1. Journal of Electronics and Communication Engineering & Technology (JECET)ISSN
2347–4181 (Print), ISSN 2347 – 419X (Online), Volume 1, Issue 1, July-December(2013)
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CLUSTERED CONDUCTION OF VOIP ROUTING TOPOLOGY FOR
802.11 WLAN
Mohammed Sirajuddin1,
Dr D. Rajya Lakshmi2
and Dr Syed Abdul Sattar3
1
Royal Institute of Technology and Science, Chevella, Hyderabad, India
2
Gitam Institute of Technology, Gitam University, Vizag, India
3
Royal Institute of Technology and Science, Chevella, Hyderabad, India
ABSTRACT
In this Paper, we propose a routing topology for VoIP transmission over 802.11 WLANs
that referred as clustered conduction of VoIP routing (CCVR) topology, which is Quality of
Service Centric. A novel scheduling mechanism introduced to differentiate the capricious loss of
the data packet and transmission delay. The aim of the proposal is to achieve a clustered
approach in the tasks involved in VoIP routing. The expected clustered approach splits the
routing functionalities such as scheduling and buffering process.
Keyword: 802.11, WLAN, QoS, ad hoc network, CCVR
1. INTRODUCTION
Real-time VoIP systems are suitably more and more popular in a range of
applications. The broad choice of applications embraces group collaboration, remote medical
diagnosis/treatment, conferencing systems, on-demand video services, and distance/remote
sensing and monitoring [1]. It is clear that the Internet is integrating a Global Mobile system
and different Clusters of systems into a big standard IP network [2]. IP based 802.11 WLAN
to be our technology to take VoIP applications. We chose a micro-mobility topology as a
solution to the performance and scalability issues of 802.11 WLAN [3].VoIP applications
have very unusual necessities from applications for which the internet was initially designed.
VoIP applications are real-time applications that need a certain amount of bandwidth to make
sure the bit-rate wanted by each media stream and harsh delay difference wants to keep away
from buffer underflow at the receivers. We require a mechanism to monitor the network non-
intrusively to notice any change. Moreover, for supporting VoIP streams, a definite means
for supporting mixed quality of service (QoS) required.
The paper is organized as follows: Section 2 briefly describes the related work.
Section 3 describes the proposed clustered conduction and ordering of services. Section 4
describes the Simulation and performance analysis and Section 5 concludes the paper.
Journal of Electronics and Communication Engineering &
Technology (JECET)
ISSN 2347-4181 (Print)
ISSN 2347-419X (Online)
Volume 1, Issue 1, July-December (2013), pp. 27-32
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2. RELATED WORK
Many researchers have addressed the issue of supporting QoS for IP based 802.11
WLAN by using scheduling algorithms. Simple scheduling algorithms such as First- Come-
First-Served (FCFS) were first recommended. In [4], the early deadline-first (EDF) algorithm
is extensive for scheduling real-time traffic in an IP-based 802.11 WLAN network. An
algorithm called TBLB (Token Bank Leaky Bucket) [5], which is using admission control
and device policing to provide real-time VoIP traffic streams in packet-switched networks
[5]. The drawback of this approach is that the modeling overhead rise linearly with the
number of flows present in the network.Fair queuing proposed approach that is specific to
real-time traffic [6]. Increasing the weight for specific Clusters may usually result in better
performance with respect to delay. However, it is a complex task to find suitable values for
the weight, even in an ideal generalized processor sharing (GPS) scheduler.
Recently, it seems to have been agreed upon that type specific architectures will be a
viable solution for providing service guarantees in the Internet. Compared with stream
specific architectures, since type specific architectures are working with simpler algorithms
for implementing QoS guarantees, and hence, they can be deployed with minor changes to
the network architecture.
3. CLUSTERED CONDUCTION AND ORDERING OF SERVICES
The proposed clustered approach buffers the VoIP data by Clustering that data into
different clusters based on the data properties and for each Cluster a different buffer cluster will
be allotted.
The model we use relies on using adaptive real-time scheduling method that takes into
account the characteristics of the wireless channel as well as the QoS requirements for each
traffic flow. The diverse parameters are considered in developing this model and their
description follows.
• Each cluster of traffic is connected to the relevant cluster of the buffer that buffers the
packets in the form of FIFO, which can be referred as packet clustering. The packet that
streams into a network through a gateway router will be moved to the relevant buffer cluster
that selected from a cluster to which that packet belongs to. The packet clustering process not
aware of the stream to which that ingress packet belongs to.
• In clustered buffering each cluster is having a finite size, which can be differ from the
sizes of other clusters, but the total size of all clusters together must be equal to the actual
buffer. This can be referred as Clustered Inflow Buffering
• Upon an ingress packet moved to its buffer cluster, the service priority ranks will be
normalized according to the load at individual buffer clusters in order to achieve the service
agreement. In this regard, often packets can be discarded from some of buffer clusters. This
can be referred as Clustered egressing
As a matter of generalizing, enhanced delay tolerance and utmost packet drop
possibility and ability of detecting fair changes in target network can be considered as QoS
aspects for VoIP data transmission.
Service priority ranking under service scheduler must be done under QoS aspects. This
process can be elaborated with an example that follows let us consider VoIP traffic at a
gateway router. Here the ingress is Clusterified into three clusters and the same number of

3. Journal of Electronics and Communication Engin
2347–4181 (Print), ISSN 2347 – 419X
buffer clusters are used to store these
prioritized in the same order such that Cluster one get top priority, cluster 2 is in second order
and cluster 3 follows cluster 1 and cluster 2 with rank 3. Then few QoS aspects can be def
as follows:
The delay in cluster 2 data must be double to the delay at cluster 1 data: This QoS aspect helps
to achieve the better throughput at less Frame Overhead. This QoS aspect can be generalized as
delay in a cluster A data must be double to th
follows cluster B rank in order.
The packet loss in cluster 3 must be equal to packet loss in cluster 2: This QoS aspect
helps to achieve the better bandwidth
can be generalized such that the packet loss ratio of two clusters that are sequenced in given
priority ranks must be same.
The enhanced delay of packets at cluster 3 must be less than the max delay threshold:
This QoS aspect fixes the finite state
transmission; hence the packets that can’t survive beyond the max delay time can be dropped
from the buffer to accommodate for other capable packets in the stream. This QOS aspect helps
to achieve the effective buffer management. This QoS aspect can be generalized such that the
max delay threshold of the cluster that stands lost in the priority rank order must set to finite
state.
Because we desire to maintain complete guarantees and do not use admission
a set of service guarantees may be infeasible in some
delay bound and loss rate bound together at burst of traffic is not practically possible. If
inconsistency observed in service guarantees then some of the service guarantees need to be
tranquiled. For instance, to achieve absolu
relaxed, and the same way to achieve the delay guarantees loss guarantees need to be relaxed.
In the assumption of QoS guarantees with precedence order that uses to conclude
the constraints to be relaxed against to
Figure 1: Architecture of clustered transmission in wireless 802.11
nd Communication Engineering & Technology (JECET)ISSN
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these three Cluster Clusters of ingress. If the clusters are being
prioritized in the same order such that Cluster one get top priority, cluster 2 is in second order
and cluster 3 follows cluster 1 and cluster 2 with rank 3. Then few QoS aspects can be def
The delay in cluster 2 data must be double to the delay at cluster 1 data: This QoS aspect helps
to achieve the better throughput at less Frame Overhead. This QoS aspect can be generalized as
delay in a cluster A data must be double to the delay in a cluster B data, if cluster A rank
The packet loss in cluster 3 must be equal to packet loss in cluster 2: This QoS aspect
better bandwidth utilization with fewer Frame Overhead. This QoS a
can be generalized such that the packet loss ratio of two clusters that are sequenced in given
The enhanced delay of packets at cluster 3 must be less than the max delay threshold:
This QoS aspect fixes the finite state for delay enhanced by a buffer cluster during the
hence the packets that can’t survive beyond the max delay time can be dropped
from the buffer to accommodate for other capable packets in the stream. This QOS aspect helps
tive buffer management. This QoS aspect can be generalized such that the
max delay threshold of the cluster that stands lost in the priority rank order must set to finite
Because we desire to maintain complete guarantees and do not use admission
a set of service guarantees may be infeasible in some occasions. As an instance, achieving
delay bound and loss rate bound together at burst of traffic is not practically possible. If
inconsistency observed in service guarantees then some of the service guarantees need to be
tranquiled. For instance, to achieve absolute bounds the proportional guarantees need to be
relaxed, and the same way to achieve the delay guarantees loss guarantees need to be relaxed.
In the assumption of QoS guarantees with precedence order that uses to conclude
ainst to inconsistent service guarantees.
Architecture of clustered transmission in wireless 802.11 WLAN
eering & Technology (JECET)ISSN
December(2013)
three Cluster Clusters of ingress. If the clusters are being
prioritized in the same order such that Cluster one get top priority, cluster 2 is in second order
and cluster 3 follows cluster 1 and cluster 2 with rank 3. Then few QoS aspects can be defined
The delay in cluster 2 data must be double to the delay at cluster 1 data: This QoS aspect helps
to achieve the better throughput at less Frame Overhead. This QoS aspect can be generalized as
e delay in a cluster B data, if cluster A rank
The packet loss in cluster 3 must be equal to packet loss in cluster 2: This QoS aspect
utilization with fewer Frame Overhead. This QoS aspect
can be generalized such that the packet loss ratio of two clusters that are sequenced in given
The enhanced delay of packets at cluster 3 must be less than the max delay threshold:
for delay enhanced by a buffer cluster during the
hence the packets that can’t survive beyond the max delay time can be dropped
from the buffer to accommodate for other capable packets in the stream. This QOS aspect helps
tive buffer management. This QoS aspect can be generalized such that the
max delay threshold of the cluster that stands lost in the priority rank order must set to finite
Because we desire to maintain complete guarantees and do not use admission control,
. As an instance, achieving
delay bound and loss rate bound together at burst of traffic is not practically possible. If
inconsistency observed in service guarantees then some of the service guarantees need to be
te bounds the proportional guarantees need to be
relaxed, and the same way to achieve the delay guarantees loss guarantees need to be relaxed.
networks

4. Journal of Electronics and Communication Engineering & Technology (JECET)ISSN
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There are two requirements that a relative separation model should meet:
Cluster space balancing is the network operator should be able to regulate the cluster spacing
between clusters based on their criteria.
Autonomous Cluster Ordering is the separation is consistent (a higher cluster is better
or at least no worse than a lower cluster) and the relative ordering between clusters should be
met autonomous of load condition and time scales.
The scheduling algorithm functions as follows.
For each event of advent, the service rate allotment of traffic clusters is modified so that
all QoS service guarantees are met. If there exists no possible rate allocation that meets all
services guarantee then transmission losses due to traffic drop either from a new advent or from
the current backlog.
In the model we propose, the number of the queues is identical to the number of the separation
clusters. The state history is maintained at a clustering stage, not on a flow state as with others
future separation algorithms. Thus, the state record of a Cluster then is to consider to the
transmission of a packet from a Cluster or falling of a packet from a Cluster. For Cluster
priority, our scheduler will grantee Cluster iC will obtain improved or at least no worse service
than Cluster 1+iC .
4. SIMULATION AND RESULTS ANALYSIS
We present an estimate of the model discussed in section (3) using NS-2 network
simulator. Our aims are (1) to conclude if and how well the preferred QoS is can be achieved
by differentiation; (2) that the scheduling algorithm can reach controllable and predictable
interruption and packet loss separation; and (3) to compare our algorithm with existing models.
We test the algorithm for combining TCP and UDP traffic; we also want to study the level of
quality of service and end-to-end flow that can be received with the proposed per-node
guarantees. The relationship between the CIP nodes is 45 Mbps, and sources relate to the
Gateway by 126 Mbps links. Each 40 to 60 Mbps link has a dissemination delay of 3 to 4 ms;
each 126 Mbps association has a dissemination delay of 1 ms. Cluster 1, 2 and 3 only consist of
TCP transmission, and the 4th cluster only consists of UDP transmission flow. All flows
consist of packets with a fixed range of 500 Bytes, and the experiment lasts 70 seconds of
simulated time. The offered load is asymmetric, since primarily Cluster 1 contributes 10% of
the aggregate traffic, Cluster 2 contributes 20%, Cluster 3 contributes 30% and Cluster 4
contributes 40%. The complete delay and packet loss constraints for Cluster 1 are 2ms and 1%
correspondingly. The comparative division issues are set of four for the ratio of delays of two
successive clusters, and of four for the ratio of failure rates of two consecutive clusters. We
compared the performance of buffering approach along with standard service scheduling
currently in use and clustered buffering and dynamic service scheduling with priority ranks that
we proposed.
The same load is agreeing to all the paths with a regular interval of 10000 mille sec.
Given load in kbs is shown in fig 6.2. The fig 6.3 concludes the step up of proposed QoS
centric clustered buffering and dynamic service scheduling approach over buffering and
service scheduling model in use. The Frame delivery ratio evaluation between proposed and
existing models is compared in fig 6.4 that elevate the performance scalability as minimum
Frame Overhead in the clustered

5. Journal of Electronics and Communication Engineering & Technology (JECET)ISSN
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Figure 2: Weight in bytes sent by source node of the path [in regular interval of 10000 mille
sec]
Figure 3: Frame delivery ratio: between cluster buffering with dynamic service scheduling
and single buffer strategy with standard service scheduling
Figure 4: Frame Overhead comparison between proposed and existing models

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5. CONCLUSION
This paper proposed a Clustered conduction of VoIP routing (CCVR) topology for
wireless 802.11 WLAN networks. We estimate the performance of the proposed model during
wide simulation study with traffic of audiovisual data combination. Our simulation study has
revealed that the proposed algorithm attain considerable performance beneath QoS issues. The
projected clustered buffering and adaptive flow scheduling model is exceedingly flexible in
that it can offer a large range of QoS features. In future this work can be advanced such that the
proposed model is qualitative in mathematically prioritizing and scheduling services.
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