The Experts below are selected from a list of 7704 Experts worldwide ranked by ideXlab platform
Joaquin Torressospedra - One of the best experts on this subject based on the ideXlab platform.
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off line evaluation of Indoor Positioning Systems in different scenarios the experiences from ipin 2020 competition
IEEE Sensors Journal, 2021Co-Authors: Francesco Potorti, Valérie Renaudin, Miguel Ortiz, Joaquin Torressospedra, Antonio Jimenez, Fernando Seco, Antoni Pereznavarro, Darwin Quezadagaibor, Ni Zhu, Ryosuke IchikariAbstract:Every year, for ten years now, the IPIN competition has aimed at evaluating real-world Indoor localisation Systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of Systems, technologies, and methods for Indoor Positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for Indoor Positioning. The results in terms of participation and accuracy of the proposed Systems have been encouraging. The best performing competitors obtained a third quartile of error of 1m for the Smartphone Track and 0.5m for the Footmounted IMU Track. While not running on physical Systems, but only as algorithms, these results represent impressive achievements.
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a meta review of Indoor Positioning Systems
Sensors, 2019Co-Authors: German M Mendozasilva, Joaquin Torressospedra, Joaquin HuertaAbstract:An accurate and reliable Indoor Positioning System (IPS) applicable to most Indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of Indoor Positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys.
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a radiosity based method to avoid calibration for Indoor Positioning Systems
Expert Systems With Applications, 2018Co-Authors: Oscar Belmontefernandez, Joaquin Torressospedra, Raul Montoliu, Emilio Sansanosansano, Daniel ChiaaguilarAbstract:Abstract Due to the widespread use of mobile devices, services based on the users current Indoor location are growing in significance. Such services are developed in the Machine Learning and Experst Systems realm, and ranges from guidance for blind people to mobile tourism and Indoor shopping. One of the most used techniques for Indoor Positioning is WiFi fingerprinting, being its use of widespread WiFi signals one of the main reasons for its popularity, mostly on high populated urban areas. Most issues of this approach rely on the data acquisition phase; to manually sample WiFi RSSI signals in order to create a WiFi radio map is a high time consuming task, also subject to re-calibrations, because any change in the environment might affect the signal propagation, and therefore degrade the performance of the Positioning system. The work presented in this paper aims at substituting the manual data acquisition phase by directly calculating the WiFi radio map by means of a radiosity signal propagation model. The time needed to acquire the WiFi radio map by means of the radiosity model dramatically reduces from hours to minutes when compared with manual acquisition. The proposed method is able to produce competitive results, in terms of accuracy, when compared with manual sampling, which can help domain experts develop services based on location faster.
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off line evaluation of mobile centric Indoor Positioning Systems the experiences from the 2017 ipin competition
Sensors, 2018Co-Authors: Joaquin Torressospedra, German M Mendozasilva, Antonio Jimenez, Adriano Moreira, Tomas Lungenstrass, Stefan Knauth, Fernando Seco, Antoni Pereznavarro, Maria Joao Nicolau, Antonio CostaAbstract:The development of Indoor Positioning solutions using smartphones is a growing activity with an enormous potential for everyday life and professional applications. The research activities on this topic concentrate on the development of new Positioning solutions that are tested in specific environments under their own evaluation metrics. To explore the real Positioning quality of smartphone-based solutions and their capabilities for seamlessly adapting to different scenarios, it is needed to find fair evaluation frameworks. The design of competitions using extensive pre-recorded datasets is a valid way to generate open data for comparing the different solutions created by research teams. In this paper, we discuss the details of the 2017 IPIN Indoor localization competition, the different datasets created, the teams participating in the event, and the results they obtained. We compare these results with other competition-based approaches (Microsoft and Perf-loc) and on-line evaluation web sites. The lessons learned by organising these competitions and the benefits for the community are addressed along the paper. Our analysis paves the way for future developments on the standardization of evaluations and for creating a widely-adopted benchmark strategy for researchers and companies in the field.
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Indoorloc platform a public repository for comparing and evaluating Indoor Positioning Systems
International Conference on Indoor Positioning and Indoor Navigation, 2017Co-Authors: Raul Montoliu, Joaquin Torressospedra, Emilio Sansano, Oscar BelmonteAbstract:This paper presents the IndoorLoc Platform, a public repository for comparing and evaluating Indoor Positioning algorithms and sharing datasets. The proposed web platform can be used to download datasets, learn how some well-known algorithms work, study the implementation of those algorithms, test the methods, and even upload Indoor Positioning estimations of the user's methods to check the accuracy when comparing against the results provided by other methods already included in a ranking, among other functionalities. This paper also presents a comparative study of the accuracy of two well-known fingerprinting-based Indoor localization algorithms using the datasets included in the platform. This comparative study can be performed using the tools included in the platform.
Joaquin Huerta - One of the best experts on this subject based on the ideXlab platform.
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a meta review of Indoor Positioning Systems
Sensors, 2019Co-Authors: German M Mendozasilva, Joaquin Torressospedra, Joaquin HuertaAbstract:An accurate and reliable Indoor Positioning System (IPS) applicable to most Indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of Indoor Positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys.
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ensembles of Indoor Positioning Systems based on fingerprinting simplifying parameter selection and obtaining robust Systems
International Conference on Indoor Positioning and Indoor Navigation, 2016Co-Authors: Joaquin Torressospedra, German M Mendozasilva, Raul Montoliu, Oscar Belmonte, Fernando Benitez, Joaquin HuertaAbstract:Selecting the appropriate parameters for an Indoor Positioning system may be a difficult task due to the large number of parameter combinations. It is more complex in realistic multi-building multi-floor environments, where severe wrong building and floor errors occur but they are not highlighted in the main evaluation metric. Moreover, a selected parameter configuration, that may seem appropriate in the system validation, may not have the expected behaviour in a real deployment. In order to address these issues, an ensemble of Indoor Positioning Systems is introduced. A base estimator with 2.332 parameter combinations has been used. According to the results, this model simplifies the parameter selection and provides more robust Systems.
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comprehensive analysis of distance and similarity measures for wi fi fingerprinting Indoor Positioning Systems
Expert Systems With Applications, 2015Co-Authors: Joaquin Torressospedra, Raul Montoliu, Oscar Belmonte, Sergio Trilles, Joaquin HuertaAbstract:We introduce a study in depth of distance/similarity measures for Indoor location.Alternative measures provide better results than commonly used Euclidean distance.Choosing an appropriate non-linear representation is crucial for intensity values.Very low intensity values are representative and they should not be filtered.All the experiments are validated with a public database, so they are reproducible. Recent advances in Indoor Positioning Systems led to a business interest in those applications and services where a precise localization is crucial. Wi-Fi fingerprinting based on machine learning and expert Systems are commonly used in the literature. They compare a current fingerprint to a database of fingerprints, and then return the most similar one/ones according to: 1) a distance function, 2) a data representation method for received signal strength values, and 3) a thresholding strategy. However, most of the previous works simply use the Euclidean distance with the raw unprocessed data. There is not any previous work that studies which is the best distance function, which is the best way of representing the data and which is the effect of applying thresholding. In this paper, we present a comprehensive study using 51 distance metrics, 4 alternatives to represent the raw data (2 of them proposed by us), a thresholding based on the RSS values and the public UJIIndoorLoc database. The results shown in this paper demonstrate that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their Systems. The IPSs based on k-NN are improved by just selecting the appropriate configuration (mainly distance function and data representation). In the best case, 13-NN with Sorensen distance and the powed data representation, the error in determining the place (building and floor) has been reduced in more than a 50% and the Positioning accuracy has been increased in 1.7 m with respect to the 1-NN with Euclidean distance and raw data commonly used in the literature. Moreover, our experiments also demonstrate that thresholding should not be applied in multi-building and multi-floor environments.
Raul Montoliu - One of the best experts on this subject based on the ideXlab platform.
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a radiosity based method to avoid calibration for Indoor Positioning Systems
Expert Systems With Applications, 2018Co-Authors: Oscar Belmontefernandez, Joaquin Torressospedra, Raul Montoliu, Emilio Sansanosansano, Daniel ChiaaguilarAbstract:Abstract Due to the widespread use of mobile devices, services based on the users current Indoor location are growing in significance. Such services are developed in the Machine Learning and Experst Systems realm, and ranges from guidance for blind people to mobile tourism and Indoor shopping. One of the most used techniques for Indoor Positioning is WiFi fingerprinting, being its use of widespread WiFi signals one of the main reasons for its popularity, mostly on high populated urban areas. Most issues of this approach rely on the data acquisition phase; to manually sample WiFi RSSI signals in order to create a WiFi radio map is a high time consuming task, also subject to re-calibrations, because any change in the environment might affect the signal propagation, and therefore degrade the performance of the Positioning system. The work presented in this paper aims at substituting the manual data acquisition phase by directly calculating the WiFi radio map by means of a radiosity signal propagation model. The time needed to acquire the WiFi radio map by means of the radiosity model dramatically reduces from hours to minutes when compared with manual acquisition. The proposed method is able to produce competitive results, in terms of accuracy, when compared with manual sampling, which can help domain experts develop services based on location faster.
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Indoorloc platform a public repository for comparing and evaluating Indoor Positioning Systems
International Conference on Indoor Positioning and Indoor Navigation, 2017Co-Authors: Raul Montoliu, Joaquin Torressospedra, Emilio Sansano, Oscar BelmonteAbstract:This paper presents the IndoorLoc Platform, a public repository for comparing and evaluating Indoor Positioning algorithms and sharing datasets. The proposed web platform can be used to download datasets, learn how some well-known algorithms work, study the implementation of those algorithms, test the methods, and even upload Indoor Positioning estimations of the user's methods to check the accuracy when comparing against the results provided by other methods already included in a ranking, among other functionalities. This paper also presents a comparative study of the accuracy of two well-known fingerprinting-based Indoor localization algorithms using the datasets included in the platform. This comparative study can be performed using the tools included in the platform.
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ensembles of Indoor Positioning Systems based on fingerprinting simplifying parameter selection and obtaining robust Systems
International Conference on Indoor Positioning and Indoor Navigation, 2016Co-Authors: Joaquin Torressospedra, German M Mendozasilva, Raul Montoliu, Oscar Belmonte, Fernando Benitez, Joaquin HuertaAbstract:Selecting the appropriate parameters for an Indoor Positioning system may be a difficult task due to the large number of parameter combinations. It is more complex in realistic multi-building multi-floor environments, where severe wrong building and floor errors occur but they are not highlighted in the main evaluation metric. Moreover, a selected parameter configuration, that may seem appropriate in the system validation, may not have the expected behaviour in a real deployment. In order to address these issues, an ensemble of Indoor Positioning Systems is introduced. A base estimator with 2.332 parameter combinations has been used. According to the results, this model simplifies the parameter selection and provides more robust Systems.
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comprehensive analysis of distance and similarity measures for wi fi fingerprinting Indoor Positioning Systems
Expert Systems With Applications, 2015Co-Authors: Joaquin Torressospedra, Raul Montoliu, Oscar Belmonte, Sergio Trilles, Joaquin HuertaAbstract:We introduce a study in depth of distance/similarity measures for Indoor location.Alternative measures provide better results than commonly used Euclidean distance.Choosing an appropriate non-linear representation is crucial for intensity values.Very low intensity values are representative and they should not be filtered.All the experiments are validated with a public database, so they are reproducible. Recent advances in Indoor Positioning Systems led to a business interest in those applications and services where a precise localization is crucial. Wi-Fi fingerprinting based on machine learning and expert Systems are commonly used in the literature. They compare a current fingerprint to a database of fingerprints, and then return the most similar one/ones according to: 1) a distance function, 2) a data representation method for received signal strength values, and 3) a thresholding strategy. However, most of the previous works simply use the Euclidean distance with the raw unprocessed data. There is not any previous work that studies which is the best distance function, which is the best way of representing the data and which is the effect of applying thresholding. In this paper, we present a comprehensive study using 51 distance metrics, 4 alternatives to represent the raw data (2 of them proposed by us), a thresholding based on the RSS values and the public UJIIndoorLoc database. The results shown in this paper demonstrate that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their Systems. The IPSs based on k-NN are improved by just selecting the appropriate configuration (mainly distance function and data representation). In the best case, 13-NN with Sorensen distance and the powed data representation, the error in determining the place (building and floor) has been reduced in more than a 50% and the Positioning accuracy has been increased in 1.7 m with respect to the 1-NN with Euclidean distance and raw data commonly used in the literature. Moreover, our experiments also demonstrate that thresholding should not be applied in multi-building and multi-floor environments.
Prashant Krishnamurthy - One of the best experts on this subject based on the ideXlab platform.
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analysis of wlan s received signal strength indication for Indoor location fingerprinting
Pervasive and Mobile Computing, 2012Co-Authors: Kamol Kaemarungsi, Prashant KrishnamurthyAbstract:An Indoor Positioning system that uses a location fingerprinting technique based on the received signal strength of a wireless local area network is an enabler for Indoor location-aware computing. Data analysis of the received signal strength indication is very essential for understanding the underlying location-dependent features and patterns of location fingerprints. This knowledge can assist a system designer in accurately modeling a Positioning system, improving Positioning performance, and efficiently designing such a system. This study investigates extensively through measurements, the features of the received signal strength indication reported by IEEE 802.11b/g wireless network interface cards. The results of the statistical data analysis help in identifying a number of phenomena that affect the precision and accuracy of Indoor Positioning Systems.
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on clustering rss fingerprints for improving scalability of performance prediction of Indoor Positioning Systems
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments, 2008Co-Authors: Nattapong Swangmuang, Prashant KrishnamurthyAbstract:We previously developed an analytical model in [8] to predict the precision and accuracy performance of Indoor Positioning Systems using location fingerprints. A by-product of the model is the ability to eliminate unnecessary fingerprints to reduce the number of fingerprints in a radio map for comparison without loss in performance. This model enables computation of an approximate probability distribution of location selection, by employing a proximity graph to extract neighbor and non-neighbor sets of a given fingerprint. However, employing the model 'as is' in a system with many location fingerprints requires determining a single large proximity graph derived from all location fingerprints which may involve significant computational effort. In this paper, we consider two techniques to divide location fingerprints into smaller clusters. Separate proximity graphs for each cluster are now used to predict performance and eliminate unnecessary location fingerprints. Results show that the computational effort can be reduced, creating a more scalable analytical model, while still predicting and enabling good precision performance.
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design of Indoor Positioning Systems based on location fingerprinting technique
2005Co-Authors: Prashant Krishnamurthy, Kamol KaemarungsiAbstract:Positioning Systems enable location-awareness for mobile computers in ubiquitous and pervasive wireless computing. By utilizing location information, location-aware computers can render location-based services possible for mobile users. Indoor Positioning Systems based on location fingerprints of wireless local area networks have been suggested as a viable solution where the global Positioning system does not work well. Instead of depending on accurate estimations of angle or distance in order to derive the location with geometry, the fingerprinting technique associates location-dependent characteristics such as received signal strength to a location and uses these characteristics to infer the location. The advantage of this technique is that it is simple to deploy with no specialized hardware required at the mobile station except the wireless network interface card. Any existing wireless local area network infrastructure can be reused for this kind of Positioning system. While empirical results and performance studies of such Positioning Systems are presented in the literature, analytical models that can be used as a framework for efficiently designing the Positioning Systems are not available. This dissertation develops an analytical model as a design tool and recommends a design guideline for such Positioning Systems in order to expedite the deployment process. A system designer can use this framework to strike a balance between the accuracy, the precision, the location granularity, the number of access points, and the location spacing. A systematic study is used to analyze the location fingerprint and discover its unique properties. The location fingerprint based on the received signal strength is investigated. Both deterministic and probabilistic approaches of location fingerprint representations are considered. The main objectives of this work are to predict the performance of such Systems using a suitable model and perform sensitivity analyses that are useful for selecting proper system parameters such as number of access points and minimum spacing between any two different locations.
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modeling of Indoor Positioning Systems based on location fingerprinting
International Conference on Computer Communications, 2004Co-Authors: Kamol Kaemarungsi, Prashant KrishnamurthyAbstract:In previous years, Positioning Systems for Indoor areas using the existing wireless local area network infrastructure have been suggested. Such Systems make use of location fingerprinting rather than time or direction of arrival techniques for determining the location of mobile stations. While experimental results related to such Positioning Systems have been presented, there is a lack of analytical models that can be used as a framework for designing and deploying the Positioning Systems. In this paper, we present an analytical model for analyzing such Positioning Systems. We develop the framework for analyzing a simple Positioning system that employs the Euclidean distance between a sample signal vector and the location fingerprints of an area stored in a database. We analyze the effect of the number of access points that are visible and radio propagation parameters on the performance of the Positioning system and provide some preliminary guidelines on its design.
Liying Fan - One of the best experts on this subject based on the ideXlab platform.
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Indoor Positioning Systems based on visible light communication state of the art
IEEE Communications Surveys and Tutorials, 2017Co-Authors: Junhai Luo, Liying FanAbstract:Advances in visible light communication (VLC) technology and the ubiquity of illumination facility have led to a growing interest in VLC-based Indoor Positioning. Numerous techniques have been proposed to obtain better system performance. In this paper, we survey over 100 papers ranging from pioneering papers to the state-of-the-art in the field to present the Positioning technology. Not only the light emitting diode technology, modulation method and types of receivers are compared but also a novel taxonomy method is proposed. In this paper, VLC-based Indoor Positioning Systems (VLC-based-IPSs) are classified based on the methods used: 1) mathematical method; 2) sensor-assisted method; and 3) optimization method. Different from other survey works, we emphasize and analyze the accuracy of VLC-based-IPS in the experiment and simulation environments. Meanwhile, this paper illustrates challenges, countermeasures, and lessons learned.