cac-olsr: extending olsr to provide admission control in wireless mesh networks

CAC-OLSR: Extending OLSR to Provide AdmissionControl in Wireless Mesh Networks

Eduardo Pompeo da Silva Mineiro •

Debora Christina Muchaluat-Saade

Received: 29 June 2013 / Accepted: 13 May 2014

� Springer Science+Business Media New York 2014

Abstract This work presents an admission control

mechanism for multi-hop wireless mesh networks based on

the IEEE 802.11 standard and the OLSR routing protocol.

This mechanism, called CAC-OLSR, aims at ensuring that

traffic flows with quality of service (QoS) requirements,

especially voice and video, are only admitted in the mesh

network if it has available resources in order to provide

flow requirements. In addition, QoS requirements of pre-

viously admitted traffic flows cannot be violated. The

proposal was evaluated with NS-2 and Evalvid simulations.

Keywords Quality of service � QoS � Admission control �Wireless mesh networks � OLSR � CAC-OLSR

1 Introduction

Technological advances coupled with cost reduction have

stimulated the implementation of wireless networks based

on the IEEE 802.11 standard [1]. In addition, there is a

growing trend in providing different services over IP net-

works, such as voice and video, which require a minimum

level of quality of service (QoS). Some QoS mechanisms

that have been widely disseminated and applied to wired

networks need special treatment in wireless scenarios,

especially because of bandwidth restrictions and huge

channel quality variability.

Regarding QoS in IEEE 802.11 wireless networks, it is

important to consider the IEEE 802.11e amendment [2],

which was incorporated into the original standard in 2007.

This amendment established four traffic categories, voice,

video, best effort and background, with different channel

access priority, through the configuration of some param-

eters such as frame spacing, contention window size and

transmission opportunity.

However, only traffic differentiation based on channel

access priority is not sufficient to guarantee the QoS

required by some multimedia applications. For example, it

does not avoid traffic congestion when many flows of the

same access category are injected in the network. This will

probably generate delay increase and throughput decrease

observed by each flow.

In the case of voice transmission, for example, in order

to guarantee quality of calls, network capacity must not be

violated [3]. Unfortunately, this condition is not always

satisfied when applying traffic differentiation only.

In this context, it is necessary to provide an admission

control mechanism, which should be responsible for the

entry of new network flows so that the QoS required by

them is met, without violating the requirements demanded

by previously admitted flows [4].

The IEEE 802.11 standard suggests an admission con-

trol mechanism for wireless networks operating in the

infrastructure mode. On the other hand, it does not define

any admission control mechanism aimed at wireless mesh

networks, where Nodes communicate in ad hoc mode using

multiple wireless hops.

This paper proposes an admission control mechanism

for multi-hop wireless mesh networks based on the IEEE

802.11 distributed access mode. The proposed mechanism,

E. P. da Silva Mineiro (&)

MıdiaCom Labs, Telecommunications Engineering Department,

Fluminense Federal University, Niteroi, Brazil

e-mail: [email protected]

D. C. Muchaluat-Saade

Computer Science Department, Fluminense Federal University,

Niteroi, Brazil

e-mail: [email protected]

123

Int J Wireless Inf Networks

DOI 10.1007/s10776-014-0242-z

called call admission control OLSR (CAC-OLSR), is an

extension to the optimized link state routing (OLSR) [5]

routing protocol, which is widely used in wireless mesh

networks [6, 7].

In addition to admission control, CAC-OLSR has the

ability to reserve channel time resources for voice and

video access categories. If those reserved resources are not

fully used, they can be destined to other access categories

in order to optimize network resource use.

This article is an extended version of [8]. It describes in

details the CAC-OLSR admission control mechanism,

showing the results of a real video transmission using the

Evalvid toolset [9], including peak signal-to-noise ratio

(PSNR) analysis of received frames and end-to-end delay

of received frames in terms of a cumulative distribution

function. Furthermore, it details results of CAC-OLSR QoS

violation recovery, including throughput per flow analysis.

The rest of the paper is structured as follows. Section II

discusses related work. Sect. 3 proposes CAC-OLSR.

Section 4 presents a performance analysis through simu-

lations made in Network Simulator (NS-2) [10] and Eval-

vid [9]. Finally Sect. 5 covers the conclusions and future

work.

2 Related Work

The work of Yang and Kravets [11], referenced in many

articles on the subject, proposed an admission control for

ad hoc networks based on channel occupation. In summary,

each Node decides about the admission of a new flow by

comparing the estimated occupation of the channel caused

by the incoming traffic to the available channel resources,

measured by carrier detection. If the former is smaller than

the latter, the flow can be admitted.

In another article [12], Chakeres and Belding-Royer

used the same principle of Yang and Kravets [11], but

proposed a zone extension to be considered by each Node

when measuring channel occupation. This is due because

of ‘‘hidden terminals’’, which can lead to false admissions.

Both works did not take into account the IEEE 802.11e

access categories. Also, they did not detail how their

mechanisms would work in a multi-hop network.

Chakeres and Belding-Royer [12] suggested an associ-

ation with reactive routing protocols, where the admission

of new flows occurs during the route discovery process, but

they did not mention how the mechanism could be applied

to proactive protocols like OLSR [5]. Also, they did not

consider intraflow interference, which will be addressed in

the next section.

Lindgren and Belding-Royer [13] presented an approach

for dealing with intraflow interference. They proposed a

factor called contention counter, which must be applied

during the resource estimation to be consumed by a new

flow. That work also proposed the admission control pro-

cess during route discovery, a fact that excludes the

application of the mechanism in networks whose routing

protocol is proactive, such as OLSR [5]. They did not take

into account the IEEE 802.11e access categories.

Ahn et al. [14] proposed a mechanism called SWAN,

which means service differentiation in stateless wireless ad

hoc networks. SWAN does not depend on a specific routing

protocol. It uses rate control for user datagram protocol

(UDP) and transmission control protocol (TCP) best effort

traffic, and sender-based admission control for UDP traffic.

Admission control is always executed by the source Node,

which sends a probe to the destination Node in order to

check the available bandwidth in the network and to

compare it with the bandwidth required by the new flow.

SWAN did not take into account the IEEE 802.11e access

categories.

Cerveira and Costa [15] proposed an extension to the ad

hoc on-demand distance vector (AODV) protocol [16]

adding an admission control mechanism similar to those

mentioned so far. However, it differentiates the channel

time occupation between QoS and best-effort traffic,

ignoring the latter in the process of admitting new flows.

Through that strategy, best effort traffic will only occupy

network resources not used by flows that demand QoS. The

authors also considered the use of IEEE 802.11e access

categories and assumed that best effort traffic uses TCP as

the transport protocol. They also considered intraflow

interference.

Su and Su [4] also proposed a mechanism for AODV

protocol called single phase admission control (SPAC).

SPAC considers bandwidth estimation during route dis-

covery process in order to admit or not a new flow.

Nguyen and Minet [17] presented an admission control

proposal for the OLSR protocol [5]. In fact, the article

proposed a modified version of OLSR, which considered

bandwidth allocation and channel interference for route

selection, besides admission control. The latter is based on

two steps: feasibility and acceptability. In the first step, the

incoming traffic Node compares the bandwidth required by

the flow to the available bandwidth on each Node of the

path (the available bandwidth of each Node is disseminated

via Hello messages). If the required bandwidth is smaller

than the smallest available bandwidth among all Nodes in

the path, the flow may be accepted. In the acceptability

step, there is a check for the interference caused by the new

flow in the vicinity of the Nodes along the path. The pro-

posal neither considered IEEE 802.11e, nor addressed the

issue of intraflow interference.

Badis and Agha [18] proposed an admission control for

the QOLSR [19] protocol. The scheme is based on sending

preliminary messages called check request (CREQ) and


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check reply (CREP), which carry the incoming flow

requirements in terms of bandwidth, delay and jitter. Each

Node in the path checks whether or not such requirements

can be met and forwards the message along only in the

positive case. If the CREQ Node source receives a CREP,

the corresponding flow is accepted. That work dealt with

intraflow interference, but did not consider IEEE 802.11e.

As far as it was possible for the current work to inves-

tigate, the mechanism that will be presented in the fol-

lowing section is the first admission control mechanism, to

be proposed for a proactive routing protocol, that addresses

all the following features: a wireless network composed of

multiple hops; the IEEE 802.11e access categories; pre-

sents a treatment for inter and intraflow interferences and

deals with QoS violation.

Besides all the features above, this paper considers the

reservation of channel time resources for voice and video

access categories. If not used, those resources can be used

by other traffic. This is important because if it is not con-

sidered and, for example, the network capacity is fully used

by previously admitted video flows, the admission control

mechanism might not allow new voice flows, even though

they belong to the highest priority category. CAC-OLSR,

in this case, would stop the latest admitted video flow in

order to admit voice traffic until the resources reserved for

this traffic was reached. This issue is a new insight into the

admission problem, since it is not considered by any of the

mentioned related works.

3 CAC-OLSR

The CAC-OLSR proposal, which means call admission

control OLSR, uses as main criterion for flow admission

the comparison between current channel time occupation

and the estimated occupation demanded by a new flow.

Furthermore, it uses the IEEE 802.11e access categories,

treats inter and intraflow interference and deals with QoS

violation.

In order to facilitate the understanding of the proposed

mechanism, this section is structured as follows. Sec-

tion 3.1 presents the channel occupation measurement

method, while Sect. 3.2 shows how the estimated occupa-

tion demanded by a new flow is done. Section 3.3 describes

how inter and intra flow interferences are treated. QoS

violation is discussed in Sects. 3.4 and 3.5 presents the

admission policy.

3.1 Channel Occupation Measurement

In order to measure channel occupation, carrier detection is

observed for a given period of time. This can be done in IEEE

802.11 by monitoring a function called PHYCS—PHY

carrier sense, which indicates if the channel is idle or busy.

However, this work considers that a channel is busy only

if it is occupied by QoS traffic (voice and video access

categories). In other words, the channel must be considered

idle during transmission or reception of traffic without QoS

(best effort and background access categories). This strat-

egy was adopted to prevent non-admission of QoS flows

because of channel occupation with traffic that does not

require QoS. It is considered here, as adopted in [15], that

the lower channel access priority and the use of TCP will

automatically reduce best effort and background traffic

category throughput in the presence of QoS traffic.

In summary, channel occupation measurement observes

carrier detection periodically during a certain period

(100 ms by default) and returns the percentage occupation

caused by frames belonging to voice and video access

categories. If the frame access category can not be identi-

fied, that frame is also considered for channel occupation

measurement.

3.2 Channel Occupation Estimation for a New Flow

Channel occupation for a new flow can be estimated if the

time required for one single frame transmission and the

flow frame rate are known.

Based on IEEE 802.11, the time required for success-

fully transmitting a frame, without request to send (RTS)

and clear to send (CTS) [1] procedures, is given by Eq. (1).

Tframe ¼ T Dataf g þ SIFS þ ACK þ AIFS

þ T Backofff g ð1Þ

In (1), T_{Data} is the time required for a single data

frame transmission; short interframe space (SIFS) is the

interframe spacing before an ACK transmission, which

corresponds to the acknowledgment of the data frame

previously sent; arbitration interfame space (AIFS) is the

interframe spacing before the transmission of another data

frame, depending on its access category. Finally,

T_{Backoff} represents the time wasted with the conten-

tion process as described in IEEE 802.11.

SIFS and AIFS values can be easily obtained in IEEE

802.11 recommendations. Moreover, T_{Data} and ACK

transmission time can be calculated knowing the frame size

and transmission rate information.

T_{Backoff} time can be estimated based on the con-

tention window average value multiplied by the duration of

a time slot, which can also be obtained in the IEEE 802.11

standard.

After estimating the time required for one data frame

transmission (1), it is necessary to check the amount of

frames sent by a given flow during an observation period.

As a result, it is possible to estimate the percentage of


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channel time occupation that will be consumed by that

flow.

3.3 Interference

3.3.1 Interflow Interference

It is considered as interflow interference, one that happens

among two or more different flows that compete for

channel access. The correct treatment of this type of

interference is very important to ensure that the admission

of a new flow neither will interfere with previously

admitted flows, nor will violate their QoS requirements.

To mitigate this problem, it is necessary to verify the

available resources in terms of channel availability, both in

the Nodes along the path of the new flow, as well as in their

neighbors that are located in the interference region.

CAC-OLSR combats interflow interference by propa-

gating channel occupation observed by each Node via

OLSR Hello messages [5]. Each Node that receives a Hello

message stores the channel occupation information in its

one and two-hop neighbor tables, which are already

maintained by OLSR. This proposal considers that a Node

interference region consists of its one and two-hop neigh-

bors only.

Thus, each Node along the path can verify whether it

has, as well as its one and two-hop neighbors have, the

necessary resources to meet the new flow demand during

the admission control process.

3.3.2 Intraflow Interference

Intraflow interference happens when Nodes that forward

packets of the same flow are interfering with each other.

This will probably cause flow throughput decrease, even

though each individual Node has enough channel resource

availability. To illustrate this phenomenon, Fig. 1 shows a

scenario where Node 1 is transmitting a flow to Node 5.

The circles with plain line represent the data transmis-

sion or reception range for each Node, while the dashed

one represents the carrier sensing range for Node 3.

Observe that Node 3 is in the detection region of Nodes 1,

2, 4 and 5, which prevents it from transmitting simulta-

neously with any of them. As a result, free time channel

available resources should be reduced by a factor of 5.

Thus, it is necessary to compute at each Node, the number

of Nodes involved in the intraflow interference region in

order to correctly estimate the resources that will be

demanded by a new flow.

CAC-OLSR protocol considers that intraflow interfer-

ence should be estimated by calculating a factor called

contention counter (CC) [13]. This factor sums, in each

Node, the number of Nodes belonging to the intraflow

interference region, limited to a maximum of 5, i.e., the

previous two, the two latter and the Node itself. For

example, the CC factor of Node 3 in Fig. 1 equals 5.

Finally, this factor should be applied to estimate channel

time occupation demanded by a new flow. For example, if

after applying the method described in Section B, a value

of 5 % of occupation was reached, each Node along the

path must consider that the flow will consume 5 % 9 CC

of channel time capacity.

3.4 QoS Violation

An admission control mechanism must deal with QoS

violation, which may happen, for example, because of false

admissions, Node mobility, changes in a Node neighbor-

hood or due to variations in signal propagation conditions.

In CAC-OLSR, every Node periodically monitors

channel occupation with QoS flows. If one of them detects

that occupation is greater than a threshold called ‘‘Viola-

tion Threshold’’, the network is near congestion and one or

more flows need to be stopped.

In this case, CAC-OLSR first checks if the reserved

resources for video access category were exceeded. If so,

an internet control message protocol (ICMP) message is

sent to the source Node of the last admitted video flow in

order to stop it. If not, voice access category reserved

resources were exceeded. Then an ICMP message is sent to

Fig. 1 Intraflow interference

Fig. 2 QoS violation mechanism


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the source Node of the last admitted voice flow in order to

stop it. Figure 2 illustrates the proposed QoS violation

mechanism.

In order to avoid synchronization, each Node starts to

check channel occupation in a different time. Moreover

there must be an interval of, at least, the observation period

(100 ms) between measurements for resource update

purposes.

The ‘‘Violation Threshold’’ must be a value greater than

the one used by the admission control algorithm to admit

new flows, as it will be seen in the next subsection. Also, it

must be neither too low to avoid unnecessary flow inter-

ruptions nor too high to avoid QoS violation.

3.5 Admission Policy

The admission policy includes the criteria to be used for

deciding if a new flow can be admitted in the network.

These criteria are based on definitions and proposals pre-

sented in the previous sections. To make it clearer, Fig. 3

shows a simplified flowchart of the proposed mechanism,

which is explained as follows.

Upon receiving a data packet, CAC-OLSR checks

whether it was originated in a client of the mesh Node

itself. If not, the packet is normally routed as in the original

OLSR. Included in this action is the premise that if a Node

receives a packet to be forwarded, then it has already

passed through the admission process at the source mesh

Node and therefore should immediately be forwarded.

If the packet was originated in a client of the mesh Node

itself, then it checks if it has associated QoS, i.e., whether it

will be forwarded as voice or video categories. This checking

is done by consulting the internet protocol (IP) header type of

service (ToS) or differentiated services code point (DSCP)

field. The ToS/DSCP value is used for mapping the packet to

the appropriate IEEE 802.11e access category (voice, video,

best effort or background) [2].

If the packet does not demand QoS, i.e., if it belongs to

best effort or background categories, then it should be

normally forwarded, unless channel occupation with QoS

traffic is over a configurable threshold. This threshold must

be smaller than the ‘‘Violation Threshold’’, as seen in the

previous section. If channel occupation with QoS traffic is

greater than the threshold chosen, the packet is discarded,

avoiding network congestion.

It is important to observe that traffic without QoS will

pass through CAC-OLSR admission control mechanism

only if the network is not congested. Its lower channel

access priority and the use of TCP in most cases regulate

that traffic injection into the network by itself.

Back to the flowchart in Fig. 3, if the flow has associated

QoS (voice and video categories), then the source mesh

Node will check whether it has previously been admitted

and still has an entry in the admission table. If so, the

packet is immediately sent. If not, a new admission process

is started.

In fact, before starting a new admission process, it

checks if the flow has already been rejected within a

minimum time interval required for a new admission pro-

cess to be initiated. If so, the flow should again be rejected

It is worth to notice that it was necessary to create a flow

table in each Node database with the admission, accepted

and rejected flows. Each entry in that table has a timer.

Flows without packet transmission for a given time will be

Fig. 3 Admission flowchart


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excluded and will have to pass through the admission

process again. Rejected flows will remain on the table until

the timer times out and a new admission attempt will not be

allowed for them during this period.

After the previous step, the Node checks if the channel

occupation with QoS traffic is smaller than the threshold

value and if it is smaller than the reserved resources value

for the access category of the new flow. If both conditions

are not satisfied, the flow must be rejected. On the other

hand, if one of them is met, the Node sends a CREQ

message, which must be answered by the destination with a

CREP. It is important to notice that if only the second

condition is met and the channel occupation with QoS

traffic is higher than the threshold, it means that the other

access category (voice or video) exceeded its reserved

resources and its latest flow must be interrupted (QoS

violation mechanism).

CREQ and CREP aims at avoiding inter and intraflow

interference problems, as already explained. Each Node in

the path, upon receiving a CREQ, checks the channel

occupation perceived by itself and all its one and two-hop

neighbors. Those occupations are compared to the value

estimated for the new flow, which includes the CC factor.

If the Node itself or any of its neighbors has no available

resources, CREQ should be discarded. On the other hand, if

all Nodes have the necessary resources, CREQ must be

forwarded to the next Node in the path until the destination

Node is reached.

The destination Node must process CREQ and send a

CREP, which will follow the path in the reverse way.

Again, each Node will check for available resources before

forwarding CREP. If the source Node receives CREP, it

will process the message and finally admit the flow if it has

available resources. The source Node has a timer for

receiving the corresponding CREP. After timeout, the flow

is considered rejected.

4 Perfomance Analysis

In order to analyze the effectiveness and performance of

CAC-OLSR, simulations were done using Network Simu-

lator NS-2 version 2.34 [10] and Evalvid [9]. The results

were compared to the standard OLSR protocol [5] and to

the SWAN [14] admission control mechanism. SWAN [14]

was chosen because an request for comments (RFC) draft

was written based on it and because its NS-2 code was

available [20].

4.1 Simulation Scenarios

For evaluation of CAC-OLSR, a mesh network scenario

composed of 10 fixed Nodes randomly placed in an area of

500 m 9 500 m was configured in NS-2 [10]. The random

position was provided by the setdest tool, available in NS-2.

As in wireless mesh networks, backbone mesh Nodes are

usually fixed, mobility was not considered in this scenario.

All simulations described here were performed using the

standard OLSR protocol, CAC-OLSR and SWAN control

admission mechanism.

As the NS-2 version employed did not have an imple-

mentation of OLSR, the code developed by the University

of Murcia, UM-OLSR, version 0.8.7 [21], was used, which

is totally adherent to RFC 3626 [5] considering the hop

count metric.

The IEEE 802.11e access categories were considered

only for OLSR and CAC-OLSR evaluation, since SWAN

does not consider them. These access categories were

developed for NS-2 by TKN group [22] and the parameters

for each one were set up according to the IEEE 802.11

recommendation [1].

All simulations were done without the use of RTS/CTS

[1]. All scenarios used IEEE 802.11g in ad hoc mode and

the two ray ground propagation model. The transmission

range was set to 250 m, while the carrier sensing range, to

550 m.

Network traffic was generated as follows: to model

voice and video flows, two constant bit rate (CBR) traffic

sources were respectively implemented, one with 160-byte

packet size sent every 20 ms (64 Kbps) and another with

1,280-byte packet size transmitted every 10 ms (1,024

Kbps). Best effort and background traffic were modeled as

file transfer protocol with 1,300 and 1,500-byte packet

sizes respectively. At each 20 s, a new flow from each type

(voice, video, best effort and background) was randomly

injected in the network. The first four flows, however,

began after 30 s of simulation, to ensure the correct routing

protocol convergence. The total simulation time was 431 s.

The threshold values adopted were 95 % for QoS violation

and 90 % for the admission process. The reserved channel

time resources values for QoS categories were 55 % for

voice and 35 % for video. Those values can be customized

in a different scenario.

Fig. 4 QoS violation recovery scenario


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For each simulation, the following metrics were com-

puted: average end-to-end delay, average throughput per

flow and average aggregate throughput. In addition to these

metrics, the average number of admitted flows and the

additional control overhead injected into the network were

also measured for CAC-OLSR.

A second test was done in order to evaluate the

quality of a real video transmission. The same scenario

described above was set in NS-2 with some differences

in the network traffic. In this test, one flow from each

access category (voice, video, best effort and

background) was injected every 10 s, starting after 30 s.

Besides that, a real video called ‘‘News’’ [9] was injected

after 50 s. ‘‘News’’ is a quarter common intermediate

format video with average throughput of 310 Kbps and

duration of 12 s. This video was encoded and prepared

for transmission in NS-2 using the Evalvid tool set. The

video content was repeated four times, so it lasted 48 s.

Source and destination Nodes were randomly selected. At

the destination Node, each received video frame PSNR

was calculated and converted to a mean opinion score

(MOS) score.

Fig. 5 Average end-to-end

delay for voice flows


delay for video flows


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Finally, for the purpose of evaluating CAC-OLSR QoS

violation recovery, a different scenario was employed, as

shown in Fig. 4. In this scenario, horizontal mobility was

used to force QoS violation.

Voice and video flows, with the same traffic model

defined before, were started every 20 s from Nodes 0 and 3

to Nodes 2 and 5 respectively. Nodes 0 to 2 and Nodes 3 to

5 were separated by a distance of 1,000 m. After 300 s of

simulation, Nodes 3 to 5 moved horizontally and stopped

10 m away from Nodes 0 to 2. Average end-to-end delay

and average throughput per flow were measured.

4.2 Results

The results were obtained from an average of 30 simula-

tions for each mechanism, SWAN, OLSR ? 802.11e and

CAC-OLSR ? 802.11e, and for each scenario, with a

95 % confidence interval.

Fig. 7 Average throughput per

flow for voice flows

Fig. 8 Average throughput per

flow for video flows


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4.2.1 Average End-to-End Delay

This metric considers the average end-to-end delay suf-

fered by packets sent in each 20 s interval. Figures 5 and 6

show average delay results for voice and video flows.

Observing Fig. 5, it can be seen that voice flows average

delay reached almost 15 s with the OLSR ? 802.11e and

25 s with SWAN. On the other hand, CAC-OLSR was able

to keep average delay of all voice flows bellow 150 ms, an

acceptable value for voice applications. Considering video

flows, CAC-OLSR maintained the delay below 500 ms.

4.2.2 Average Throughput per Flow

The average throughput per flow corresponds to the aver-

age bits per second transmitted per flow at each 20 s

interval. Figures 7 and 8 show throughput results for voice

and video flows.

Fig. 9 Average aggregate

throughput for QoS traffic

Fig. 10 Average aggregate

throughput for non-QoS traffic


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Only CAC-OLSR was able to deliver the required

average throughput per flow, i.e., 64 Kbps for voice and

1,024 Kbps for video.

4.2.3 Average Aggregate Throughput

The average aggregate throughput corresponds to the

average bits per second transmitted by all traffic flows at

each 20 s time interval. Figure 9 shows the average

aggregated throughput for QoS traffic, while Fig. 10 shows

throughput for non-QoS traffic.

In the case of aggregate throughput for QoS traffic,

CAC-OLSR presents the best result, as shown in Fig. 9,

probably because of less time spent with medium access

control layer retransmissions. For non-QoS traffic, as

shown in Fig. 10, CAC-OLSR also presents the best

results, although after 271 s, the average aggregate

throughput remained below 50 Kbps for all mechanisms.

4.2.4 Admitted Flows

Figure 11 shows the average number of voice and video

admitted flows. OLSR ? 802.11e does not have an

admission control mechanism. So, with 431 s of simula-

tion, there were 21 voice and 21 video flows in the

network.

SWAN admitted an average of 9 voice and 11 video

flows. As SWAN could not satisfy QoS metric require-

ments for all flows, it admitted more flows than the network

capacity was able to support. This is probably due to the

fact that the mechanism does not consider traffic between

transmission and carrier sensing ranges when admitting a

new flow.

CAC-OLSR admitted an average of 8 voice and almost

2 video flows after 431 s of simulations. In addition to

satisfying QoS requirements for all admitted flows,

Fig. 11 Admitted Flows

Table 1 CAC-OLSR additional control overhead

Description Average number of CREQ or

CREP packets (%)

Average number of

control packets

Sent

packets

464.57 (5.57 %) 8,343.10

Amount of

bytes

44,311.40 (4.04 %) 1,097,641.40

Fig. 12 PSNR of received frames


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CAC-OLSR also presented better results in terms of

aggregate throughput. So, the proposed mechanism

admitted an appropriate number of flows.

4.2.5 Admission Control Overhead

One concern about adopting CAC-OLSR is the additional

overhead introduced into the network, mainly because of

CREQ and CREP messages. However, as shown in

Table 1, this increase was, on average, less than 6 %

considering the number of packets. In fact, for an average

of 8,343.10 control packets sent by CAC-OLSR, 464.57

were CREQ or CREP messages, which represent 5.57 % of

the total. It is noteworthy that the other packets are usually

sent by the original OLSR protocol [5] (Hello, topology

control messages—TC, among others). With respect to the

additional amount of bytes injected into the network

because of CREQ and CREP, the simulations showed an

increase of only 4.04 %.

4.3 Real Video Transmission

Using the Evalvid tool set, the PSNR ratio of a real video

flow received frames was measured.

As it can be seen in Fig. 12, the PSNR ratio of original

video frames was very close to 45 dB. CAC-OLSR was

able to preserve the PSNR ratio above 40 dB for more than

95 % of received frames. The same did not happen with the

other protocols. OLSR received most frames in 20–30 dB

PSNR ratio interval, while SWAN, in 15–20 dB interval.

From the PSNR ratio of received frames, Evalvid can

calculate the percentage number of frames classified in

each MOS score. For this purpose, Table 2 is applied.

Results are shown in Fig. 13.

CAC-OLSR presented the best results, with almost

100 % of received frames classified as ‘‘Excellent’’, as the

original video. On the other hand, OLSR received only

30 % of the frames as ‘‘Good’’ and ‘‘Excellent’’, while

SWAN received most of frames with ‘‘Bad’’ quality.

Table 2 PSNR–MOS conversion

PSNR (dB) MOS

[37 5 (Excelent)

31–37 4 (Good)

25–31 3 (Fair)

20–25 2 (Poor)

\20 1 (Bad)

Fig. 13 Received frames per MOS

Fig. 14 CDF of average end-

to-end delay


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Finally, the Evalvid tool set was also able to show the

cumulative distribution function (CDF) in terms of

received frames average end-to-end delay. Figure 14

shows the results.

As it can be seen, with CAC-OLSR, 100 % of frames

were received with a delay lower than 0.5 s. On the other

hand, with original OLSR and SWAN, almost 10 % and

20 % of frames, respectively, were received with a delay of

20 s or higher.

4.4 QoS Violation Recovery

The scenario proposed to evaluate QoS violation recovery,

as previously described in Sect. 4.1, is extremely severe.

Both groups of Nodes are put together when each one has

almost 100 % of its channel resources consumed by voice

and video flows. This evaluation was done only with CAC-

OLSR, with and without the QoS violation recovery

mechanism.


voice delay


video delay


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Figures 15 and 16 show that with the QoS violation

recovery, voice and video end-to-end average delay were

reestablished after a severe neighborhood change. The

same did not happen without the violation mechanism.

For throughput, Fig. 17 shows that QoS violation

recovery was able to reestablish voice throughput

requirements (64 Kbps). In the case of video, after 431 s of

simulation, average throughput per flow was recovered to

Fig. 17 Average throughput

per voice flow

Fig. 18 Average throughput

per video flow


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almost 90 % of the demanded value, as it can be observed

in Fig. 18.

5 Conclusions

This paper presented CAC-OLSR, an admission control

mechanism for IEEE 802.11e wireless mesh networks. It

uses the OLSR routing protocol. The proposal considered

available channel time resources in order to admit new

flows and presented an approach to deal with inter and

intraflow interferences. CAC-OLSR also deals with QoS

violation and provided the ability to reserve channel time

resources for voice and video access categories.

The proposed mechanism is the first admission control

mechanism for a proactive routing protocol, such as OLSR,

which addresses all the following features: considers a

wireless network composed of multiple hops; considers the

IEEE 802.11e access categories; presents a treatment for

inter and intraflow interferences and deals with QoS

violation.

Performance results provided by CAC-OLSR, under the

considered scenarios, were quite satisfactory compared to

those produced by SWAN and by the original OLSR pro-

tocol using the IEEE 802.11e access categories. There was a

significant reduction in the end-to-end average delay and an

increase in the aggregate throughput. Moreover, CAC-

OLSR was able to maintain the throughput demanded by

voice and video flows. The additional-control-byte overhead

introduced by the admission mechanism was less than 5 %.

For future works, CAC-OLSR is going to be imple-

mented in openwrt-based mesh routers in order to be

evaluated in a real network. Also, scalability needs to be

investigated in a scenario with a large number of mesh

Nodes and flows, as well as the mechanisms behavior with

Node mobility. Another future work is the improvement of

channel occupation estimation for a new flow using a sta-

tistical approach.

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http://www.tkn.tu-berlin.de/research/evalvid

http://www.tkn.tu-berlin.de/research/evalvid

http://www.isi.edu/nsnam/ns/

http://www.isi.edu/nsnam/ns/

http://comet.columbia.edu/swan/

http://comet.columbia.edu/swan/

Eduardo Pompeo da SilvaMineiro is a telecommunica-

tions engineer and received the

M. Sc. degree in Telecommu-

nication Engineering from the

Fluminense Federal University

in 2011. His research interests

include computer networks and

multimedia systems.

Debora Christina Muchaluat-Saade is an associate professor

of the Computer Science

Department of the Fluminense

Federal University since 2002.

She is a computer engineer and

received the M. Sc. and D. Sc.

degree in Computer Science

from the Pontifical Catholic

University of Rio de Janeiro

(PUC-Rio), Brazil. She has been

the leader of several research

projects funded by Brazilian

agencies, such as CNPq,

FAPERJ, FINEP and RNP. Her

research interests include computer networks and multimedia

systems.


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cac-olsr: extending olsr to provide admission control in wireless mesh networks

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