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Indoor Positioning Technologies for Internet-of-Things
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Indoor Positioning Technologies for Internet-of-Things

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

Deadline for manuscript submissions: 25 March 2025 | Viewed by 9485

Special Issue Editors

Dr. Marco Passafiume

E-Mail Website
Guest Editor
CNIT/RaSS Lab, Consorzio Nazionale Interuniversitario delle Telecomunicazioni, Pisa, Italy
Interests: computer science; signal processing; communications and networking; RF design; embedded systems development; machine learning
Dr. Stefano Maddio

E-Mail Website
Guest Editor
Department of Information Engineering, University of Florence, Florence, Italy
Interests: RF design; antennas and propagation; EM theory; communication and networking
Dr. Giovanni Collodi

E-Mail Website
Guest Editor
Department of Information Engineering, University of Florence, Florence, Italy
Interests: RF design; MMIC design; communications and networking

Special Issue Information

Dear Colleagues,

The new Internet-of-Things paradigms have so far pushed the concept of interconnectivity between users and between users and their underlying environment. The spatial density of connected devices has dramatically increased; thus, to avoid clashes between communication channels, scenario awareness has become of primary importance. The widening of link bandwidths, as required by the increased communication data rates, has led to an increase in operative frequencies. As a result, spatial coverage of transmitters has dramatically decreased, and dynamic beamsteering for high-directional arrays has become mandatory. New communication technologies, such as 5G, mostly address these new challenges by varying the network topology, i.e., introducing such concepts as picocells and adaptive beam antenna arrays, which are reliant on position awareness for optimal network routing.

In addition to addressing technical needs, high data rates enable users and network-connected nodes to continuously exchange huge amounts of information, thus necessitating data contextualization to organize the flow of all the collected data.

Considering the scenario of overall networking, indoor positioning has become a service of main importance for both network protocol and user application layers. Hence, a strict requirement is the ease of integrating positioning functionality into pre-existent protocol stacks and hardware. Possible application scenarios span from augmented reality to home automation as well as robotics, advanced data management, and healthcare.

Every generic device connected to the network should be able to achieve position awareness; hence, minimizing the need for specific devices is mandatory. Furthermore, the positioning service should be implemented without demanding configuration and calibration phases, as this would place significant limitations on application portability.

This Special Issue aims to collect outstanding and innovative research works proposing plug-n-play and calibration-free indoor positioning technologies aimed at providing users with indoor positioning functionality as a transparent and user-friendly service totally integrated into user devices.

Dr. Marco Passafiume
Dr. Stefano Maddio
Dr. Giovanni Collodi
Guest Editors

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 positioning
  • user experience
  • RSSI
  • wireless sensor networks
  • Wi-Fi
  • bluetooth
  • home automation
  • tracking
  • robotics
  • multimedia

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Published Papers (7 papers)

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Research

22 pages, 3257 KiB  
Article
Tag-Array-Based UHF Passive RFID Tag Attitude Identification of Tracking Methods
by Honggang Wang, Sicheng Li, Yurun Zhou, Yongli Wang, Ruoyu Pan and Shengli Pang
Sensors 2024, 24(19), 6305; https://doi.org/10.3390/s24196305 - 29 Sep 2024
Viewed by 797
Abstract
Attitude information is as important as position information in describing and localizing objects. Based on this, this paper proposes a method for object attitude sensing utilizing ultra-high frequency passive RFID technology. This method adopts a double tag array strategy, which effectively enhances the [...] Read more.
Attitude information is as important as position information in describing and localizing objects. Based on this, this paper proposes a method for object attitude sensing utilizing ultra-high frequency passive RFID technology. This method adopts a double tag array strategy, which effectively enhances the spatial freedom and eliminates phase ambiguity by leveraging the phase difference information between the two tags. Additionally, we delve into the issue of the phase shift caused by coupling interference between the two tags. To effectively compensate for this coupling effect, a series of experiments were conducted to thoroughly examine the specific impact of coupling effects between tags, and based on these findings, a coupling model between tags was established. This model was then integrated into the original phase model to correct for the effects of phase shift, significantly improving the sensing accuracy. Furthermore, we considered the influence of the object rotation angle on phase changes to construct an accurate object attitude recognition and tracking model. To reduce random errors during phase measurement, we employed a polynomial regression method to fit the measured tag phase information, further enhancing the precision of the sensing model. Compared to traditional positioning modes, the dual-tag array strategy essentially increases the number of virtual antennas available for positioning, providing the system with more refined directional discrimination capabilities. The experimental results demonstrated that incorporating the effects of inter-tag coupling interference and rotation angle into the phase model significantly improved the recognition accuracy for both object localization and attitude angle determination. Specifically, the average error of object positioning was reduced to 12.3 cm, while the average error of attitude angle recognition was reduced to 8.28°, making the method suitable for various practical application scenarios requiring attitude recognition. Full article
(This article belongs to the Special Issue Indoor Positioning Technologies for Internet-of-Things)
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17 pages, 4853 KiB  
Article
Enhancing UWB Indoor Positioning Accuracy through Improved Snake Search Algorithm for NLOS/LOS Signal Classification
by Fang Wang, Lingqiao Shui, Hai Tang and Zhe Wei
Sensors 2024, 24(15), 4917; https://doi.org/10.3390/s24154917 - 29 Jul 2024
Cited by 2 | Viewed by 1010
Abstract
Non-line-of-sight (NLOS) errors significantly impact the accuracy of ultra-wideband (UWB) indoor positioning, posing a major barrier to its advancement. This study addresses the challenge of effectively distinguishing line-of-sight (LOS) from NLOS signals to enhance UWB positioning accuracy. Unlike existing research that focuses on [...] Read more.
Non-line-of-sight (NLOS) errors significantly impact the accuracy of ultra-wideband (UWB) indoor positioning, posing a major barrier to its advancement. This study addresses the challenge of effectively distinguishing line-of-sight (LOS) from NLOS signals to enhance UWB positioning accuracy. Unlike existing research that focuses on optimizing deep learning network structures, our approach emphasizes the optimization of model parameters. We introduce a chaotic map for the initialization of the population and integrate a subtraction-average-based optimizer with a dynamic exploration probability to enhance the Snake Search Algorithm (SSA). This improved SSA optimizes the initial weights and thresholds of backpropagation (BP) neural networks for signal classification. Comparative evaluations with BP, Particle Swarm Optimizer–BP (PSO-BP), and Snake Optimizer–PB (SO-BP) models—performed using three performance metrics—demonstrate that our LTSSO-BP model achieves superior stability and accuracy, with classification accuracy, recall, and F1 score values of 90%, 91.41%, and 90.25%, respectively. Full article
(This article belongs to the Special Issue Indoor Positioning Technologies for Internet-of-Things)
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19 pages, 5536 KiB  
Article
Enhanced Indoor Positioning Using RSSI and Time-Distributed Auto Encoder-Gated Recurrent Unit Model
by Zhe Wei, Zhanpeng Zhou, Shuyan Yu and Jialei Chen
Sensors 2024, 24(15), 4815; https://doi.org/10.3390/s24154815 - 24 Jul 2024
Viewed by 804
Abstract
This study presents a novel approach to indoor positioning leveraging radio frequency identification (RFID) technology based on received signal strength indication (RSSI). The proposed methodology integrates Gaussian Kalman filtering for effective signal preprocessing and a time-distributed auto encoder-gated recurrent unit (TAE-GRU) model for [...] Read more.
This study presents a novel approach to indoor positioning leveraging radio frequency identification (RFID) technology based on received signal strength indication (RSSI). The proposed methodology integrates Gaussian Kalman filtering for effective signal preprocessing and a time-distributed auto encoder-gated recurrent unit (TAE-GRU) model for precise location prediction. Addressing the prevalent challenges of low accuracy and extended localization times in current systems, the proposed method significantly enhances the preprocessing of RSSI data and effectively captures the temporal relationships inherent in the data. Experimental validation demonstrates that the proposed approach achieves a 75.9% improvement in localization accuracy over simple neural network methods and markedly enhances the speed of localization, thereby proving its practical applicability in real-world indoor localization scenarios. Full article
(This article belongs to the Special Issue Indoor Positioning Technologies for Internet-of-Things)
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16 pages, 5520 KiB  
Article
A Combined Filtering Method for ZigBee Indoor Distance Measurement
by Zhe Wei and Zhanpeng Zhou
Sensors 2024, 24(10), 3164; https://doi.org/10.3390/s24103164 - 16 May 2024
Cited by 1 | Viewed by 990
Abstract
Indoor distance measurement technology utilizing Zigbee’s Received Signal Strength Indication (RSSI) offers cost-effective and energy-efficient advantages, making it widely adopted for indoor distance measurement applications. However, challenges such as multipath effects, signal attenuation, and signal blockage often degrade the accuracy of distance measurements. [...] Read more.
Indoor distance measurement technology utilizing Zigbee’s Received Signal Strength Indication (RSSI) offers cost-effective and energy-efficient advantages, making it widely adopted for indoor distance measurement applications. However, challenges such as multipath effects, signal attenuation, and signal blockage often degrade the accuracy of distance measurements. Addressing these issues, this study proposes a combined filtering approach integrating Kalman filtering, Dixon’s Q-test, Gaussian filtering, and mean filtering. Initially, the method evaluates Zigbee’s transmission power, channel, and other parameters, analyzing their impact on RSSI values. Subsequently, it fits a signal propagation loss model based on actual measured data to understand the filtering algorithm’s effect on distance measurement error. Experimental results demonstrate that the proposed method effectively improves the conversion relationship between RSSI and distance. The average distance measurement error, approximately 0.46 m, substantially outperforms errors derived from raw RSSI data. Consequently, this method offers enhanced distance measurement accuracy, making it particularly suitable for indoor positioning applications. Full article
(This article belongs to the Special Issue Indoor Positioning Technologies for Internet-of-Things)
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14 pages, 4911 KiB  
Article
Transfer Learning for Indoor Localization Algorithm Based on Deep Domain Adaptation
by Jiahao Wang, Yifu Fu, Hainan Feng and Junxiang Wang
Sensors 2023, 23(23), 9334; https://doi.org/10.3390/s23239334 - 22 Nov 2023
Viewed by 1078
Abstract
In application, training data and test data collected via indoor positioning algorithms usually do not come from the same ideal conditions. Changes in various environmental conditions and signal drift can cause different probability distributions between the data sets. Existing positioning algorithms cannot guarantee [...] Read more.
In application, training data and test data collected via indoor positioning algorithms usually do not come from the same ideal conditions. Changes in various environmental conditions and signal drift can cause different probability distributions between the data sets. Existing positioning algorithms cannot guarantee stable accuracy when facing these issues, resulting in dramatic reduction and the infeasibility of the positioning accuracy of indoor location algorithms. Considering these restrictions, domain adaptation technology in transfer learning has proven to be a promising solution in past research in terms of solving the inconsistent probability distribution problems. However, most localization algorithms based on transfer learning do not perform well because they only learn a shallow representation feature, which can only slightly reduce the domain discrepancy. Based on the deep network and its strong feature extraction ability, it can learn more transferable features for domain adaptation and achieve better domain adaptation effects. A Deep Joint Mean Distribution Adaptation Network (DJMDAN) is proposed to align the global domain and relevant subdomain distributions of activations in multiple domain-specific layers across domains to achieve domain adaptation. The test results demonstrate that the performance of the proposed method outperforms the comparison algorithm in indoor positioning applications. Full article
(This article belongs to the Special Issue Indoor Positioning Technologies for Internet-of-Things)
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18 pages, 955 KiB  
Article
Channel State Information Based Indoor Fingerprinting Localization
by Rongjie Che and Honglong Chen
Sensors 2023, 23(13), 5830; https://doi.org/10.3390/s23135830 - 22 Jun 2023
Cited by 3 | Viewed by 2093
Abstract
Indoor localization is one of the key techniques for location-based services (LBSs), which play a significant role in applications in confined spaces, such as tunnels and mines. To achieve indoor localization in confined spaces, the channel state information (CSI) of WiFi can be [...] Read more.
Indoor localization is one of the key techniques for location-based services (LBSs), which play a significant role in applications in confined spaces, such as tunnels and mines. To achieve indoor localization in confined spaces, the channel state information (CSI) of WiFi can be selected as a feature to distinguish locations due to its fine-grained characteristics compared with the received signal strength (RSS). In this paper, two indoor localization approaches based on CSI fingerprinting were designed: amplitude-of-CSI-based indoor fingerprinting localization (AmpFi) and full-dimensional CSI-based indoor fingerprinting localization (FuFi). AmpFi adopts the amplitude of the CSI as the localization fingerprint in the offline phase, and in the online phase, the improved weighted K-nearest neighbor (IWKNN) is proposed to estimate the unknown locations. Based on AmpFi, FuFi is proposed, which considers all of the subcarriers in the MIMO system as the independent features and adopts the normalized amplitudes of the full-dimensional subcarriers as the fingerprint. AmpFi and FuFi were implemented on a commercial network interface card (NIC), where FuFi outperformed several other typical fingerprinting-based indoor localization approaches. Full article
(This article belongs to the Special Issue Indoor Positioning Technologies for Internet-of-Things)
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10 pages, 502 KiB  
Communication
MLP-mmWP: High-Precision Millimeter Wave Positioning Based on MLP-Mixer Neural Networks
by Yadan Zheng, Bin Huang and Zhiping Lu
Sensors 2023, 23(8), 3864; https://doi.org/10.3390/s23083864 - 10 Apr 2023
Cited by 2 | Viewed by 1926
Abstract
Millimeter wave (MMW) communication, noted for its merit of wide bandwidth and high-speed transmission, is also a competitive implementation of the Internet of Everything (IoE). In an always-connected world, mutual data transmission and localization are the primary issues, such as the application of [...] Read more.
Millimeter wave (MMW) communication, noted for its merit of wide bandwidth and high-speed transmission, is also a competitive implementation of the Internet of Everything (IoE). In an always-connected world, mutual data transmission and localization are the primary issues, such as the application of MMW application in autonomous vehicles and intelligent robots. Recently, artificial intelligence technologies have been adopted for the issues in the MMW communication domain. In this paper, MLP-mmWP, a deep learning method, is proposed to localize the user with respect to MMW communication information. The proposed method employs seven sequences of beamformed fingerprints (BFFs) to estimate localization, which includes line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. As far as we know, MLP-mmWP is the first method to apply the MLP-Mixer neural network to the task of MMW positioning. Moreover, experimental results in a public dataset demonstrate that MLP-mmWP outperforms the existing state-of-the-art methods. Specifically, in a simulation area of 400 × 400 m2, the positioning mean absolute error is 1.78 m, and the 95th percentile prediction error is 3.96 m, representing improvements of 11.8% and 8.2%, respectively. Full article
(This article belongs to the Special Issue Indoor Positioning Technologies for Internet-of-Things)
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