction of arrival, among others). In one form or in another, we can usually find a static device. Furthermore, the evaluation of Radio Frequency-based positioning systems has commonly relied on measurements from static evaluation locations, being a continuous evaluation less frequent in solutions relying on, for instance, fingerprinting or based on signal strength. However, there is a need for a paradigm shift in indoor positioning systems as dynamic conditions are being slowly introduced. This paper introduces an exploratory analysis of a novel approach to be integrated into existing indoor positioning solutions based on Bluetooth Low Energy-based Direction-of-Arrival solutions. The core idea is to allow the infrastructure sensing the environment to adjust the orientation of the antennas, enhance the coverage, and provide better positioning of the devices being tracked. i.e., this approach will enable the system to evolve over time, continuously adapting to current environmental conditions. We describe the low-cost infrastructure needed to enable self-orientation for a commercial Bluetooth Low Energy board providing Direction-of-Arrival measurements.},

keywords = {Antennas, Indoor localization, Indoor positioning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pascacio2022b,

title = {A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning},

author = {Pavel Pascacio-de-los-Santos and Joaquín Torres-Sospedra and Sven Casteleyn and Elena Simona Lohan},

doi = {https://doi.org/10.1109/IJCNN55064.2022.9892484},

isbn = {978-1-7281-8671-9},

year  = {2022},

date = {2022-09-30},

booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},

pages = {1-9},

publisher = {IEEE},

abstract = {In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.},

keywords = {Bluetooth Low Energy, Indoor positioning, machine learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Quezada2022d,

title = {SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning},

author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro},

doi = {10.1109/IPIN54987.2022.9918146},

isbn = {978-1-7281-6218-8},

year  = {2022},

date = {2022-09-06},

booktitle = {2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)},

number = {1-8},

publisher = {IEEE},

abstract = {Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accuracy. The proposed combination of supervised and unsupervised models was tested in 17 public datasets, providing an extensive analysis of its performance. As a result, the positioning error has been reduced in more than 70% of them.},

keywords = {deep learning, Indoor positioning, machine learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Quezada2022b,

title = {Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification},

author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro},

doi = {https://doi.org/10.1109/ICL-GNSS54081.2022.9797021},

year  = {2022},

date = {2022-06-19},

booktitle = {2022 International Conference on Localization and GNSS (ICL-GNSS)},

pages = {1-6},

publisher = {IEEE},

abstract = {Machine learning models have become an essential tool in current indoor positioning solutions, given their high capa-bilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1 %).},

keywords = {Indoor positioning, machine learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Quezada2022c,

title = {Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets},

author = {Darwin Quezada-Gaibor and Lucie Klus and Joaquín Torres-Sospedra and Elena Simona Lohan and Jari Nurmi and Carlos Granell-Canut and Joaquín Huerta-Guijarro},

doi = {https://doi.org/10.1109/MDM55031.2022.00079},

isbn = {978-1-6654-5176-5},

year  = {2022},

date = {2022-06-10},

booktitle = {2022 23rd IEEE International Conference on Mobile Data Management (MDM)},

pages = {349-354},

publisher = {IEEE},

abstract = {Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this paper, we offer a novel and straightforward data cleansing algorithm for WLAN fingerprinting radio maps. This algorithm is based on the correlation among fingerprints using the Received Signal Strength (RSS) values and the Access Points (APs)'s identifier. We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset. We evaluated the proposed method on 14 independent publicly-available datasets. As a result, an average of 14% of fingerprints were removed from the datasets. The 2D positioning error was reduced by 2.7% and 3D positioning error by 5.3% with a slight increase in the floor hit rate by 1.2% on average. Consequently, the average speed of position prediction was also increased by 14%.},

keywords = {Data science, Indoor positioning, Wi-Fi fingerprint},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jimenez2021a,

title = {Affinity Propagation Clustering for Older Adults Daily Routine Estimation},

author = {Ana Jiménez Martín and Ismael Miranda Gordo and Juan Jesús García Domínguez and Joaquín Torres-Sospedra and Sergio Lluva Plaza and David Gualda Gómez},

doi = {https://doi.org/10.1109/IPIN51156.2021.9662579},

year  = {2021},

date = {2021-12-15},

booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation},

publisher = {IEEE},

abstract = {This work proposes a system that allows estimating and monitoring daily routine changes in a sensorized home through Machine Learning and Affinity Propagation clustering techniques. Older adults often have low-activity and rather routine lives, which means that these routines can be an indicator of their physical and cognitive state in order to lead an independent life and healthy ageing. Therefore, it is important to be able to generate precise routines, as well as to monitor them, to trigger alarms in case of significant variations. This proposal defines routines based on the time spent in each of the monitored rooms. The daily time in each room is estimated trough a Bluetooth Low Energy-based indoor localization system. The localization is obtained through the Bluetooth received signal strength, which is processed with different supervised algorithms and fused with the acceleration measured by the mobile receiver, obtaining an accuracy above 96 %. From these data, the sample has been synthetically expanded to generate four different routines, on which the proposed algorithm based on Principal Component Analysis and Affinity Propagation clustering has been tested, obtaining very promising results.},

keywords = {Indoor positioning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pendao2021a,

title = {Dioptra – A Data Generation Application for Indoor Positioning Systems},

author = {Cristiano Pendão and Ivo Silva and Adriano Moreira and Joaquín Torres-Sospedra},

doi = {https://doi.org/10.1109/IPIN51156.2021.9662585},

year  = {2021},

date = {2021-12-15},

booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation},

publisher = {IEEE},

abstract = {Indoor Positioning Systems (IPSs) based on different approaches and technologies have been proposed to support localization and navigation applications in indoor environments. The fair benchmarking and comparison of these IPSs is a difficult task since each IPS is usually evaluated in very specific and controlled conditions and using private data sets, not allowing reproducibility and direct comparison between the reported results and other competing solutions. In addition, testing and evaluating an IPS in the real world is difficult and time-consuming, especially when considering evaluation in multiple environments and conditions. To enhance IPS assessment, we propose Dioptra, an open access and user-friendly application to support research, development and evaluation of IPSs through simulation. To the best of our knowledge, Dioptra is the first application specially developed to generate synthetic datasets to promote reproducibility and fair benchmarking between IPSs.},

keywords = {Indoor positioning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Aranda2021a,

title = {Finding Optimal BLE Configuration for Indoor Positioning with Consumption Restrictions},

author = {Fernando J. Aranda and Felipe Parralejo and Teodoro Aguilera and Fernando J. Álvarez and Joaquín Torres-Sospedra},

doi = {https://doi.org/10.1109/IPIN51156.2021.9662563},

year  = {2021},

date = {2021-12-15},

booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation},

publisher = {IEEE},

abstract = {Bluetooth Low Energy (BLE) fingerprinting has gained a lot of research effort in recent years due to flexibility in both beacons placement and configuration. Different works have addressed the effect of the configuration parameters, mainly the transmission power (Tx) and period (Ts), over positioning accuracy but not on the system lifespan and the trade-off between these two. In this work, different configurations of one, three and six slots have been tested over the same experimental setup. Positioning accuracy was obtained using different variations of the Weighted k-Nearest Neighbours (Wk-NN) algorithm, and the system lifespan was estimated using the actual current consumption and transmission mechanism for each configuration. Experimental results have shown that Tx and the number of slots can be adjusted to optimize this trade-off; meanwhile, changes in Ts worsen Wk-NN results more than in the other parameters, showing that the minimum Ts is always the best option.},

keywords = {Indoor positioning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Quezada2021a,

title = {Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm},

author = {Darwin Quezada-Gaibor and Joaquín Torres-Sospedra and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro},

doi = {https://doi.org/10.1109/IPIN51156.2021.9662612},

year  = {2021},

date = {2021-12-15},

booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation},

publisher = {IEEE},

abstract = {Nowadays, several indoor positioning solutions sup-port Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. In order to minimize the time response, many solutions have been proposed along the time. The most common solution is to divide the data set into clusters. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance similarity (clusters). Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting.},

keywords = {Indoor positioning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bellavista2021a,

title = {New trends in indoor positioning based on WiFi and machine learning: A systematic review},

author = {Vladimir Bellavista-Parent and Joaquín Torres-Sospedra and Antoni Perez-Navarro},

doi = {https://doi.org/10.1109/IPIN51156.2021.9662521},

year  = {2021},

date = {2021-12-15},

booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation},

publisher = {IEEE},

abstract = {Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented.},

keywords = {Indoor positioning, machine learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Torres-Sospedra2021c,

title = {Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets},

author = {Joaquín Torres-Sospedra and Ivo Silva and Lucie Klus and Darwin Quezada-Gaibor and Antonino Crivello and Paolo Barsocchi and Cristiano Pendão and Elena Simona Lohan and Jari Nurmi and Adriano Moreira},

doi = {https://doi.org/10.1109/IPIN51156.2021.9662560},

year  = {2021},

date = {2021-12-15},

booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation},

publisher = {IEEE},

abstract = {The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.},

keywords = {Indoor positioning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Klus2021a,

title = {Transfer Learning for Convolutional Indoor Positioning Systems},

author = {Roman Klus and Lucie Klus and Jukka Talvitie and Jaakko Pihlajasalo and Joaquín Torres-Sospedra and Mikko Valkama},

doi = {https://doi.org/10.1109/IPIN51156.2021.9662544},

year  = {2021},

date = {2021-12-15},

booktitle = {Proceedings of the Eleventh International Conference on Indoor Positioning and Indoor Navigation},

publisher = {IEEE},

abstract = {Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions.},

keywords = {Indoor positioning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pascacio2021c,

title = {A Lateration Method based on Effective Combinatorial Beacon Selection for Bluetooth Low Energy Indoor Positioning},

author = {Pavel Pascacio-de-los-Santos and Joaquín Torres-Sospedra and Sven Casteleyn},

doi = {10.1109/WiMob52687.2021.9606419},

isbn = {978-1-6654-2854-5},

year  = {2021},

date = {2021-10-15},

booktitle = {2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)},

pages = {397-402},

publisher = {IEEE},

abstract = {Nowadays, the Bluetooth Low Energy (BLE) technology joined with the Received Signal Strength Indicator technique has became a popular approach in Indoor Positioning System, thanks to the wide availability of BLE in anchors and wearable devices and the straightforward implementation of both. Consequently, methods based on geometric properties of anchors, as lateration, are capable of enhancing the positioning accuracy exploiting the growing availability of anchors and their rich geometric distribution in indoor environments. On the downside, an inappropriate selection of anchors decreases the positioning accuracy estimation. Therefore, integrating an effective beacon selection method can enhance the reliability and accuracy of these methods. In this paper, we present a novel and straightforward Lateration indoor positioning method based on effective combinatorial BLE beacon selection. The combinatorial BLE selection approach relies on a geometrical analysis (difference of triangle areas), of each beacon combination, considering the reference beacons’ position with the estimated position using lateration, and with a globally calculated virtual target position as reference. The real-world experiment demonstrated that the proposed method improves the traditional lateration with 5% to 16%, considering different evaluation metrics.},

keywords = {Indoor positioning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{CasanovaMarques2021a,

title = {Anonymous Attribute-based Credentials in Collaborative Indoor Positioning Systems},

author = {Raúl Casanova-Marqués and Pavel Pascacio-de-los-Santos and Jan Hajny and Joaquín Torres-Sospedra },

doi = {http://dx.doi.org/10.5220/0010582507910797},

isbn = {978-989-758-524-1},

year  = {2021},

date = {2021-09-01},

booktitle = {Proceedings of the 18th International Conference on Security and Cryptography (SECRYPT 2021)},

pages = {791-797},

publisher = {SciTePress},

abstract = {Collaborative Indoor Positioning Systems (CIPSs) have recently received considerable attention, mainly because they address some existing limitations of traditional Indoor Positioning Systems (IPSs). In CIPSs, Bluetooth Low Energy (BLE) can be used to exchange positioning data and provide information (the Received Signal Strength Indicator (RSSI)) to establish the relative distance between the actors. The collaborative models exploit the position of actors and the relative position among them to allow positioning to external actors or improve the accuracy of the existing actors. However, the traditional protocols (e.g., iBeacon) are not yet ready for providing sufficient privacy protection. This paper deals with privacy-enhancing technologies and their application in CIPS. In particular, we focus on cryptographic schemes which allow the verification of users without their identification, so-called Anonymous Attribute-based Credential (ABC) schemes. As the main contribution, we presen t a cryptographic scheme that allows security and privacy-friendly sharing of location information sent through BLE advertising packets. In order to demonstrate the practicality of our scheme, we also present the results from our implementation and benchmarks on different devices.},

keywords = {geoprivacy, Indoor positioning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Torres-Sospedra2021b,

title = {Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning},

author = {Joaquín Torres-Sospedra and Fernando J. Aranda and Fernando J. Álvarez and Darwin Quezada-Gaibor and Ivo Silva and Cristiano Pendão and Adriano Moreira},

doi = {http://dx.doi.org/10.1109/VTC2021-Spring51267.2021.9448947},

isbn = {978-1-7281-8965-9},

year  = {2021},

date = {2021-06-15},

booktitle = {Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)},

pages = {1-5},

publisher = {IEEE},

abstract = {Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.},

keywords = {Indoor positioning, Wi-Fi fingerprint, Wi-Fi mapping},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Mendoza-Silva2021a,

title = {Local-level Analysis of Positioning Errors in Wi-Fi Fingerprinting},

author = {Germán Martin Mendoza-Silva and Joaquín Torres-Sospedra and Joaquín Huerta-Guijarro},

doi = {https://doi.org/10.1109/VTC2021-Spring51267.2021.9448936},

isbn = {978-1-7281-8964-2},

year  = {2021},

date = {2021-06-15},

booktitle = {Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)},

pages = {1-5},

publisher = {IEEE},

abstract = {Nowadays, Location Based Services run over a net of heterogeneous devices (mainly smartphones) with different location capabilities thanks to, for instance, signals of opportunity as Wi-Fi. In contrast to professional deployments in controlled scenarios, the positioning error highly depends not only on the environment but also on the location. Traditional metrics for evaluating indoor positioning system may fail in obtaining lower-level details on the reported results. This paper introduces a way to perform a local-level analysis of the positioning errors. Our approach is based on analyses of the position-wise variance of positioning errors.},

keywords = {Indoor positioning, Wi-Fi fingerprint},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pascacio2021b,

title = {Smartphone Distance Estimation Based on RSSI-Fuzzy Classification Approach},

author = {Pavel Pascacio-de-los-Santos and Sven Casteleyn and Joaquín Torres-Sospedra},

doi = {https://doi.org/10.1109/ICL-GNSS51451.2021.9452226},

isbn = {978-1-7281-9645-9},

year  = {2021},

date = {2021-06-01},

booktitle = {Proceedings of the 2021 International Conference on Localization and GNSS (ICL-GNSS)},

pages = {1-6},

publisher = {IEEE},

abstract = {Positioning people indoors has known an exponential growth in the last few years, especially thanks to Bluetooth Low Energy (BLE) technology and the Received Signal Strength Indicator (RSSI) technique. This approach is available in wearable devices, is easy to implement and has energy consumption advantages. However, the relative distance calculation is inaccurate, as the strength of BLE signals significantly fluctuates in indoor environments. Typical coping mechanisms, such as path-loss propagation models, require mathematical modeling and time-consuming calibration, that depend on the environment. In this paper, we propose a novel distance estimator based on RSSI-fuzzy classification of the BLE signals. Fuzzy-logic improves the robustness and accuracy of RSSI-based estimators, does not require an explicit propagation model and is easy and intuitive to (graphically) tune (using basic statistical analysis). The estimator's suitability and the feasibility to provide an easy implementation were experimentally demonstrated in two scenarios with real-world data.},

keywords = {Indoor positioning, machine learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Quezada-Gaibor2020,

title = {Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices},

author = {Darwin Quezada-Gaibor and Lucie Klus and Joaquín Torres-Sospedra and Simona Elena Lohan and Jari Nurmi and Joaquín Huerta-Guijarro},

doi = {http://www.doi.org/10.1109/ICUMT51630.2020.9222411},

isbn = {978-1-7281-9281-9},

year  = {2020},

date = {2020-09-17},

booktitle = {Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 5-8 October 2020. Online event},

pages = {208-213},

keywords = {A-wear, Indoor positioning, Internet of things, wearables, Wi-Fi mapping},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Torres-Sospedra2020,

title = {New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting},

author = {Joaquín Torres-Sospedra and Darwin Quezada-Gaibor and Germán Martín Mendoza-Silva and Jari Nurmi and Yevgeni Koucheryavy and Joaquín Huerta-Guijarro},

doi = {http://www.doi.org/10.1109/ICL-GNSS49876.2020.9115419 },

isbn = {978-1-7281-6455-7},

year  = {2020},

date = {2020-07-02},

booktitle = {2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2020},

pages = {1-6},

publisher = {IEEE},

abstract = {Wi-Fi fingerprinting is a popular technique for Indoor Positioning Systems (IPSs) thanks to its low complexity and the ubiquity of WLAN infrastructures. However, this technique

may present scalability issues when the reference dataset (radio map) is very large. To reduce the computational costs, k-Means Clustering has been successfully applied in the past. However, it is a general-purpose algorithm for unsupervised classification. This paper introduces three variants that apply heuristics based on radio propagation knowledge in the coarse and fine-grained searches. Due to the heterogeneity either in the IPS side (including radio map generation) and in the network infrastructure, we used an evaluation framework composed of 16 datasets. In terms of general positioning accuracy and computational costs, the best proposed k-means variant provided better general positioning

accuracy and a significantly better computational cost –around 40% lower– than the original k-means.},

keywords = {A-wear, Indoor positioning, Wi-Fi fingerprint},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Karmacharya2020b,

title = {Sensor Fusion and Well Condition Triangle Approach for BLE-based Indoor Positioning},

author = {Amrit Karmacharya and German Martín Mendoza-Silva and Joaquín Torres-Sospedra },

editor = {A. Ometov and Jaarmi Nurmi and Elena Simona Lohan and Joaquín Torres-Sospedra and H. Kuusniemi (Eds)},

url = {http://ceur-ws.org/Vol-2626/paper6.pdf },

issn = {1613-0073 },

year  = {2020},

date = {2020-06-15},

booktitle = {Proceedings of the International Conference on Localization and GNSS (ICL GNSS 2020) CEUR Workshop Proceedings. Tampere, Finland, June 2-4 2020},

volume = {2626},

publisher = {CEUR},

abstract = {GPS has been a de-facto standard for outdoor positioning. For indoor positioning different systems exist. But there is no general solution to fit all situations. A popular choice among service provider is Bluetooth Low Energy (BLE) based Indoor Positioning System (IPS) . BLE has low cost, low power consumption, and it is compatible with newer smartphones. This paper introduces two ways for accuracy improvement i) a new algorithm for BLE-based IPS based on well-condition triangle and ii) fusion of BLE position estimates with IMU position estimates was implemented. Fusion generally gives better results but a noteworthy result from fusion was that the position estimates during turns were accurate. When used separately, both BLE and IMU estimates showed errors in turns. Fusion with IMU improved the accuracy of BLE based positioning.},

keywords = {Indoor positioning, Mastergeotech},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Torres-Sospedra2019b,

title = {Exploiting Different Combinations of Complementary Sensor's data for Fingerprint-based Indoor Positioning in Industrial Environments},

author = {Joaquín Torres-Sospedra and A. Moreira and Germán Martín Mendoza-Silva and M. J. Nicolau and Miguel Matey-Sanz and I. Silva and Joaquín Huerta-Guijarro and C. Pendão },

doi = {10.1109/IPIN.2019.8911758 },

isbn = {978-1-7281-1788-1 },

year  = {2019},

date = {2019-12-01},

booktitle = {Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy. },

publisher = {IEEE},

abstract = {Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliability of position estimates is a key requirement. One successful approach to deal with signal fluctuations is to average the signals collected simultaneously through independent Wi-Fi interfaces. Another successful approach is to average the estimates provided by models built on independent radio maps. This paper explores multiple combinations of both approaches and determines the procedure to select the best model based on them through a simulated environment. The evaluation of the proposed model in a real-world industrial scenario shows that the positioning error (according to different metrics including the 95th and 99th percentiles) is highly improved with respect to the traditional fingerprint.},

keywords = {Indoor positioning, Sensors, Wi-Fi fingerprint},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Li2019,

title = {Survey on Indoor Map Standards and Formats},

author = {K. L. Li, and S. Zlatanova and Joaquín Torres-Sospedra and Antoni Pérez-Navarro and C. Laoudias and A. Moreira},

isbn = {978-1-7281-1788-1 },

year  = {2019},

date = {2019-12-01},

booktitle = {Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy},

publisher = {IEEE},

abstract = {With the adoption of indoor positioning solutions, which enable for a variety of location-based spatial services, a number of indoor map standards and formats have been proposed in the last decade. As each of these indoor map standard has its own purpose, the strengths and weaknesses are necessary to be understood and analyzed before selecting one of them for a given application. The Indoor Map Subcommittee has been established under IPIN/ISC in 2017. Among others, the goal of this working group is to compare available indoor map standards, provide a guideline for their application and advise on changes to their standardization development organizations if necessary. In this paper we present a survey of indoor map standards as an achievement of the subcommittee. The scope of the survey covers official standards such as IFC of BuildingSmart, IndoorGML and CityGML of OGC, and Indoor OpenStreetMap. We present several use-cases to show and discuss how to build indoor maps.},

keywords = {Indoor positioning, OGC, Standards},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jiménez2019,

title = {Tools for smartphone multi-sensor data registration and GT mapping for positioning applications},

author = {Antonio R. Jiménez and Fernando Seco and Joaquín Torres-Sospedra },

isbn = {978-1-7281-1788-1 },

year  = {2019},

date = {2019-12-01},

booktitle = {Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy},

publisher = {IEEE},

abstract = {Nowadays smartphones have impressive sensing and computation capabilities, allowing the registration and processing of multiple sources of information. This power enables the creation of useful applications, such as seamless location both outdoors and indoors. Research teams pay less interest in standardizing the acquisition and processing of sensor data than to research and innovation tasks, so each group develops its own private software tools to collect data. We want to contribute by creating a framework that allows a more coherent datastream registration and algorithm performance comparison. In this paper we present an open-source framework to make possible multi-sensor registration, which includes GetSensorData, our logging Android application. In order to ease the creation and sharing of experiments among different researchers around the world, the framework also includes the data format definition, the data parsers and the procedures to calibrate maps and to define

the ground-truth trajectory for subsequent position algorithm performance comparison. Finally, we review applications of these tools in the IPIN competition as well as in teaching activities},

keywords = {Indoor positioning, mobile GIS, Sensors},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Gómez-Cambronero2019,

title = {Mixed reality escape room to promote learning in indoor environments},

author = {Águeda Gómez-Cambronero and Alberto González-Pérez and Ignacio Miralles-Tena and Sven Casteleyn},

doi = {10.21125/edulearn.2019.1412 },

isbn = {978-84-09-12031-4  },

year  = {2019},

date = {2019-09-01},

booktitle = {EDULEARN19 Proceedings of 10th International Conference on Education and New Learning Technologies},

pages = {5857-5864},

publisher = {IATED},

address = {Palma de Mallorca},

keywords = {geogames, Indoor positioning, RyC-Casteleyn, videogame},

pubstate = {published},

tppubtype = {inproceedings}

}