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Smart Wireless Indoor Localization

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

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 11259

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Special Issue Editors

Prof. Dr. Xiansheng Guo

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Guest Editor

Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: UAV; sensors; trajectory localization; indoor localization; transfer learning; deep learning; target recognition; IoT; edge-computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Nirwan Ansari

E-Mail Website
Guest Editor

New Jersey Inst Technol, Dept Elect & Comp Engn, Adv Networking Lab, Newark, NJ 07102, USA
Interests: indoor positioning; Internet of Things; green communications and networking; cloud computing; drone-assisted networking; various aspects of broadband networks

Prof. Dr. Gang Wang

E-Mail Website
Guest Editor

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China
Interests: source localization; statistical singal processing; convex optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of wireless signals has shown great potential in solving localization problems, especially in GPS-denied indoor environments. However, a number of practical issues, including multipath, NLOS, the large scale of the data, heterogeneous data, and the presence of measurement error, still need to be properly addressed in order to achieve reliable localization in real-world scenarios. With the adaptation of artificial intelligence (in the context of machine learning, transfer learning, reinforcement learning, deep learning, etc.), the focus has been on making wireless indoor localization smarter and more effective. In addition, the recent breakthrough in wireless communication technologies (in the context of IoT, crowdsensing, massive MIMO, intelligent surfaces, etc.) provides a transformative means of turning the wireless environment into a programmable smart entity. This link between the intelligent control of the indoor environment and self-learning artificial intelligence poses many challenges that call for novel approaches and rethinking of the entire localization architecture to meet requirements in accuracy, reliability, budget, and more.

We invite authors from both industry and academia to submit original research and review articles that cover the design, implementation, and optimization, with a specific focus on waveforms, protocols, and positioning algorithms in the following topics (not an exhaustive list):

Waveform and protocol design for indoor localization;
MIMO, massive MIMO, and intelligent reflecting surface (IRS) for indoor localization;
Indoor localization for IoT environment;
Crowdsourcing and sensing approaches for indoor localization;
Artificial-intelligence-enhanced indoor localization;
Multi-sensor fusion for indoor localization;
Transfer learning solutions in indoor localization;
Multi-agent systems for indoor localization;
Novel location-based services and applications;
Multi-object localization in indoors;
Indoor rigid body localization;
Evolutionary computing.

Prof. Dr. Xiansheng Guo
Prof. Dr. Nirwan Ansari
Prof. Dr. Gang Wang
Guest Editors

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Keywords

indoor localization
wireless signals
multimodal fusion
artificial intelligence
machine learning
waveform optimization
multi-agent
transfer learning

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Published Papers (4 papers)

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Research

17 pages, 628 KiB

Open AccessArticle

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

by Jian Huang, Haonan Si, Xiansheng Guo and Ke Zhong

Sensors 2022, 22(23), 9127; https://doi.org/10.3390/s22239127 - 24 Nov 2022

Cited by 2 | Viewed by 1926

Abstract

Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the “negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least

17.15 %

smaller average localization errors (ALEs) than existing transfer learning-based positioning methods, which further validates the effectiveness and superiority of our algorithm. Full article

(This article belongs to the Special Issue Smart Wireless Indoor Localization)

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38 pages, 5375 KiB

Open AccessArticle

OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning

by Hailu Tesfay Gidey, Xiansheng Guo, Ke Zhong, Lin Li and Yukun Zhang

Sensors 2022, 22(23), 9044; https://doi.org/10.3390/s22239044 - 22 Nov 2022

Cited by 3 | Viewed by 2406

Abstract

In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing with the problem of signal distribution variations, especially in a dynamic indoor environment. The reasons are: collecting a sufficient number of training instances is costly, the training instances may arrive online, the feature spaces of the target and source domains may be different, and negative knowledge may be transferred in the case of a redundant source domain. In this work, we proposed an online heterogeneous transfer learning (OHetTLAL) algorithm for IPS-based RSS fingerprinting to improve the positioning performance in the target domain by fusing both source and target domain knowledge. The source domain was refined based on the target domain to avoid negative knowledge transfer. The co-occurrence measure of the feature spaces (

C_{m i}^{p}

) was used to derive the homogeneous new feature spaces, and the features with higher weight values were selected for training the classifier because they could positively affect the location prediction of the target. Thus, the objective function was minimized over the new feature spaces. Extensive experiments were conducted on two real-world scenarios of datasets, and the predictive power of the different modeling techniques were evaluated for predicting the location of a mobile device. The results have revealed that the proposed algorithm outperforms the state-of-the-art methods for fingerprint-based indoor positioning and is found robust to changing environments. Moreover, the proposed algorithm is not only resilient to fluctuating environments but also mitigates the model’s overfitting problem. Full article

(This article belongs to the Special Issue Smart Wireless Indoor Localization)

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39 pages, 3766 KiB

Open AccessArticle

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting

by Hailu Tesfay Gidey, Xiansheng Guo, Ke Zhong, Lin Li and Yukun Zhang

Sensors 2022, 22(22), 8720; https://doi.org/10.3390/s22228720 - 11 Nov 2022

Cited by 6 | Viewed by 3566

Abstract

Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station,

d_{j r}

(iv) correlation of the measurements,

ρ_{r j}^{a i}

and (v) signal propagation parameters

σ_{C}

and

γ

. Full article

(This article belongs to the Special Issue Smart Wireless Indoor Localization)

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20 pages, 2814 KiB

Open AccessArticle

WiFi Indoor Location Based on Area Segmentation

by Yanchun Wang, Xin Gao, Xuefeng Dai, Ying Xia and Bingnan Hou

Sensors 2022, 22(20), 7920; https://doi.org/10.3390/s22207920 - 18 Oct 2022

Cited by 10 | Viewed by 2560

Abstract

Indoor positioning is the basic requirement of future positioning services, and high-precision, low-cost indoor positioning algorithms are the key technology to achieve this goal. Different from outdoor maps, indoor data has the characteristic of uneven distribution and close correlation. In areas with low data density, in order to achieve a high-precision positioning effect, the positioning time will be correspondingly longer, but this is not necessary. The instability of WiFi leads to the introduction of noise when collecting data, which reduces the overall performance of the positioning system, so denoising is very necessary. For the above problems, a positioning system using the DBSCAN algorithm to segment regions and realize regionalized positioning is proposed. DBSCAN algorithm not only divides the dataset into core points and edge points, but also divides part of the data into noise points to achieve the effect of denoising. In the core part, the dimensionality of the data is reduced by using stacking auto-encoders (SAE), and the localization task is accomplished by using a deep neural network (DNN) with an adaptive learning rate. At the edge points, the random forest (RF) algorithm is used to complete the localization task. Finally, the proposed architecture is verified on the UJIIndoorLoc dataset. The experimental results show that our positioning accuracy does not exceed 1.5 m with a probability of less than 87.2% at the edge point, and the time is only 32 ms; the positioning accuracy does not exceed 1.5 m with a probability of less than 98.8% at the core point. Compared with indoor positioning algorithms such as multi-layer perceptron and K Nearest Neighbors (KNN), good results have been achieved. Full article

(This article belongs to the Special Issue Smart Wireless Indoor Localization)

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