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Sensors for Occupancy and Indoor Positioning Services
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Sensors for Occupancy and Indoor Positioning Services

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 20473

Special Issue Editor

Prof. Dr. Alejandro Santos Martínez Sala

E-Mail Website
Guest Editor
Department of Information and Communication Technologies, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
Interests: indoor positioning systems; IoT; wireless sensor networks; innovation; technology transfer

Special Issue Information

Dear Colleagues,

COVID-19 is driving demand for new services based on indoor positioning technologies related to location capacity counting and proximity reporting. This kind of data can be analyzed over time for better decision-making and management regarding a facility. People may collaborate by using a particular app on their smartphone or by carrying a custom device, but research must also deal with the issue that people may not collaborate in downloading an app on their smartphones or in using wearables. These challenging services will be useful and much needed by private sites such as industrial facilities and offices as well as public sites such as universities, hospitals, malls, airports or railway stations. Some solutions are based on the smartphone, using Bluetooth Low Energy or Wi-Fi combined with IMU sensors, or based on custom devices such as a bracelet or a wristband using Ultra-Wideband (UWB) or Bluetooth Low Energy. In addition, IoT solutions could potentially allow the deployment of these services.  

This Special Issue aims to collect high-quality research papers and review articles focusing on recent advances in occupancy and social distance monitoring based on indoor positioning systems. Original, high-quality contributions that have not been published before and are not currently under review by other journals or conferences are sought.

Prof. Dr. Alejandro Santos Martínez Sala
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Indoor occupancy and social distance monitoring
  • Smartphone zone-based indoor positioning
  • Active and passive indoor wireless positioning
  • IoT and sensor data fusion applied to location capacity accounting
  • Wi-Fi
  • Bluetooth Low Energy
  • Ultra-Wideband (UWB)
  • Machine learning and statistical techniques applied to occupancy estimation
  • Innovative services based on occupancy and social distance estimation
  • Performance evaluation of testbed and real deployments

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Published Papers (6 papers)

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Research

22 pages, 2065 KiB  
Article
Indoor Content Delivery Solution for a Museum Based on BLE Beacons
by David Verde, Luís Romero, Pedro Miguel Faria and Sara Paiva
Sensors 2023, 23(17), 7403; https://doi.org/10.3390/s23177403 - 25 Aug 2023
Cited by 4 | Viewed by 1439
Abstract
The digital transformation advancement enables multiple areas to provide modern services to their users. Culture is one of the areas that can benefit from these advances, more specifically museums, by presenting many benefits and the most emergent technologies to the visitors. This paper [...] Read more.
The digital transformation advancement enables multiple areas to provide modern services to their users. Culture is one of the areas that can benefit from these advances, more specifically museums, by presenting many benefits and the most emergent technologies to the visitors. This paper presents an indoor location system and content delivery solution, based on Bluetooth Low Energy Beacons, that enable visitors to walk freely inside the museum and receive augmented reality content based on the acquired position, which is done using the Received Signal Strength Indicator (RSSI). The solution presented in this paper was created for the Foz Côa Museum in Portugal and was tested in the real environment. A detailed study was carried out to analyze the RSSI under four different scenarios, and detection tests were carried out that allowed us to measure the accuracy of the room identification, which is needed for proper content delivery. Of the 89 positions tested in the four scenarios, 70% of the received signals were correctly received throughout the entire duration of the tests, 20% were received in an intermittent way, 4% were never detected and 6% of unwanted beacons were detected. The signal detection is fundamental for the correct room identification, which was performed with 96% accuracy. Thus, we verified that this technology is suitable for the proposed solution. Full article
(This article belongs to the Special Issue Sensors for Occupancy and Indoor Positioning Services)
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18 pages, 7507 KiB  
Article
A Wi-Fi-Based Passive Indoor Positioning System via Entropy-Enhanced Deployment of Wi-Fi Sniffers
by Poh Yuen Chan, Ju-Chin Chao and Ruey-Beei Wu
Sensors 2023, 23(3), 1376; https://doi.org/10.3390/s23031376 - 26 Jan 2023
Cited by 8 | Viewed by 3604
Abstract
This study presents a Wi-Fi-based passive indoor positioning system (IPS) that does not require active collaboration from the user or additional interfaces on the device-under-test (DUT). To maximise the accuracy of the IPS, the optimal deployment of Wi-Fi Sniffers in the area of [...] Read more.
This study presents a Wi-Fi-based passive indoor positioning system (IPS) that does not require active collaboration from the user or additional interfaces on the device-under-test (DUT). To maximise the accuracy of the IPS, the optimal deployment of Wi-Fi Sniffers in the area of interest is crucial. A modified Genetic Algorithm (GA) with an entropy-enhanced objective function is proposed to optimize the deployment. These Wi-Fi Sniffers are used to scan and collect the DUT’s Wi-Fi received signal strength indicators (RSSIs) as Wi-Fi fingerprints, which are then mapped to reference points (RPs) in the physical world. The positioning algorithm utilises a weighted k-nearest neighbourhood (WKNN) method. Automated data collection of RSSI on each RP is achieved using a surveying robot for the Wi-Fi 2.4 GHz and 5 GHz bands. The preliminary results show that using only 20 Wi-Fi Sniffers as features for model training, the offline positioning accuracy is 2.2 m in terms of root mean squared error (RMSE). A proof-of-concept real-time online passive IPS is implemented to show that it is possible to detect the online presence of DUTs and obtain their RSSIs as online fingerprints to estimate their position. Full article
(This article belongs to the Special Issue Sensors for Occupancy and Indoor Positioning Services)
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14 pages, 1275 KiB  
Article
Efficient Clustering for Continuous Occupancy Mapping Using a Mixture of Gaussian Processes
by Soohwan Kim and Jonghyuk Kim
Sensors 2022, 22(18), 6832; https://doi.org/10.3390/s22186832 - 9 Sep 2022
Cited by 1 | Viewed by 1677
Abstract
This paper proposes a novel method for occupancy map building using a mixture of Gaussian processes. Gaussian processes have proven to be highly flexible and accurate for a robotic occupancy mapping problem, yet the high computational complexity has been a critical barrier for [...] Read more.
This paper proposes a novel method for occupancy map building using a mixture of Gaussian processes. Gaussian processes have proven to be highly flexible and accurate for a robotic occupancy mapping problem, yet the high computational complexity has been a critical barrier for large-scale applications. We consider clustering the data into small, manageable subsets and applying a mixture of Gaussian processes. One of the problems in clustering is that the number of groups is not known a priori, thus requiring inputs from experts. We propose two efficient clustering methods utilizing (1) a Dirichlet process and (2) geometrical information in the context of occupancy mapping. We will show that the Dirichlet process-based clustering can significantly speed up the training step of the Gaussian process and if geometrical features, such as line features, are available, they can further improve the clustering accuracy. We will provide simulation results, analyze the performance and demonstrate the benefits of the proposed methods. Full article
(This article belongs to the Special Issue Sensors for Occupancy and Indoor Positioning Services)
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26 pages, 4447 KiB  
Article
A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting
by Ninh Duong-Bao, Jing He, Luong Nguyen Thi, Khanh Nguyen-Huu and Seon-Woo Lee
Sensors 2022, 22(15), 5709; https://doi.org/10.3390/s22155709 - 30 Jul 2022
Cited by 6 | Viewed by 2309
Abstract
In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. [...] Read more.
In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. To tackle this challenge, in this paper, we propose a novel WiFi fingerprinting method that uses the valued tolerance rough set theory–based classification method. In the offline phase, the conventional received signal strength (RSS) fingerprinting database is converted into a decision table. Then a new fingerprinting database with decision rules is constructed based on the decision table, which includes the credibility degrees and the support object set values for all decision rules. In the online phase, various classification levels are applied to find out the best match between the RSS values in the decision rules database and the measured RSS values at the unknown position. The experimental results compared the performance of the proposed method with those of the nearest-neighbor-based and the random statistical methods in two different test cases. The results show that the proposed method greatly outperforms the others in both cases, where it achieves high accuracy with 98.05% of right position classification, which is approximately 50.49% more accurate than the others. The mean positioning errors at wrong estimated positions for the two test cases are 1.71 m and 1.99 m, using the proposed method. Full article
(This article belongs to the Special Issue Sensors for Occupancy and Indoor Positioning Services)
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21 pages, 5214 KiB  
Article
An Indoor Positioning Method Based on UWB and Visual Fusion
by Pingping Peng, Chao Yu, Qihao Xia, Zhengqi Zheng, Kun Zhao and Wen Chen
Sensors 2022, 22(4), 1394; https://doi.org/10.3390/s22041394 - 11 Feb 2022
Cited by 24 | Viewed by 3384
Abstract
Continuous positioning and tracking of multi-pedestrian targets is a common concern for large indoor space security, emergency evacuation, location services, and other application areas. Among the sensors used for positioning, the ultra-wide band (UWB) is a critical way to achieve high-precision indoor positioning. [...] Read more.
Continuous positioning and tracking of multi-pedestrian targets is a common concern for large indoor space security, emergency evacuation, location services, and other application areas. Among the sensors used for positioning, the ultra-wide band (UWB) is a critical way to achieve high-precision indoor positioning. However, due to the existence of indoor Non-Line-of-Sight (NLOS) error, a single positioning system can no longer meet the requirement for positioning accuracy. This research aimed to design a high-precision and stable fusion positioning system which is based on the UWB and vision. The method uses the Hungarian algorithm to match the identity of the UWB and vision localization results, and, after successful matching, the fusion localization is performed by the federated Kalman filtering algorithm. In addition, due to the presence of colored noise in indoor positioning data, this paper also proposes a Kalman filtering algorithm based on principal component analysis (PCA). The advantage of this new filtering algorithm is that it does not have to establish the dynamics model of the distribution hypothesis and requires less calculation. The PCA algorithm is firstly used to minimize the correlation of the observables, thus providing a more reasonable Kalman gain by energy estimation and the denoised data, which are substituted into Kalman prediction equations. Experimental results show that the average accuracy of the UWB and visual fusion method is 25.3% higher than that of the UWB. The proposed method can effectively suppress the influence of NLOS error on the positioning accuracy because of the high stability and continuity of visual positioning. Furthermore, compared with the traditional Kalman filtering, the mean square error of the new filtering algorithm is reduced by 31.8%. After using the PCA-Kalman filtering, the colored noise is reduced and the Kalman gain becomes more reasonable, facilitating accurate estimation of the state by the filter. Full article
(This article belongs to the Special Issue Sensors for Occupancy and Indoor Positioning Services)
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32 pages, 7911 KiB  
Article
An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening
by Steve H. L. Liang, Sara Saeedi, Soroush Ojagh, Sepehr Honarparvar, Sina Kiaei, Mahnoush Mohammadi Jahromi and Jeremy Squires
Sensors 2021, 21(1), 50; https://doi.org/10.3390/s21010050 - 24 Dec 2020
Cited by 15 | Viewed by 6515
Abstract
To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the [...] Read more.
To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data. Full article
(This article belongs to the Special Issue Sensors for Occupancy and Indoor Positioning Services)
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