Pub Date : 2025-04-07DOI: 10.1109/JISPIN.2025.3558465
Luca De Nardis;Marco Savelli;Giuseppe Caso;Federico Ferretti;Lorenzo Tonelli;Nadir Bouzar;Anna Brunstrom;Özgü Alay;Marco Neri;Fouzia Elbahhar Bokour;Maria-Gabriella Di Benedetto
Existing proposals for positioning in narrowband Internet of Things (NB-IoT) networks based on range estimation are characterized by either low accuracy or lack of compliance with 3GPP standards. While range-free approaches taking advantage of machine learning (ML) have been recently proposed as a potential way forward, their evaluation has been carried out only in simulated environments, with the exception of weighted $k$ nearest neighbors (W$k$NN), recently tested on experimental data. This work investigates five ML strategies for range-free positioning in NB-IoT networks, based on W$k$NN and its combination with preprocessing and classification algorithms as well as on artificial neural networks (ANNs). The strategies are evaluated on experimental data and are compared based on a set of key performance indicators measuring both positioning performance and processing load. Two different datasets taken at different times and locations were adopted, enabling the validation of strategies optimized on one testbed on the other, as well as the study of the impact of dataset features on performance. Results show that range-free positioning using ML is a viable solution in commercial NB-IoT networks, and that W$k$NN and ANNs are at the two extremes in terms of a performance/complexity tradeoff; intermediate tradeoffs can be achieved by combining W$k$NN with preprocessing techniques and classification models.
{"title":"Range-Free Positioning in NB-IoT Networks by Machine Learning: Beyond W$k$NN","authors":"Luca De Nardis;Marco Savelli;Giuseppe Caso;Federico Ferretti;Lorenzo Tonelli;Nadir Bouzar;Anna Brunstrom;Özgü Alay;Marco Neri;Fouzia Elbahhar Bokour;Maria-Gabriella Di Benedetto","doi":"10.1109/JISPIN.2025.3558465","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3558465","url":null,"abstract":"Existing proposals for positioning in narrowband Internet of Things (NB-IoT) networks based on range estimation are characterized by either low accuracy or lack of compliance with 3GPP standards. While range-free approaches taking advantage of machine learning (ML) have been recently proposed as a potential way forward, their evaluation has been carried out only in simulated environments, with the exception of weighted <inline-formula><tex-math>$k$</tex-math></inline-formula> nearest neighbors (W<inline-formula><tex-math>$k$</tex-math></inline-formula>NN), recently tested on experimental data. This work investigates five ML strategies for range-free positioning in NB-IoT networks, based on W<inline-formula><tex-math>$k$</tex-math></inline-formula>NN and its combination with preprocessing and classification algorithms as well as on artificial neural networks (ANNs). The strategies are evaluated on experimental data and are compared based on a set of key performance indicators measuring both positioning performance and processing load. Two different datasets taken at different times and locations were adopted, enabling the validation of strategies optimized on one testbed on the other, as well as the study of the impact of dataset features on performance. Results show that range-free positioning using ML is a viable solution in commercial NB-IoT networks, and that W<inline-formula><tex-math>$k$</tex-math></inline-formula>NN and ANNs are at the two extremes in terms of a performance/complexity tradeoff; intermediate tradeoffs can be achieved by combining W<inline-formula><tex-math>$k$</tex-math></inline-formula>NN with preprocessing techniques and classification models.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"53-69"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10950079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1109/JISPIN.2025.3570258
Grigorios G. Anagnostopoulos;Paolo Barsocchi;Antonino Crivello;Cristiano Pendão;Ivo Silva;Joaquín Torres-Sospedra
Transparency and verifiability have long been regarded as cornerstones of the scientific ethos and practice. However, persistent reproducibility challenges across numerous disciplines have brought renewed attention to the imperative for widespread adoption of open science practices. These considerations are particularly relevant to the research field of indoor positioning. Open data and open code sharing are gradually gaining traction in the field, but are still far from standard practice. This study comprehensively evaluates the extent of the adoption of open science practices within the community of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), by systematically analyzing all reference papers from the 2019 to 2024 editions of the IPIN. The work thoroughly examines the open data and code usage, and the use of other types of open materials while performing a particular close-up review of the open data that are leveraged in these studies. Our findings reveal that 21.7% of papers use open research data, 8.3% utilize open code, and 20.2% incorporate other open materials. However, only 6.8% of papers provide both open data and code. Moreover, emerging patterns and intuitive best practices are highlighted. The complete characterization of all reviewed publications is publicly available. This study brings to light the need for wider adoption of open science practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes in the field of indoor positioning.
{"title":"Comprehensive Assessment of Open Science Practices in Indoor Positioning: Open Data, Code, and Material","authors":"Grigorios G. Anagnostopoulos;Paolo Barsocchi;Antonino Crivello;Cristiano Pendão;Ivo Silva;Joaquín Torres-Sospedra","doi":"10.1109/JISPIN.2025.3570258","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3570258","url":null,"abstract":"Transparency and verifiability have long been regarded as cornerstones of the scientific ethos and practice. However, persistent reproducibility challenges across numerous disciplines have brought renewed attention to the imperative for widespread adoption of open science practices. These considerations are particularly relevant to the research field of indoor positioning. Open data and open code sharing are gradually gaining traction in the field, but are still far from standard practice. This study comprehensively evaluates the extent of the adoption of open science practices within the community of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), by systematically analyzing all reference papers from the 2019 to 2024 editions of the IPIN. The work thoroughly examines the open data and code usage, and the use of other types of open materials while performing a particular close-up review of the open data that are leveraged in these studies. Our findings reveal that 21.7% of papers use open research data, 8.3% utilize open code, and 20.2% incorporate other open materials. However, only 6.8% of papers provide both open data and code. Moreover, emerging patterns and intuitive best practices are highlighted. The complete characterization of all reviewed publications is publicly available. This study brings to light the need for wider adoption of open science practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes in the field of indoor positioning.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"175-194"},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11003858","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-06DOI: 10.1109/JISPIN.2025.3567374
Reza Ahmadvand;Sarah Safura Sharif;Yaser Mike Banad
This study introduces a novel distributed cloud-edge framework for autonomous multi-unmanned aerial vehicle (UAV) systems that combines the computational efficiency of neuromorphic computing with nature-inspired control strategies. The proposed architecture equips each UAV with an individual spiking neural network (SNN) that learns to reproduce optimal control signals generated by a cloud-based controller, enabling robust operation even during communication interruptions. By integrating spike coding with nature-inspired control principles inspired by tilapia fish territorial behavior, our system achieves sophisticated formation control and obstacle avoidance in complex urban environments. The distributed architecture leverages cloud computing for complex calculations while maintaining local autonomy through edge-based SNNs, significantly reducing energy consumption and computational overhead compared to traditional centralized approaches. Our framework addresses critical limitations of conventional methods, including the dependence on premodeled environments, computational intensity of traditional methods, and local minima issues in potential field approaches. Simulation results demonstrate the system's effectiveness across two different scenarios: first, the indoor deployment of a multi-UAV system made up of 15 UAVs, and second, the collision-free formation control of a moving UAV flock, including six UAVs considering the obstacle avoidance. Due to the sparsity of spiking patterns, and the event-based nature of SNNs on average for the whole group of UAVs, the framework achieves almost 90% reduction in computational burden compared to traditional von Neumann architectures implementing traditional artificial neural networks.
{"title":"Neuromorphic Digital-Twin-Based Controller for Indoor Multi-UAV Systems Deployment","authors":"Reza Ahmadvand;Sarah Safura Sharif;Yaser Mike Banad","doi":"10.1109/JISPIN.2025.3567374","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3567374","url":null,"abstract":"This study introduces a novel distributed cloud-edge framework for autonomous multi-unmanned aerial vehicle (UAV) systems that combines the computational efficiency of neuromorphic computing with nature-inspired control strategies. The proposed architecture equips each UAV with an individual spiking neural network (SNN) that learns to reproduce optimal control signals generated by a cloud-based controller, enabling robust operation even during communication interruptions. By integrating spike coding with nature-inspired control principles inspired by tilapia fish territorial behavior, our system achieves sophisticated formation control and obstacle avoidance in complex urban environments. The distributed architecture leverages cloud computing for complex calculations while maintaining local autonomy through edge-based SNNs, significantly reducing energy consumption and computational overhead compared to traditional centralized approaches. Our framework addresses critical limitations of conventional methods, including the dependence on premodeled environments, computational intensity of traditional methods, and local minima issues in potential field approaches. Simulation results demonstrate the system's effectiveness across two different scenarios: first, the indoor deployment of a multi-UAV system made up of 15 UAVs, and second, the collision-free formation control of a moving UAV flock, including six UAVs considering the obstacle avoidance. Due to the sparsity of spiking patterns, and the event-based nature of SNNs on average for the whole group of UAVs, the framework achieves almost 90% reduction in computational burden compared to traditional von Neumann architectures implementing traditional artificial neural networks.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"165-174"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10989580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Achieving an exact localization is a complex and essential issue for autonomous aerial vehicles (AAVs) due to their three-directional high-speed mobility. Identifying the accurate flying position of AAVs for resource management and task reallocation is still challenging. In these scenarios, the position of the AAVs must be identifiable in a timely and precise manner. A bioinspired metaheuristic hybrid model was proposed to overcome the shortcomings of inaccurate altitude and improve the AAVs' flying positional coordinates. The proposed model incorporates the particle swarm optimization (PSO) with a fuzzy logic technique. PSO is used to find the optimal or near-optimal positions for the AAVs by minimizing localization error across a wide search space. Once the PSO has determined a feasible solution, fuzzy logic is applied for fine tuning the position based on real-time environmental factors (e.g., signal strength, sensor data, or global positioning system errors). This combination achieved both global efficiency (via PSO) and local precision (via fuzzy logic), ensuring robust localization even in noisy or dynamic conditions during AAVs flight operations. The model, compared to the state-of-the-art model, shows more accuracy in AAV localization with real-time operational data.
{"title":"OUL-HMT: Optimized AAV Localization Using Hybrid Metaheuristic Techniques","authors":"Awadhesh Dixit;Meka Naga Nandini Devi;Firoj Gazi;Md Muzakkir Hussain","doi":"10.1109/JISPIN.2025.3567375","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3567375","url":null,"abstract":"Achieving an exact localization is a complex and essential issue for autonomous aerial vehicles (AAVs) due to their three-directional high-speed mobility. Identifying the accurate flying position of AAVs for resource management and task reallocation is still challenging. In these scenarios, the position of the AAVs must be identifiable in a timely and precise manner. A bioinspired metaheuristic hybrid model was proposed to overcome the shortcomings of inaccurate altitude and improve the AAVs' flying positional coordinates. The proposed model incorporates the particle swarm optimization (PSO) with a fuzzy logic technique. PSO is used to find the optimal or near-optimal positions for the AAVs by minimizing localization error across a wide search space. Once the PSO has determined a feasible solution, fuzzy logic is applied for fine tuning the position based on real-time environmental factors (e.g., signal strength, sensor data, or global positioning system errors). This combination achieved both global efficiency (via PSO) and local precision (via fuzzy logic), ensuring robust localization even in noisy or dynamic conditions during AAVs flight operations. The model, compared to the state-of-the-art model, shows more accuracy in AAV localization with real-time operational data.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"142-151"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10989237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-06DOI: 10.1109/JISPIN.2025.3567258
Thomas Edridge;Manon Kok
Recently, it has been shown that odometry is possible only using data from a magnetometer array. In this work, we analyze the uncertainty of the pose change estimate using a magnetometer array. We derive an analytical expression for the pose change covariance to analyze the estimation uncertainty in Monte Carlo simulations. Under certain conditions, we demonstrate that using a magnetometer array, it is possible to estimate the position and orientation change with submillimeter and subdegree precision between two consecutive time-steps. Moreover, we also demonstrate that when constructing a magnetometer array, magnetometers should be placed in the direction of movement to maximize the positional and rotational precision, with at least four magnetometers per unit of length-scale. In addition, we illustrate that to minimize positional and rotational drift to under a few percentages and degrees of the distance traveled, submillimeter and subdegree magnetometer alignment errors are necessary. Similarly, bias errors smaller than a few percent of the magnitude of the magnetic field variations are necessary. The Monte Carlo simulations are verified using experimental data collected with a 30-magnetometer array. The experimental data show that when insufficient magnetic field anomalies are in close proximity, the changes in positions are estimated poorly, while significant orientation information is still obtained. It also shows that when the magnetometer array is in close proximity to sufficient magnetic field anomalies, the overall trajectory traveled by a magnetometer array can be accurately estimated with a horizontal error accumulation of less than a percentage of the distance traveled.
{"title":"Position and Orientation Estimation Uncertainty Using Magnetometer Arrays for Indoor Localization","authors":"Thomas Edridge;Manon Kok","doi":"10.1109/JISPIN.2025.3567258","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3567258","url":null,"abstract":"Recently, it has been shown that odometry is possible only using data from a magnetometer array. In this work, we analyze the uncertainty of the pose change estimate using a magnetometer array. We derive an analytical expression for the pose change covariance to analyze the estimation uncertainty in Monte Carlo simulations. Under certain conditions, we demonstrate that using a magnetometer array, it is possible to estimate the position and orientation change with submillimeter and subdegree precision between two consecutive time-steps. Moreover, we also demonstrate that when constructing a magnetometer array, magnetometers should be placed in the direction of movement to maximize the positional and rotational precision, with at least four magnetometers per unit of length-scale. In addition, we illustrate that to minimize positional and rotational drift to under a few percentages and degrees of the distance traveled, submillimeter and subdegree magnetometer alignment errors are necessary. Similarly, bias errors smaller than a few percent of the magnitude of the magnetic field variations are necessary. The Monte Carlo simulations are verified using experimental data collected with a 30-magnetometer array. The experimental data show that when insufficient magnetic field anomalies are in close proximity, the changes in positions are estimated poorly, while significant orientation information is still obtained. It also shows that when the magnetometer array is in close proximity to sufficient magnetic field anomalies, the overall trajectory traveled by a magnetometer array can be accurately estimated with a horizontal error accumulation of less than a percentage of the distance traveled.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"152-164"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10989651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article proposes an indoor position-estimation method that integrates visible light positioning (VLP) with pedestrian dead reckoning (PDR), using a smartphone's built-in ambient light sensor (ALS) offering lower power consumption than a camera and inertial sensor. In the proposed method, the user's position is first estimated via PDR and the positioning results for areas where VLP using ALS (ALS-VLP) is available are corrected by using pose graphs that resolve simultaneous localization and mapping. Experiments were conducted with eight users walking a route measuring 141.67 m for five laps. The results indicated an average error of 11.30 m when only PDR was used, with a substantial reduction to 2.03 m when the proposed method was used. Limitations and challenges related to practical use scenarios of the proposed method clarified through the experiments are discussed.
{"title":"ALS+PDR: Indoor Pedestrian Dead Reckoning Using a Smartphone Ambient Light Sensor","authors":"Sosuke Otsuka;Yusei Onishi;Mananari Nakamura;Hiromichi Hashizume;Masanori Sugimoto","doi":"10.1109/JISPIN.2025.3541991","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3541991","url":null,"abstract":"This article proposes an indoor position-estimation method that integrates visible light positioning (VLP) with pedestrian dead reckoning (PDR), using a smartphone's built-in ambient light sensor (ALS) offering lower power consumption than a camera and inertial sensor. In the proposed method, the user's position is first estimated via PDR and the positioning results for areas where VLP using ALS (ALS-VLP) is available are corrected by using pose graphs that resolve simultaneous localization and mapping. Experiments were conducted with eight users walking a route measuring 141.67 m for five laps. The results indicated an average error of 11.30 m when only PDR was used, with a substantial reduction to 2.03 m when the proposed method was used. Limitations and challenges related to practical use scenarios of the proposed method clarified through the experiments are discussed.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"43-52"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-30DOI: 10.1109/JISPIN.2025.3536396
Eudald Sangenis;Chi-Shih Jao;Andrei M. Shkel
In this article, we propose an approach to enhance zero-velocity-update (ZUPT)-aided inertial navigation systems (INSs) with a time series support vector machine (SVM) forecaster algorithm. The approach is based on the inclusion in ZUPT algorithm the time correlation of velocity threshold values based on classification of 19 distinct pedestrian activities determined from a foot-mounted inertial measurement unit. The classification enhances the traditional ZUPT-aided INS by first optimizing the threshold in the detector called stance hypothesis optimal detection and second adjusting zero-velocity measurement variances for each categorized locomotion type. Experimental validation involved three subjects, each conducting 10 trials of indoor navigation, encompassing activities, such as walking, fast walking, jogging, running, sprinting, walking backward, jogging backward, and sidestepping, over a nearly 100 [m] path. The trained time series SVM classifier achieved a 90.04% average classification accuracy, resulting in an improvement in navigation accuracy by a factor of 250 as compared to a standalone INS and by a factor of 3 as compared to a traditional ZUPT-aided INS solution. Comparable improvements in the vertical drift of the navigation solution have been also demonstrated.
{"title":"Effect of Adding Time Correlation to SVM-Based Motion Classification in Pedestrian Navigation","authors":"Eudald Sangenis;Chi-Shih Jao;Andrei M. Shkel","doi":"10.1109/JISPIN.2025.3536396","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3536396","url":null,"abstract":"In this article, we propose an approach to enhance zero-velocity-update (ZUPT)-aided inertial navigation systems (INSs) with a time series support vector machine (SVM) forecaster algorithm. The approach is based on the inclusion in ZUPT algorithm the time correlation of velocity threshold values based on classification of 19 distinct pedestrian activities determined from a foot-mounted inertial measurement unit. The classification enhances the traditional ZUPT-aided INS by first optimizing the threshold in the detector called stance hypothesis optimal detection and second adjusting zero-velocity measurement variances for each categorized locomotion type. Experimental validation involved three subjects, each conducting 10 trials of indoor navigation, encompassing activities, such as walking, fast walking, jogging, running, sprinting, walking backward, jogging backward, and sidestepping, over a nearly 100 [m] path. The trained time series SVM classifier achieved a 90.04% average classification accuracy, resulting in an improvement in navigation accuracy by a factor of 250 as compared to a standalone INS and by a factor of 3 as compared to a traditional ZUPT-aided INS solution. Comparable improvements in the vertical drift of the navigation solution have been also demonstrated.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"32-42"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858374","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/JISPIN.2025.3534200
Fanchen Bao;Stepan Mazokha;Jason O. Hallstrom
Pedestrian mobility data is valuable to data-driven decision-making for city planning, emergency response, and more. Thanks to the ubiquity of Wi–Fi-enabled devices, pedestrians may be colocalized with their devices using Received Signal Strength Indicator (RSSI) measurements from Wi–Fi probe requests, passively and privately. While shown to be feasible in controlled outdoor environments, few have used this method outdoors in production environments. In this article, we continue the work on the Mobility Intelligence System (MobIntel) and apply RSSI-based passive localization on data collected from the 500 and 400 blocks of Clematis Street in West Palm Beach, FL. We present an open-source dataset used in our study, which, to the best of our knowledge, is the first public Wi–Fi RSSI dataset for localization purposes in an outdoor environment. We then introduce a three-stage localization model that first classifies a test sample to a city block, followed by a sidewalk within the city block, and ends with an estimation of x-coordinate within the sidewalk. While we formulate the problem and validate our solution within an outdoor context, the work is equally applicable to large indoor environments. It achieves a mean localization error of 3.16 and 4.21 m, with 73% and 66% chance of reaching an error $le$4 m, and 17% and 21% of the data discarded due to poor quality in the 500 and 400 block, respectively. We also highlight the challenges when dealing with real-world RSSI data, analyze the model's tolerance to missing data, and propose solutions to improve localization performance.
{"title":"RSSI-Based Passive Localization in the Wild, At Streetscape Scales","authors":"Fanchen Bao;Stepan Mazokha;Jason O. Hallstrom","doi":"10.1109/JISPIN.2025.3534200","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3534200","url":null,"abstract":"Pedestrian mobility data is valuable to data-driven decision-making for city planning, emergency response, and more. Thanks to the ubiquity of Wi–Fi-enabled devices, pedestrians may be colocalized with their devices using Received Signal Strength Indicator (RSSI) measurements from Wi–Fi probe requests, passively and privately. While shown to be feasible in controlled outdoor environments, few have used this method outdoors in production environments. In this article, we continue the work on the Mobility Intelligence System (MobIntel) and apply RSSI-based passive localization on data collected from the 500 and 400 blocks of Clematis Street in West Palm Beach, FL. We present an open-source dataset used in our study, which, to the best of our knowledge, is the first public Wi–Fi RSSI dataset for localization purposes in an outdoor environment. We then introduce a three-stage localization model that first classifies a test sample to a city block, followed by a sidewalk within the city block, and ends with an estimation of x-coordinate within the sidewalk. While we formulate the problem and validate our solution within an outdoor context, the work is equally applicable to large indoor environments. It achieves a mean localization error of 3.16 and 4.21 m, with 73% and 66% chance of reaching an error <inline-formula><tex-math>$le$</tex-math></inline-formula>4 m, and 17% and 21% of the data discarded due to poor quality in the 500 and 400 block, respectively. We also highlight the challenges when dealing with real-world RSSI data, analyze the model's tolerance to missing data, and propose solutions to improve localization performance.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"3 ","pages":"13-31"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854656","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/JISPIN.2025.3526540
{"title":"2024 Index IEEE Journal of Indoor and Seamless Positioning and Navigation Vol. 2","authors":"","doi":"10.1109/JISPIN.2025.3526540","DOIUrl":"https://doi.org/10.1109/JISPIN.2025.3526540","url":null,"abstract":"","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"343-353"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10832483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1109/JISPIN.2023.3348000
{"title":"IEEE Journal of Indoor and Seamless Positioning and Navigation Publication Information","authors":"","doi":"10.1109/JISPIN.2023.3348000","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3348000","url":null,"abstract":"","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817733","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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