# A Survey of Application of Machine Learning in Wireless Indoor Positioning
Systems

Amala Sonny, Abhinav Kumar, , and Linga Reddy Cenkeramaddi  This work was
supported in part by the Department of Science and Technology (DST),
Government of India, under Indo-Norway Joint Project. The work of Dr. Abhinav
Kumar was also supported in part by TiHAN Faculty Fellowship.Amala Sonny, and
Abhinav Kumar are with the Department of Electrical Engineering, Indian
Institute of Technology, Hyderabad, Sangareddy, Telengana, 502284, India
(e-mail<EMAIL_ADDRESS>abhinavkumar@ee.iith.ac.in).Linga Reddy
Cenkeramaddi is with the ACPS Group, Department of ICT, University of Agder,
4879 Grimstad, Norway (e-mail: linga.cenkeramaddi@uia.no).

###### Abstract

Indoor human positioning has become increasingly important for applications
such as health monitoring, breath monitoring, human identification, safety and
rescue operations, and security surveillance. However, achieving robust indoor
human positioning remains challenging due to various constraints. Numerous
attempts have been made in the literature to develop efficient indoor
positioning systems (IPSs), with a growing focus on machine learning (ML)
based techniques. This paper aims to compare and analyze current ML-based
wireless techniques and approaches for indoor positioning, providing a
comprehensive review of enabling technologies for human detection,
positioning, and activity recognition. The study explores different input
measurement data, including RSSI, TDOA, etc., for various IPSs. Key
positioning techniques such as RSSI-based fingerprinting, Angle-based, and
Time-based approaches are examined in conjunction with various ML methods. The
survey compares the positioning accuracy, scalability, and algorithm
complexity, with the goal of determining the suitable technology in various
services. Finally, the paper compares distinct datasets focused on indoor
localization, which have been published using diverse technologies. Overall,
the paper presents a comprehensive comparison of existing techniques and
localization models.

###### Index Terms:

Angle of Arrival (AoA), Indoor positioning systems, Localization, Machine
Learning, Neural networks, Radio Frequency, Sensors, Time of Arrival (ToA),
Wireless Systems.

##  I Introduction

Indoor human detection and positioning have been gaining much attention in
recent times. Indoor positioning systems have the objective of determining the
real-time location of users or devices within buildings, employing a range of
technologies. The demand for indoor positioning-based military and commercial
applications spurred the development of this domain immensely. It is
empowering the Internet of Things (IoT) by introducing various applications
for personal navigation, security and context awareness [1]. IoT integrates
rapidly into human life in different ways such as health monitoring for
elderly people, activity recognition in smart homes, breath monitoring
systems, human identification and detection in safety and rescue operations,
security surveillance, and for intrusion detection systems [2].

The lack of Line-of-Sight (LoS) between satellites and the receivers renders
the Global Positioning System (GPS) and Global Navigation Satellite System
(GLONASS) unsuitable for indoor accuracy. Poor indoor connectivity has spurred
research for alternative localization methods. Various signals, including Wi-
Fi, Bluetooth [3], FM radio [4], and radio-frequency identification (RFID)
[5], are being explored for this purpose. Systems employing Received Signal
Strength Indicator (RSSI), OFDM channel state information (CSI), antenna
arrays, RFID tags, Wi-Fi fingerprinting, or visible LED lights have achieved
sub-meter accuracy, albeit requiring users to carry a wireless device as an
access point (AP) [6]. Fingerprinting techniques are prevalent, involving
offline training to generate a radio map and online estimation for location
detection using RSS data [7]. However, device heterogeneity may introduce
anomalies and challenging a robust indoor positioning [8, 9].

Among the existing indoor positioning technologies, vision and sensor network-
based approaches face limitations due to privacy concerns, complexity, and the
necessity for extensive device coverage. Various systems utilize RADAR [10],
SONAR [11], lasers, dedicated sensor networks [12], and cameras [13] for
indoor positioning and activity recognition. Vision-based systems, reliant on
high-resolution cameras and line-of-sight (LoS) conditions, are constrained by
illumination requirements and privacy concerns [14]. Wearable sensor-based
methods necessitate specialized hardware, potentially inconveniencing users,
especially the elderly and children, with high installation and maintenance
costs [15]. Commercial examples like Xbox Kinect [16] utilize depth sensing
and vision for motion recognition. However, RADAR and SONAR-based systems
primarily detect motion direction via Doppler shift, limiting their
applicability in real-life settings due to existing drawbacks in performance
and practicality.

Figure 1: Organization of the Paper.

Thus, wireless signals-based positioning, activity sensing, and recognition
serve as complementary techniques due to the widespread deployment of wireless
devices [17]. This passive sensing method, attracting increasing interest,
offers device-free detection without privacy concerns, applicable for indoor
positioning, gesture recognition, activity recognition, and motion tracking
[18, 19, 20]. Its versatility extends to smart homes, healthcare, security,
safety systems, and entertainment applications. Lots of techniques have been
proposed based on this using a set of receivers or USRP SDR devices. However,
device-free human detection and activity recognition is a very challenging
task. Recent studies demonstrate that human presence and activities can
significantly affect wireless signals, with multipath signals reflecting,
refracting, and scattering. However, environmental factors like multipath
fading, non-line-of-sight (NLoS), obstructions, crowd density, and weather
conditions complicate the wireless signal in indoor environment. Selecting
appropriate mathematical models for signal analysis and target localization is
crucial, with triangulation, fingerprinting, and multilateration being common
techniques [21]. Machine learning (ML) tools, such as multi-layer perceptron
(MLP), radial basis function (RBF), k-nearest neighbors algorithm (k-NN), and
recurrent neural network (RNN), address non-linearities and enhance data
analysis accuracy. Various ML models have been employed in indoor positioning
systems, utilizing signals from multiple access points (APs) [3] in wireless
sensor networks (WSNs) [22, 23] and neural networks for fingerprint-based
approaches, improving positioning performance in WLAN environments.

Therefore, this survey paper offers a comprehensive examination of current ML-
based approaches in wireless indoor positioning. It seeks to provide an in-
depth comparison of these technologies, covering various ML methodologies in
terms of accuracy, cost-effectiveness, and more. Additionally, the paper
explores the wide array of localization technologies applied in indoor
positioning, and publicly available datasets relevant to indoor positioning.
Since a combined survey covering all these subdomains is not available, this
paper can act as a guide for referencing the state-of-the-art approaches in
this emerging area. The primary motivation behind this work is to shed light
on this intricate domain and address research challenges within it. Our goal
is to furnish readers with essential references to deepen their understanding
and foster further contributions in this area. The paper’s organization is
outlined in Fig. 1.

The structure of the paper is as follows: Sections II and III give insights
into the background and Indoor Positioning Systems (IPSs), respectively.
Section IV outlines various Machine Learning (ML) techniques employed in
diverse IPSs. The comparison and analysis of various positioning technologies
are presented in Section V. Section VI showcases publicly accessible datasets
in IPSs employing different technologies. Lastly, Section VII outlines
forthcoming trends in IPS.

##  II Background

Table I: Comparison of existing survey papers in Indoor positioning. Title |  Year |  ML-based |  VLC |  IR |  LiDAR |  Sound |  RADAR |  Magnetic Sensor |  Wi-Fi |  BLE |  RFID |  UWB |  WSN |  LTE |  Zigbee
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
This Work |  2023 |  |  |  |  |  |  |  |  |  |  |  |  |  |
A. S. Yaro et al. [29] |  2023 |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
A. Shastri  et al. [13] |  2022 |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
P. S. Farahsari et al. [49] |  2022 |  |  |  ✗ |  ✗ |  |  ✗ |  ✗ |  |  |  |  |  ✗ |  |
M. Elsanhoury et al. [37] |  2022 |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗
P. Roy $\&$ C. Chowdhury [28] |  2022 |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
Yang et al. [41] |  2021 |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  |  |  |  |  ✗ |  ✗ |
F. Alam et al.[39] |  2021 |  |  |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
P. Roy $\&$ C. Chowdhury[50] |  2021 |  |  ✗ |  ✗ |  ✗ |  |  |  |  |  |  |  ✗ |  ✗ |  ✗ |  ✗
N. Singh et al.[30] |  2021 |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
A. Nessa et al. [51] |  2020 |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  |  |  |  ✗ |  ✗ |
F. Zafari et al. [42] |  2019 |  ✗ |  |  ✗ |  ✗ |  |  ✗ |  ✗ |  |  |  |  |  |  ✗ |
N. Saeed et al. [34] |  2019 |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗
F. Khelifi et al. [35] |  2019 |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗
W. Liu  et al. [31] |  2019 |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
R. F. Brena  et al. [45] |  2017 |  ✗ |  |  |  ✗ |  |  ✗ |  |  |  ✗ |  |  ✗ |  |  ✗ |
S. Xia et al.[33] |  2017 |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
A.Khalajmehrabadi  et al. [17] |  2017 |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
A. K. Paul $\&$ T. Sato[36] |  2017 |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗
J.Xiao et al. [52] |  2016 |  |  ✗ |  ✗ |  ✗ |  |  ✗ |  |  |  |  |  |  ✗ |  |
A. M. Ossain $\&$ W.-S. Soh [8] |  2015 |  ✗ |  ✗ |  |  ✗ |  ✗ |  |  ✗ |  |  |  |  |  ✗ |  ✗ |  ✗
S.He $\&$ S.-H. G. Chan [9] |  2015 |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
M.A. Al-Ammar  et al. [32] |  2014 |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
F. Ijaz et al. [38] |  2013 |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗
K. Al Nuaimi $\&$ H. Kamel [44] |  2011 |  ✗ |  |  |  ✗ |  |  |  ✗ |  ✗ |  ✗ |  ✗ |  |  ✗ |  ✗ |  ✗
T. Teixeira et al. [46] |  2010 |  ✗ |  ✗ |  |  |  |  |  ✗ |  |  ✗ |  |  |  ✗ |  ✗ |  ✗
Y. Gu  et al. [47] |  2009 |  ✗ |  ✗ |  |  ✗ |  |  |  |  |  |  |  |  ✗ |  ✗ |  ✗
I. Guvenc $\&$ C.-C.Chong [40] |  2009 |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  |  |  ✗
H. Liu  et al. [48] |  2007 |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  ✗ |  |  |  |  |  ✗ |  |  ✗
Figure 2:  Classification of IPS-based on positioning technology. Vision-based
techniques are not covered in this article.

Wireless signals encounter multipath effects from reflection, refraction, and
scattering in both indoor and outdoor environments, posing challenges for
accurate location tracking. Indoor Positioning Systems (IPS) address indoor
navigation difficulties using technologies like Wi-Fi, Bluetooth, and UWB,
enhancing location-based services, asset tracking, and security in places such
as malls, airports, and offices [26]. IPS-based systems are crucial for modern
indoor navigation, meeting the demand for precise location data and
facilitating mapping, optimization, and improvement of indoor spaces. Unlike
outdoor environments dominated by GPS, indoor settings face signal attenuation
and multipath complexities. IPSs systematically gather data to evaluate scenes
and positioning methods, improving accuracy and effectiveness in locations
like malls and airports.

Here, we are comparing some of the most referred IPS-based surveys in the
literature in terms of tasks involved, technology adoption, infrastructure
deployment, data collection approach, signal analysis, and algorithm testing.
Survey outcomes inform IPS deployment for asset tracking, security
enhancement, and personalized services [27]. Bridging the gap between
technology and practical solutions, IPS surveys are pivotal in the evolving
indoor landscape.

The papers [9, 17, 28, 29, 30, 31, 32] and [33] are IPS-based surveys, with an
exclusive focus on Wi-Fi-based methods. The papers [9, 17, 33] discuss the
popularity of indoor location-based services (ILBS) and the use of Wi-Fi
(802.11) as a promising signal for indoor positioning, particularly Wi-Fi
fingerprinting. Meanwhile, [28, 32] explores indoor positioning technologies,
techniques, and algorithms, challenges, proposed solutions, categorization,
and potential enhancements for accurate location tracking.

The surveys in [29, 30] identify the factors involving offline (RSS map
creation) and online (user positioning) phases, factors affecting performance,
discusses their impact, mitigation strategies, and future trends in 2D RSS
fingerprinting-based I-WLS. Additionally, [31] provides a comprehensive review
of indoor positioning methods based on Wi-Fi’s CSI, comparing geometry-based
and fingerprint-based positioning systems and discussing multi-source fusion
technologies and the transition from traditional ML to deep learning methods.

The papers [34, 35, 36] delve into Wireless Sensor Networks (WSNs)-based
positioning, covering both range-based and range-free methods. They explore
measurement techniques, applications, and challenges like accuracy, cost,
complexity, and scalability. Survey [37] focuses on Ultra-WideBand (UWB)
positioning systems, highlighting advantages for indoor positioning and smart
logistics. The study in [38] compares real-time indoor location systems,
emphasizing RF and ultrasonic technologies, and highlighting ultrasonic
systems’ fine-grained location capabilities at low costs. Similarly, [13]
reviews mmWave communication and radar devices for indoor positioning and
sensing, emphasizing their potential in 5G and 6G networks, stressing the need
for improved hardware and integration. Passive RF or wireless sensing
approaches in IPSs are unobtrusive and well-researched. The survey in [39]
reviews non-RF techniques like visible light, infrared, and electric field
sensing, discussing limitations and future directions. And [40] offers a
unified survey of Time-of-Arrival (TOA)-based positioning and NLoS mitigation
algorithms, covering bounds, estimators, NLoS techniques, challenges, and
future research. This comprehensive review explores wireless device
localization through TOA from base stations, encompassing various algorithms
with varying accuracies, complexities, and robustness against NLoS effects.

The surveys conducted by [41, 42] provide an extensive overview of indoor
positioning techniques encompassing Angle of Arrival (AoA), Time of Arrival
(ToF), Round trip Time of Flight (RToF), and Received Signal Strength (RSS),
utilizing technologies like Wi-Fi, RFID, UWB, and Bluetooth. These surveys
emphasize human users and devices, spotlighting system strengths, and
assessing aspects such as energy efficiency, cost, and accuracy. In
particular, [41] delves into supervised (SVM, KNN, NN) and unsupervised
(isolation forest, k-means, EM) approaches, as well as Bayesian filtering
methods. In a similar vein, [8, 43, 44, 45, 46] explore emerging solutions and
introduce additional evaluation criteria, assessing various proposed
positioning systems through both traditional and novel criteria. Notably, [46]
compares active vs. passive sensors, single-modality vs. sensor fusion
approaches, and instrumented vs. non-instrumented settings. Wireless IPSs have
gained traction in applications like asset tracking and inventory management.
In this context, [47, 48] furnish an overview of existing wireless technology-
based solutions, categorizing techniques and systems. The focus lies on
analyzing triangulation, scene analysis, and proximity, with a special
emphasis on location fingerprinting. Furthermore, the study presented in [47]
delves into personal networks (PNs) that interconnect users’ devices via
various communication technologies to offer location-aware services.

The articles [49, 50, 51, 52] provide an extensive overview of diverse indoor
positioning methods utilizing technologies such as Visible Light Communication
(VLC), Infrared (IR), Sound, Wi-Fi, and Bluetooth Low Energy (BLE) etc., The
focus is on machine learning-based techniques employed by these approaches to
create IPSs. The referenced paper offers a detailed exploration of machine
learning methods for indoor positioning using technologies like Wi-Fi and
Bluetooth. It examines traditional algorithms, feature selection, and emerging
Deep Learning techniques, addressing challenges and advancements in the field.

The paper covers the applicability of machine learning, basic principles,
challenges, and recent advancements, offering insights into the future of
indoor positioning research. The survey in [49, 52] presents a comprehensive
survey of wireless indoor localization from a device perspective, reviewing
recent advances in both modes, emphasizing smartphone integration and human-
centric device-free approaches. It compares schemes based on accuracy, cost,
scalability, and energy efficiency, and addresses intrinsic challenges and
open research issues in the field. The discussion in [49] extends to location-
based services (LBSs), real IoT applications, and notable vendors, offering
valuable insights for future IoT-driven research in positioning services.

A comprehensive technological comparison of the surveys discussed is presented
in Table. I. The analysis indicates that this study excels in terms of
encompassing a wide range of technologies associated with IPSs.

##  III Indoor Positioning System (IPS)

Indoor positioning systems (IPS) aim to determine the location of users or
devices within buildings using diverse technologies in real-time. As defined
in [54], an IPS must furnish the real-time location of entities within
physical environments like gyms, hospitals, and shopping malls. Ideally, such
systems should swiftly update target locations while covering vast areas. The
data offered by IPSs varies based on user requirements; some provide absolute
target locations by referencing predefined areas, crucial for precise tracking
and navigation. Others offer relative position information, detecting the
status of different target components. Proximity location, estimating
approximate target locations, is another data type obtainable from IPSs. For
instance, IPSs may indicate the room where a patient resides in a hospital.
Each type serves distinct purposes, catering to diverse applications and user
needs.

IPSs rely on a diverse set of components to accurately determine location
within indoor environments. Sensing technology, including Wi-Fi, Bluetooth,
and RFID sensors, forms the foundational elements for gathering device
positions and signals [55, 56]. Various IPSs employ these technologies
independently or in combination to sense the environment and attribute data
accordingly. The classification of IPSs based on positioning technology is
outlined in Fig. 2. In this survey, we are covering all the technologies
mentioned in Fig. 2 except the vision-based approaches considering the length
of the study. Each technology offers distinct advantages and limitations, with
selection based on the service requirements of the IPS.

Data preprocessing plays a crucial role in refining raw sensor data for
accurate positioning calculations. Techniques such as noise reduction, data
fusion, signal strength calibration, and map matching are essential for
enhancing accuracy and reliability [57, 58, 59]. Moreover, outlier detection
methods and data privacy measures are pivotal for robust positioning while
ensuring user privacy [60, 61]. However, preprocessing aspects related to IPSs
are not within the scope of this survey. Ultimately, a robust data analysis
engine, powered by algorithms and machine learning, refines accuracy and
updates real-time positions, forming the framework for IPSs. These
interconnected components enable a variety of applications, including
navigation services and asset tracking. A visual representation of the typical
IPS building blocks is provided in Fig. 3.

Figure 3: Components of an Indoor Positioning System.

###  III-A Positioning Techniques

In this section, we explore various indoor positioning methods used to
determine the location of objects within indoor environments. We will discuss
each of the main methods in detail.

####  III-A1 Triangulation

RSSI, Angle of Arrival (AoA) and Time of Arrival (TOA) are methods for target
position calculation by using the geometric properties of triangles [62, 63].
Among these, TOA is the most accurate and complex and is unaffected by
Multipath effects in indoor scenarios. RSS and TOA require at least 3
reference nodes (RNs) for localization. The basic principle of localizing a
target in 2D or 3D position using TOA or RSS is given in Fig. 4. Localization
in 2D or 3D space via TOA or RSS depends on knowing the positions of 2 or 3
RNs respectively. The method estimates the transmitter’s location based on
angles and distances between the RNs, improving accuracy with more RNs
particularly effective with Line of Sight (LoS) conditions. Bluetooth 5.1 uses
a similar approach for its positioning system. The three RNs - RN1, RN2, RN3
have geographical coordinates at (x1,y1), (x2,y2), (x3,y3) respectively. The
exact location of the target at $(x,y)$ can be estimated using the angles
$(\alpha,\beta,\gamma)$ and the distance between RNs in Fig. 4. AOA is less
complex and it requires only two reference points. However, it may have less
accuracy if the target is far away.

####  III-A2 Trilateration

rilateration, a technique for determining the position of objects in 2D or 3D
space using distances from known reference nodes (RNs), calculates
intersections of circles (2D) or spheres (3D) centered at these points. It is
widely used in GPS, indoor positioning, and robotics, along with challenges
from measurement errors and signal interference. Increasing the number of
reference nodes improves system reliability and accuracy. Fig. 4 demonstrates
2-D localization, where the object’s position intersects at least three
circles/spheres from anchor nodes. System reliability improves with more
anchor nodes, aiding accuracy. $(x,y)$ denotes the unknown object’s
coordinates, while $(x1,y1),(x2,y2),(x3,y3)$ represent known RNs [64, 65].

a) Tringulation b) Trilateration c) Proximity

Figure 4:  Positioning Techniques

####  III-A3 Fingerprinting

Fingerprinting positioning enhances IPS accuracy by incorporating prior
location data. It comprises offline mapping using RSSI signals stored in a
database and online comparison of current RSSI values to determine the nearest
point, thus locating the target. Matching methods encompass deterministic and
probabilistic algorithms, with the latter offering superior performance albeit
with increased computational demands. Techniques like KNN and SVM provide
satisfactory results, while DNNs with SDAE yield precise fingerprinting
outcomes. Despite its effectiveness, fingerprinting is laborious, time-
consuming, and reliant on predefined maps, necessitating remapping for
environmental changes. Various strategies are employed to mitigate these
challenges [66, 67, 68].

####  III-A4 Proximity

In certain situations, pinpointing precise object locations might be
unnecessary, with an approximate zone around the subject proving sufficient.
Beacons detect objects approaching Points of Interest (PoIs), with the
object’s detection zone determined by adjusting Tx-power. Proximity location
sensing compares the target’s position with a known location in the same
environment. If a target is detected by any detector within the scenario, its
location is deemed within that detector’s proximity area [47]. In Fig. 4, each
square denotes the proximity area of detectors $D1,D2$, and $D3$, tracking
targets $T1$ and $T2$ by detectors D1 and D2 respectively. Target T3 remains
untracked as it’s outside any detector’s proximity. While unable to provide
absolute target location like other methods, it suffices for estimating target
presence in a room. Proximity positioning solely offers proximity data,
prompting IPSs to integrate multiple techniques for enhanced accuracy in
diverse applications like hospitals and malls.

####  III-A5 Trajectory

Trajectory-based Indoor Positioning Systems (IPS) utilize object or user
movement patterns to determine positions in indoor settings. Unlike methods
relying solely on instant measurements, trajectory-based IPSs leverage
continuous motion data from mobile devices or sensors. These data, including
accelerations, rotations, or step counts, undergo analysis using algorithms to
estimate object trajectories. Comparing these trajectories with reference data
or employing map-matching techniques allows the system to determine the
current object position [69].

###  III-B Sensing Techniques

This section discusses sensing techniques derived directly from receive
signals of the corresponding networks. Signals propagate through either LoS or
NLoS paths indoors. LoS paths allow accurate distance estimation but pose
challenges indoors due to obstructions. NLoS paths introduce additional
attenuation from obstacles, causing distance estimation errors. Hence, various
techniques are necessary for precise localization, as discussed in this
section [70].

####  III-B1 Signal Based

This method is the most straightforward and frequently employed in indoor
localization settings. It relies on measuring the power and strength of the
received signals.

#####  Received Signal Strength (RSS)

RSS-based localization, integral to Distance-Based Localization (DBL), relies
on trilateration. RSSI, indicating the power received by a receiver from the
radio signal, is a prevalent data type derived from networks like GSM, WLAN,
GPS, and Bluetooth [71, 72, 73]. It involves using RSS measurements to
estimate the distance between the user device and at least three reference
points, enabling geometric calculations for spatial coordinates determination,
as illustrated in Fig. 5. Measurement-Based Localization (MBL) similarly
utilizes RSS measurements from reference points to ascertain the user device’s
position, often requiring a central controller or ad-hoc communication network
for data processing. Both model-based [74, 75] and fingerprint-based [76]
localization approaches leverage this data. The model-based method employs a
log-normal path loss model to estimate distances to User Equipment (UE) [77].
Despite its ubiquity, RSS suffers in indoor scenarios due to fluctuations.
While the RSS-driven approach is cost-effective, its accuracy is compromised,
especially where line-of-sight is obstructed, leading to decreased precision
due to signal attenuation and multipath fading [67, 68]. Although filtering
methods can mitigate some issues, achieving high localization accuracy without
sophisticated algorithms remains

#####  Channel State Information (CSI)

CSI is information that represents the estimates of the channel of a
communication link. It is a fine-grained measurement of the physical layer
properties including the amplitude and the phase of each orthogonal subcarrier
in the channel. It describes how a signal is propagating from the transmitter
to the receiver and the extent of scattering, fading, and power decay
throughout the propagation. Recent studies showed the capability of CSI
measurements for localization because of the multipath effects [78, 79].

####  III-B2 Time Based

This technique relies on measuring the propagation time of signals. The
distance between RNs and the mobile node (MN) is calculated by multiplying the
time of propagation with the signal speed.

#####  Time of Arrival (TOA)

TOA, denoting the absolute time for a signal to travel from an RN to a UE
[40], offers energy-efficient real-time localization. However, precise clock
synchronization from at least 3 RNs is necessary for accuracy [63]. Assuming
the signal’s transmission at time 0 from the UE and reception by the ith RN at
time $t_{i}$, $t_{i}$ serves as TOA. The UE’s location is determined by
intersecting circles centered at each RN with $d_{i}$ as radius [64, 65, 80].
Cheung $et\leavevmode\nobreak\ al.$ proposed an approach for localizing UE in
both LoS and NLoS scenarios [81]. Consequently, the separation between
entities $RN_{i}$ and $UE$ can be determined employing equation

$d_{i}=(t_{2}-t_{1})\times c$

a) RSSI b) TOA c) TDOA

Figure 5:  Positioning Techniques

#####  Time Difference Of Arrival (TDOA)

The location of the UE is identified by using three or more remote receivers
(RNs) capable of detecting signals of interest. Each RN is time-synchronized
to capture the reference signal, enabling the calculation of the distance
between the UE and RNs based on differences in arrival times. Multiple
receivers provide hyperbolic curves as solutions, with the UE located at their
intersection [82, 83, 84]. The system of hyperbola equations can be resolved
through linear regression [48] or Taylor-series expansion approximation. Fig.
5 demonstrates the utilization of three distinct Reference Nodes (RNs) to
determine the two-dimensional location of any target, illustrating the
hyperbolas formed from RN measurements for location determination. Similar to
ToA techniques, TDoA precision depends on factors like signal bandwidth,
receiver sampling rate, and unobstructed line of sight. Stringent
synchronization is crucial, although TDoA requires synchronization only among
transmitters, unlike ToA techniques that necessitate synchronization between
the transmitter (Tx) and receiver (Rx).

##### Round trip Time of Flight (RToF)

Round-trip time of Flight (RToF) measures the spatial gap between MN and RNs
by timing a signal’s journey from MN to RN and back. Similar to time-of-flight
(ToF), RToF calculates the full round-trip ToF. RToF requires moderately
synchronized clocks between MN and RNs, unlike ToF. However, its precision is
influenced by factors like sampling rate and signal bandwidth, more so due to
double signal transmission. Latency in receiver response is a concern,
especially in short-range systems like indoor localization [85, 86, 87].

####  III-B3 Angle-based

Angle-based methods utilize signal angles, such as radio waves or light, to
estimate user or device positions by measuring arrival angles from multiple
RNs, enabling precise localization.

a) AoA b) PoA

Figure 6:  Positioning based on AoA and PoA

#####  Angle of arrival (AOA)

In AOA-based localization, the UE’s position is determined where pairs of
angular direction lines intersect, each circular line formed with the mobile
target situated on it. Unlike TOA and TDOA methods, AoA only requires two RNs
equipped with antenna arrays. Although more flexible than TOA and RSSI
methods, AoA-based localization faces limitations due to obstructions,
multipath propagation, and the lack of LoS. OFDM signals have helped alleviate
multipath issues. While three measurements suffice for 3D localization and two
for 2D, AoA requires extensive hardware and suffers from reduced accuracy
proportional to the distance from the RN due to multipath reflection and
shadowing [88]. Fig. 6 illustrates AoA’s application in approximating the
user’s position by leveraging signal reception angles.

##### Phase of Arrival(PoA)

Phase of Arrival (PoA) techniques utilize the phase or phase difference of the
carrier signal to determine the distance between sender and receiver. The
assumption is that signals from anchor nodes or the user device are perfectly
sinusoidal with identical frequency and no phase deviation. Methods for
estimating the range between the Tx and Rx using PoA include considering a
finite transit delay (Di) as a fraction of the signal wavelength. Incident
signals reach different antennas within the array with distinct phase
disparities, enabling user location derivation. PoA can enhance localization
precision when combined with RSSI, ToA, and TDoA, but its reliance on
unobstructed line-of-sight poses challenges for accuracy, especially in indoor
environments.

Table II: COMPARISON OF ML-BASED TOOLS FOR IPS.  Tool |  Complexity |  Interpretability |  Linearity Status |  Computational Cost |  Outlier Sensitivity |  Memory Intensity |  Flexibility |  Robustness to Noise |  Variance
---|---|---|---|---|---|---|---|---|---
SVM [90, 91] |  Medium |  Low |  Linear $\&$ non-Linear |  Low |  High |  Low |  Low |  Low |  Low
PCA [78] |  Medium |  Low |  Linear |  High |  High |  Low |  Low |  High |  Low
ANN [92, 93, 94] |  Medium |  Medium |  Linear $\&$ non-Linear |  Medium |  High |  High |  Medium |  Low |  Medium
DNN[97, 98] |  High |  Medium |  Linear $\&$ non-Linear |  High |  High |  Medium |  High |  Medium |  High
RNN [100, 101] |  High |  Low |  Linear $\&$ non-Linear |  High |  High |  Low |  Low |  Medium |  High
LSTM [102] |  High |  Medium |  Linear $\&$ non-Linear |  Medium |  High |  High |  Medium |  High |  High
MLP [23, 103] |  Medium |  Low |  Linear $\&$ non-Linear |  Low |  Medium |  Medium |  Medium |  High |  High
CNN [104, 105] |  High |  High |  Linear $\&$ non-Linear |  High |  High |  High |  High |  Medium |  High
RBF [107, 108] |  Medium |  High |  Linear $\&$ non-Linear |  Low |  Low |  Low |  Medium |  High |  High
KNN [109, 110] |  Low |  High |  Linear $\&$ non-Linear |  High |  High |  High |  Low |  Medium |  High
RF [135, 211] |  Medium |  High |  Linear $\&$ non-Linear |  Low |  Low |  High |  High |  High |  High

##  IV PIONEERING SENSING TECHNOLOGIES FOR INDOOR POSITIONING

The process of localization involves analyzing data from various sources to
determine the location of an object or individual. In the literature, several
ML tools have been utilized to analyze and classify the data in different
localization approaches. These tools include supervised and unsupervised
learning algorithms, deep learning methods, clustering techniques, and
regression models. By adopting various ML tools, researchers have been able to
achieve high accuracy and efficiency in localization applications, making them
a valuable asset in the field of location-based services. The choice of ML
model for an IPS depends on the specific use case and data characteristics. By
utilizing these models, IPS can provide accurate and reliable location
tracking and data analysis for a variety of indoor environments and
applications. A comprehensive comparison of these models, considering factors
such as complexity, interpretability, and flexibility, is provided in Table
II. This section explores innovative sensing technologies designed for
different IPSs, employing sensors like Wi-Fi, Bluetooth, and magnetic fields
for device/object localization. Integrating these sensors with ML models
boosts IPS precision, enabling reliable location data for navigation,
tracking, and automation. Table. III compares these technologies based on
coverage area, cost, and energy efficiency.

Table III: INDOOR POSITIONING TECHNOLOGIES.  Technology |  Coverage |  Cost |  Energy |  Computation |  Accuracy |  Advantages |  Disadvantages
---|---|---|---|---|---|---|---
GPS |  5m |  Low |  Low |  Low |  Low |  • Highly ubiquitous • Wide Area Coverage |  • Poor indoor performance
• Signal Quality Variability
• Signal Interference:
IR |  5m |  Low |  Low |  Low |  High |  • Low Signal Interference
• Enhanced privacy
• Fine-Grained Positioning
|  • Line-of-Sight Requirement
• Sensitivity to Lighting Conditions
• Limited Range

Magnetic |  10m |  High |  Low |  Medium |  Low |  • No pre-deployed infrastructure is required
• Magnetic field is everywhere and relatively stable |  • Complexity increases as the number of sensors increases
• Accuracy depends on the variation in magnetic field.
WLAN |  45m |  Low |  High |  Medium |  High |  • Use existing communication networks
• Majority of devices available nowadays are equipped with WLAN connectivity
• LOS is not required |  • Highly influenced by environmental changes.
• Security concerns
VLC |  60m |  Low |  Medium |  Medium |  High |  • Supports larger bandwidth
• Secured communication
• No interference due to EM radiations
• Easy to install
|  • Interference issues from other light sources
• Requires both source and receiver should be in LOS
• Easily affected by atmospheric absorption, shadowing, and beam dispersion
RADAR |  70m |  Medium |  Low |  Medium |  High |  • High Robustness
• LOS is not required
• Highly scalable |  • Limited resolution
• High chances of signal interference
• Complex signal processing
WSN |  100m |  Low |  Low |  Medium |  Medium |  • Suitable for the non-reachable places
• Can accommodate new devices at any time.
• Flexible |  • Low Speed
• Security issues
• Highly influenced by walls, and microwave
RFID |  100m |  Low |  Low |  Medium |  Medium |  • LOS is not required between RF transmitters and receivers
• Penetrate through solid and non-metal objects |  • Less compatible with other technologies
• The data signal is affected by antenna
• Security issues.
Zigbee |  100m |  Low |  Medium |  Low |  Medium |  • Low power consumption • Offer low latency
Offers interoperability between devices from different manufacturers. |  • Interference
• Limited Data Throughput
• Limited Network Size.
UWB |  200m |  High |  Medium |  Medium |  High |  • Penetrate through walls
• No interference with existing RF systems |  • Interference due to metalic and liquid materials
Bluetooth |  400m |  Low |  Low |  Low |  Medium |  • Do not require LOS
• A lighter standard
• Highly ubiquitous |  • Relatively expensive
• High radio interference
Cellular |  8km-35km |  High |  Low |  Medium |  High |  • Mobile phones can be used
• No interference with other devices operating at the same frequency. |  • Low reliability
Acoustic |  - |  High |  Medium |  High |  High |  • No need of LoS
• Low Interference
• Available on Smartphones |  • Sensitive to environmental conditions
Ultrasonic |  - |  High |  Medium |  High |  High |  • No need of LoS
• Low Interference
• Available on Smartphones |  • Sensitive to temperature and pressure
• No penetration though some solids
Table IV: VLC $\&$ IR-based indoor positioning systems $\&$ solutions. Technology |  Positioning algorithm |  Complexity |  Scalability |  Cost |  Paper
---|---|---|---|---|---
Visible Light |  ANN |  Medium |  Low |  Low |  [92, 93, 94, 113, 115, 95]
Visible Light |  Regression NN |  Medium |  Medium |  Low |  [100]
Visible Light |  DNN |  High |  Low |  Low |  [114]
PIR Sensor |  DT |  Low |  Low |  Low |  [122]
PIR Sensor |  XGB, SVM, LR, RF |  Medium |  Low |  Low |  [123]
PIR Sensor |  ANN |  Medium |  High |  Low |  [124]
PIR Sensor |  CNN-BiLSTM |  High |  Low |  Low |  [118]
PIR Sensor |  Active transfer learning |  High |  Low |  Low |  [121]
PIR Sensor |  CNN |  High |  Medium |  Low |  [117]
PIR Sensor |  DCNN, LSTM, RNN |  High |  High |  Low |  [119, 120]
IR Sensor |  SVM |  Low |  Low |  Low |  [125, 126]
IR Thermal Sensor |  DCNN |  Low |  Low |  Low |  [128]
IR Thermal Sensor |  RF, GNB, kNN, SVM |  Medium |  Low |  Low |  [129]
IR Thermal Sensor |  SVM,FFNN |  Medium |  High |  Low |  [130]
IR Thermal Sensor |  DNN |  High |  Low |  Low |  [127]

###  IV-A Visible Light Communication (VLC)-based

Visible light communication (VLC) using LEDs has garnered attention due to its
energy efficiency and cost-effectiveness, especially for indoor illumination,
and its resilience to electromagnetic interference. Consequently, research
efforts have intensified to improve indoor visible light positioning (VLP)
systems. Photo-diodes (PDs) are preferred over image sensors for VLC detection
due to their affordability and simplicity, particularly with the RSS
algorithm, which is more reliable in larger scenarios [95]. However, RSS-based
VLP suffers from positioning errors, prompting researchers to explore ML-based
methods to enhance accuracy.

Various ANN-based visible light indoor positioning systems are proposed in
[92], [93] and [94]. In [92], an LED bulb is considered as the transmitter,
and a photo diode is considered as the receiver. The communication channel is
modeled as a visible light channel influenced by multipath effects which is
removed by the application of ANN. The system used the Combined Deterministic
and Monte Carlo (CDMMC) method for localization. In [93], the system used a
back-propagation ANN with optical camera communications where LED lights are
clustered into blocks and these coordinates are encoded using under sampled
modulation scheme. The location of the camera receiver) is localized using the
estimated coordinates of the blocks. Two indoor positioning methods using VLS
-based on ANN are proposed by Shencheng $et\leavevmode\nobreak\ al.$ [94]. In
this approach, the first method creates 4 networks that are used for training
and estimating the coordinates of the target location. The second method
creates only one network with 4 input layers. The system divides the entire
indoor space into small blocks. The receiver captures the signal sent by each
LED with ID information with which the system localizes the receiver. The
transmitted optical signal is modulated using asymmetric clipped optical
orthogonal frequency division multiplexing (ACO-OFDM) technology. These 2
methods are robust and achieved a mean position error of 3.29cm and 2.78cm,
respectively.

The utilization of VLC combined with a genetic algorithm in indoor
localization, as presented in [95], involves calculating 3D coordinates within
the optical wireless environment using multipath reflections. A modified
genetic algorithm facilitates global optimization for 3D position estimation
without prior knowledge of device height or orientation. ANN processes first-
order reflections, achieving a low average localization error of 1.02 cm. 3D
visible light positioning (3DVLP) proposed in [100] excels in accuracy and
consistency. It employs a regression neural network to estimate real-time
target location. Image sensors capture AoA information from transmitting LED
lights for network input. The offline phase preprocesses the data extracted
from reference points, which trains the network using Adam optimizer. The
trained network in the online phase detects target locations with a mean error
of 1.1 cm.

Sayed $et\leavevmode\nobreak\ al.$ introduced two VLC-based methods to locate
a moving user in [113]. VLC transmitters convey location info through visible
light, which a photodetector carried by the target receives. Trilateration and
neural network approaches are used for instantaneous prediction. For LOS, the
system’s max positioning error is 2.9 cm; for NLoS, it’s 8.1 cm, with the
neural network outperforming trilateration. In [114], a Position Estimation
Deep Neural Network (PE-DNN) aided VLC system is introduced to address
complexity and compatibility issues. X. Lin’s approach employs a DNN for
processing data, enabling 2D location estimation with just one LED
transmitter. Achieving centimeter-level accuracy, the system attains a minimum
positioning error of 4.18 cm. In [115], a trained neural network mitigates the
impact of indoor diffuse channels in VLC positioning. Utilizing RSS data and
backpropagation, the algorithm achieves an average positioning error of 6.59
cm.

VLC-based IPSs encounter challenges due to signal blockage, lighting
conditions, and LoS constraints. Complex algorithms for angle estimation and
decoding, specialized hardware, and integration with lighting systems affect
cost and scalability. Addressing these is essential for improving VLC-based
IPS accuracy and feasibility. A comparison of VLC-based IPSs considering
factors like complexity, scalability, and cost is given in Table. IV.

###  IV-B Infrared (IR)-based

. In Infrared (IR) technology for IPS, electromagnetic radiation beyond the
visible light spectrum is utilized. An IR system comprises an emitter diode
emitting bursts of non-visible light as infrared signals. A receiving photo
diode captures these light pulses for processing. IR positioning functions in
active or passive modes, with system reliability contingent on optical signal
properties such as directivity and obstacle response, encompassing
reflectivity and scattering [35]. In IR-based IPSs, the LOS requirement poses
challenges due to occluded no-detection zones from the transmitter or sensor.
This technology is frequently applied in robotics, smart homes, and indoor
navigation systems to furnish real-time location information and enhance user
experiences indoors.

Numerous research works have been suggested for IPS employing CNN as the
analytical technique [117, 118, 119, 120]. Recent developments target reduced
sensor density through analyzing analog output [118]. Using PIR and domain
expertise, [118] introduces a CNN-based multi-person localization system with
modules for person count and location determination. It attains 76$\%$ density
reduction while retaining accuracy, employing two-stage networks for signal
separation, detection, extraction, and localization in complex scenarios.
Comparing classical and deep learning algorithms for analog PIR-based human
movement detection, [117] demonstrates CNN’s superior real-time detection and
accuracy, even with limited data, while assessing aspects like scalability and
real-time performance. Leveraging the PIR sensor’s analog output’s reflection
of temperature changes, [119] proposes a CNN-LSTM model capturing temporal
dependencies for accurate location estimation, validated in intricate
scenarios. Similarly, [120] presents a PIR-based system and dataset, applying
CNN, RNN, and CNN-RNN for device-free localization feasibility. Dataset
conversion yields a remarkable 0.25m distance error, spotlighting PIR’s
accurate localization potential [120].

An alternative method within PIR-based Indoor Positioning Systems involves
utilizing thermal imaging to improve localization accuracy[121, 122, 123]. In
[121], a thermal imaging-based localization system proves useful in
emergencies, utilizing active transfer learning for accurate location
determination in dark conditions. Meanwhile, [122] presents a mobile robot
equipped with an IR thermal imaging camera for enhanced localization precision
through overlapping fields of view and a Decision Tree classifier, achieving
96$\%$ accuracy in controlled settings but facing constraints (83.3$\%$) in
unconstrained scenarios. In passive human localization, [123] employs infrared
thermal imaging cameras to detect emitted IR radiation, employing a machine
learning-based approach utilizing human body temperature for precise person-
to-camera distance and localization estimation. Factors like head position,
size, and temperature statistics enable sub-meter accuracy, comparing ML tools
such as XGB, RF, SVM, and LR for person-to-camera distance estimation.

Another application of IR thermal imaging sensors is to use them for precise
tracking of patients and caregivers. This can be facilitated through discreet
approaches such as utilizing low-resolution infrared sensors to protect
privacy[119, 125, 126]. The objective of the research in [125] is to develop a
cost-effective indoor localization system that prioritizes privacy, achieved
by employing a network of infrared sensors with the help of an SVM classifier.
Similarly, [126] also proposed a system to achieve motion detection and group
proximity modeling with an 8x8 infrared sensor array using SVM. Each pixel
provides temperature readings, forming scenes with different human group
configurations and walking directions. Motion direction is inferred using
cross-correlation analysis, while the SVM classifier estimates the number of
human subjects in each scene. Similarly, the proposed approach in [127]
utilizes a low-resolution infrared array sensor and Deep Learning to achieve
up to 97$\%$ accuracy in detecting up to 3 people’s presence and 100$\%$
accuracy in determining their absence. The study in [128] focuses on
unobtrusive human posture recognition for health monitoring, especially for
the elderly with the help of an infrared sensor-based wireless network and
DCNN. Another low-resolution thermal imaging sensor for real-time localization
in smart buildings is proposed in [129]. Different sensors with varying
resolutions are analyzed for their localization performance. The paper
presents a unified processing algorithm pipeline and proposes various
algorithms for data preprocessing, feature extraction, and localization. A
privacy-preserving indoor localization system using IR thermal imaging sensors
and machine learning to collect and analyze toilet usage data in an office is
proposed in [130]. Evaluating occupancy patterns contributes to the evidence-
based design of sanitary spaces and adds to scholarship on indoor localization
methods. The comparison of these LiDAR-based systems is given in Table. V.

IPSs utilizing IR face significant challenges. LoS dependency affects accuracy
due to obstacles, while interference from ambient light sources disrupts
signal integrity. Multipath effects introduce complexities in signal
processing. Signal attenuation caused by distance, absorption, and scattering
complicates distance estimation. Limited signal range necessitates dense
sensor deployment, raising infrastructure costs. Ensuring privacy amid data
capture and infrastructure maintenance adds complexity. Addressing these
challenges requires holistic solutions integrating technology, algorithms, and
system design. The table in IV provides a comparison of IR-based IPSs
considering factors like complexity, scalability, and cost.

###  IV-C LiDAR-based

LiDAR-based IPS utilizes Light Detection and Ranging (LiDAR) technology to
enable precise and accurate localization of objects or devices within indoor
environments. LiDAR technology emits laser pulses and measures the time it
takes for the pulses to return after bouncing off surfaces, creating detailed
3D maps of indoor spaces. These maps can then be used for various
applications, including indoor navigation, tracking, and automation. The study
proposed in [131] fuses CNN-predicted depth maps with direct monocular
Simultaneous Localization and Mapping (SLAM) depth measurements. This fusion
approach improves reconstruction quality in challenging areas. The depth
prediction also aids in absolute scale estimation, addressing a limitation of
monocular SLAM. Additionally, a framework is introduced to fuse semantic
labels with dense SLAM, enhancing scene reconstruction from a single view.
Similar to this, a DL architecture for real-time dense mapping and tracking is
proposed in [132]. Synthetic viewpoints aid incremental tracking, simplifying
the learning process. The proposed system accumulates information in a cost
volume for accurate depth estimations by combining depth measurements and
image-based priors. [133, 134] introduced a system that seamlessly integrates
fingerprinting-based indoor localization with minimal data collection effort.
LiDARs are leveraged to tag Wi-Fi scans during regular user movement, reducing
human intervention. These systems can efficiently build the fingerprint
database using even a single LiDAR, allowing reuse across buildings and
minimizing deployment costs. An alternative strategy for incorporating LiDARs
into IPSs involves the utilization of compact wearable micro LiDARs for
localization [135]. Such a system is introduced in [135] for accurate human
identification in real-time life-logging applications. The system utilizes 3D
point cloud data extracted from the LiDAR sensor, removing noise and
background through Spatio-temporal density clustering and fisher vector
representations. A random forest classifier is trained on the extracted
features, achieving 99.9$\%$ accurate subject identification.

LiDAR-based IPSs face distinct challenges. Firstly, LiDAR sensors require LoS
visibility, hindering accurate positioning in obstructed environments.
Secondly, dense indoor spaces can lead to multiple reflections and scattering,
causing signal distortions. Thirdly, the cost and complexity of LiDAR sensors
can limit their widespread adoption. Moreover, real-time processing of LiDAR
data demands powerful computational resources. Calibrating LiDAR sensors for
accurate measurements and addressing issues related to sensor drift and
environmental changes also pose challenges. These factors collectively impact
the accuracy and feasibility of these systems.

Table V: LiDAR and Sound-based indoor positioning systems $\&$ solutions. Technology |  Positioning algorithm |  Complexity |  Scalability |  Cost |  Paper
---|---|---|---|---|---
LiDAR Sensor |  RF |  Low |  Medium |  High |  [135]
LiDAR Sensor $\&$ RSSI |  LSTM,SVM |  Medium |  Medium |  High |  [133, 134]
LiDAR Sensor |  DL |  High |  Low |  High |  [132]
LiDAR Sensor |  CNN |  High |  Medium |  High |  [131]
Acoustic |  DNN |  High |  Medium |  Medium |  [138, 139]
Acoustic |  PNN |  High |  Medium |  High |  [136]
Acoustic |  DNN |  High |  Medium |  High |  [137]
Acoustic |  CRNN |  High |  Medium |  High |  [140]
Acoustic |  CNN |  High |  Medium |  High |  [141]
Ultrasonic |  SVM |  High |  Medium |  High |  [142]
Ultrasonic |  SVM |  High |  Medium |  High |  [143]
Ultrasonic |  SVM |  High |  Medium |  High |  [144]

###  IV-D Sound-based

Sound Source Localization (SSL) is a vital process that determines the origin
of sound in an environment, crucial for applications like acoustic
surveillance, robot localization, virtual reality, and human-computer
interaction. SSL methods utilize microphone arrays strategically placed to
capture sound signals, analyzing time delay, phase differences, or intensity
variations to estimate sound source direction. Techniques like TDOA, IDOA, and
PDOA are employed, often combined with signal processing algorithms, to
achieve accurate localization in diverse scenarios and noise conditions. SSL’s
significance lies in enhancing situational awareness, aiding robotic
navigation, and enabling immersive audio experiences in various real-world
settings.

####  IV-D1 Acoustic-based

SSL finds utility in various domains like human-robot interaction,
teleconferencing, and automatic speech recognition [136]. For instance, SSL
enables robots to locate patients efficiently. Nonetheless, SSL poses
challenges due to multipath effects, high reverberation, and low signal-to-
noise ratio (SNR). To tackle these issues, [137] introduced an SSL algorithm
employing a probabilistic neural network (PNN) called a generalized cross-
correlation classification algorithm (GCA). GCA transforms the SSL problem
into a likelihood-based nonlinear classification using PNN. To enhance
accuracy and Direction of Arrival (DOA) estimation, they utilized the weighted
location decision method (WLDM). Compared to existing methods, their approach
yields azimuth angle estimation errors averaging 4.6∘ and elevation angle
estimation errors averaging 3.1∘. Additionally, a DNN-based SSL system for
indoor environments is proposed in [136]. It efficiently locates sound sources
in various positions. Microphones capture sound signals, and spatial features
are extracted and represented as likelihood surfaces. An encoder compresses
input data, while decoders estimate source locations.

Many more works are proposed for SSL using various ML tools [138, 139]. In
[138], an unsupervised adapting DNN is used for classification. The influence
of unknown reverberant environments is highly challenging in SSL. However,
they solved this problem by using unsupervised adaption of parameters of DNNs
to the collected sound signals. [139] studied the potential of DL-based time-
frequency (T-F) masking in SSL. It enhances DOA estimation, especially in
reverberant conditions. Speech-dominant T-F units containing relatively clean
phases for DOA estimation are identified by DNN. Another SSL technique using
convolutional recurrent neural network for joint sound event localization is
proposed in [140]. This system is capable of detecting multiple overlapping
sound events in 3D space. The network feeds the sequences of consecutive
spectrogram time frames as inputs. It maps to two outputs parallelly : sound
event detection and localization in 3D space. Here, detection is considered as
a multi-label classification problem and localization is a multi-output
regression task. This system uses the phase and magnitude component of the
spectrogram calculated on each audio channel as the feature separately.

One more paper proposed a similar approach in SSL using CNN [141]. In this
paper, a supervised learning method for estimating the DOA of multiple
speakers is proposed. Hence, the estimation of multi-speaker DOA is considered
a multi-class multi-label classification problem. Here, the phase component of
the short-time Fourier transform (STFT) coefficients of the received signals
from the microphone are fed into the CNN, and the feature estimation of DOA is
carried out during training.

####  IV-D2 Ultrasound-based

Systems using ultrasound signals for location estimation are inspired by the
navigation techniques of bats at night. From this idea [142] proposed an
Active Bat positioning system to provide 3D localization for the tags. Tags
are small tracked devices carried by people and of dimension 7.5cm $x$ 3.5cm
$x$ 1.5 cm. Active Bat system adopts ultrasonic technology and a triangulation
approach to detect the location of the tag-carrying person.

The paper [143] introduces the Acoustic Location Processing System (ALPS), a
platform that enhances BLE transmitters with ultrasound for improved ranging
accuracy in location-aware applications. By placing three or more beacons and
performing a calibration sequence, precise beacon locations and room geometry
are computed. ALPS utilizes time-synchronized ultrasonic transmitters and
achieves an estimated 16.1cm 3D beacon location error and 19.8cm room
measurements error. It can identify NLoS signals with over 80$\%$ accuracy and
track a user’s location within 100cm. SVM is employed to filter out NLoS
signals during user localization after installation. Another work [144]
focuses on sound-based localization technologies for indoor positioning,
compatible with smartphones and cost-effective. However, the NLoS phenomenon
poses challenges. The study proposes an efficient approach using SVM with a
radial-based function kernel to identify NLoS components by characterizing
acoustic channels. Nine novel features are extracted, achieving an overall
98.9$\%$ classification accuracy with a large dataset of over 10 thousand
measurements. The SVM with RBF kernel outperforms traditional classifiers in
acoustic NLoS identification. The comparison of these works is given in Table.
V.

IPSs that utilize acoustic and ultrasonic signals face significant challenges.
Sound waves are easily affected by reflections, absorptions, and diffractions,
causing signal distortions and inaccuracies in position estimation. Background
noise further hampers signal quality, affecting accuracy. Precise
synchronization and timing are essential for distance calculation while
varying propagation due to environmental factors adds complexity. These
challenges collectively influence the reliability and performance of sound-
based IPSs.

###  IV-E RADAR-based

RADAR (Radio Detection And Ranging) systems, utilizing electromagnetic waves,
locate objects by transmitting high-frequency radio waves reflected off
encountered objects, like airplanes or ships. By analyzing the reflected
wave’s characteristics, RADAR determines object range, direction, and speed.
In Indoor Positioning Systems (IPS), RADARs have diverse applications,
including human detection, tracking, and localization. IPS benefits from
RADARs’ unique traits, like their ability to penetrate obstacles and high
measurement accuracy for distances, velocities, and angles. In [10], Bahl et
al. presented a RADAR-based IPS similar to k-Nearest Neighbours (kNN),
offering improved accuracy using signal propagation models. There are
different RADAR types, like pulsed, continuous-wave, and FMCW RADARs, each
have distinct advantages and are suitable for specific applications. We
emphasize FMCW RADAR due to its widespread use in localization tasks.

Table VI: RADAR-based indoor positioning systems $\&$ solutions. Technology |  Frequency |  Positioning algorithm |  Complexity |  Scalability |  Cost |  Paper
---|---|---|---|---|---|---
IWR6843 Radar |  60GHz |  DNN |  Medium |  Low |  Medium |  [155]
IWR6843 Radar |  60GHz |  DBSCAN |  Medium |  Low |  Medium |  [157]
AWR1243 Radar |  79GHz |  SVM |  Medium |  Low |  Medium |  [161]
60 GHz sensing Radar |  60 GHz |  LSTM |  Medium |  High |  High |  [148]
AWR1443 Radar |  77-81 GHz |  CNN |  High |  Low |  Low |  [149]
IWR1443BOOST Radar |  77GHz |  GaitCube |  High |  Low |  Medium |  [147]
AWR1642 Radar |  77GHz |  CNN |  High |  Low |  Medium |  [152]
IWR1443 Radar |  76-81 GHz |  DBSCAN |  High |  Low |  Medium |  [159]
AWR1843BOOST Radar |  77GHz |  RNN AutoEncoder |  High |  Low |  Medium |  [160]
AWR1443 Radar |  76-81 GHz |  DBSCAN |  High |  Low |  High |  [151]
IWR1642 Radar |  77GHz |  CNN |  High |  Low |  High |  [156, 158]
INRAS RadarLog devic |  77GHz |  DBSCAN, DCNN |  High |  High |  Medium |  [150]
AWR1642BOOST Radar |  77GHz |  DCNN |  High |  High |  Medium |  [153]

####  IV-E1 Frequency Modulated Continuous Wave (FMCW)-based

Frequency Modulated Continuous Wave (FMCW) radar uses frequency modulation to
determine the range of targets, emitting a continuous wave with a linearly
varying frequency. The received signal is mixed with a reference signal,
producing an Intermediate Frequency (IF) signal which is proportional to the
range of the target. By measuring the frequency difference between the
transmitted and received signals, FMCW radar can determine the range of the
target [146]. FMCW radar can also provide information on the target’s velocity
and AoA, making it a popular choice for indoor positioning systems. Modern
radars often use FMCW modulation along with Millimeter wave (mmWave)
technology to provide accurate range and velocity information. mmWave radars
are sensors with high resolution and low power, capable of detecting targets
and providing range information regardless of weather conditions or location.
Various systems have adopted mmWave-based point cloud data processing for
object detection and localization. FMCW radars can have a beamwidth and field
of view (FoV) adaptable up to 120o, with a maximum range of 300m. Multiple
radars can be cascaded to achieve a wider FoV. Time-frequency analysis is also
used to investigate further properties of the data, increasing the complexity
of signal processing. Therefore, ML-based approaches have been adopted to
process the radar’s data. When mapping the radar signal onto the environment,
the resulting data points are densely packed in terms of range, angle, and
velocity. Within radar sensing, a notable algorithm for this purpose is the
density-based spatial clustering of applications with noise (DBSCAN) [13].
DBSCAN employs a density-based approach to group points based on their
proximity in regions of high density while disregarding isolated points. The
algorithm starts by selecting an initial point as a core point due to its
dense neighborhood. Other points within the neighborhood of the core point,
within a specified radius, are considered reachable points. DBSCAN evaluates
the neighborhood density of each reachable point and designates new core
points from those within dense neighborhoods. By connecting these dense
neighborhoods, clusters with diverse shapes are formed, consisting of closely
connected points. This iterative process continues until no additional
reachable points exceed the minimum density requirement.

FMCW mmWave radar has emerged as a prominent technology for human detection
and tracking, capitalizing on its capabilities like high resolution and
material penetration. Notably, [147] introduced an inconspicuous human
recognition system employing a novel 3D gait data cube representation, derived
from FMCW mmWave radar micro-Doppler and micro-range signatures. Signal
processing techniques, known as GaitCube, facilitated automatic human walking
detection and segmentation, extracting gait data cubes for analysis. Other
studies like [148, 149, 150, 151] proposed diverse human detection,
identification, and tracking systems utilizing FMCW radars. mmSense[148]
harnessed mmWave’s distinct capabilities, employing LSTM-based classification
to detect and localize multiple individuals without devices. milliMap [149]
offered an indoor mapping system for low-visibility settings, integrating
lidar during training for noise mitigation, and leveraging spectral responses
of mmWave reflections for object identification. Meanwhile, [150] introduced a
system employing backscattered mm-wave signals for simultaneous tracking and
recognition of individuals navigating indoor spaces, employing KFs and DNNs
for multiple-person scenarios. Finally, [151] demonstrated a real-time
tracking system using two mmWave radars with DBSCAN to efficiently detect and
track individuals. These approaches underscore the potential of FMCW mmWave
radar for diverse indoor applications.

Human motion behavior detection using FMCW radars is a burgeoning research
domain within sensing technology. These radars analyze micro-Doppler
signatures generated by human body movements to detect various activities,
postures, and individuals, and even monitor vital signs. The paper [152]
presents a real-time human behavior detection system using AWR1642 radar,
employing micro-Doppler information captured by the radar and a CNN for
classification. Similarly, [153] introduces a patient behavior detection
system combining mmWave radar and deep CNN for real-time recognition of
patients’ behaviors. The radar tracks and monitors patients, analyzing their
scattering point cloud’s Doppler pattern using a deep CNN for accurate
behavior recognition. Human motion identification is addressed in [154, 155],
[154] employs parallel signal processing methods involving statistical
analysis and deep learning-based classification of radar spectrograms. And
[155] utilizes a complex-weighted learnable pre-processing module named
CubeLearn to directly extract features from raw radar signals, subsequently
employing an end-to-end DNN for consistent classification accuracy. These
studies collectively demonstrate FMCW radars’ potential for sophisticated
human behavior analysis and identification. The application of mmWave radar in
sensing extends to human pose estimation, a crucial aspect of human-computer
interaction. This involves accurately recognizing body parts like shoulders,
wrists, and ankles, enabling the creation of dynamic human body skeletons.
This technique finds use in intelligent surveillance, gaming, activity
analysis, and smart homes. Pose estimation approaches are presented in [156,
157, 158]. In [156], a system uses data from two radar sensors to generate
heatmaps, which a CNN then converts into human poses. Training the network
involves coordinated heatmaps from radar and camera inputs. The approach in
[157] categorizes postures (standing, sitting, lying) for an individual
indoors using DBSCAN. Likewise, [158] introduces mm-Pose, a real-time system
detecting over 15 skeletal joints via mmWave radar reflection signals. It
employs a CNN with a forked architecture to estimate the 3D positions of
skeletal joints. These approaches illustrate mmWave radar’s potential in
accurate human pose estimation for diverse applications.

Another emerging application of FMCW mmWave radar is human activity
recognition (HAR). By analyzing the reflected radar signals, such as Doppler
or micro-Doppler signatures, it is possible to detect and recognize different
activities performed by individuals, such as walking, running, sitting, or
gesturing. [159] has explored the utilization of FMCW mmWave radars in
combination with ML techniques (DBSCAN) as part of their human activity
recognition (HAR) approaches. The integration of FMCW radars into health
monitoring systems represents a specific subset of HAR. These systems are
designed to monitor various aspects of human activity, including fall
detection [160] and identifying sleep positions [161], among other
functionalities. All these studies are compared in Table. VI

Usually, radar signals face multipath reflections, resulting in imprecise
distance measurements and positioning. Further, signal interference from
various electronic devices and sources can compromise accuracy. Sophisticated
signal processing is essential to discern and analyze distinct reflections.
Additionally, the presence of dynamic objects or individuals in the
surroundings can influence performance. These combined challenges influence
the dependability and resilience of RADAR-based IPSs.

Table VII: Magnetic Sensor-based indoor positioning systems $\&$ solutions. Technology |  Positioning algorithm |  Complexity |  Scalability |  Cost |  Paper
---|---|---|---|---|---
Geomagnetic Sensor |  TCN, LSTM |  Low |  High |  Low |  [165]
Geomagnetic Sensor |  RNN |  Medium |  Low |  Medium |  [164]
Geomagnetic Sensor |  DNN |  High |  Medium |  Low |  [167, 168, 169]
Geomagnetic Sensor |  ELM |  High |  Medium |  Low |  [173]
Geomagnetic Sensor |  DNN |  High |  Medium |  High |  [162]
Magnetic Sensor |  ANN |  Medium |  Low |  Low |  [163]
Magnetic Sensor |  XGBoost |  Medium |  Low |  Low |  [166]
Magnetic Sensor |  ANN |  Medium |  Medium |  Low |  [170]
Magnetic $\&$ light sensors |  Deep LSTM |  Medium |  High |  Medium |  [102]
Magnetic Sensor |  DCNN, mkNN |  High |  High |  Low |  [171]
Magnetic Sensor |  CNN |  High |  High |  Low |  [172]

###  IV-F Magnetic Sensors-based

Magnetic sensors are integral to IPS, detecting Earth’s magnetic field changes
indoors to ascertain device position and orientation. Variations caused by
objects or architecture create distinct patterns for localization. Geomagnetic
fingerprinting involves comparing real-time readings with a magnetic database.
Offering stability and simplicity, magnetic sensors are preferred in indoor
settings with unreliable RF signals. Machine learning, like recurrent neural
networks, enhances accuracy by analyzing magnetic data sequences,
strengthening IPS performance. Positioning and tracking systems using magnetic
signals offer high accuracy even in NLoS scenarios [162].

Existing magnetic positioning methods encounter challenges in wide-area
accuracy due to magnetic data ambiguity, often requiring multiple sensors.
Addressing this, [162] proposes an indoor system utilizing distorted
geomagnetic fields. Features from magnetic sequences are extracted, and fed
into a trained neural network alongside a magnetic map, yielding 2D locations
with 80$\%$ accuracy. Similarly, [163] employs an ANN with 5 hidden layer
neurons for nonlinear input-output mapping, enhancing self-sensing active
magnetic bearing systems.

Existing RF-based indoor positioning algorithms are unsuitable for large-scale
areas like airports due to proportional positioning errors. A geomagnetic
sensor-based method proposed in [164] uses stable geomagnetic data for indoor
localization. Object movement affects geomagnetic signals, and RNN models
track signal variations for target position detection. RNNs recognize time-
varying sensor data sequences. Training and testing occur using the indoor
space’s magnetic field map, tuning hyperparameters. TensorFlow and CUDA
Toolkit are used, achieving positioning errors of 0.51m and 1.04m for medium
and large spaces, respectively. Another magnetic localization method is
proposed using a multi-scale temporal convolutional network (TCN) and LSTM in
[165]. Time-series preprocessing enhances geomagnetic signal discernibility.
TCN expands feature dimensions while preserving LSTM time-series
characteristics. The proposed stacking framework of multi-scale TCN and LSTM
demonstrates effective indoor magnetic localization. DeepML, another
smartphone-based indoor localization system, employs LSTM networks using
magnetic and light sensors. Bi-modal images are generated via preprocessing
and fed into the network for training, enabling new device localization. It’s
tested across varying environments, showcasing its consistency [102].

In [166], the authors introduce an indoor localization test-bed, achieving a
high accuracy of 98$\%$ through XGBoost algorithms applied to magnetometer
sensors and Wi-Fi access points, and examine classifier performance with
varying test data sizes. Studied in [167, 168], enhances smartphone indoor
localization by combining Wi-Fi RSSI and magnetic field data through DNN. It
encompasses offline learning to extract intrinsic features from multi-class
fingerprints and an online serving phase. Instead of Wi-Fi signals, [169]
explored indoor localization using stable geomagnetic sensor signals. A DNN
model and an RNN track unique geomagnetic field signal sequences caused by
object movement for positioning. Basic RNN and LSTM versions are trained on
magnetic field maps of medium and large-scale indoor testbeds. Similarly,
[170] utilizes smartphone sensors like magnetometer, accelerometer, and
gyroscope for indoor localization. It employs fingerprinting based on magnetic
flux intensity patterns, mitigating database updating and device heterogeneity
issues. ANN aids user state identification (walking/stationary) with 95$\%$
accuracy. The study in [171] proposed a multi-sensor fusion approach to
mitigate device dependency and enhance accuracy. A DCNN recognizes indoor
scenes to refine localization, while a magnetic field pattern database
minimizes device reliance. Modified K nearest neighbor (mKNN), pedestrian dead
reckoning, and an EKF further refine localization. A CNN-based IPS utilizing
magnetic patterns (MP) for localization is proposed in [172]. A database of MP
is created, and CNN matches user-collected MP to estimate positions, employing
a voting mechanism for accuracy. An optimized geomagnetic positioning system
using an enhanced genetic algorithm (EGA) and extreme learning machine (ELM)
is proposed in [173]. EGA optimizes ELM’s parameters, achieving meter-level
accuracy with robustness and faster construction. All the papers mentioned in
this section are analyzed in terms of complexity. scalability and cost in
Table. VII. However, magnetic sensor-based IPS encounters issues such as
signal variability, device heterogeneity, limited range, dynamic environment
effects, and the need for accurate real-time updates, impacting the system’s
accuracy and reliability.

###  IV-G Sensors-based

Sensors generate outputs proportional to the environmental conditions they are
exposed to such as sound, temperature, pressure, light, etc., [174]. Sensors
can be widely divided into two categories, active sensors which can interact
with the environment, and passive sensors which can only receive the data.
Sensor-based indoor positioning systems consist of multiple sensors at
predefined locations to detect and track the position of a person or a device
[175]. The study in [176] introduces a Cascaded DNN (CDNN) that utilizes
smartphone sensor data to accurately localize objects in indoor environments,
catering to the needs of individuals living alone or securing belongings. The
CDNN employs a tree structure of independent Deep Neural Networks (DNNs) for
prediction.

Table VIII: WSN-based indoor positioning systems $\&$ solutions.  Technology |  Positioning algorithm |  Complexity |  Scalability |  Cost |  Paper
---|---|---|---|---|---
WSN |  PNN |  Low |  Medium |  Low |  [180]
WSN |  ANN |  Medium |  Low |  Low |  [12]
WSN - Crossbow IRIS, PSO DV-Hop |  ANN |  Medium |  Medium |  Low |  [96], [178]
WSN |  RBF |  Medium |  Medium |  Low |  [21]
WSN |  DNN |  High |  Low |  Low |  [179]
WSN |  RBF-NN |  High |  Medium |  Low |  [181]
WSN |  Deep extreme learning machine |  High |  Medium |  Medium |  [177]
Zigbee |  kNN |  Low |  Medium |  Low |  [185]
Zigbee |  ANN |  Low |  Medium |  Low |  [183]
Zigbee |  BP-ANN |  Medium |  Low |  Low |  [187]
Zigbee |  QPSO-GRNN |  Medium |  Medium |  Low |  [184]
Zigbee |  Weighted Nearest Algorithm |  Medium |  High |  Low |  [186]

###  IV-H Wireless Sensor Networks (WSN)-based

Wireless Sensor Networks (WSNs) interconnect sensors for tracking,
surveillance, and intrusion detection, eliminating wired connections. They’re
vital in areas like forest fire-prone zones and high-risk areas where wired
networks are impractical. Accurate location data for sensor nodes, along with
transmitted or received data, ensures WSN efficiency. Despite traditional
methods’ shortcomings, new techniques using TDOA data with neural networks
improve node location accuracy [12]. This employs two ANN models: Back
Propagation Network (BPN) and Radial Basis Function (RBF), extracting distance
information between anchor and sensor nodes. RBF outperforms BPN in accuracy,
enhancing the system’s precision in determining node locations.

A fingerprint-based indoor localization approach using WSN is proposed in
[96]. RSSI data collected from a real environment is used to train the feed-
forward ANN to improve accuracy. The system performs better on statistical
data than raw data. Similarly, an algorithm utilizing deep learning, extreme
learning machines, and high-level features extracted by an autoencoder to
enhance localization performance is proposed in [177]. Additionally, the
method gradually increases the training data to update the fingerprint
database and improve performance. Since the wireless channels are vulnerable
to various factors such as multipath effects and shadowing, modeling a proper
propagation loss channel might be difficult and thus the RSSI-based
positioning approaches can be erroneous. To avoid all these limitations, [178]
proposed an indoor positioning technique based on NN and grid sensor training
phase to localize the sensors accurately. The RSS data from the grid is used
to train the NN. The estimation accuracy is directly proportional to the grid
density and it is reliant on the variation in RSS data. An optimization-aided
DNN for location prediction using distance-based features like AoA and RSSI is
proposed in [179]. In this work, a hybrid model, the Lion Assisted Firefly
Algorithm (LAFA), enhances localization accuracy. The study presents a
parametric analysis of the LAFA algorithm, assessing performance and
variations. S. Rajaee $et\leavevmode\nobreak\ al.$ [180] addresses a
positioning approach in ad-hoc wireless networks, where the nodes and anchors
are uniformly distributed in a squared area. The approach uses a probabilistic
neural network (PNN) for estimating the location of unknown nodes. The
proposed system uses Independent Component Analysis (ICA) to reduce the
dimension of the data and to remove the unwanted data which helps to simplify
the data processing. By the use of ICA, the energy consumption is reduced by
43$\%$. The minimum positioning error for the system is 16$\%$. Guangzhu
$et\leavevmode\nobreak\ al.$ proposed a WSN-based localization technique for
tracking moving targets in real time in [181]. This system works on predicting
the location of the moving target in chain-type WSNs using an RBF neural
network. The target tracking prediction problems perform well in real-time.
The major shortcoming of this work is that the prediction algorithm will
perform well in single-target tracking problems. The performance of the system
in multi-target tracking prediction is limited. A similar system is proposed
in [21]. This study explores NNs for solving localization issues in noisy
distance measurements in WSNs. Comparing MLP, RBF and RNN, alongside KF
variants, it finds that RBF offers the best accuracy but the highest resource
requirements.

The study in [182] explores device-free wireless localization and activity
recognition, estimating a person’s location and activity without equipping
them with a device. It uses radio image processing to characterize human
behavior influence on Wi-Fi signals. Unlike traditional methods, it considers
correlated CSI measurements from multiple channels as a radio image. Deep
learning is employed to extract optimized deep features from image features,
achieving excellent performance in location and activity estimation in
cluttered laboratory experiments.

####  IV-H1 Zigbee-based

Zigbee, a decentralized mobile ad-hoc network, finds application in low-power,
low-data-rate scenarios such as home automation, data collection, and short-
range data transfer. Operating at a data transfer rate of approximately 250
kbits/sec, Zigbee’s strength lies in its energy efficiency and long battery
life, albeit with a range limited to 10-100 meters. Its secure networking is
facilitated by 128-bit symmetric encryption keys. Various studies explore
Zigbee’s potential in indoor localization, primarily utilizing the RSS
fingerprinting method with Artificial Neural Networks (ANNs). Optimization
algorithms like Quantum Particle Swarm Optimization (QPSO) are integrated,
enhancing localization precision. Zigbee-assisted indoor localization (ZIL)
enhances Wi-Fi-based positioning by capturing mixed Wi-Fi signals, employing
novel fingerprint-matching algorithms and K Nearest Neighbors (KNN) techniques
for accuracy improvement. Additionally, location calculations in certain
scenarios employ algorithms such as nearest, weighted nearest, and Bayesian
algorithms, yielding enhanced accuracy over longer distances. This approach is
particularly beneficial for larger indoor spaces or underground environments,
augmenting location-based services. In a distinct application, a Wireless
Sensor Network (WSN) coupled with Back Propagation Artificial Neural Network
(BP-ANN) is utilized for indoor localization of Alzheimer’s patients,
demonstrating the versatility of Zigbee technology in healthcare. A
comparative analysis of these studies is provided in Table. VIII.

While WSN and Zigbee-based IPSs are widely adopted, they encounter notable
challenges. Firstly, signal propagation is vulnerable to obstacles and
environmental conditions, causing inaccuracies in distance measurements and
positioning outcomes. Secondly, the constrained range and limited data
transfer speed of Zigbee can undermine coverage and data transmission
reliability. Thirdly, network congestion can emerge from the growing device
count, impacting both communication quality and positioning precision. These
combined challenges collectively influence the resilience and effectiveness of
IPSs based on WSN and Zigbee technologies.

Table IX: RFID-based indoor positioning systems $\&$ solutions.  Technology |  Positioning algorithm |  Complexity |  Scalability |  Cost |  Paper
---|---|---|---|---|---
RFID |  ANN |  Low |  Low |  Low |  [196]
RFID |  ANN |  Low |  Medium |  Low |  [194]
RFID |  RBF |  Low |  Medium |  Low |  [108]
RFID |  CMTL |  Low |  Medium |  Medium |  [200]
RFID |  AIS-RBF-NN |  Low |  High |  Low |  [107]
RFID |  ANN |  Low |  High |  Low |  [116], [191]
RFID |  ANN |  Low |  High |  Low |  [199]
Active RFID - RSS |  kNN |  Medium |  Low |  Low |  [109]
RFID |  ANN |  Medium |  Medium |  Low |  [195]
RFID |  PSO-ANN |  Medium |  High |  Low |  [192]
RFID |  ANN |  Medium |  High |  Low |  [190],[197]
RFID |  RBF |  Medium |  High |  Low |  [198]
RFID- RSSI |  BP-ANN |  High |  Medium |  Low |  [193]

###  IV-I Radio Frequency Identification (RFID)-based

An RF-compatible circuit facilitates electromagnetic transmission for data
storage and retrieval. Essential components of a basic RFID system include
RFID readers, RFID tags, and elements for communication. The system transmits
and receives data within a predetermined radio frequency and protocol,
operating in either passive or active mode [188].

Passive RFID systems serve as alternatives to traditional bar code technology,
being smaller, simpler, and more cost-effective compared to active systems.
These tags operate without a battery, reflecting the RF signal from the reader
to the receiver, with information added by modulating the reflected signal.
However, their operational range is limited to 1-2 meters, making them
unsuitable for larger areas. Commonly used frequency bands include LF, HF,
UHF, and microwave frequencies [48]. Active RFID systems utilize similar
frequency ranges, featuring small transceivers that communicate their ID upon
interrogation. These systems typically offer a larger range and find
applications like SpotON, a 3D location sensing system utilizing radio signal
strength analysis for object location detection [189].

In [192], a Particle Swarm Optimization Artificial Neural Network (PSO-ANN)
algorithm for RFID indoor positioning is proposed, using PSO to optimize ANN
weights and thresholds. This cost-efficient RFID IPS is compared with BPANN,
ANN, and LANDMARC models. The approach establishes the relationship between
RSSI and tag position, enhancing accuracy. A Gaussian filter is employed for
data processing, mitigating environmental effects. Achieving an average
positioning error of 0.6482 m, the method leverages non-contact two-way
communication for data transfer. In a similar vein, [193] presents a Genetic
Algorithm-Backpropagation Neural Network approach, combining GA’s optimum
searching with BPNN’s optimization for RSSI-based indoor positioning. Also,
[194] introduces a high-accuracy indoor location system using active RFID and
neural network classification. In [108], a Radial Basis Function Neural
Network (RBFNN) with Virtual Reference Tags is employed for RFID-based target
localization, achieving a consistent accuracy with a positioning error of
0.472 m, while selecting optimal network architectures.

In [195], an integrated wireless platform proposes an adaptive RFID-derived
RSSI-based indoor location sensing technique. Utilizing a fuzzy neural network
architecture, the method adapts to environmental parameters. Active readers
and tags are used to enable long-distance transmission, achieving less than 1
m positioning error with fewer tags and readers. The approach in [196]
introduces an intelligent tag strength prediction algorithm using
backpropagation learning in neural networks for RFID tag position detection.
It predicts tag signal strength under varying conditions and achieves over
90$\%$ accuracy in estimating target positions. While [197] presents an RFID
hybrid positioning method employing a neural network phased array antenna for
indoor RF localization. Combining AOA and RSSI, it scans the search plane
using phased array antenna radiation beams, achieving a mean positioning error
of 0.32m for 10 locations.

In [198], an algorithm is presented for estimating target location in dynamic
indoor environments like warehouses with changing layouts. By utilizing RSSI
and passive UHF tags as references, a trained RBFNN reduces the Localized
Generalization Error (L-GEM) for 2D warehouse positioning. Similarly, [199]
employs multiple neural networks and a genetic algorithm to estimate indoor
positions from RSSI data collected from reference tags, achieving an average
error of 2.4 m and a maximum error of 5.21 m. Additionally, [200] explores
active RFID for positioning moving targets using Cluster-based Movable Tag
Localization (CMTL) with kNN and ANN, achieving an average positioning error
of 0.77 m. These studies are compared in Table IX. Despite numerous approaches
for IPSs utilizing RFID technology, challenges persist, including fluctuations
in signal strength, positioning inaccuracies, environmental factors, and tag-
related configurations, underscoring the need to address these issues for
improved reliability and accuracy.

Table X: Bluetooth-based indoor positioning systems $\&$ solutions. Technology |  Positioning algorithm |  Complexity |  Scalability |  Cost |  Paper
---|---|---|---|---|---
Bluetooth |  RF |  Low |  Low |  Low |  [211]
Bluetooth |  RF |  Low |  Medium |  Low |  [213]
Bluetooth -RSSI |  Denoising Auto Encoder |  Low |  High |  Low |  [201]
Bluetooth |  KNN, SVM, NN |  Medium |  Low |  Low |  [204]
Bluetooth |  RNN |  Medium |  Low |  Low |  [101]
Bluetooth |  CNN, PCA, t-SNE |  Medium |  Low |  Low |  [210]
Bluetooth |  NN |  Medium |  Low |  Low |  [212]
Bluetooth |  ANN |  Medium |  Medium |  Low |  [202, 203]
Bluetooth-RSS |  XGBoost |  Medium |  Medium |  Low |  [208]
Bluetooth |  Weighted Nearest Neighbors |  Medium |  High |  Low |  [206]
Bluetooth-RSS, AoA |  CNN-PCA |  High |  Low |  Low |  [209]
Bluetooth-RSS |  CNN |  High |  High |  Low |  [205]

###  IV-J Bluetooth-based

Bluetooth, a wireless technology used for short-range communication among
various devices, like computers and smartphones, employs affordable
transceiver microchips and radio systems, ensuring minimal power consumption.
Its operational range, typically around 10-15 meters, varies based on factors
such as materials and has a widespread standard operating frequency of 2.4 GHz
with a lower bit rate. Bluetooth-based indoor localization, favored for its
presence in mobile devices, cost-effectiveness, and energy efficiency,
utilizes unique IDs for precise tag location [3], proving valuable for
tracking and room-specific tasks.

The Denoising Autoencoder-based Bluetooth Low Energy (BLE) indoor localization
(DABIL) [201] employs a 3D indoor positioning system utilizing Bluetooth data.
Utilizing a denoising autoencoder, it extracts relevant fingerprints from RSSI
data to create a 3D reference point database. This method demonstrates
improved vertical and horizontal accuracies with a 1.27m positioning error.
Neural networks are commonly utilized for Bluetooth-based localization. For
instance, in one approach [202], a cost-effective system utilizes neural
networks for user orientation, achieving a 0.5m accuracy. Another study [203]
employs Bluetooth in phones for neighboring positions, using a 6-neuron hidden
layer with a 17$\%$ error rate. Recurrent networks in [101] focus on real-time
Bluetooth localization with a 10m error. In [204], kNN surpasses neural
networks and SVM for indoor Bluetooth positioning with less than 1m error.
Bluetooth RSS in [205], employing CNNs, attains 93.33$\%$ accuracy and less
than 1.4m error, suitable for multi-floor settings, effectively covering large
areas.

The study in [206] proposes a BLE-based indoor localization system utilizing
Android devices to create continuous radio maps with BLE ”iBeacons” and Wi-Fi
access points. Stationary object localization is assessed using Wi-Fi, BLE,
and their combination, with optimal parameter selection through Weighted
Nearest Neighbors (WNN). Another study in [207] proposes a remote indoor
positioning system using kNN analysis and a portable BLE tag, applicable to
tracking individuals in various scenarios. The demand for precise indoor
localization arises from superstores, smart homes, and disaster management
needs. Addressing the complexity of indoor settings, [208] describes an
explainable indoor localization (EIL) technique employing BLE’s RSSI with a
gradient boosting machine. It achieves 98.04$\%$ accuracy within 1.5m in a
superstore environment.

This study in [209] explores IoT-driven location-based services, particularly
in indoor Bluetooth localization using Bluetooth 5.1’s AOA function. They
proposed a DL-based algorithm that fuses RSSI and AOA features through PCA and
KF. CNN extracts deep-level features from RSSI and AOA, followed by
concatenation and Softmax layer classification. The authors of [210] also
proposed an indoor localization system using Bluetooth fingerprinting and CNN.
A unique approach transforms wireless signal data into images using a blurring
technique to simulate signal diffusion. Additionally, two-dimensional
reduction algorithms, PCA and t-SNE, are compared. An evolutionary algorithm
configures the solution with varied transmission power levels. Results
demonstrate a promising accuracy of nearly 94$\%$, highlighting the potential
of this technique for enhanced indoor localization systems.

An improved RSSI-based fingerprinting method, employing data augmentation and
ML algorithms for XY-position identification of user nodes relative to anchor
nodes, is proposed in [211]. RF achieved a 96$\%$ test accuracy, outperforming
other techniques, ensuring precision, and compatibility with ML. In [212], a
smartphone-based indoor location method utilizing BLE Beacons’ RSSI values is
suggested. ML is used to create a distance estimator from RSSI readings.
TensorFlow is employed to calculate intersection points of peripheral lines,
facilitating position estimation based on the geometric median. Additionally,
[213] enhances accuracy by utilizing multiple anchors and radio channels. RF
proves most effective, achieving over 99$\%$ classification accuracy. These
methods are compared in Table.X. Despite Bluetooth technology’s energy
efficiency, device compatibility, and cost-effectiveness in IPS, challenges
like limited range, signal interference, accuracy variation, and privacy
concerns persist. Overcoming these challenges necessitates careful
implementation and innovative solutions for optimal performance in indoor
positioning systems.

###  IV-K Wi-Fi-based

Table XI: Wi-Fi-based indoor positioning systems $\&$ solutions.  Technology |  Positioning algorithm |  Complexity |  Scalability |  Cost |  Paper
---|---|---|---|---|---
RF- RSSI |  ANN |  Low |  Low |  Medium |  [96]
Wi-Fi-RSSI |  Bayesian Network |  Low |  Low |  Medium |  [220]
Wi-Fi -RSS |  DNN,LDA |  Low |  Medium |  Low |  [99]
Wi-Fi-5g |  DNN |  Low |  Medium |  Low |  [247]
Wi-Fi |  DNN |  Low |  Medium |  Low |  [246, 217]
Wi-Fi-RSSI |  Bayesian location estimator |  Low |  Medium |  Low |  [219]
Wi-Fi - CSI |  PCNN |  Low |  Medium |  Low |  [244]
Wi-Fi |  ANN |  Low |  Medium |  Low |  [222]
Wi-Fi- Signal Strenth |  kNN, Viterby-like algorithm |  Low |  High |  Low |  [145, 10]
Wi-Fi RSSI |  PCA |  Low |  Medium |  Medium |  [229]
Wi-Fi-RSSI |  Bayesian network |  Low |  Medium |  Medium |  [224]
Wi-Fi - CSI |  RF |  Low |  Medium |  High |  [240]
Wi-Fi-RSSI |  SVM |  Low |  Medium |  High |  [91]
Wi-Fi, Magnetic Field |  DNN |  Medium |  Low |  Low |  [215]
Wi-Fi |  DNN |  Medium |  Low |  Medium |  [248]
Wi-Fi - CSI |  1D-CNN |  Medium |  Low |  Medium |  [239]
Wi-Fi - RSSI |  CNN |  Medium |  Medium |  Medium |  [218], [230]
Wi-Fi- CSI |  CNN |  Medium |  High |  Low |  [250, 251, 242]
Wi-Fi - RSSI |  kNN |  Medium |  High |  Low |  [111, 112]
Wi-Fi - RSSI |  MMLP |  Medium |  High |  Low |  [24]
Wi-Fi - RSS |  MLP, SVM |  Medium |  High |  Low |  [23, 90]
Wi-Fi - RSSI |  kNN |  Medium |  High |  Medium |  [234]
Wi-Fi-RSSI |  Joint Clustering |  Medium |  High |  Medium |  [111, 112]
Wi-Fi fingerprint |  D-CNN |  Medium |  High |  Medium |  [230]
Wi-Fi - RSSI |  DNN |  Medium |  High |  Medium |  [216, 97]
Wi-Fi RSS |  D-CNN,SVM |  Medium |  High |  Medium |  [214]
Wi-Fi - CSI |  CNN |  Medium |  High |  Medium |  [238]
Wi-Fi MIMO channel |  CNN |  Medium |  High |  High |  [249]
Wi-Fi - CSI |  1-DCNN-LSTM |  High |  Low |  Low |  [69]
Wi-Fi - CSI |  DL |  High |  Low |  Medium |  [236]
Wi-Fi - CSI - MIMO |  DNN |  High |  Low |  High |  [237]
Wi-Fi,LTE,Magnetometer data |  pattern matching algorithm |  High |  Medium |  Low |  [233]
Wi-Fi-RSSI |  DNN, Auto-encoder |  High |  Medium |  Medium |  [227]
Wi-Fi-RSS |  DNN-CNN-DS |  High |  Medium |  Medium |  [226]
Wi-Fi -RSSI |  DNN, KF |  High |  Medium |  Medium |  [245]
Wi-Fi - CSI |  DNN |  High |  Medium |  Medium |  [98, 252, 243]
Wi-Fi-RSS |  LDA, SVM, KNN, RF |  High |  Medium |  Medium |  [231]
Wi-Fi - CSI |  DCNN |  High |  High |  Low |  [79, 241]
Wi-Fi-RSSI |  PaCNN-LSTM |  High |  High |  Medium |  [228]

Wi-Fi, a widely adopted wireless technology, links devices to the internet via
routers, offering a range of 20 to 150 meters, extendable through overlapping
Access Points (APs), making it ideal for localization systems due to its
global presence [214]. Its speed and range adhere to IEEE protocol standards,
with localization relying on ranging-based and fingerprinting-based methods.
WLAN, a local wireless network, operates in limited areas like schools and
campuses, using technologies such as Frequency Hopping Spread Spectrum (FHSS)
and Direct Sequence Spread Spectrum (DSSS). APs, acting as routers, connect to
the internet, typically utilizing the 2.4 GHz frequency with a range of 50-100
meters. Wi-Fi and WLAN-based IPS leverage these technologies for precise and
dynamic localization within enclosed spaces, revolutionizing indoor
navigation. By capitalizing on the proliferation of access points, routers,
and beacons, these systems enable the tracking and positioning of individuals
and objects in real-time, revolutionizing how we navigate and interact with
indoor spaces. While Wi-Fi and WLAN are frequently used interchangeably, in
this context, we are addressing them under the Wi-Fi-based category.

####  IV-K1 Wi-Fi Fingerprinting-Based

In fingerprinting-based methods, creating a fingerprint map and matching it to
online fingerprint data requires significant computing resources. However,
indoor scenarios’ complexity often leads to poor data quality during offline
data collection for fingerprint databases. Therefore, employing ML in
fingerprint-based indoor positioning is preferable, reducing computing
resource usage without sacrificing accuracy. Additionally, this approach
facilitates floor distinction.

##### RSSI-Based

RSSI-based fingerprinting in Indoor Positioning Systems (IPS) serves to
estimate the location of mobile devices within indoor environments by
measuring the power level of Wi-Fi signals received from nearby Access Points
(APs) or routers. While offering simplicity, low-cost implementation, and
compatibility with most Wi-Fi-enabled devices, RSSI-based methods are
susceptible to environmental changes like signal interference, multipath
effects, and signal strength variations due to obstacles. Nonetheless, RSSI-
based fingerprinting remains widely used, especially in environments where
additional infrastructure installation isn’t feasible, such as public spaces
and shopping malls [97]. Recent years have seen an exploration of Deep Neural
Networks (DNNs) in RSSI-based IPSs, with systems utilizing DNNs to classify
and regress Wi-Fi user positions in indoor environments [99, 215, 216, 217].
For instance, A. Adege et al. introduced a DNN-based indoor localization
technique employing Wi-Fi [99], achieving high accuracy by preprocessing RSS
data with Linear Discriminant Analysis (LDA) and selecting the strongest RSS
values. Other techniques like Deep Positioning [215] combine RSSI and magnetic
field data for improved accuracy, while scalable approaches like the one
proposed by K. S. Kim et al. [216] utilize hierarchical DNN architectures for
multi-floor classification with consistent performance. Additionally, in
[218], a Wi-Fi-based approach for localization of Mobile Nodes (MNs) is
introduced, employing RSSI to create location fingerprints and employing a
Convolutional Neural Network (CNN) on time series data to enhance accuracy,
achieving a mean error of 2.77m in predicting coordinates.

In [219], Roos $et\leavevmode\nobreak\ al.$ proposed an IPS. This grid-based
Bayesian estimator achieved over 50$\%$ accuracy within 1.5 meters in a small
office. Similarly, [220] employed a Bayesian probabilistic method for device
localization. Battiti $et\leavevmode\nobreak\ al.$ in [23] developed a system
using an MLP-based classifier and OSS training, yielding less than 3 meters
error with only 5 signal strength samples. Its advantage lies in lower
sensitivity to overfitting. [221] compares neural network and nearest neighbor
classifiers, achieving 72$\%$ accuracy within 1 meter. An MLP in [23] maps RSS
data to user location with an average accuracy of 2.3 meters, leveraging Wi-Fi
infrastructure while addressing privacy. [24] introduces the Modular Multi-
Layer Perceptron (MMLP) for enhanced accuracy in location estimation, managing
uncertainty factors.

In [111, 112], the Horus system employs a joint clustering technique for
probabilistic location estimation. Each person’s location coordinate is
treated as distinct classes, and $L_{i}$ is selected based on the highest
likelihood to minimize distance error. The experiments achieved over 90$\%$
accuracy within 2.1 meters, with improved accuracy observed as the number of
samples per location increased. [90] presents an indoor positioning method
using SVM and statistical learning theory, showcasing SVM’s low error rate as
a classifier and its regression version for mobile user positioning. Another
approach in [91] determines device location using RSSI from APs, employing an
SVM-based fingerprinting algorithm for real-time analysis. [25] introduces the
Discriminant-Adaptive Neural Network (DANN) for Wi-Fi client positioning,
utilizing RSS from APs to construct an accurate RSS-position relationship.
Additionally, [222] proposes an ANN-based approach for real-time target
location detection and room type identification with median positioning errors
of 5.46m and 3.75m respectively. Finally, [223] presents a hybrid WiFi and
WSN-based approach using ANN, achieving an average distance error reduction to
1.05 meters, surpassing GA optimization.

In their work [224], Ladd $et\leavevmode\nobreak\ al.$ presented a grid-based
Bayesian algorithm employing the 802.11 standard for robot localization. Using
RSS data from 9 APs, the host employs a probabilistic model to compute the
position likelihood from a pool of locations, refining the results by
considering the mobile host’s limited maximum speed. Achieving over 83$\%$
accuracy within 1.5 meters, this method proves effective for robot
localization. Similarly, [225] adopts a practical Bayesian approach with the
802.11 architecture for topological localization within office buildings,
reducing training time without sacrificing accuracy. J. Zou et al. proposed a
Wi-Fi localization system [226] utilizing RSS with a deep regression model
named DNN-CNN-DS, comprising DNN, CNN, and Dempster-Shafer. An Auto-Encoder
initializes the DNN weights, optimized by minimizing the mean square error
between model output and real location. DFLAR, a device-free wireless
localization and activity recognition technique [227], employs deep learning
to recognize activities and gestures based on the target’s influence on nearby
wireless links. Similarly, the approach in [228] enhances indoor Wi-Fi
localization accuracy by approximately 22$\%$ through improved contrastive
learning and a parallel fusion network, PaCNN-LSTM. Another system in [229]
enhances WiFi indoor localization efficiency and performance by utilizing PCA.

Another popular strategy in fingerprinting for indoor positioning involves
utilizing fingerprint images derived from RSSI data. Numerous approaches based
on fingerprint image processing and deep learning are presented in [214, 230,
231]. In [214], a dilated CNN is trained on RSS images, and prediction errors
are utilized to train an SVR model, verified using the UJIIndoorLoc dataset
[232]. Alternatively, in [230], Wi-Fi and magnetic signals are transformed
into fingerprint images, with a CNN learning mappings to actual positions,
showcasing robust learning and accuracy. Similarly, [231] introduces MFMCF,
leveraging multi-pattern fingerprints and various classifiers (KNN, SVM, RF)
to enhance localization accuracy by constructing a composite fingerprint set
(CFS) with LDA from SSD, HLF, and RSS. Additionally, [233] presents Wi-LO, an
indoor localization system that boosts Wi-Fi-based accuracy by integrating LTE
and magnetometer data, overcoming mismatches by combining different data types
at each location. Furthermore, another study [234] proposes an ML framework
employing Bag-of-Features and kNN classification, surpassing existing models
in both simulations and real-time experiments.

##### CSI-Based

CSI-based fingerprinting in IPS utilizes detailed information from Wi-Fi
signals’ CSI to create distinct fingerprints for various indoor locations.
This approach offers high accuracy and resilience in complex indoor
environments but requires precise calibration and dense reference point
deployment [98]. Recent studies have extensively employed DL for CSI-based
fingerprinting in IPS [235, 236, 237, 238, 239], providing automatic feature
learning, noise resilience, adaptability to new environments, and real-time
processing [240, 241, 242, 243, 244]. Combining Wi-Fi signals with other
methods can enhance accuracy, as seen in [245, 246], where autoencoders
reduced data dimensions, aiding in position estimation. Additionally, systems
like BiLoc [247] and [79] leverage DL to estimate location using 5-GHz Wi-Fi
CSI, achieving promising accuracy. Meanwhile, [248] introduces data rate (DR)
fingerprinting for passive localization, addressing challenges like low
resolution and fluctuations by employing various strategies such as
transmission power levels and dynamic nearest neighbors matching.

In [249], researchers explore utilizing Massive MIMO channels with CNN for
localization, leveraging the sparse structure of these channels to achieve
fractional wavelength positional accuracy. Another method, ConFi, employs CNN
for Wi-Fi localization [250]. It treats time-frequency metrics from CSI data
as images and utilizes a 5-layer CNN for classification, demonstrating robust
performance with a 2.7 m localization error. Additionally, a CSI image-based
indoor localization method is introduced in [251], forming an RGB image with
phase differences and amplitudes from different antennas, and employing a CNN
for classification. Another CSI-based indoor fingerprinting system, DeepFi,
trains all DNN weights as fingerprints using deep learning in the offline
phase, employing a greedy algorithm for complexity reduction. In the online
phase, a probabilistic data fusion method based on the RBF is utilized for
location estimation.

####  IV-K2 Wi-Fi Ranging-Based

A Wi-Fi IPS that utilizes trajectory CSI observed from predetermined routes
instead of stationary locations, addressing the limitations caused by
multipath fading is proposed in [69]. The proposed IPS employs a one-
dimensional convolutional neural network-long short-term memory (1DCNN-LSTM)
architecture to leverage the spatial and temporal information of trajectory
CSI. Additionally, a generative adversarial network (GAN) helps enlarge the
training dataset, reducing the cost of trajectory CSI collection. All these
studies are analysed in Table. XI.

Table XII: Cellular and UWB-based indoor positioning systems $\&$ solutions.  Technology |  Positioning algorithm |  Complexity |  Scalability |  Cost |  Paper
---|---|---|---|---|---
Cellular |  SVM, NN |  Medium |  Medium |  High |  [261]
Cellular |  kNN |  Medium |  High |  Medium |  [110]
Cellular |  DNN |  Medium |  High |  High |  [255],[260], [256],[257],[258]
Cellular -5G |  SVM,KF |  High |  Medium |  High |  [262]
Cellular -5G |  DNN |  High |  High |  High |  [263]
UWB |  MLP, RPF |  Low |  Low |  High |  [103]
UWB |  ANN |  Low |  Medium |  High |  [265], [266]
UWB |  CNN |  Medium |  Low |  High |  [105, 106]
UWB |  PCA, MC-SVM |  Medium |  Medium |  High |  [268]
UWB- RSSI |  ANN |  Medium |  High |  High |  [267]
UWB |  LSTM |  Medium |  High |  High |  [86]
UWB |  PNN |  High |  Low |  High |  [269]
UWB |  DNN - LSTM |  High |  Medium |  High |  [87]
UWB |  CNN |  High |  Medium |  High |  [104]

Wi-Fi signals usually encounter several challenges including signal
propagation obstruction, multipath effects, and non-line-of-sight scenarios,
leading to inaccuracies. Achieving high accuracy is difficult due to dynamic
environments, infrastructure dependency, and privacy concerns. Calibration and
maintenance are crucial, and interference in crowded channels can impact
accuracy. Implementation complexity, cost, and power consumption pose further
issues, requiring advanced algorithms, hardware improvements, and robust
signal processing techniques for reliable Wi-Fi-based IPSs.

###  IV-L Cellular

Localization methods utilizing cellular data encounter limited popularity due
to intense multipath noise and setup heterogeneity, leading to signal strength
variation and low accuracy within a range of 50-200 meters [253, 254]. The
concept proposed in [254] involves employing wide signal strength fingerprints
for indoor localization, incorporating the six strongest GSM cells and up to
29 additional channel readings. While this enhances accuracy through increased
dimensionality, such channels may hinder efficient communication. Moreover,
this technique is adaptable to IS-95 CDMA and 3G technologies. Another study
[255] introduces a device-agnostic fingerprinting-based localization method
using deep learning, ensuring consistent performance with minimal power usage
and enhancing robustness and system transparency.

SoloCell, described in [256], employs deep learning to optimize indoor
positioning using signal strength history from cell towers. With 7 data
collection modules, its performance was evaluated across diverse Android
devices. MonoDCell [257], another cellular-based method, enhances data
collection efficiency and robustness through deep LSTM networks and multiple
modules, maintaining accuracy within 0.95 to 1.4 meters across different
Android implementations. Additionally, OmniCells [258] utilizes auto-encoders
for device-invariant RSS data learning, ensuring consistent median
localization accuracy even for unknown devices. CellinDeep [259] utilizes DL
for nonlinear cellular data-location relations, ensuring medium accuracy and
93.45$\%$ power efficiency. Another approach in [260] suggests DL-based data
augmentation techniques.

In [261], a novel Long-Term Evolution (LTE) communication infrastructure-based
environment sensing (LTE-CommSense) system is introduced, offering a non-
intrusive, low-cost indoor positioning solution. Utilizing USRP B210, LTE data
is gathered and analyzed using SVM and NN for detection of static and mobile
occupants, achieving high classification accuracies. [262] proposes a 5G-based
navigation method employing ML-based software-defined receiver (SDR) for ToA
estimation. Another 5G-based indoor positioning approach is suggested in
[263], employing RSSI and DNN for localization. The comparison of these
studies is listed in Table. XII. However, cellular signals being optimized for
outdoor use, the signal undergoes degradation in indoors, hindering accuracy.
Additionally, signal fluctuations, interference, and infrastructure reliance
pose challenges for cellular-based IPSs, impacting their effectiveness and
precision.

###  IV-M Ultra-wideband (UWB)

UWB, unlike typical RFID systems, utilizes multiple frequency bands ranging
from 3.1 GHz to 10.6 GHz [264]. It relies on ultra-short pulses narrower than
1 nm and a low duty cycle (1:1000), facilitating easy separation of original
signals from multipath ones. UWB signals transmit for shorter durations than
RFID signals and operate across a broad frequency range. Additionally, UWB
tags require less power compared to conventional RF tags, and their
interference with other RF signals is minimal due to signal type and spectrum
differences.

Various indoor localization techniques employing UWB data and neural networks
are investigated in [265, 266, 267, 268]. These methods utilize channel
impulse responses (CIR) from UWB indoor propagation measurements to construct
fingerprint databases and estimate locations with positioning errors ranging
from 0.081m to 2m. Some address NLoS challenges, exhibiting enhanced stability
with updated databases. Others compare neural network models like MLP and RBF,
exploiting UWB’s high-time resolution for accurate positioning. Additionally,
a CNN-based system in [106] accurately estimates distances, showing promise
for indoor localization in unfamiliar environments with reduced complexity and
low latency. In [103], a subterranean UWB-based fingerprinting radio
localization system assisted by neural networks achieves improved stability
and performance. Comparing two neural network models, MLP and RBF, yields
positioning errors of 0.5m and 0.1524m, respectively. Another UWB-based
strategy in [105] employs a back-propagating neural network to mitigate NLoS
effects, while [106] introduces a CNN-based method for precise distance
estimation without SNR estimation, thereby reducing latency and computational
complexity. UWB signals for indoor localization offer accurate results with
minimized multipath effects. TOA-based distance estimation between ANs and UWB
tags is influenced by TOA errors and LoS conditions. A deep learning approach
employing LSTM networks achieves a mean localization error of 7 cm, surpassing
conventional methods [86, 87]. Recent research explores UWB for indoor
localization, proposing a weighted indoor positioning algorithm that combines
LSTM and DNN to analyze five UWB signal features, leading to enhanced
positioning accuracy under severe NLoS conditions.

In [268], a novel radar system combines UWB with ML, specifically, Multi-Class
Support Vector Machine (MC-SVM), not only to localize targets but also to
understand their locations effectively. Learning from extensive UWB signal
data enables an evolving scheme to provide meaningful data for end-user
appreciation. Experimental results affirm the effectiveness of the proposed
algorithm. Another approach in [269] concentrates on enhancing UWB indoor
localization using a Naive Bayes (NB) ML algorithm. Evaluation through
Receiving Operating Curves (ROC) demonstrates significant enhancements in UWB
localization accuracy, particularly in LoS and NLoS conditions. Table. XII
compares all these studies regarding complexity, scalability, and cost.

UWB-based IPSs encounter challenges like multipath interference, NLoS accuracy
decline, and vulnerability to environmental conditions. Addressing these
issues for reliable indoor positioning in dynamic settings is intricate.
Interference from other wireless systems affects precision, while scalability
and cost-efficiency challenges arise from requiring multiple UWB anchors.
Advanced algorithms and hardware optimizations are crucial for synchronization
and power consumption management.

Table XIII: INDOOR DATASETS. Name |  Technology |  Published Year |  Dimension |  No. of Points |  Scale |  Paper
---|---|---|---|---|---|---
CRAWDADa King  et al. |  RSSI |  2008 |  1D |  146080, 6600 |  221 $m^{2}$, 1 Floor |  [285]
KIOS dataset |  Wi-Fi RSSI |  2013 |  1D |  2100 |  560 $m^{2}$, 1 Floor |  [286]
UJIIndoorLoc |  Wi-Fi |  2014 |  3D |  19,937, 1111 |  108703 $m^{2}$ |  [232]
UJIIndoorLoc-Mag |  Magnetic |  2015 |  1D |  270, 11 |  260 $m^{2}$, 1 Floor |  [270]
KTH/RSS DATASET |  RSSI |  2016 |  1D |  1689 |  400 $m^{2}$, Multiple Rooms |  [279]
Z. Tóth $\&$ J. Tamás |  WLAN,BLE |  2016 |  3D |  1571 |  3 Floors |  [281]
Barsocchi et al.[287] |  Wi-Fi, Geo-Magnetic |  2016 |  1D |  680, 460 |  185 $m^{2}$, 3 Rooms, 4 Hallways |  [287]
Matterpport3D |  RGB-D Image |  2017 |  3D |  194400 |  90 Building-scale scenes |  [290]
Tampere database |  Wi-Fi |  2017 |  3D |  687, 3951 |  22570 $m^{2}$, 4 Floors |  [278]
Minho database |  Wi-Fi-RSSI |  2017 |  1D |  4973, 810 |  1000 $m^{2}$ |  [280]
IPIN2017 Tutorial |  Wi-Fi-RSSI |  2017 |  1D |  927, 702 |  School corridor |  [288]
InLoc |  RGB-D Image |  2018 |  3D |  277 |  Five Floors, 2 Buildings |  [289]
Baronti et al. |  BLE |  2018 |  1D |  2598 |  185 $m^{2}$, 7 Rooms |  [273]
Bristol database |  BLE - RSSI |  2018 |  1D |  1571 |  Multiple Houses |  [274]
D. Sikeridis et al. |  BLE |  2018 |  3D |  - |  3 Floors |  [275]
Mendoza-Silva et al. |  Wi-Fi |  2018 |  3D |  576, 3120 |  308.4 $m^{2}$, 2 Floors |  [276]
JUIndoorLoc |  RSSI |  2019 |  3D |  23,904, 1460 |  882 $m^{2}$, 5 Floors |  [271]
S. Sadowski  et al. |  Zigbee, Bluetooth, Wi-Fi |  2020 |  1D |  73, 32 |  33 $m^{2}$-79 $m^{2}$, 3 Rooms |  [284]
F. Potortì  et al. |  Wi-Fi,Magnetic,Camera |  2020 |  3D |  - |  6000 $m^{2}$, 3 Floors |  [277]
SODIndoorLoc database |  Wi-Fi |  2022 |  3D |  21,205, 2720 |  8000 $m^{2}$, 3 Builings,3 Floors |  [272]
P. Pascacio et al. |  BLE, RSSI |  2022 |  1D |  178487 $\pm$ 85824 |  180 $m^{2}$ |  [282]
TUJI1 Dataset |  Wi-Fi |  2023 |  1D |  6752, 2147 |  Multiple Offices |  [283]

##  V IPS Datasets

Indoor Positioning System (IPS) datasets are collections of data that have
been gathered in indoor environments with the goal of developing and
evaluating indoor localization and tracking algorithms. These datasets are
invaluable for researchers, engineers, and developers working on improving
indoor positioning technologies. Table XIII contains details of various
datasets collected using different technologies for developing IPSs.

The UJIIndoorLoc dataset [232] spans a university campus of approximately
110,000 $m^{2}$, created with 20 individuals and 25 Android devices,
containing 19,937 training and 1,111 validation/test data points.
UJIIndoorLoc-Mag [270] explores magnetic field variations with inertial sensor
data. The JUIndoorLoc database [271] encompasses a five-floor building at
Jadavpur University, with 25,364 samples collected from four Android devices,
including 23,904 training and 1,460 testing samples, and 172 APs. SODIndoorLoc
database [272] is a Wi-Fi-based dataset covering 8000 $m^{2}$ with three
buildings (1-3 floors), including 105 pre-installed APs (56 single-band, 49
dual-band), 1802 points, 1630 RPs, 272 Testing Points (TPs), 23,925 samples,
and three scenes: office, meeting, and seamless hall corridor. Other datasets
include Baronti et al.’s indoor BLE dataset [273] for localization and
tracking, the Bristol database [274], and [275], both BLE-based indoor
positioning datasets supporting location-aware systems, crowd apps, and
building management. The first one covers multiple rooms in different houses
and is collected using a wearable component package based on Texas Instruments
CC2650 system-on-chip (SoC). The latter collected from beacons carried by 46
individuals moving on three different floors of a university building.

The Library dataset [276] is comprised of Wi-Fi RSS data spanning 25 months
from a university library measuring 308 $m^{2}$, featuring 576 training and
3,120 test samples collected via 620 APs. The IPIN 2019 Competition [277]
assessed personal positioning systems across buildings, floors, and diverse
scenarios, highlighting accurate positioning methods for real-world
comparisons. The competition encompassed two adjacent buildings with outdoor
and indoor areas totaling 1000 $m^{2}$ and 6000 $m^{2}$ respectively, spread
over three floors with a path length exceeding 500 m. The Tampere database
[278] gathered 4,648 fingerprints from 21 devices in a 22,570 $m^{2}$
university building, offering 687 training and 3,951 testing fingerprints.
Additionally, the dataset [279] provides RSS data from indoor and outdoor
environments covering approximately 400 $m^{2}$, including hallways, rooms,
and an abandoned steel factory in Dortmund, Germany, recorded via an odometer.
The Minho database [280], collected at the University of Minho, Portugal,
comprises 5,783 fingerprints, with 4,973 training samples, and covers around
1000 $m^{2}$ using 11 APs. Dataset [281] introduces a hybrid indoor
positioning dataset with WLAN, Bluetooth, and Magnetometer data. Unlike
previous single-technology datasets, it covers 50$\%$ of a three-story
building in which each floor covers an area of 1425 $m^{2}$ and the corridors
and the hall cover approximately 465.75 $m^{2}$. Furthermore, BLE dataset
[282] facilitates mobile-based indoor positioning investigations, encompassing
calibration, NLoS ranging, and real office scenarios within a 10.76 m x 16.71
m area. It comprises 14 bookcases, 7 concrete columns, and 3 work sections
with desks, chairs, and computers. Lastly, dataset [283] from UJI, Spain,
presents various office and corridor settings with diverse wireless coverage,
including Wi-Fi RSS, BLE RSS, accelerometer, and GNSS data, gathering
approximately 9,000 annotated samples using GetSensorData 2.1, annotated with
coordinates.

Raspberry Pi 3 Model Bs were utilized to establish a WiFi-based WLAN,
utilizing onboard antennas in [284]. The CRAWDAD [285], KIOS [286], and IPIN
2017 [288] Tutorial datasets consist of RSS fingerprints of small areas with
varying distances between location points. Dataset [289] is an image-based
collection comprising 277 RGBD panoramic images from scanning two Washington
University buildings with a Faro 3D scanner, each housing around 40M colored
3D points. Split into five scenes (DUC1, DUC2, CSE3, CSE4, CSE5) representing
different floors, they are aligned to floor plan areas ranging from 23.5 to
185.8 $m^{2}$. Matterport3D [290] is another image-based dataset featuring
194,400 RGB-D images, obtained through 10,800 panoramas using a Matterport
camera. It offers unique features like human-height viewpoints, global camera
pose consistency, instance-level semantic segmentations, and data collected
from private home living spaces, distinguishing it from previous datasets.

##  VI Future Directions

Figure 7: Future trends in IPSs.

IPSs are revolutionizing the way we navigate and interact with indoor spaces.
As we move further into the future, the role and capabilities of indoor
positioning systems are poised to become increasingly prominent and
transformative. These systems offer the potential to enhance various aspects
of our lives, from improving efficiency in industries to enhancing user
experiences in retail, healthcare, and beyond. A visual depiction illustrating
the forthcoming trends in IPSs through the utilization of diverse technologies
is presented in Fig. 7.

Future research into Wi-Fi fingerprint-based localization should explore the
viability of CSI as a reliable method, leveraging its fine-grained
characteristics for precise positioning. Incorporating vision into Wi-Fi
localization offers expanded possibilities for richer location data. Ensuring
accessibility for physically impaired individuals is crucial for inclusive
indoor positioning services. Developing floor recognition for multi-level
indoor localization enables seamless tracking across various floors.
Optimizing deployment strategies is vital for creating practical Wi-Fi-based
indoor positioning systems that fulfill diverse application needs. Large-scale
location-based data mining holds promise for revealing valuable applications
and insights. Integrating localization systems with 5G infrastructure must
address challenges concerning power consumption and cost. Leveraging
cooperative positioning techniques and data fusion from multiple 5G devices
can enhance real-time localization accuracy. Key considerations for
5G-oriented systems include addressing NLoS scenarios and implementing robust
methodologies for multi-path conditions and floor-level identifications.
Integration of dead-reckoning techniques with visual place recognition can
enhance pedestrian localization. While ensemble models and integrating NN-
based methods with KF-based methods improve accuracy and reliability, careful
deliberation is necessary due to complexity and latency concerns.

Challenges in WSN-based IPSs vary with applications, network scales, and
environments, necessitating cost-effective solutions, diverse sensor
integration, scalability, and computational complexity management. Balancing
accuracy and cost-effectiveness, particularly in range-free techniques, is
crucial. The future of UWB positioning entails hardware advancements and
innovative algorithms for applications like multi-robot positioning and smart
logistics. Integrating low-earth orbit satellites with UWB, GNSS, and inertial
sensors expands indoor and outdoor navigation possibilities.

Ultrasonic-based IPSs need to tackle environmental challenges like noise and
multipath propagation to boost location accuracy. Acoustic IPS research needs
to prioritize real-time tracking, energy efficiency, multi-user localization,
and robustness. ML algorithms and sensor fusion can further enhance
performance. Optical IPSs should focus on accuracy, scalability, and high
update rates. Integrating INS, GNSS, and magnetic sensors can improve system
performance. Cost-effective solutions are essential for widespread adoption.
In mmWave IPSs, advanced hardware and scalable algorithms are crucial for
faster localization and multi-AP management. Integrating ML presents
challenges that require reduced reliance on extensive training datasets.
Combining mmWave radar with camera-based systems and adapting IEEE 802.11ay
for passive sensing hold potential for advanced applications.

The future of indoor and outdoor localization hinges on leveraging ML tools to
broaden their applicability. ML effectively tackles noisy environments and
NLoS conditions, improving data analysis. Key research focuses on floor
detection in multi-storey buildings, demanding 3D localization techniques
empowered by ML-based 3D location estimation. Neural networks aid in feature
extraction, ensuring robust results for tasks like human detection. ML
simplifies localization, accommodating diverse scenarios with suitable
hardware. Cost-effective deployment enables large-scale data mining, enabling
applications like indoor landmark discovery. CSI’s robustness in
fingerprinting may supplant RSSI, while integrating vision, sound detection,
and motion sensors enhances localization, especially for the physically
challenged. Despite challenges, ML-based indoor positioning gains traction,
meeting global demands. The evolving landscape demands innovative indoor
positioning systems, fostering competition and advancement in the field.

##  VII Conclusion

Human localization plays a pivotal role in various applications, from
monitoring elderly health to safety operations. Consequently, extensive
research has been dedicated to developing efficient indoor positioning
systems. Hence, this paper explores different machine learning-based indoor
localization approaches discussed in the literature along with their
performance comparisons. Through the assessment and comparison of diverse
machine learning methods, this study aims to contribute to the advancement of
indoor positioning systems and their application in diverse domains. The
survey also encompasses publicly available datasets pertinent to indoor