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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18 (2017) pp. 7154-7161
© Research India Publications. http://www.ripublication.com
7154
The Distribution of Path Loss Exponent in 3D Indoor Environment
Nur Atina Mohamad Razali 1, Mohamed Hadi Habaebi, Nurul Fariza Bt. Zulkurnain,
Md Rafiqul Islam and Al-Hareth Zyoud
Department of Electrical and Computer Engineering, Faculty of Engineering, International Islamic University Malaysia (IIUM), 53100 Gombak, Kuala Lumpur, Malaysia.
Corresponding author1Orcid ID: 0000-0002-2161-5763
Abstract
Wireless communications in indoor environment such as
wireless sensor network and WiFi is slowing becoming the
norm of everyday business, especially in industrial indoor
settings. Tracking flying objects in an indoor environment
controlled via wireless links require proper understanding of
the path loss distribution in a 3D environment. Such a setting is
rarely discussed - if any - in the open literature where the path
loss exponent is generalized over the whole 3D indoor space.
This study, however, attempts to address minute changes in
path loss exponent distribution in such environment. An
extensive measurement campaign was conducted in our
university laboratory mimicking an industrial indoor setting
where drones are supposed to operate. Our initial study
involved a placement of 2.4 GHz transmitter at different
heights with 0.00 m, 0.61 m, 1.36m and 1.88 m while varying
the receiver antenna location over a 3D grid of 1 meter
resolution in x, y, and z-axes directions. The received signal
strength indicator (RSSI) readings were recorded to derive the
path loss exponent at every grid point. Interestingly, results
indicate that path loss exponent is definitely affecting along the
z-axis (vertically) but still can be averaged over x-y- axes
(horizontally). Increasing the transmitter or receiver height
decreases the path loss exponent averaged over the s-x axes
grid lines. Furthermore, due to furniture clutter on the floor and
the availability of clear LOS components at higher links, the
path loss becomes a function of the height. The results confirm
further the furniture clutter and the floor proximity do add up
to the path loss exponent due to lack of LOS links while higher
links experience smaller path loss exponent. This is quite
important as it has never been reported in the open literature
before.
INTRODUCTION
Wireless Sensor Networks (WSN) is a collection of small, low
cost and randomly placed sensor nodes which connected by
wireless communication to form a sensor area [1]. Nodes can
sense and monitor the environment to gather the data and send
it to the user through base stations. Network sensing coverage
reflects the performance of a wireless sensor network in
monitoring and tracking an object towards the application. The
coverage is considered as one of the problems in identifying the
quality offered by wireless sensor network [2]. In this study, it
focuses on the distribution of path loss exponent in 3D indoor
environments. 3D consists of x, y and z plane to focus on the
3D space for real applications of wireless sensor network. The
application of wireless sensor network in indoor system could
help in many areas, such as health care, safety, military,
automation, manufacturing, tracking and others. It can provide
good accuracy towards the applications by determining the path
loss exponent.
In developing 3D indoor environment using wireless sensor
network, the goal is to find the distribution of path loss
exponent using radio signal. Radio sensing involves estimating
the distance between transmitter and receiver [3]. Thus, the
method of received signal strength based technique is
considered. Received signal strength indication (RSSI) is a
measurement of the power level received by radio signal. The
higher the RSSI number, the stronger the signal received. In
addition, for maximum coverage the placement on sensor node
is needed[4], [5].Generally, the measurement of 3D axis
locations in indoor environment will be recorded and stored in
the database together with the responding locations. Then,
unknown location will be determined by measuring the signals
and comparing it to the previous measured values. In indoor
environment, the accuracy of localization is significantly
impacted by signal propagation due to the diffraction, reflection
and absorption from the surrounding medium such as furniture,
door, table, and even a person. As such method in few studies
[6], [7] path loss configuration is needed for estimating path
loss signal in an indoor environment, [8]verification area, and
a study by [9] did on different path loss for each link which
shows a higher accuracy. Therefore, in this paper it focused on
distribution of path loss exponent on each 3D point.
The remaining sections of the paper are organized as follows.
Section 2 elaborates the related work that involved wireless
sensor network and path loss configuration. Section 3 defines
method used in received strength signal. Section 4 discussed
the simulation results for path loss distribution. Finally, the
conclusion and recommendation has been presented in Section
5.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18 (2017) pp. 7154-7161
© Research India Publications. http://www.ripublication.com
7155
RELATED WORKS
WSN is connected by wireless media to form a sensor field by
using sensor nodes. Sensor nodes in wireless sensor networks
have an architecture that can sense, communicate, compute,
store and control the physical parameters of the environment.
This study focus on indoor environment using WSN. The nodes
can sense and monitor the surrounding location in order to
gather the data and send it to the user through base stations [1].
Before a node sending the data to another nodes, the node must
localize itself. The location information is collected in order to
provide the exact location as a database.
Path loss is propagation losses that caused by the natural
expansion of the radio signal such as absorption when the
signal passes through non transparent media, diffraction when
part of the radio signal is obstructed by an opaque object and
scattering when dimension of objects are smaller than the
wavelength. Different environment need different parameters
to describe propagation signals. In indoor environment a few
techniques have been discussed such as received strength signal
(RSS), angle of arrival (AOA), time of arrival (TOA), time
difference of arrival, phase difference of arrival (PDOA) and
other[1], [10]–[12]. A path loss model correlates received
signal power density with the transmitter to estimate the
distance by expression of path loss exponent, where in [13]
general path loss modelling is summarized. Table 1 below
shows the typical values according to different environment
[14].
Table 1: Path loss exponent based on different environment
Environment Path loss exponent
Free space 2
Urban area 2.7 – 3.5
Suburban area 3 – 5
Indoor line-of-sight 1.6 – 1.8
Obstructed in building 4-6
Obstructed in factories 2-3
The equation (1) can show the general principle and describes
the relationship. It reflects the basic theory that indicates the
average received signal power declines logarithmically with the
propagated distance. This is always taken into account in both
the theoretical and propagation models.
𝑃𝐿 = 𝑃𝐿0 + 10 𝑛 log10 (𝑑
𝑑0)
(1)
Where PL is path loss in dB, PL0 is path loss at reference
point d0, d is distance between transmitter and receiver and n is
path loss exponent which is environment dependent. Moreover,
the quantitative comparisons of various path loss models for
WSN also required distance variable [13], [15] for different
frequencies and scenarios. Path loss have different
characteristics at multiple frequency which provide more
accurate measurement results as it depended on frequency [16].
For many scenarios, path loss exponent distribute in different
ways [17], [18] Plus, the different path loss exponent in normal
distribution is affected for localization accuracy [9]. However,
this study only required measurement at 2.4 GHz frequency,
3D indoor environment in finding path loss distribution.
The main difficulty lies in communication signals for an indoor
environment are to face interference and attenuation from
multi-path, reflection, deflection, diffraction and channel
fading. Furthermore, signal propagation scattering due to
equipment, furniture and people being present, especially in
indoors environment [14], plays a major hindrance. To
overcome this problem, this experiment addresses a RSSI based
method for estimating the signal path loss parameter in an
indoor environment. The advantages of using RSSI-based
method are it does not require extra dedicated hardware and it
can be used for both indoor and outdoor applications by
changing the channel model and renegotiating an acceptable
level of accuracy and minimum mean square error (MMSE).
However, most of the current research works focus on 2D [6],
[17], [19]–[24] work instead of 3D. As sensor dimensions
shrink, it is necessary to find the distribution of path loss
exponent in a 3D environment instead of approximate 2D.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18 (2017) pp. 7154-7161
© Research India Publications. http://www.ripublication.com
7156
METHODOLOGY
(a) (b)
Figure 1: (a) 3D indoor environment in laboratory (b) Transmitter and receiver
Figure 2: Coordinate or receiver, Rx and transmitter, Tx
The study starts with an experimental setup for hardware
preparation in 3D indoor environment. The indoor environment
is conducted out in Communications Laboratory at
International Islamic University Malaysia see Fig. 1(a) with
approximately 14m x 7m area. The laboratory have been
divided into grid point 1m x 1m resolution with different
heights to create 3D environment. The experiment used Xbee
Pro see Fig. 1(b) as transmitter and receiver to collect the RSS
value. All the measurements is stored in the database. The
software installation used Matlab for creating algorithm,
mathematical analysis and developing models or graph.
In this experiment, from Fig. 2 Tx indicate the transmitter and
Rx indicate the receiver. All the point are placed at each
coordinates with different height at 0.00m, 0.61m, 1.36m and
1.88m in order to collect the RSS value. The measurement of
coordinates, and RSS value is stored in the database for further
path loss configuration. In the model equation, this study used
log-normal shadowing equation for calculation to determine the
value of path loss exponent.
𝑃𝐿 = 10 𝑛 log10 (𝑑
𝑑0) + 20 log10 (
𝑓
𝑓0) + 20 log10 (
4𝜋
𝑐)
(2)
Where do is a fixed reference distance with 1 meter, f at
frequency 2.4kHz, c is speed of light. The measurement of
RSSI at each 3D coordinates is taken and stored in the database.
From equation (1), the value of path loss exponent,n is
calculated.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18 (2017) pp. 7154-7161
© Research India Publications. http://www.ripublication.com
7157
RESULTS AND SIMULATION
(a) (b)
(c) (d)
Figure 3: Distribution of path loss when transmitter at height 0.00m (a) Receiver at height 0.00m (b) Receiver at height 0.61m
(c) Receiver at height 1.36m (d) Receiver at height 1.88m
(a) (b)
(c) (d)
Figure 4: Distribution of path loss when transmitter at height 0.61m (a) Receiver at height 0.00m (b) Receiver at height 0.61m
(c) Receiver at height 1.36m (d) Receiver at height 1.88m
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18 (2017) pp. 7154-7161
© Research India Publications. http://www.ripublication.com
7158
(a) (b)
(c) (d)
Figure 5: Distribution of path loss when transmitter at height 1.36m (a) Receiver at height 0.00m (b) Receiver at height 0.61m
(c) Receiver at height 1.36m (d) Receiver at height 1.88m
(a) (b)
(c) (d)
Figure 6: Distribution of path loss when transmitter at height 1.88m (a) Receiver at height 0.00m (b) Receiver at height 0.61m
(c) Receiver at height 1.36m (d) Receiver at height 1.88m
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18 (2017) pp. 7154-7161
© Research India Publications. http://www.ripublication.com
7159
Table 2: Value of average, maximum, minimum, variance and standard deviation
Average Maximum Minimum Variance Standard
deviation
Fig. 3
(a) 5.2512 9.2708 3.3123 1.3116 1.7204
(b) 4.8484 8.8524 2.8332 1.1544 1.3327
(c) 4.2905 9.5501 2.5992 1.1761 1.3832
(d) 3.7645 6.3931 2.6279 0.7923 0.6278
Fig. 4
(a) 4.4750 7.6235 2.6557 0.9885 0.9771
(b) 4.7742 9.2708 2.8168 1.2396 1.5365
(c) 4.2768 6.8291 2.9546 0.7945 0.6312
(d) 4.1505 7.0804 2.5678 0.8856 0.7843
Fig. 5
(a) 4.2997 7.1358 2.3818 1.0596 1.1228
(b) 4.6163 7.8053 2.5847 1.1360 1.2905
(c) 4.4198 9.9352 2.4839 1.2368 1.5297
(d) 4.0878 7.8371 2.3123 1.1118 1.2360
Fig. 6
(a) 4.2437 8.0125 2.1089 1.3309 1.7714
(b) 4.2492 6.9112 2.3529 0.8700 0.7570
(c) 4.4911 8.2993 2.5289 1.3150 1.7292
(d) 4.2515 9.2708 2.5992 1.3878 1.9259
From the experiment, the RSS value received from transmitter
is being calculated to develop the value of path loss exponent at
each coordinate. From all the measurement and calculation of
received signal strength in dBm and the calculated value of path
loss exponent at each coordinate, the data is simulated using
Matlab. As the laboratory have been divided into grid point 1m
x 1m resolution, it develops a 3D indoor environment.
Fig. 3, Fig. 4, Fig. 5 and Fig. 6 showed the distribution of path
loss exponent in 3D indoor environment. The transmitter is
placed at coordinate (0,0) with four difference heights. The
results showed distribution of path loss exponent at different
height of the receiver. Fig. 3 shows distribution at height 0.00
meter, Fig. 4 shows distribution at height 0.61 meter, Fig. 5
shows distribution at height 1.36 meter and Fig. 6 shows
distribution of height 1.88 meter. Based on Table 2, the value
of average, maximum, minimum, variance and standard
deviation on each level is calculated. The average value for path
loss exponent are between 3.7645 to 5.2512. At each level of
the transmitter, it is showed that maximum value is located
when the receiver at 0.00m height where Fig.3 is 9.5501, Fig. 4
is 9.2708, Fig. 5 is 9.8352 and Fig. 6 is 9.2708. From the data,
overall minimum value is 2.1089. Where the variance of path
loss distribution are between 0.7923 to 1.3878 and with
standard deviation between 0.6278 to 1.9259.
Each level have different distribution as the path loss exponent
is dependent towards the environment. The distribution of path
loss exponent is effects by diffraction, refraction, reflection,
absorption and multipath of received signal strength between
the transmitter and receiver. Moreover, the laboratory is full of
medium such as furniture, table, chair, fan, lamp, computer,
wire and more. These medium propagates the distribution of
path loss exponent in 3D indoor environment. From all the
figures, the value of path loss exponent is scattered around the
3D indoor environment.
CONCLUSION
In this paper, measurements for RSSI in 3D indoor
environment have been conducted with four different vertical
placements of the receiver antenna with respect to the
transmitter antenna coordinates. The distribution of path loss
exponent was found to vary inversely as a function of the
transmitter or receiver height. This confirms the effect of the
furniture clutter on the floor, together with floor proximity,
adding to the path loss exponent of links close to the floor. The
distance between the transmitter and the receiver also affects
the signal as expected as the link becomes longer. However, the
path loss can be averaged over the horizontal links. These
measurements confirms early hypothesis proposed previously
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18 (2017) pp. 7154-7161
© Research India Publications. http://www.ripublication.com
7160
by the authors in [25]. The results confirm further the furniture
clutter and the floor proximity do add up to the path loss
exponent due to lack of LOS links while higher links
experience smaller path loss exponent. This is quite important
as it has never been reported in the open literature before.
However, in order to confirm conclusiveness of the results,
further investigation in different 3D indoor residential,
industrial, office, medical facility, and other environments is
necessity.
ACKNOWLEDGEMENT
This work is supported partially by International Islamic
University Malaysia (IIUM) Grant No. RIGS16-362-0526.
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