The Impact of Transmission Opportunity (TXOP) on the
Performance of Priority based Contention based Scheduling
Strategies in Multi-hop Mesh Networks
Sajid M. Sheikh, Riaan Wolhuter and Herman A. Engelbrecht
Department of Electrical and Electronic Engineering, University of Stellenbosch,
Private Bag X1, Matieland, 7602, South Africa
Keywords: EDCA, Fairness, MAC, Wireless Mesh Networks, Scheduling, IEEE802.11e, Priority Scheduling.
Abstract: Wireless Mesh Networks (WMNs) face multiple problems. An increase in the number of hops for packets to
reach the destination results in an increase in contention for the medium which also results in an increase in
the collision rates. The enhanced distributed channel access (EDCA) mechanism was developed to provide
differentiated services to data with different priority levels in the IEEE 802.11e standard. The EDCA is a
distributed, contention-based channel access mechanism of the hybrid coordination function (HCF) which
results in an unfairness problem where higher priority data can starve lower priority data. We adopt the
EDCA architecture for heterogeneous data in telemetry and IoT applications to address these problems of
EDCA in multi-hop mesh networks. An adaptive weighted round robin (AWRR) scheduling strategy has
been proposed and tested on multi-hop networks in our previous work. With the AWRR strategy, although
packet loss is reduced, the end-to-end delay increases with high and medium priority data compared to
EDCA in WMNs. In this paper we investigate the effect of the Transmission Opportunity (TXOP) bursting
on the global performance in a WMN through setting up simulations in OMNeT++ using the INETMANET
framework. Simulation results have shown that using TXOP–bursting in the priority based scheduling
which follows the concept of schedule before backup helps reduce packet loss as well as reduce the end-to-
end delay. TXOP further optimizes the performance of AWRR.
1 INTRODUCTION
Wireless Mesh Networks (WMNs) have been
viewed as a promising technology for low cost
deployments for telemetry networks in rural areas as
well as for extending network coverage compared to
other solutions such as fiber optic, cellular networks,
Wi-Max or VSATs (iDirect 2009; Hammond and
Paul, 2006). WMNs have also attracted deployment
in many applications due to their self healing
properties where data can use an alternate route to
send data to the destination in the event when a link
becoming faulty (Akyildiz et al., 2005). Despite
these advantages, WMNs experience some
performance limitations. As stated in (Akyildiz et
al., 2005; Sheikh et al., 2015 and Pathak and Dutta,
2011) some of the main challenges are (1) the drop
in throughput with an increase in the number of hops
for data to reach the destination, (2) an increase in
the contention for the medium which results in an
increase in the collision rates and (3) a starvation
problem (fairness) affecting lower priority data with
the use of the enhanced distributed channel access
(EDCA) scheduling mechanism. Data that traverses
multiple hops to reach the destination usually
experience more contention to access the medium
compared to nodes closer to the destination (Denko
and Obaidat, 2009). The unfairness problem takes
place as the nodes closer to the receiver are given a
higher chance to transmit their data than those
progressively further way (Denko and Obaidat,
2009). EDCA has an internal node contention
mechanism such that when two data packets try to
transmit data on the medium at the same time, the
higher priority data is given access to the medium
and the lower priority data behaves as if a collision
occurred on the medium and exponentially increases
it’s contention window size resulting in starvation
for lower priority data (Chen, 2011 and Telenor et
al., 2005).
In many implementations and research, WMNs
usually use the EDCA scheduling strategy which
enhances the popular carrier sense multiple access
Sheikh, S., Wolhuter, R. and Engelbrecht, H.
The Impact of Transmission Opportunity (TXOP) on the Performance of Priority based Contention based Scheduling Strategies in Multi-hop Mesh Networks.
DOI: 10.5220/0005949201130120
In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - Volume 6: WINSYS, pages 113-120
ISBN: 978-989-758-196-0
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
113
with collision avoidance (CSMA/CA) for data of
different priority levels. The original CSMA/CA was
designed for wireless local area networks (WLANs)
based on signal-hop networks (Denko and Obaidat,
2009). To address the fairness and contention
increase problems in WMNs, a novel distributed
contention based mechanism called adaptive
weighted round robin (AWRR) in (Sheikh et al.,
2015) was proposed. Weighted round robin (WRR)
scheduling had been applied before for WiMax
scheduling as in (Guesmi and Maaloul, 2013), for
single hop WLANs IEEE802.11 based networks in
(Kuppa and Prakash, 2004; Farn and Chang, 2005
and Lee et al., 2005, but not for multi-hop WMNs.
With this proposed scheme, packet loss is reduced as
well as improvement in fairness to address starvation
as the internal collisions mechanism is removed.
With this strategy different nodes might be
transmitting data from a different queue as there are
guaranteed slots for different queues which results in
an increase in end-to-end delay for high and medium
priority data and a reduction for low priority data.
EDCA contains a contention free period known
as Transmission Opportunity (TXOP). When one of
the priority classes gain access to the channel to send
data, multiple frames can be transmitted in the
duration of the TXOP without the need to sense the
channel again and perform the back-off period (Inan
et al., 2007; Suzuki et al., 2006 and Min et al., 2008.
This condition is only valid as long as the duration
does not exceed the TXOP limit set. Each packet
transmission in the TXOP duration is separated by
Short Inter-frame Spacing (SIFS). If the TXOP limit
is set to 0, only one frame can be sent when the
priority class gains access to the channel (Inan et al.,
2007). Transmitting of multiple frames during the
TXOP is also referred to bursting as many packets
are transmitted continuously (Suzuki et al., 2006).
In the study in (Hu et al., 2012), it is shown that
TXOP with Quality of Service (QoS) differentiation
helps improve the system performance. The AWRR
strategy has been tested without focus on the
integration of TXOP. In this paper, we carry out a
comparative analysis of EDCA and AWRR with and
without the implementation of TXOP bursting in
multi-hop mesh networks. In (Reddy et al., 2007),
the proposed strategy focuses on dynamically
changing the TXOP limit values. In (Reddy et al.,
2007), an Adaptive-TXOP (A-TXOP) is proposed
where the TXOP interval is dynamically adjusted
based on the packets in the queue. The TXOP in our
understanding also contributes to a form of
unfairness as it allows multiple consecutive packets
to be transmitted of the same priority class.
Therefore, if high priority data is starving lower
priority data; it is expected than TXOP will result in
further unfairness.
EDCA was mainly designed for multimedia
applications such as voice and video which can
tolerate small amounts of packet loss but require less
end-to-end delay (Gao et al., 2005). There are many
non-delay sensitive applications that require a high
degree of reliability (less packet loss) QoS over
delay. This is to say that they can tolerate slightly
more delay provided it is within the tolerable ranges.
Examples of these applications are smart rural
applications such as smart grid, smart buildings,
smart farming and smart health (Sheikh et al., 2015).
These applications carry heterogeneous type of data
having different priority levels running on the same
communication network. In (Sheikh et al., 2015), the
requirements of these applications have been
classified into three categories, namely high,
medium and low priority. For EDCA to be used in
these applications to carry data of different priority
levels, it will have to be able to provide a high
degree of reliability as well as provide end-to-end
delay within tolerable ranges.
The novel contribution of this paper is that we
investigate the impact of TXOP (also know as
bursting) on the performance of EDCA and AWRR.
With EDCA packets from the different queues
concurrently try to access the medium by performing
their back-off countdown in parallel. With AWRR,
the packets only perform back-off after they have
been selected for transmissions. The rest of this
paper is organised as follows. Section 2 presents an
overview of EDCA which is a contention based
strategy in the IEEE802.11e standard. In section 3,
we present an overview of the AWRR scheduling
strategy. In section 4, an overview of the simulation
setup and performance metrics used for the analysis
are presented. In Section 5, the results and presented
and in section 6, the paper is concluded.
2 PRIORITY SCHEDULING IN
THE IEEE802.11E STANDARD
In this section we present a brief overview of the
EDCA scheduling strategy which is used as the
baseline strategy in this paper. EDCA is an
enhancement of CSMA/CA which is widely used in
many WMNs implementations.
In the IEEE802.11 standard, the Medium Access
Control (MAC) layer has two access mechanisms,
namely the contention based method called
WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems
114
distributed coordination function (DCF) and the
contention–free method called point coordination
function (PCF) (Maamar et al., 2011). The PCF is
the infrastructure based technique while DCF is the
distributed technique where the devices content for
the medium (Kaveh Pahlavan, 2002). The PCF is
used less in WMNs implementations due to the
difficulty of achieving time synchronisation globally
within a network.
With DCF, data of different priority is treated
equally and in a first in first out (FIFO) transmission
queue scheduling strategy. To provide differentiated
services the IEEE802.11e standard was proposed.
The IEEE 802.11e standard is based on both the
centrally-controlled and contention based medium
access mechanism (Kaveh Pahlavan, 2002). The
hybrid coordination function (HCF) is used in IEEE
802.11e which combines the aspects of DCF and
PCF with enhanced QoS mechanisms to provide
service differentiation providing both distributed and
centrally controlled channel access mechanisms.
EDCA is the distributed, contention-based channel
access mechanism of HCF (Poonguzhali, 2014).
Table 1: EDCA parameters.
AC Traffic Type
AIFS
No.
CW
min
CW
max
TXOP limit
802.11a PHY
TXOP limit
802.11b PHY
AC[3] Background 7 31 1023 0 0
AC[2] Best Effort 3 31 1023 0 0
AC[1] Video 2 15 31 3.008ms 6.016ms
AC[0] Voice 2 7 15 1.504ms 3.264ms
Figure 1: Reference EDCA implementation model for
IEEE802.11e (Sheikh et al., 2015).
Figure 2: TXOP Limit.
EDCA consists of more than one queue for data of
different priority levels known as access categories
(ACs). Each one of these ACs has specific
parameters associated with it as shown in table 1.
These parameters are designed such that high
priority data have smaller values than lower priority,
giving the higher priority data a higher chance to
access the channel (Pan et al., 2009).
Data is mapped at the MAC layer into the
corresponding AC. EDCA introduces a new
interframe spacing called Arbitration IFS (AIFS).
AIFS is the minimum time period for which the
medium must be sensed idle before an Enhanced
Distributed Channel Access Function (EDCAF) may
start transmission or back-off. The period is
depended on the AIFSN, CWmin and CWmax
values as shown in table 1. The higher priority ACs
have smaller CWmin and CWmax values compared
to lower priority ACs (Poonguzhali, 2014). For each
of the ACs, the corresponding AIFSN, CW values
and TXOP limit values are also shown in table 1.
Figure 1 shows the implementation scheduling
structure of EDCA. If any queue has data, data is
scheduled after sensing the medium to be idle for the
AIFS period and CW backoff period depending on
the priority class.
TXOP is a time interval during which a station
can send multiple frames one after the other
separated by a SIFS period as shown in figure 2. In
the EDCA standard, the TXOP limit is set to 3.264
ms for voice data and 6.016 ms for video data if the
IEEE 802.11b standard is used and to 1.504ms for
voice data and 3.008ms for video data if the IEEE
802.11a standard is used. For data, the TXOP–
bursting is set to 0 (Suzuki et al., 2006). These
TXOP limit values have been setup to suit voice and
video QoS required and packet sizes.
3 ADAPTIVE WEIGHTED
ROUND ROBIN (AWRR)
SCHEDULING STRATEGY
In this section we briefly explain how the AWRR
scheduling strategy works. To this AWRR strategy
we integrate a TXOP mechanism and test this
strategy with different TXOP limit values for data.
With AWRR, information from the header on the
type of application the packet is coming from is used
to classify and place the frames in the different
priority queues at the MAC layer. Weights are
assigned to the different priority queues. In our case
we have assigned 50% for high priority data, 30%
The Impact of Transmission Opportunity (TXOP) on the Performance of Priority based Contention based Scheduling Strategies in
Multi-hop Mesh Networks
115
for medium priority data and 20% for low priority
data. Based on these weights, we assign 10 slots to
these queues. The numbers of slots assigned to the
different queues can be changed. They are
application dependent and are
dependent on how much
transmission probability chance you want to assign to the
different queues (Sheikh et al., 2015). Table 2 presents the
slots assigned to the different queues depending on which
queues have
data. The weights only apply if all the
queues have data. Figure 3 shows the complete
overview of the AWRR strategy. The frame only
gets transmitted after performing the AIFS and back-
off according to the priority data (Sheikh et al.,
2015).
With the AWRR scheduling strategy, only one
frame gets scheduled at a time as compared to
EDCA where the frames from the different queues
contend for the medium. After the scheduling
process, the AIFS period and back off are carried out
before transmission on the medium takes place.
There is no internal contention mechanism in
AWRR. To perform the investigations in the paper,
the TXOP limit mechanism is added after the back-
off period.
4 SIMULATION SETUP
Many telemetry and Internet of Things (IoT)
applications such as smart grid, home-automation,
health-care monitoring are characterized as
consisting of heterogeneous data in the network.
These heterogeneous data have different priority
levels depending on the applications. To investigate
the effect of TXOP on EDCA and the AWRR
scheduling strategy for data, simulations were setup
in OMNeT++ using the INETMANET framework.
Table 3 presents the simulation parameters. The
standard IEEE802.11e model with AC[0] for high
priority data, AC[1] for medium priority data and
AC[2] for low priority data was used. The traffic
types are heterogeneous with different priority
levels. The two ray ground propagation model was
used to represent the physical environment as the
main focus of testing these strategies are for rural
smart applications. Usually the numbers of obstacles
or buildings are less in most rural areas in Africa.
The two ray model was used as in rural areas,
predominantly these two rays exist, i.e. direct rays
and the reflected rays due to less development in
rural areas. Shadowing models are more suitable for
more developed areas with more obstacles. User
Data Protocol (UDP) packets at the transport layer
having sizes of 512 bytes were used as UDP
Table 2: Queue slots assigned (Sheikh et al., 2015).
Does queue have data? Adaptive Queue Weights Assigned
High
Medium Low High Medium Low
No
No Yes 0 0 1
No
Yes No 0 1 0
Yes
No No 1 0 0
No
Yes Yes 0 3 2
Yes
No Yes 5 0 2
Yes
Yes No 5 3 0
Yes
Yes Yes 5 3 2
Figure 3: AWRR scheduling strategy (Sheikh et al., 2015).
applications such as Trivial File Transfer Protocol
(TFTP) and Domain Name Systems (DNS) use a
default packet size of 512 bytes. UDP was used as it
does not establish connections between the source
and destinations (connectionless) and also there is no
retransmission of lost packets [40]. The use of UDP
packets helps to determine the unreliability of the
network at the lower layers through packet loss
measures. On the other hand, Transmission Control
Protocol (TCP) is connection oriented and also
feedback is received for delivered packets
(Xylomenos and Polyzos, 1999).
A 5x5 square grid topology was used for the
investigation with measurements being done at the
source and sink nodes being the furthest apart in the
network as shown in figure 4. Source 1 and Sink 1
are other nodes also in communication to have a
scenario with data links also in communication. The
transmission range of each node is set such that each
node can only communicate with its adjacent nodes.
Square grid topologies present higher contention
levels with a high number of neighbouring nodes.
This helps to access the performance in extreme
cases.
Five test cases with different TXOP limit values
were setup with EDCA and AWRR as shown in
table 4. For each of these test cases, the performance
was tested over different data transmission rates with
constant bit rate (CBR) data as shown in table 5.
Each test with each test case and each data
transmission date rate was repeated 10 times with
different seed numbers generated by the random
number generator utility in OMNeT++. Each seed
number was 10000 apart. The errors bars show the
WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems
116
95% confidence level.
The performance metrics used in this paper are:
1. Number of Collisions: There are two types of
collisions that can take place with the EDCA.
These are internal and external collisions.
Internal collisions take place in the node if
EDCA is used. In AWRR no internal collisions
take place. External collisions take place on the
channel when packets collide physically. The
total number of collisions is calculated as:
TotalnumberofCollisionspersecond
Externalcollisions Internalcollisions
simulationtimein seconds
(1)
2. End-to-end Delay: This is the average time delay
by a packet to arrive at the destination from the
source.
3. Percentage Packet Loss (%): This is the measure
of the percentage of packets lost from the source
to the destination. This value was measured at
the destination as (Periyasamy and Karthikeyan,
2014):
Packe
t
Loss %
#o
f
Packetstransmitted # o
f
packetsrecieved
100
#o
f
Packetstransmitted
(2)
4. Jain Fairness Index (JFI): It measures how fairly
or unfairly the resources are shared among the
nodes. Equation 3 presents the JFI value where x
i
is the normalized throughput of station i, and n is
the number of flows in the WMN. A JFI of 1
indicates absolute fairness and a JFI of 0 absolute
unfair resource distribution (Deng and Han,
2009). In our case n=3 as we investigate the
fairness for 3 data classes namely for high,
medium and low priority classes.

,
,
,….,

∑


(3)
0

,
,
,….,
1
5 RESULTS
Figure 5 presents the number of collisions that took
place in the network with EDCA and AWRR with
the different TXOP test cases. In the TXOP tests, the
value for the low priority data TXOP is kept smaller
than the TXOP limit values for high and medium
priority data as normally the higher priority data
need to be transmitted with higher importance. For
AWRR in TXOP test cases 1 to 5, the number of
collisions per millisecond starts at 4.12 per ms with
case 1 and reduces to 3.87 per ms in case 4. With
EDCA, the numbers of collisions stay approximately
the same, despite using TXOP, due to the internal
Table 3: Simulation Parameters.
Network Setup
Topology type 5 by 5 Grid Topology
Terrain Area 2.2km x 2.2km = 4.84km
2
IEEE Standard IEEE 802.11g
Propagation Model Two Ray Ground Model
Routing Protocol OLSR
Data rate 54Mbits/s
Transport Protocol UDP Packets
Packet Size 512bytes
Table 4: TXOP test cases.
Case 1 Case 2 Case 3 Case 4 Case 5
High Priority Data
No TXOP 1ms 2ms 3ms 4ms
Medium Priority Data
No TXOP 0.5ms 1.5ms 2.5ms 3.5ms
Low Priority Data
No TXOP 0 1ms 1ms 1ms
Table 5: Data transmission test cases.
High Priority
Data
(Packets/sec)
Medium
Priority Data
(Packets/sec)
Low Priority
Data
(Packets /sec)
Data Case 1
50 50 50
Data Case 2
50 50 100
Data Case 3
50 100 50
Data Case 4
50 100 100
Data Case 5
100 50 50
Data Case 6
100 50 100
Data Case 7
100 100 50
Data Case 8
100 100 100
Figure 4: Test Topology.
collision mechanism being present which starves
lower priority data. The higher priority data have a
smaller collision window range and hence the
chances of collisions are high. With AWRR, the
reduction in collision with higher TXOP values is
observed since when a higher priority data gains
access to the medium, more higher priority data can
be transmitted without having to contend for the
medium as they have a higher collision possibility.
AWRR also allows more packets from other classes
to be transmitted on the medium compared to
EDCA.
For brevity, the consolidated average packet loss
over all the data transmission data rates with the
different TXOP test cases are presented in figures 6
to 8. From figure 6, we can observe that for high
priority data using EDCA, there is a packet loss of
50.8% in case 1 (No TXOP) and this reduces to
The Impact of Transmission Opportunity (TXOP) on the Performance of Priority based Contention based Scheduling Strategies in
Multi-hop Mesh Networks
117
50.4% until TXOP case 3. For high priority data
using AWRR, there is a packet loss of 44.8% in case
1 (No TXOP) and this reduces to 37.6% until TXOP
case 3. Therefore, a further packet loss reduction of
7% with AWRR for high priority data is observed.
From figure 7, we can observe that for medium
priority data using EDCA, there is a packet loss of
45.5% in case 1 and this reduces to 44.5% until
TXOP case 3. For medium priority data using
AWRR, there is a packet loss of 42.5% in case 1 and
this reduces to 36.6% until TXOP case 3. Therefore,
a further packet loss reduction of 1% for EDCA and
5.9% with AWRR for medium priority data is
observed. From figure 8, we can observe that for low
priority data using EDCA, there is a packet loss of
40.5% in case 1 and this reduces to 36.9% until
TXOP case 3. For low priority data using AWRR,
there is a packet loss of 39.1% in case 1 (No TXOP)
and this reduces to 33% until TXOP case 3.
Therefore, a further packet loss reduction of 1% for
EDCA and 6.1% with AWRR for low priority data
is observed. It is observed that increasing TXOP
beyond case 3 does not affect packet loss any further
for either EDCA or AWRR.
It is observed that TXOP does not significantly
affect packet loss in EDCA, but does significantly
reduce the packet loss in AWRR. The TXOP limit
allows multiple frames to be transmitted in this
TXOP duration without the need to contend for the
medium for each frame in the queue. The TXOP
only comes into play when more than one frame is
present in the queue. During the TXOP the medium
is sensed as being busy by the other nodes
contending for the medium and therefore, this results
in fewer collisions for the extra frames transmitted
during this period. The packet loss is reduced until a
point and further increasing the TXOP has no effect
on the performance. This is as a result that there are
no packets queued up further for the longer TXOP to
come into play.
With AWRR, the back-off countdown is started
only after scheduling a packet, while with EDCA,
the packets in the different queues in a node perform
the count down simultaneously. After a transmission
takes place, the queues that were already counting
down for back-off start from where they left off,
while with AWRR a new back-off is started. This
Figure 5: Number of Collisions.
Figure 6: Average packet loss for hig
h
priority data in all TXOP test cases.
Figure 7: Average packet loss for mediu
m
priority data in all TXOP test cases.
Figure 8: Average packet loss for lo
w
priority data in all TXOP test cases.
Figure 9: Average end-to-end delay for
high priority data in all TXOP test
cases.
Figure 10: Average end-to-end delay for
medium priority data in all TXOP test
cases.
Figure 11: Average end-to-end delay
for low priority data in all TXOP test
cases.
WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems
118
increases the chances of collision on the medium
with EDCA.
The consolidated average end-to-end over all the
data transmission test cases for the different TXOP
test cases are shown in figures 9 to 11. From figure 9
we observe an end-to-end delay of 5.3ms for high
priority data and 56ms for AWRR in case 1. The
end-to-end delay drops to 5ms with EDCA until case
3 and drops to 34ms for AWRR. A reduction in the
end-to-end delay of 23ms is observed with AWRR.
From figure 10 for case 1, we observe an end-to-end
delay of 8.2ms with EDCA and 34ms with AWRR.
The end-to-end delay drops to 8ms with EDCA until
case 3 and drops to 25ms for AWRR. A reduction in
the end-to-end delay of 9ms is observed with
AWRR. From figure 11 for case 1 it is observed that
EDCA has an end-to-end of 69ms and 33ms for
AWRR. The end-to-end delay drops to 49ms with
EDCA until case 3 and drops to 26ms for AWRR. A
reduction in the end-to-end delay of 7ms is observed
with AWRR and 20ms with EDCA. Higher end-to-
end delay reductions are observed with AWRR as
AWRR does not starve lower priority data and gives
a higher chance for data from other classes to be
transmitted as mentioned earlier.
The Jain’s fairness index values are presented in
figure 12. No significant change in fairness is
observed. Since the internal collision mechanism is
absent in AWRR, AWRR has a higher fairness than
EDCA. Application of TXOP with AWRR is
expected to improve fairness at higher loads as
lower priority data are given a fair opportunity and
are not being starved.
It must be noted that these strategies have a retry
limit of 7. The packets that collide increase the end-
to-end delay. These results are for heavy load
scenarios. With low loads, performance is better.
Figure 12: Jain’s Fairness Index in all TXOP test cases.
6 CONCLUSIONS
The problem with contention based scheduling
strategies is that they require monitoring of the
channel before data can be transmitted. The
advantage of TXOP is that multiple packets from the
same queue can be transmitted without the need of
continuously performing the contention period. With
AWRR using TXOP, a reduction in collisions has
been shown as the channel is sensed as being busy
by the other nodes during the TXOP period and the
other packets within the TXOP period of the same
data class can successful transmit. Retransmission of
collided packets waste channel bandwidth and
reduce the overall performance of the network.
Bandwidth is a critical factor in rural telemetry
networks.
In this study, we have observed that with the
application of TXOP to the AWRR, packet loss for
high priority data can be reduced by 7%, 5.9% for
medium priority data and 6.1% for low priority data.
The AWRR strategy does not have an internal
collision mechanism in the nodes and also the nodes
only contend for the medium after it is decided
which device will access the medium. Little, if any
improvement with EDCA is observed with EDCA in
WMNs due to the starvation and internal contention
mechanism present. However, TXOP application to
AWRR has shown significant packet loss and end-
to-end delay reduction for all data priority classes.
With AWRR a high increase in end-to-end delay
for high and medium priority packet is observed
compared to EDCA as AWRR gives higher chances
for packets from other priority data classes to be
transmitted on the medium. In a multi-hop scenario,
this results in the end-to-end delay increase for high
and medium priority data only. The TXOP period
helps lower this delay by a significant amount. A
delay reduction of 23ms for high priority, 9ms for
medium and 7ms for low priority data are observed.
This paper has shown that the performance of
AWRR can be further improved and optimised by
the use of TXOP limit values. In EDCA, back-off is
performed concurrently between the parallel queues,
while with AWRR, it is performed after the packet is
selected for scheduling which shows more positive
results with the use of TXOP.
Simulation results show an improvement in
performance in terms of reliability, end-to-end delay
and fairness. The proposed strategy, AWRR with
TXOP is a promising technique for implementation
in smart rural applications such as smart grid, smart
buildings, smart health and smart agriculture as a
low cost option to build and extent networks as
observed through simulation results.
The Impact of Transmission Opportunity (TXOP) on the Performance of Priority based Contention based Scheduling Strategies in
Multi-hop Mesh Networks
119
ACKNOWLEDGEMENTS
The authors will like to thank the reviewers for their
comments. This research was supported by the
University of Botswana and the South African
National Research Foundation (NRF) under the
THRIP project TP13081327740.
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