ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY &
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AZOJETE December 2020. Vol. 16(4):733-740
Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria.

Print ISSN: 1596-2490, Electronic ISSN: 2545-5818
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Corresponding author’s e-mail address: keso2rhyme@yahoo.com 733

ORIGINAL RESEARCH ARTICLE

AN EFFICIENT TECHNIQUE FOR THE REDUCTION OF PACKET LOSS
IN WIRELESS SENSOR NETWORK

K. K. Nnamani*, A. D. Usman and K. A. Abubilal
Department of Electronics and Telecommunications Engineering, Ahmadu Bello University, Zaria, Nigeria

*Corresponding author’s email address: keso2rhyme@yahoo.com

1.0 Introduction
A wireless sensor networks (WSNs) possesses a lot of issues such as limited energy,
heterogeneous traffic, finite bandwidth, finite memory capacity, finite processing time (Swain
and Nanda, 2014) and congestion (Rezaee and Pasandideh, 2017). Congestion usually occurs
when the traffic load introduced into the network exceeds the network handling capacity at a
particular time. Congestion results in energy wastage, decrease in throughput, increase in
collisions, retransmission and an increase in packet delay and packet loss (Antoniou et al.,
2013). Congestion control and avoidance are the actions taken to manipulate the network
traffic, to prevent congestion and eventually congestion collapse (Antoniou et al., 2013).

Techniques to mitigate congestion essentially involves congestion detection, congestion
notification and finally congestion control (Narawade and Kokelar, 2017). Rate reduction
(traffic control) and the establishment of a different route (Resource control), are the two
basic methods of controlling congestion (Sergiou et al., 2014). Traffic control: congestion is
mitigated using this method, by reducing the amount of traffic load infused into the network
(Ghaffari, 2015). The different categories of traffic control techniques is shown in Figure 1
(Kafi et al., 2014).

ARTICLE
INFORMATION

ABSTRACT
This paper, presents an efficient technique for the reduction of packet losses in
wireless sensor network (WSN), which occurs because of buffer overflow and
subsequently congestion. A key design feature for wireless sensor network is to
have less packet loss, as more packet loss degrades the Quality of service
(QoS) of the transmitted information and retransmitting such, a packet can
use up the energy of the nodes. Regardless of a number of efforts made to
alleviate this problem, networks still experiences packet losses because of
congestion and topology change. A congestion avoidance algorithm, which is
based on an active queue management technique, was proposed in this work.
The new technique, called a Refined Adaptive Random Early Detection
(RARED) technique was used to control the maximum packet drop probability
to be within 0.01 and 0.5, which is equivalently between 1% and 50%, thereby
reducing the rate of packet loss and increasing the throughput. Simulation
results obtained using Network simulator 2, showed that the proposed
algorithm attained a reduction in packet loss of 5.7% against the existing
Priority-based Fairness Rate Control (PFRC) in WSN.

© 2020 Faculty of Engineering, University of Maiduguri, Nigeria. All rights reserved.

Submitted 3 December, 2019
Revised 7 August, 2020
Accepted 18 October, 2020.

Keywords:
Congestion
Packet loss
Refined Adaptive Random
Early Detection
priority-based fairness rate
control.

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Nnamani et al.: An Efficient Technique for the Reduction of Packet Loss in Wireless Sensor Network. AZOJETE,16(4):733-740. ISSN
1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: keso2rhyme@yahoo.com 734

Figure 1: Traffic Control Categories (Kafi et al.,2014)

2. Review of Related Work
Brahma et al., (2012), presented traffic management in WSN: Decoupling congestion control
and fairness. The congestion control algorithm adaptively determines a fair transmission rate to
each node that is highly efficient. However, a prioritized architecture was not considered,
which would have improved the throughput of the algorithm. Antoniou et al., (2013), presented
congestion control in WSNs built on the flocking behavior of birds. The underlining idea is to
direct packets to form flock and move in the direction of the sink node as the avoid obstacles
on their paths (dying nodes and congested areas) in a robust manner. However, the node is
passively listening to the channel, increasing the energy consumption of the network. This will
ultimately lead to packets loss, when some of the nodes run out of energy, as there will be
fewer nodes available to mitigate congestion. Jaiswal and Yadav (2013), studied fuzzy based
adaptive congestion control (FBACC) in wireless sensor networks. The author made use of
fuzzy logic system (FLS) for estimation of congestion and adapts to the changing traffic with low
packet loss. Congestion was detected and mitigated by considering buffer occupancy, traffic
rate and participants as input to the FLS. However, the use of fuzzy logic estimation involves
difficult computations, which that needs a lot of processing energy. This causes energy loss and
increased energy consumption. This eventually lead to loss of packets as there will be fewer
number of number available for transmission of data. Rezaee et al., (2013), implemented an
optimized congestion management protocol for healthcare wireless sensor networks. A novel
Active Queue Management (AQM) technique is used to avoid congestion and to provide
quality of service. However, congestion cannot be prevented for non-sensitive data. Rezaee et
al., (2014), implemented HOCA: Healthcare Aware Optimized Congestion Avoidance and
control protocol for wireless sensor networks. The author implemented a data centric
congestion control scheme using an AQM technique. There are four phases of operation of
HOCA, namely: request dissemination phase, event occurrence reporting phase, route
establishment phase and data forwarding and rate adjustment stage. However, packets
experience longer delays when they go through longer routes to the sink node. Swain and
Nanda (2014), presented Priority-based Adaptive Rate Control for Wireless Sensor Networks.
A tree topology was used Real time and Non-real time traffic classes were considered and
higher priority was assigned to the real time traffic. However, fairness in the distribution of the
available bandwidth was not considered, which would have improved the throughput.
Narawade and Kokelar (2016), investigated ACSRO: Adaptive cuckoo search-based rate
adjustment for optimized congestion avoidance and control in wireless sensor networks.
Congestion was initially avoided, by the adaptation of the packet drop probability using a virtual
queue, where less important packets are dropped. However, there is an increased complexity
and delay encountered in solving the multi-objective objective function. Swain and Nanda (2017)

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Arid Zone Journal of Engineering, Technology and Environment, December, 2020; Vol. 16(4):733-740. ISSN 1596-2490; e-ISSN 2545-
5818; www.azojete.com.ng

Corresponding author’s e-mail address: keso2rhyme@yahoo.com 735

studied Priority-Based Fairness Rate Control in Wireless Sensor Networks. The author
presented a rate control technique that is concerned with the fair distribution of bandwidth,
while considering the priority of the different traffic classes connected to various nodes.
However, congestion cannot be detected in advance, which would have improved the quality of
service of the algorithm.

3. Improved Algorithm: RARED
This work puts forward an efficient technique for reducing packet loss in WSN. To mitigate the
issue of packet loss as a result of buffer overflow, which eventually leads to congestion. The
developed technique, addressed the issue of packet loss by, detecting congestion in advance,
when the congestion index exceeds a predefined threshold (maximum). This helps in reducing
packet loss as a result of buffer overflow emanating from congestion.
The equation used to denote the packet loss is given below (Swain and Nanda, 2017):

Trxin
sink = i∈C(sink)Trxout

i� (1)

The equation, used in denoting the congestion index Ic , is given as follows (Liu et al., 2013):

Ic =

0,
Pmax (qk(i)−Mintℎ

k )

Maxtℎ
k i −Mintℎ

k i

1,

qk(i) ≤ Mintℎ
k i

Mintℎ
k i < qk(i) < Maxtℎ

k i

qk(i) ≥ Maxtℎ
k i

(2)

The new transmission rate from the sink node to the child node is given (Swain and Nanda,
2017):

Trxouti =

Trxouti + ∆Trxsink

Lsink
.
Pgl
i

Pgl
sink , if∆Trxsink > 0

Trxouti

∆Trxsink
.
Pgl
i

Pgl
sink if∆Trxsink < 0

(3)

Equations (4) and (5) is used to keep the minimize the packet losses (Tahiliani et al., 2011):

α= 0.25 x
qk(i)−target

target
x Pmax (4)

β = 1- 0.17 x target−qek(i)

target−Minth
k i (5)

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Nnamani et al.: An Efficient Technique for the Reduction of Packet Loss in Wireless Sensor Network. AZOJETE,16(4):733-740. ISSN
1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: keso2rhyme@yahoo.com 736

4.0 Simulation Environment
Network simulator 2.35 was used in the simulation of this work. Network simulator 2.35 was
used to compare the performance of the RARED and the existing priority-based fairness rate
control algorithm (PFRC). The simulation parameters used in this work are shown in Table 1.
Furthermore; Figure 3 shows the flowchart of the RARED and the Network simulator 2 code
used to design the RARED and PFRC respectively.

Table 1: Simulation Parameters Used in this Work (Swain and Nanda, 2017)

Parameter Value/Protocol

Simulator
r

NS-2 version 2.35

Simulation area 1000m *1000m

Mac layer IEEE 802.11
Number of sensor nodes 11

Data packet length 129 bytes

IFQ length 50 packets

Transmission range 281.84mW

Operating frequency 5 GHz

Traffic source CBR

Routing protocol Destination sequenced distance vector

Β 0.1
Simulation time 100s
Queue Type RARED and Priority queueing

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Arid Zone Journal of Engineering, Technology and Environment, December, 2020; Vol. 16(4):733-740. ISSN 1596-2490; e-ISSN 2545-
5818; www.azojete.com.ng

Corresponding author’s e-mail address: keso2rhyme@yahoo.com 737

Figure 3: Flowchart of the developed RARED algorithm (Swain & Nanda (2017)

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Nnamani et al.: An Efficient Technique for the Reduction of Packet Loss in Wireless Sensor Network. AZOJETE,16(4):733-740. ISSN
1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: keso2rhyme@yahoo.com 738

Average service time at the sink node
###################################################

$stime = (1-$val(l))*$val(stime) + $val(l)*$val(stime)
Maximum transmission rate of the parent node and child node
##############################

$outTx_rate = $agv_serviceTime_sinkNode{$landa}
$maxTx_rate = $outTx_rate * $globalPriority_sinkNode{$contact_nodes} /

$globalPriority_sinkNode{$contact_nodes} * $val(qssl) / $val(qscnl)
- Implementation of Refined ARED - Start ********************************************/
void REDQueue::updateMaxP_refined_adaptive(double new_ave, double now)

double part = 0.48*(edp_.th_max - edp_.th_min);
// AIMD rule to keep target Q~1/2(th_min+th_max)
if ( new_ave < edp_.th_min + part && edv_.cur_max_p > edp_.bottom) {

// we increase the average queue size, so decrease max_p
edv_.cur_max_p = edv_.cur_max_p * (1.0 - (0.17 * ((edp_.th_min + part) -

new_ave) / ((edp_.th_min + part) - edp_.th_min)));
edv_.lastset = now;
double maxp = edv_.cur_max_p;

Figure 4: Network simulator Code for Designing the RARED and the PFRC (Swain & Nanda,
2017)

5. Results and Discussion
The packet loss was calculated using equation 1. From the plot, it can be observed that the
developed RARED scheme displayed less amount of packet loss compared to the existing PFRC
scheme. This was as a result of the RARED algorithm that detects congestion before the
overflows, thereby preventing reducing packet loss as a result of congestion. The packet loss is
also reduced by using the RARED algorithm, by ensuring that the average queue size is
between the minimum and maximum thresholds respectively. Equation (6) shows the
mathematical expression for obtaining the percentage reduction in packet loss from the plot
shown in figure 5.

Percentage Improvement = RARED−PFRC
RARED

x 100 (6)

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Arid Zone Journal of Engineering, Technology and Environment, December, 2020; Vol. 16(4):733-740. ISSN 1596-2490; e-ISSN 2545-
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Corresponding author’s e-mail address: keso2rhyme@yahoo.com 739

100.000080.000060.000040.000020.00000.0000
0.0000

100.0000

200.0000

300.0000

400.0000

Simulation Time (Sec)

P
a
ck

e
t 

L
o

ss
 (

B
p

s)

np1_RARED_loss.tr

np1_PFRC_loss.tr

Figure 5: Plot of packet loss for the two protocols against simulation time

Figure 5 shows the result for the performance of PFRC and RARED in terms of packet loss
against simulation time. The loss of the network was tested against simulation time from 0 to
100seconds. Equation 1 is used to generate Figure 5. From the result, it can be observed that
the RARED experienced lower packet loss when compared to the existing PFRC. This is as a
result of RARED algorithm that detects congestion in advance before buffer overflow, thereby
resulting in lower packet loss. The fluctuations as evident in the plot are as a result of the
varying conditions of the network.

Table 2: Packet loss against simulation time
Time(s) 0 20 40 60 80 100 Average
PFRC 0 390 395 398 380 400 327.2
RARED 0 365 368 370 355 400 309.7

The data in Table 2 were obtained through Network Simulator version 2.35. The values in
Table 2 is computed by using equation 6. From Table 2, it can be observed that the packet loss
of the RARED showed a reduction of 5.7% when compared to PFRC.

6. Conclusion
From the discussed results, it can be seen that the developed RARED technique successfully
improved the performance of the existing Priority-based Fairness Rate Control algorithm. The
developed RARED algorithm outperformed the existing priority-based fairness rate control
algorithm, in terms of packet loss. The developed RARED algorithm achieved a reduction in
packet loss of 5.7%. This reduction in packet loss was made possible by the use of RARED
technique, which functions proactively by detecting congestion in advance congestion before
the buffer overflows.

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Nnamani et al.: An Efficient Technique for the Reduction of Packet Loss in Wireless Sensor Network. AZOJETE,16(4):733-740. ISSN
1596-2490; e-ISSN 2545-5818, www.azojete.com.ng

Corresponding author’s e-mail address: keso2rhyme@yahoo.com 740

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