The Distribution of Path Loss Exponent in 3D Indoor …[6], [7] path loss configuration is needed...

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18 (2017) pp. 7154-7161

© Research India Publications. http://www.ripublication.com

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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.



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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.



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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.



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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)





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(a) (b)

(c) (d)



(a) (b)

(c) (d)





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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



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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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The Distribution of Path Loss Exponent in 3D Indoor …[6], [7] path loss configuration is needed...

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