.

1. Hybrid Nearest Neighbour and Feed-forward Neural
Networks Algorithm for Indoor Positioning System
Tuan A. Z. Rahman∗, Nordin Ramli∗, M. A. A. Rahman‡ and Hafizal Mohamad§
∗Wireless Network and Protocol Research Laboratory, MIMOS Berhad 57000 Kuala Lumpur, Malaysia
‡Department of Physics, Faculty of Science, Universiti Putra Malaysia 43400 UPM Serdang, Selangor, Malaysia
§Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia 71800 Nilai, Negeri Sembilan, Malaysia
∗zahidi.rahman@mimos.my and nordin.ramli@mimos.my
Abstract—Accurate localization of people and mobile devices
in building plays important roles in order to deliver helpful
location-based services such as direction and object finding,
and content delivery. This paper presents a hybrid feed-forward
neural networks (FNNs)-based indoor localization system trained
by means of recently developed metaheuristic algorithm. The
received signal strength (RSS) from the access points (APs) is
employed as input to the proposed algorithm to estimate the user
location in the wireless local area network (LAN) environment.
The performance of developed hybrid kNN-FNNs algorithms
were compared with the basic k-NN algorithm in term of error
distance achieved. The comparative study has shown that the
developed hybrid kNN-FNNs algorithm is more efficient when
estimating the user location with accuracy of 86.39 percent in
comparison to its predecessor algorithm (with 69.67 percent) as
the error distance was minimized using metaheuristic algorithm.
Index Terms—artificial neural networks, indoor positioning,
metaheuristic, k-nearest neighbours, received signal strength
I. INTRODUCTION
Nowadays, indoor positioning systems (IPS) have become
very popular area of research due to high demand in develop-
ment of smart buildings and IoT objects tracking for com-
mercial purposes [1-3]. RSS-based fingerprint among other
established techniques in IPS received widely attention due
to advantages such as no requirement of extra hardware at
both sender and receiver sides; utilization of already existing
infrastructure; easily implementable; and non essential need
of propagation model building which may or may not depict
real signal propagation at run time [4]. Various positioning
prediction algorithms have been developed in the past decades
to estimated the user location in WLAN environment.
As the k-nearest neighbour (k-NN) algorithm is an es-
tablished localization prediction algorithm in IPS, there are
still rooms for further improvement that can be proposed to
upgrade its prediction accuracy. In this paper, a hybrid posi-
tioning prediction algorithm has been proposed amalgamation
between a simple k-NN and feed-forward neural networks
(FNNs) algorithms and then were trained using a metaheuristic
algorithm. The rest of this paper is organized as follows:
Section II discusses regarding related works on IPS using
Wi-Fi fingerprinting method and metaheuristic-based FNNs
algorithm. The development and configuration of proposed
positioning prediction algorithm are explained in Section III.
Section IV presents the main results of developed algorithm
and comparative study on its performance. Finally, Section V
concludes the paper and gives possible future directions for
research.
II. RELATED WORK
RADAR which proposed by Bahl and Padmanabhan [5] was
the pioneer work in utilizing Wi-Fi signals and radio propa-
gation model to estimate user’s indoor position. The user’s
positions were estimated based on Wi-Fi signals received at
the access points (APs) from a laptop. k-NN algorithm with
triangulation method was employed to calculate the user’s
position resulting a median error of 2-3 meters achieved.
COMPASS developed by King et al. [6] was considered the
user’s orientation in order to improve the estimation accuracy.
The Wi-Fi signal strength were collected using a mobile device
while probabilistic algorithm was used to compute the user’s
coordinates. It successfully achieved an average error of less
than 1.65 meters in comparison to 2.26 meters achieved by
RADAR.
The affinity propagation clustering algorithm and the par-
ticle swarm optimization (PSO)-based ANNs were employed
by Li et al. [7] to develop an IPS. To reduce the maximum
location error, the affinity clustering and principle component
analysis (PCA) techniques were used, and then an ANNs
model was trained using PSO algorithm. A mean error of 1.89
meters was reported and the developed algorithm provided
a higher positioning accuracy compared to k-NN and back-
propagation (BP) algorithms.
HybLoc [8] and CEnsLoc [9] were developed by Akram et
al. for IPS using soft clustering-based random decision forest
and Gaussian Mixture Model (GMM) ensembles, respectively.
These systems employ Gaussian Mixture Model (GMM)-based
soft clustering and Random Decision Forest (RDF) ensembles
for hybrid IPS. The HybLoc algorithm demonstrated higher
accuracy and precision in comparison to kNN and ANNs
algorithms. While, the CEnsLoc algorithm highlighted 97
percent accuracy over other contested algorithms.
III. DEVELOPMENT OF HYBRID kNN-FNNS ALGORITHM
Flowchart of the proposed hybrid kNN-FNNs algorithm was
illustrated as in Fig. 1. The algorithm consist of three main
individual algorithm which are: the basic k-NN algorithm
(green box), series kNN-FNNs algorithm (blue box) and
2020 IEEE 8th Conference on Systems, Process and Control (ICSPC), 11–12 December 2020, Melaka, Malaysia
978-1-7281-8860-7 ©2020 IEEE

2. FNNs algorithm. Each algorithm will compute their own error
distance based on RSS input. An amalgamation formulated
from minimum error distance from each algorithm was per-
formed at the end of the process before output position was
estimated. The details of each algorithm for IPS using wireless
fingerprinting technique were described in the next subsection.
RSS
k-NN
Triangulation
Method
FNNs
FNNs
Minimum
Distance
XY Coordinate
k-NN
Hybrid kNN-FNNs
Basic k-NN
Series kNN-FNNs
Fig. 1. Flowchart of the developed algorithm
A. Basic kNN algorithm
The basic k-NN algorithm by means of Euclidean distance
was the foundation to this developed hybrid algorithm. The
algorithm will selected three nearest location points (from
the offline dataset) to the user location (online dataset). And
then, a simple averaging/triangulation method was employed
to estimated user location using these three points as shown
in Eqs. 1 and 2.
xe = (x1 + x2 + x3)/3 (1)
ye = (y1 + y2 + y3)/3 (2)
where, xe and ye are the estimated x and y-coordinates,
respectively.
B. Metaheuristic-based FNNs algorithm
The application of metaheuristic algorithms to train FNNs
has been a key interest among researchers around the globe
over the past two decades. In order to improve its generaliza-
tion capabilities, metaheuristic algorithms hold important role
in comparison to classical gradient-descent algorithms [10]. In
this paper, a metaheuristic algorithm named Stochastic Fractal
Search (SFS) algorithm proposed by Salimi [11] has been
employed to train FNNs for IPS purpose. The SFS algorithm
inspired by the natural phenomenon of growth and utilized
a mathematical concept called the fractal. An improvement
version of SFS algorithm using chaotic maps to upgrade its
exploration and intensification abilities is illustrated in Fig.
2 [12]. A Gauss/mouse chaotic maps is used and embedded
in Eqs. 3 and 4 which are represented the Diffusion and
First Updating Processes of SFS algorithm to replace random
normal distribution coefficients.
Start
Specify the SFS algorithm control
parameters (MDN, Gaussian Walk)
Initialize the population by
randomize a number of points, NP
Evaluate the fitness of each point
and record the best point, BP
G < Max_Gen
Perform Updating Processes
Update the jth component of Pi using
chaotic map (Eq. 2)
Display results
End
Perform Diffusion Process
Update the position of Pi using chaotic
map (Eq. 1)
G = 0
YES
G = G + 1 NO
Start
Specify the SFS algorithm control
parameters (MDN, Gaussian Walk)
Initialize the population by
randomize a number of points, NP
Evaluate the fitness of each point
and record the best point, BP
G < Max_Gen
Perform Updating Processes
Update the jth component of Pi using
chaotic map (Eq. 2)
Display results
End
Perform Diffusion Process
Update the position of Pi using chaotic
map (Eq. 1)
G = 0
YES
G = G + 1 NO
Encoding the points
Assigning the connection weights
and biases
Training and setting
Calculate the fitness function
Encoding the points
Assigning the connection weights
and biases
Training and setting
Calculate the fitness function
Start
Specify the SFS algorithm control
parameters (MDN, Gaussian Walk)
Initialize the population by
randomize a number of points, NP
Evaluate the fitness of each point
and record the best point, BP
G < Max_Gen
Perform Updating Processes
Update the jth component of Pi using
chaotic map (Eq. 2)
Display results
End
Perform Diffusion Process
Update the position of Pi using chaotic
map (Eq. 1)
G = 0
YES
G = G + 1 NO
Encoding the points
Assigning the connection weights
and biases
Training and setting
Calculate the fitness function
FNNs
CFS Algorithm
Fig. 2. Flowchart of training FNNs using CFS algorithm [12]
GW = Gaussian(γB, σ) + ( ∗ BP − α ∗ Pi) (3)
where, GW is Gaussian Walk parameter generated based on
Gaussian distribution random walk, γB is equal to |BP| and
σ is Gaussian distribution, is uniformly distributed random
numbers restricted to range of [0, 1]. While, α parameter is
generated number between 0 to 1 using Gauss/mouse chaotic
map.
P0
i (j) = Pr(j) − α ∗ (Pt(j) − Pi(j)) (4)
where P0
i is new updated position of Pi. Pr and Pt are
random selected points in the group. Again, α parameter
is employed in this First Updating Process. More details
regarding this metaheuristic algorithm can be found in [11]
and its chaotic variants in [12].
Fig. 3 shows the FNNs architecture for IPS which consists
of an input layer, a hidden layer and an output layer. Seven
nodes were employed in the input layer which include of user’s
orientation and 3 APs with their RSS signal. In this study, ten
nodes in hidden layer were selected based on heuristic method
with consideration on computational burden. Two nodes in the
2020 IEEE 8th Conference on Systems, Process and Control (ICSPC), 11–12 December 2020, Melaka, Malaysia
978-1-7281-8860-7 ©2020 IEEE

3. output layer represented the estimated location in terms of x
and y- coordinates. The chaos-enhanced SFS algorithm is used
to search for optimal weight, ω and bias, β parameters.
I1
I2
I3
I6
| f1
O1
Hidden layer
Input layer Output layer
Orentation
AP1
RSS1
X
1
2
m
11
12
1m
| f2
| fm
|
|
|
m+1
O2 Y
I7
I1
m+2
AP3
RSS3
|
|
|
7m
Fig. 3. Two layers of neural networks for indoor positioning system
IV. RESULTS AND DISCUSSION
An Intel desktop (64-bit i5) with 12 GB RAM was used
for computational process in MATLAB environment. The
experimental dataset developed through fingerprinting (FP)
collection from University of Mannheim was employed in this
study [6]. A pre-processing of the dataset was performed by
selecting 3 highest RSS values for each AP. The complete
dataset consist of 146080 Wi-Fi RSSI FPs and 6570 on-line
were utilized in training and testing processes. Twenty dataset
from offline and three dataset from on-line were used and
selected randomly in training phase of the developed algorithm
with average of 1000 times to ensure its convergence [6]. The
MDN of SFS algorithm was set to 3 with first Gaussian walk
is used. A total of 2400 function evaluations using ten Start
Points (particles) was computed.
The statistical performance of each developed algorithm
were tabulated in Table I. The performance of FNNs algorithm
is poor in comparison to the basic k-NN algorithm in terms
of median and lowest distance error, but achieved better in
maximum distance error. The FNNs was failed to generalise
the APs locations to predict the user’s position which lead to
less sensitive to small changes in RSS value in contradict to
k-NN algorithm. Then, the hybrid algorithm combining the
strength of both algorithms was trained using the improved
SFS algorithm. Fig. 4 shows the convergence plot of improved
SFS algorithm to train hybrid kNN-FNNs for IPS.
Fig. 5 shows the CDF for the contested algorithms. The hy-
brid kNN-FNNs algorithm significantly outperforms the other
contested algorithms in terms of better accuracy achieved. The
error distance in the hybrid kNN-FNNs algorithm is less than
5 meters in 86.39 percent of all measurements compared to
only 77.98, 73.93, 69.67 and 33.90 percent for parallel kNN-
FNNs, series kNN-FNNs, basic k-NN and FNNs algorithms,
TABLE I
PERFORMANCE COMPARISON BETWEEN DEVELOPED ALGORITHMS
Statistical Performance (m)
Algorithm Min. Error Max. Error Median
k-NN 0.0567 32.4178 3.1831
FNNs 0.0586 23.3341 9.6179
Series kNN-FNNs 0.0415 31.8025 3.5539
Parallel kNN-FNNs 0.0567 22.8216 2.5612
Hybrid kNN-FNNs 0.0415 13.0568 1.9290
0 500 1000 1500 2000
Number of Function Evaluations
1.8
2
2.2
2.4
2.6
2.8
3
3.2
Median
of
Error
Distance
X: 2376
Y: 1.929
Fig. 4. The convergence curve of improved SFS to train hybrid kNN-FNNs
algorithm.
respectively. Table II highlights the estimated user’s position
with lowest distance error for different algorithms. Based on
these three locations, the hybrid kNN-FNNs algorithm outper-
formed its predecessor algorithms with 1.0269m distance error
while k-NN (2.6353m) and FNNs (2.3219m), averagely.
TABLE II
THE ESTIMATED LOCATION WITH LOWEST ERROR FOR DIFFERENT
ALGORITHM
Estimated Location
Real Location k-NN FNNs Hybrid kNN-FNNs
(11.39, 5.00) (11.33, 5.00) (11.06, 6.80) (11.52, 6.12)
(8.56, 7.64) (8.67, 7.67) (12.54, 4.48) (8.52, 7.63)
(24.70, 7.70) (17.00, 7.00) (24.68, 7.65) (22.86, 7.18)
0 5 10 15 20 25 30 35
Distance Error (m)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Cumulative
Density
Function
Basic kNN
FNN
Series kNN-FNNs
Parallel kNN-FNNs
Hybrid kNN-FNNs
Fig. 5. Performance of developed algorithms in comparison to the basic kNN
algorithm.
2020 IEEE 8th Conference on Systems, Process and Control (ICSPC), 11–12 December 2020, Melaka, Malaysia
978-1-7281-8860-7 ©2020 IEEE

4. V. CONCLUSION
In this work, a new hybrid indoor Wi-Fi positioning sys-
tem based on Nearest Neighbour and Feed-forward Neural
Networks algorithms has been proposed. Hybrid methods
combine advantages of each algorithm to improve the overall
accuracy as well as generalization capability which is very
important in real world Wi-Fi fingerprinting-based indoor
positioning estimation. The error distance in the hybrid kNN-
FNNs algorithm is less than 5 meters in 86.39 percent of all
measurements in comparison to other contested algorithms.
The comparative study has shown that the developed hybrid
kNN-FNNs algorithm is more efficient in estimating the user
location in comparison to its predecessor algorithm as the error
distance was minimised using metaheuristic algorithm. The
future work includes experimental data collection of Wi-Fi
fingerprint integrated with magnetic signal in order to improve
the prediction performance of the developed algorithm.
ACKNOWLEDGMENT
The authors would like to show gratitude to MIMOS Berhad
for the funding to perform this research. The authors would
like to thank the reviewers for their valuable feedback and
comments.
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2020 IEEE 8th Conference on Systems, Process and Control (ICSPC), 11–12 December 2020, Melaka, Malaysia
978-1-7281-8860-7 ©2020 IEEE