Walch et al. [78] modified PoseNet to include LSTM units right before outputs
were produced. These units performed structured dimensionality reduction,
leading to improvements in pose estimation. Fine-tuning was performed using a
specially created dataset of wide-angle images taken $\approx$
$1\text{\,}\mathrm{m}$ apart inside a
$\mathrm{5}\mathrm{,}\mathrm{5}\mathrm{7}\mathrm{5}$ ${\mathrm{m}}^{2}$
university floor. These images were annotated with ground truth pose
information using a laser ranging system. The authors used their dataset, in
addition to the $7$-Scenes dataset, to compare the performance of their
implementation to PoseNet and a state-of-the-art SIFT-based method [147]. The
SIFT-based method outperformed both CNN-based methods on the latter dataset
but failed on the former. This is because SIFT-based methods require accurate
$3$D models to produce pose estimates, something the authors were not able to
render due to the textureless surfaces and repetitive structures of the indoor
environment. Overall, the positioning error was reduced by $\approx$
$30\text{\,}\mathrm{\char 37\relax}$, on both datasets, compared to PoseNet.

Symbolic positioning has also been investigated using feature point detectors
[59, 60]. Werner et al. [124] utilized the CNN-based AlexNet [148] as a
generic feature extractor to classify a query image to one of sixteen rooms.
They called their system Deep Mobile Visual Indoor Positioning System
(DeepMoVIPS). No fine-tuning was performed on the pre-trained network;
instead, the authors directly fed the features extracted by the first FC layer
to a Naïve Bayes classifier. These features helped their model generalize
local to global views (i.e., from small views in training to large views in
testing) well. However, this didn’t hold for the opposite case due to the
spatial invariance of features introduced by the CNN. A room classification
accuracy of $95\text{\,}\mathrm{\char 37\relax}$ was reported using global
views for both training and testing.

Ha et al. [125] proposed using Building Information Modeling (BIM) (i.e.,
digital representations of a building’s physical and functional
characteristics), to reduce the effort of site surveying. The idea was to use
synthetic images rendered from a $3$D BIM model without having to physically
survey the area. However, cross-domain image retrieval was needed since
positioning was performed using real images. Since SIFT-based matching failed,
the authors utilized a pre-trained VGG network [149] for feature extraction.
Features extracted by the fourth pooling layer were matched to those of pre-
stored BIM images through cosine similarity. The BIM image with features
closest to the features of the query image was retrieved and, hence, the
user’s position was determined. An image retrieval accuracy of
$91.61\text{\,}\mathrm{\char 37\relax}$ was reported inside a corridor with
$14$ RPs. While this approach does not require training, the retrieval
technique followed makes the response time proportional to the BIM dataset
size.

Similarly, Acharya et al. [126] proposed BIM-PoseNet, a PoseNet fine-tuned
with BIM rendered images. A virtual camera, with the same intrinsic parameters
as the real camera used for testing, was utilized to tag these images with
pose information. Cross-domain positioning was realized by performing gradient
edge-detection on both synthetic and real images before feeding them to the
network. A median positioning and orientation error of
$1.88\text{\,}\mathrm{m}$ and $None$, respectively, was reported inside a
$30\text{\,}\mathrm{m}$ long corridor. Pose errors were mainly caused by
indoor objects that appeared in the real images (e.g., furniture, decorations,
etc.) but were not included in the BIM model.

###  VI-D Hybrid Fingerprints

### CNN-based Solutions:

Liu et al. [128] proposed VMag, a fingerprinting system that fuses magnetic
field fingerprints with image fingerprints. The authors offered two reasons
for choosing these fingerprint types. First, both types do not depend on
infrastructure for operation. Second, each type complements the other since
magnetic field fingerprints are more prone to global ambiguity while image
fingerprints are more prone to local ambiguity. In their implementation, deep
learning was only leveraged for feature extraction. Image features were
extracted using a pre-trained Places-CNN [146] and fed to a separate FC
network that had the task of fusing them with magnetic field features. The
fused features, along with the user’s step length and heading information
(inferred from IMU measurements), were passed to a particle filter that output
the user’s location. Experiment results based on four different indoor
environments with a combined area of
$\mathrm{8}\mathrm{,}\mathrm{1}\mathrm{6}\mathrm{7}$ ${\mathrm{m}}^{2}$ and
$\mathrm{1}\mathrm{,}\mathrm{0}\mathrm{7}\mathrm{3}$ test RPs, showed a
positioning error of $\leq$ $1.0\text{\,}\mathrm{m}$
$78.0\text{\,}\mathrm{\char 37\relax}$ to $89.0\text{\,}\mathrm{\char
37\relax}$ of the time, depending on the environment. Nevertheless, the use of
particle filtering adversely affected positioning latency since convergence to
a reasonable positioning accuracy took as long as $20\text{\,}\mathrm{s}$.

Ishihara et al. [129] improved the accuracy of camera pose regression by
incorporating BLE RSS readings. They proposed a dual-stream CNN where one
network regresses poses from images while the other simultaneously regresses
poses from BLE measurements. The estimations of both networks were fused using
a single output layer. The weights of the former network were initialized
using the Places dataset while the weights of the latter network were randomly
initialized. A smartphone was used to collect the training data which consists
of (image,BLE) tuples labeled with ground truth pose information using a LiDAR
system. An evaluation was conducted in six different indoor locations, with a
total of $479$ BLE beacons. Compared to PoseNet, which only uses images for
pose estimation, positioning accuracy was improved by
$40\text{\,}\mathrm{\char 37\relax}$ but orientation accuracy deteriorated by
$30\text{\,}\mathrm{\char 37\relax}$. The authors didn’t offer any explanation
as to why the orientation estimation degraded.

Shao et al. [130] exploited the fact that WiFi fingerprints are generally
immune to global ambiguity but prone to local ambiguity, while the contrary is
true for magnetic field fingerprints. They proposed an architecture that
harnesses the advantages of both fingerprint types through a two-branch, two-
step training strategy. The output of two different CNNs, one for each
fingerprint type, was fed to a subsequent CNN that output the final position
estimation. Compared to using a single network, where the fingerprint types
are concatenated and fed as a single input, this strategy yielded better
results at the expense of a prolonged training process. RPs were represented
as circular cells where the fingerprints gathered inside a cell were labeled
with the coordinates of the cell’s center. Investigating the impact of cell
size on positioning error revealed that a considerable gain over (WiFi alone,
magnetic field alone) positioning was more noticeable for larger cells (i.e.,
with diameters of $10\text{\,}\mathrm{m}$ and up). This suggests that this
approach is more advantageous for large-scale environments. The authors
briefly investigated user heterogeneity by experimenting with four
participants of different heights and concluded that the difference in
positioning error between participants was insignificant. A positioning error
of $\leq$ $2\text{\,}\mathrm{m}$ $95\text{\,}\mathrm{\char 37\relax}$ of the
time was reported inside a
$\mathrm{2}\mathrm{,}\mathrm{4}\mathrm{0}\mathrm{0}$ ${\mathrm{m}}^{2}$ area
for a cell diameter of $10\text{\,}\mathrm{m}$.

### RNN-based Solutions:

Wang et al. [131] proposed DeepML, a system that blends magnetic field
fingerprints and visible light fingerprints to provide indoor positioning for
places that have little or no WiFi coverage, such as underground parking lots.
Visible light fingerprints were chosen because they share two important
characteristics with magnetic field fingerprints (i.e., omnipresence and
stability). For example, underground parking lots maintain a lighting
infrastructure in which the generated light intensity does not change over
time. DeepML uses a sliding window of magnetic field and light intensity
measurements for input and maps it to the most likely location via an LSTM
network. An additional step was introduced for online positioning, calculating
a location as a weighted average of all locations in the environment and the
system’s previous estimations for each location. While such an approach could
result in robust estimations, the authors did not discuss its implications on
positioning latency. Also, the impact of noise on light intensity measurements
caused by transient light sources, such as car headlights, was not
investigated. The authors reported a positioning error of $\leq$
$0.5\text{\,}\mathrm{m}$ $60\text{\,}\mathrm{\char 37\relax}$ of the time for
two testbeds; one with a $1$D layout of $10$ RPs and the other with a $2$D
layout of $12$ RPs, where RPs in both testbeds were separated by
$1.6\text{\,}\mathrm{m}$.

### AE-based Solutions:

Gu et. al. [127] approached indoor positioning from the perspective of
locomotion activity recognition (LAR). This stems from the idea that a user’s
symbolic location can be inferred by simply determining his/her activity. For
example, if a user’s activity is recognized as “using the elevator,” then the
user must be in an elevator. Correctly identifying such activities can aid
positioning systems in filtering out unlikely estimates. To this end, the
authors utilized an SDAE that took a two-second window of accelerometer,
gyroscope, magnetometer, and barometer measurements, at
$32\text{\,}\mathrm{Hz}$, and mapped it to $1$ of $8$ activities using a
softmax layer. These activities included elevator up/down, stairs up/down,
walking/stationary, running, and false motion (the user remains stationary
while using the phone for texting, gaming, etc.). The authors demonstrated
that some activities are more distinguishable than others. For example, stairs
up/down was sometimes misclassified as walking and vice versa. The authors
also demonstrated that the combination of four sensors yielded higher accuracy
than combinations of two/three sensors or individual sensors. An overall
F$1$-score of $0.94$ was reported; however, the authors showed that the
accuracy can vary between users depending on their movement characteristics.
While such an approach may not qualify as a stand-alone positioning technique,
it could be used by crowdsourcing methods in identifying landmarks for
fingerprint annotation.

### FC-based Solutions:

Belmonte-Hernández et al. [83] proposed SWiBluX, an indoor tracking system
that employs three wireless technologies: WiFi, BLE, and Xbee. The system uses
special anchor nodes to collect the signal emitting from a custom-built
wearable device. To account for measurement variability due to signal
absorption by a user’s body, a user’s heading angle is attached to each
measurement. This resulted in better position estimations compared to only
using signal fingerprints. An FC network, which the authors showed to
outperform $k$NN, SVM, and other shallow learning algorithms, was chosen as a
location classifier. To filter out spurious predictions, the network’s output
was further refined using Gaussian outlier detection followed by particle
filtering. While these post-processing phases improved the network’s estimates
by $25\text{\,}\mathrm{\char 37\relax}$, the corresponding computational
overhead in the offline phase was inadmissible. Moreover, commodity
smartphones currently do not provide support for Xbee. Experiments conducted
inside a $651\text{\,}{\mathrm{m}}^{2}$ testbed, in which RPs were represented
as rectangular cells of various sizes, demonstrated a mean positioning
accuracy of $0.45\text{\,}\mathrm{m}$.

###  VI-E Miscellaneous Fingerprints

### 1) UWB Fingerprints

### DBN-based Solutions:

Luo and Gao [132] used a DBN to track UWB emitters indoors. They exploited the
fact that the channel impulse response (CIR) of UWB signals vary based on the
geometry of the indoor environment and the distance between the emitter and
receiver. They used some parameters of the CIR, such as the number of
multipath components and the power of each path, as location fingerprints.
Since their system was tacking-based, these parameters were extracted from
several UWB receivers deployed in the environment. The DBN correlated the
extracted parameters to the position of the UWB emitter. During training, the
network was pre-tuned on unlabeled parameters then fine-tuned on location-
labeled parameters. The authors reported a positioning error of $<$
$1.5\text{\,}\mathrm{m}$ $90\text{\,}\mathrm{\char 37\relax}$ of the time
using only three UWB receivers in a $280\text{\,}{\mathrm{m}}^{2}$ area. This
result is better than the error achieved by an FC network of the same
architecture. The authors attributed this to the extra pre-training step.
However, it should be noted that all the experiments and data in their work
are simulation-based.

### 2) Visible Light Fingerprints

### FC-based Solutions:

Zhang et al. [133] used an FC network to regress the $2$D coordinates of a
photodetector. Four LEDs served as signal sources; each with a unique
modulation frequency (different from the background light frequency) to
prevent interference between them. The RSS of each LED was extracted by
Fourier transformation and input into the FC network. The evaluation was
performed in a small $1.8$$\times$$1.8\text{\,}{\mathrm{m}}^{2}$ area in which
the LEDs were mounted at a height of $2.1\text{\,}\mathrm{m}$. Out of the
$100$ uniformly distributed and equally spaced RPs, $20$ were chosen for
training while all $100$ RPs were used for testing. The authors investigated
the impact of training RP placement on positioning accuracy. Three settings
were used: 1) arbitrary set: RPs were randomly placed in the area; 2) even
set: RPs were uniformly distributed and equally spaced; 3) diagonal set: RPs
were placed along two diagonals. The best result was attained by the diagonal
set ($3.40\text{\,}\mathrm{cm}$), followed by the arbitrary set
($4.35\text{\,}\mathrm{cm}$), and the even set ($4.58\text{\,}\mathrm{cm}$).
Compared to a lateration-based algorithm implemented in the same area, the
positioning error achieved by the diagonal set is $91\text{\,}\mathrm{\char
37\relax}$ lower.

### 3) RFID Fingerprints

### DBN-based Solutions:

Jiang et. al. [134] utilized RFID readers and reference tags for passive
positioning. In such an implementation, the users, carrying the tags, have no
means of triggering positioning requests. Instead, the system tracks users’
positions through the readers. DBNs were employed for this purpose and
training was performed as described by Wang et al. in [106]. However,
simulated RSS measurements generated by a logarithmic path loss model were
used. The authors demonstrated that DBNs not only outperformed $k$NNs and
W$k$NNs but are also more resistant to RSS noise, which they attributed to the
robust feature learning ability of DBNs. The testbed consisted of a simulated
$12$$\times$$12\text{\,}{\mathrm{m}}^{2}$ room with six readers deployed along
two sides of the room (i.e., three readers on each side). A positioning
accuracy $<$ $2\text{\,}\mathrm{m}$ $90\text{\,}\mathrm{\char 37\relax}$ of
the time was achieved using $619$ uniformly distributed training RPs and $20$
randomly placed testing RPs.

### 4) Acoustic Fingerprints

### CNN-based Solutions:

Zhang et al. [135] used a CNN to perform sound source localization. They
simulated a $10.2$$\times$$10.2\text{\,}{\mathrm{m}}^{2}$ area with four
microphones placed at the corners. The idea was to convert the sound waveform
captured by the microphones into spectrograms and then combine them into a
single spectrogram (a $2$D image) that highlighted the amplitude and time-
delay differences between the microphones for a given location. The area was
divided into nine sectors where
$\mathrm{1}\mathrm{,}\mathrm{1}\mathrm{0}\mathrm{0}$ images were generated per
sector. The model generating the images took the reverberation effect and
white noise into account. A sector classification accuracy of
$98\text{\,}\mathrm{\char 37\relax}$ was reported. The authors demonstrated
the supremacy of their implementation, in terms of accuracy and prediction
latency, over $k$NN and SVM. However, the network was designed for a single
sound source, a specific smartphone ringtone. It would be interesting to see
whether the network would generalize to other ringtones or even a human voice.

### Discussion

This subsection summarizes the main characteristics of the different deep
learning models employed by the surveyed solutions and discusses which model
works best for which contexts.

AEs are a family of hourglass-shaped neural networks[150]. Thus, they have the
same number of neurons in the input layer as the output layer. A typical AE is
trained to reconstruct an input using an encoder-decoder approach that forces
the network to encode (compress) the input into a latent code from which the
input can be decoded (reconstructed). The latent code given by the bottleneck
layer can be used for dimensionality reduction. Thus, many of the surveyed
works have exploited this feature to address sparsity in WiFi and BLE
fingerprints. Additionally, successful applications in mitigating the effects
of fluctuating and noisy signal measurements were attained using SDAEs (common
variants of AEs that are trained to reconstruct an input from a corrupted
version of it [151]).

CNNs were first designed to combat the problem of shift, scale, and distortion
variance when classifying high dimensional patterns [152]. Thus, they are
powerful tools for extracting generic features form images. This is reflected
by the fact most of the surveyed work on image-based indoor positioning
employed CNNs as opposed to other deep learning models. Additionally, CNNs
have proven successful in positioning applications where the input data are
not necessarily images but rather transformed to resemble images.

DBNs are neural networks that use unsupervised learning to facilitate
supervised learning [23]. This is achieved through a two-step training
process, i.e., unsupervised pre-training using unlabeled data followed by
supervised fine-tuning using labeled data. Since location-annotated samples
are more expensive to obtain than unannotated ones, DBNs are most suitable for
positioning applications where only a small amount of annotated data exist.

GANs are emerging deep learning architectures that were first introduced in
$2014$ [153]. They are used to generate high-quality synthetic data from
existing authentic data. Thus, they were exploited in several of the surveyed
works to reduce the cost of site surveying by expanding the training set with
artificial fingerprints.

RNNs are popular deep learning models for dealing with sequential and time-
series data [154]. The output of the network is a function of the current
input at time $t$ in addition to the previous inputs at time $\tilde{t}<t$.
Thus, they are most suitable for positioning applications where the position
history of a user is incorporated to refine positioning estimates.

##  VII Pitfalls and Challenges

### Pitfalls:

Having reviewed the different solutions presented in the literature, this
section details the common pitfalls that should be avoided when designing deep
learning-based indoor fingerprinting systems.

#### Ill-defined materials and methods

Full disclosure of an experiment is vital for the explanation, comparison, and
reproducibility of indoor positioning research [155]. This includes providing
a detailed description of the data collection process and the testbed, the
specifications of the proposed model, and the evaluation metrics used.
Unfortunately, many of the reviewed solutions lack enough detail. For example,
some works only use a single quantity to describe the performance of the
system (i.e., positioning accuracy). Some go as far as to not specify how this
quantity was calculated. Fig. 10 displays the positioning error histograms of
three different systems. It seems that employing MSE to report accuracy was
more tempting for the first system. Similarly, MAE and median error yielded
the lowest error for the second and third systems, respectively. Therefore,
without reporting the accuracy metric and the error distribution, it is
difficult to fully characterize the performance of a given system. Moreover,
other statistics such as minimum and maximum errors, when provided with a
description of the testbed (e.g., size and location of RPs), could reveal a
system’s robustness to local/global ambiguity. Another pitfall is selective
reporting. For instance, when reporting a system’s response time, some works
exclude the time it takes to sample the fingerprint, or the time needed for
pre/post-processing and only report the prediction latency of the deep
learning model.

Figure 10: The positioning error histograms of three different fingerprinting
systems, calculated as Euclidean distance, and the corresponding MAE, MSE, and
Median error. Each histogram was obtained using
$\mathrm{1}\mathrm{,}\mathrm{8}\mathrm{5}\mathrm{0}$ testing fingerprints. All
systems produce a min. error of $0\text{\,}\mathrm{m}$ and a max. error of
$3\text{\,}\mathrm{m}$.

#### Collecting training and testing fingerprints from the same RPs

The problem with having RPs serve a dual purpose is that testing may easily
degenerate to benchmarking the system against its training samples [91]. This
is a common pitfall that could produce misleading results since over-fitting
is much harder to detect in such cases. To avoid this problem and, hence,
reflect the true generalizability of an implementation, the locations of
testing RPs should be different from those of training RPs. For example,
testing RPs could lay in between, but not on, the training RPs.

#### Post-processing that increases response time

It is observed that whenever post-processing is involved, the real-time
inference advantage offered by deep learning is often spoiled. It could be
argued that the generally minor positioning accuracy gained by post-processing
is not worth sacrificing positioning latency for. Therefore, one possible
solution is to invest more effort into designing and fine-tuning the deep
learning model instead of relying on quick, off-the-shelf models and let post-
processing do the heavy lifting. This suggestion is backed by the fact that a
lot of the reviewed solutions have achieved remarkable accuracies utilizing
architecture design and hyperparameter tuning without resorting to post-
processing.

#### Evaluation under lab conditions

Designing and evaluating a system under constrained/controlled settings (e.g.,
without the presence of moving people or constraining movements to predefined
orientations/heights) limits its applicability. Also, such restrictions rarely
apply in real-world use cases. Therefore, it is likely that the system’s
performance will deteriorate when tested under non-lab conditions since the
fingerprints will be influenced by artifacts that were never part of the
training data.

#### Data leakage and sampling bias

It has been noticed that some of the reviewed implementations unintentionally
perform data leakage and sampling bias. The problem with these pitfalls is
that they can result in practically incompetent and overly optimistic models.
A common form of data leakage is normalizing a dataset before splitting it
into training and testing sets while a common form of sampling bias is using
an imbalanced dataset without employing the right performance metrics. Some
useful resources on how to detect and avoid these subtle traps can be found in
[156, 157, 158].

#### Not considering user/device heterogeneity

Different smartphones have different software/hardware components and
different users have different carrying preferences, walking patterns, etc.
Such variations cause measurements to be inconsistent, even when collected at
the same RPs, leading to large positioning errors. While a few of the reviewed
works hinted at this issue, no practical solution was offered to combat it.
Resolving this issue is crucial because an ideal system would exhibit the same
performance regardless of heterogeneity. Though it remains an active area of
research, current proposals to address heterogeneity can be found in [159,
68]. It is worth mentioning that some of these solutions are based on
classical machine learning algorithms such as linear regression and
expectation maximization; thus, it would be worthwhile to investigate applying
deep learning to the heterogeneity problem.

### Challenges:

This section identifies the main challenges facing deep learning-based indoor
fingerprinting systems. Solving these challenges is essential so that deep
learning-based indoor fingerprinting can realize its full potential.

#### Need for large datasets

Since deep learning involves tuning a considerable number of parameters, the
need for large datasets is inevitable. Many of the reviewed systems
demonstrated how predictive performance increases with increased training
data. However, site surveying is notoriously laborious and costly. A recent
strategy created to tackle this problem is crowdsourcing. Through
crowdsourcing, the burden of fingerprint collection can be shared with users
willing to participate either actively or passively [144]. However,
crowdsourcing is not fully developed and much research is needed to address
questions such as 1) What measures are needed to ensure the integrity of
crowdsourced fingerprints? 2) What incentives are offered to users to engage
and motivate them to participate in data collection? 3) How should
inconsistent measurements caused by user/device heterogeneity be dealt with?
and 4) How should fingerprints collected through passive crowdsourcing be
annotated? Nevertheless, advances in crowdsourcing techniques will greatly
facilitate the collection of data that is desperately needed to train deep
learning models.

#### Vulnerability to adversarial attacks

Many studies have demonstrated how deep learning models are vulnerable to
adversarial attacks. Adversarial attacks refer to input samples designed by an
attacker to force a trained model into producing incorrect predictions. Note
that minute perturbations to the input features are enough to divert the
network from functioning properly. This makes it harder to distinguish genuine
inputs from adversarial ones. While the consequences of adversarial attacks
are less severe in indoor positioning than in safety-critical applications
(e.g., self-driving cars), neglecting this issue will have negative
consequences. For example, an indoor positioning system that navigates a user
to the wrong destination will likely lose the user’s trust. Recent attempts to
tackle this problem include the work of Pei et al. [160] who developed a
framework called DeepXplore to systematically generate adversarial inputs that
can be used in retraining the model to fix its erroneous behaviors. Another
example is the work of Yuan et al. [161] who identified $15$ methods to
generate adversarial inputs and described seven methods to countermeasure
them.

#### On-device deep learning

Most of the reviewed work used a smartphone for data collection; however, the
smartphone was never used for data processing (i.e., calculation of the user’s
position is not carried out on the smartphone itself but rather offloaded
externally). This is because deep learning models require sophisticated
resources to run efficiently and research for devising ways to enable deep
learning on smartphones is still ongoing. Recent developments in this area
focus on designing Artificial Intelligence (AI) chips for smartphones.
Examples of such chips include Apple’s Bionic Chip, Qualcomm’s Snapdragon 855,
Huawei’s Kirin 980, and Samsung’s Exynos 9820. In terms of software research,
active areas include model compression [162], which aims at reducing
complexity while preserving accuracy, as well as designing mobile-friendly
deep learning architectures, such as MobileNet [163], ShuffleNet [164], and
MnasNet [165]. From an indoor positioning perspective, enabling a user to use
their own device to compute his/her position is desirable for four reasons.
First, it increases the user’s privacy/security since no information must
leave the user’s device. Second, it reduces latency since no communication is
required with the server-side. Third, it increases availability since
computation is performed in a decentralized fashion. Fourth, it reduces cost
since a localization server is no longer needed. However, the impact that this
approach has on a device’s energy consumption needs to be investigated.

#### Interpretability and prediction uncertainty

A common challenge is the interpretability of deep learning models. Deep
learning models are often treated as black-box function approximators, mapping
one vector space into another. Thus, they do not provide any domain-specific
insight or knowledge. This led practitioners in risk-averse domains to favor
interpretable statistical models over accurate deep learning models. Deep
learning also lags behind Bayesian modeling in terms of quantifying prediction
uncertainty. In other words, deep learning models are incapable of obtaining
principled uncertainty estimates. The root cause behind these issues is the
lack of a coherent theoretical foundation mainly because research in deep
learning has always revolved around improving state-of-the-art accuracy. In
indoor positioning, interpretability and uncertainty quantification will
ultimately be key factors in encouraging the adoption of deep learning in
commercial settings. Luckily, researchers see a growing need to address these
limitations and work in this area is continuing. Recent methods on improving
interpretability and quantifying uncertainty are described in [166, 167] and
[168, 169], respectively. The practical value of incorporating such methods
into future deep learning-based fingerprinting systems has yet to be
appreciated.

#### Privacy and security

Given the limited processing power of smart devices, the position estimation
of deep learning-based fingerprinting systems is often performed on the server
side. As a result, systems are capable of tracking users’ movements and
gathering their personal location information. Misuse of such information
compromises users’ privacy. Therefore, a system must clearly indicate what
information is gathered from users and how this information will be used.
Also, a system must indicate how users’ information is protected against
unauthorized access. Unfortunately, it was observed that privacy and security
are not prioritized in the description of solutions and are generally
overlooked. None of the reviewed solutions laid out how the users’ location
information is handled and protected. Privacy has become particularly
concerning with the emergence of trajectory data mining [170], which makes
exploiting users’ location information for advertising and other purposes
inevitable. Positioning under the IoT brings another dimension to this problem
since a user’s location can be correlated with the location of IoT devices in
the environment. This can also be exploited to reveal further information
about the user’s health, mood, and the activities he/she performs [28].
Moreover, deep learning models themselves pose a threat to users’ information
because trained models can be attacked to identify the data records used in
training [171]. Unfortunately, there has been little research done on privacy
issues in indoor localization. A recent proposal is the P3-LOC framework
[172], a privacy-preserving, paradigm-driven framework for indoor
localization. Other remedies include training deep learning models under
differential privacy [173] and reporting statistics from end-user client
software, anonymously, with strong privacy guarantees [174]. Nevertheless,
with the ever-growing cyber security threats, privacy is becoming a major
challenge that researchers should invest more effort in addressing.

##  VIII Conclusion and Future Perspectives

The topic of indoor positioning has gained significant research interest in
recent years due to the wide range of potential applications that it enables.
Hence, it can easily be envisioned that indoor positioning will become a
critical infrastructure in the foreseeable future. Amongst the different
approaches to indoor positioning, the fingerprinting approach is the most
investigated due to its simplicity, low cost, and high accuracy. Recent
fingerprinting solutions leverage deep learning because it is a more powerful
tool than traditional shallow learning. The objective of this paper was to
provide a detailed review of these solutions. To this end, the reviewed
solutions were classified based on the fingerprint type and further sub
classified based on the deep learning model. All solutions belonging to a
certain fingerprint type were compared using a well-defined performance
evaluation framework. A detailed review of publicly available fingerprinting
datasets suitable for training deep learning models was also included.
Finally, the main pitfalls and challenges surrounding deep learning-based
fingerprinting were discussed.

Until the rise of a universally acknowledged solution for indoor positioning,
the strive for better performance will continue to be a driving force behind
the proposal of new solutions. The progress that has been made and is
continuing to be made in sensing technologies, computing capabilities and deep
learning will facilitate the development of these solutions. Also, since deep
learning is being driven by not only academia but also industry, the
development of new solutions will be further facilitated. For instance, a
community of leading companies has recently introduced the Open Neural Network
Exchange (ONNX) format [175], an ecosystem that interchangeably represents
deep learning models. In other words, ONNX enables models to be trained in one
framework and transferred to another for inference.

### Future Perspectives:

This section suggests future research directions for deep learning-based
fingerprinting.

  * •

It is easily deduced that publicly available datasets are major enablers of
indoor positioning research. While very useful, current datasets have several
limitations. First, they are limited to certain types of buildings (e.g.,
universities or research facilities). Few datasets directed at large-scale
indoor structures such as airports, shopping malls, and convention centers,
where indoor positioning is expected to be more profitable, exist [176, 177].
Second, they do not account for different transit modes (e.g., stairs,
escalators, or elevators) or carrying modes (e.g., in-pocket, texting, or
making a call). Third, they do not consider device heterogeneity because most
of them utilized Android-based devices for data collection. It should be noted
that Apple’s iOS accounted for $48.3\text{\,}\mathrm{\char 37\relax}$ of the
smartphone market share in the U.S. (as of August $2019$)[178]. Therefore, the
proposal of datasets that can address these limitations will always be of
great interest.

  * •

Several emerging IoT wireless technologies will attract growing interest as
localization enablers over the coming years. These include Sigfox, LoRA, NB-
IoT, and Wi-Fi HaLow. Understanding their potentials and limitations for
indoor localization will open new opportunities; yet, exploiting their
characteristics for indoor localization largely remains unexplored. Currently,
very few publications regarding these technologies and indoor fingerprinting
[179] can be found, whereas some initial studies are available for outdoor
fingerprinting [180, 181]. Note that these works utilize classical machine
learning algorithms; thus, it would be interesting to see how deep learning
can push performance boundaries.

  * •

While deep learning has recently seen implementations with indoor localization
techniques such as pedestrian dead rocking [182], angulation [183], lateration
[184], and device-free localization [185], its application to proximity
detection and cooperative localization has yet to come.

  * •

Most of the image-based localization reviewed work used the same loss function
originally proposed in PoseNet [64]. Therefore, one possible research
direction is to modify, or even propose, new loss functions for localization
that can result in improved accuracy and/or lowered training time [186].

  * •

Fingerprinting based on DRL is another area that has not received much
attention. In fact, out of the reviewed work, only one solution utilized DRL.
This is probably because DRL is a relatively new learning approach. However,
DRL is gaining tremendous momentum and its application in a variety of fields,
including communications and networking [187], is promising. In terms of deep
learning architectures, some of the unexplored architectures for indoor
positioning include, Residual Neural Networks [188], Convolutional
Autoencoders[189], and Conditional Generative Adversarial Networks[140].

  * •

The upcoming commercial deployment of $5$G promises ultra-reliable and low-
latency communications (URLLC) [46]. Furthermore, the reviewed solutions based
on massive MIMO transmission demonstrated the potential for delivering
centimeter-level positioning accuracy. However, massive MIMO-based solutions
are proof-of-concept because they are based on simulations or hardware
prototypes. It is expected that fingerprinting based on $5$G networks is going
to be a new frontier that encompasses its own set of challenges and
opportunities. Thus, one research direction is to investigate the feasibility
of using real massive MIMO data emitting from real $5$G cellular networks for
indoor positioning.

We hope that the information provided in this review assists investigators in
better understanding deep learning-based fingerprinting systems, encourages
new research efforts into this promising field, and paves the way for the
practical deployment of systems as commercial products.

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