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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 August 2024 | Viewed by 3871

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

Published Papers (3 papers)

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Research

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 654
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
Viewed by 1344
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 1 | Viewed by 1390
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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