qos routing for mobile adhoc networks and performance analysis using olsr protocol

8/9/2019 QoS Routing For Mobile Adhoc Networks And Performance Analysis Using OLSR Protocol

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QoS Routing For Mobile Adhoc Networks And

Performance Analysis Using OLSR Protocol

K.OudidiSi2M Laboratory

National School of Computer Science

and Systems Analysis, Rabat, Morocco

[email protected]

A.Hajami

Si2M Laboratory



[email protected]

M.Elkoutbi

Si2M Laboratory



[email protected]

Abstract-- This paper proposes a novel routing metrics based on

the residual bandwidth, energy and mobility index of the nodes.

Metrics are designed to cope with high mobility and poor

residual energy resources in order to find optimal paths that

guarantee the QoS constraints. A maximizable routing metric

theory has been used to develop a metric that selects, during theprotocol process, routes that are more stable, offer a maximum

throughput and prolong network life time. The OLSR

(Optimized Link State Routing) protocol, which is an

optimization of link state protocols designed for MANETs

(Mobile Ad hoc Networks) is used as a test bed in this work. We

prove that our proposed composite metrics (based on mobility,

energy and bandwidth) selects a more stable MPR set than the

QOLSR algorithm which is a well known OLSR QoS extension.

By mathematical analysis and simulations, we have shown the

efficiency of this new routing metric in term of routing load,

packet delivery fraction, delay and prolonging the network

lifetime.

Index Terms — Mobile Ad hoc networks, quality of service, routing protocol, routing metric, mobility, residual energy.

I. INTRODUCTION

A Mobile Ad hoc Network (MANET) is a collection of

mobile nodes working on a dynamic autonomous network.Nodes communicate with each other over the wireless medium

without need of a centralized access points or a base station.

Since there is no existing communication infrastructure,adhoc networks cannot rely on specialised routers for path

discovery and routing. Therefore, nodes in such a network are

expected to act cooperatively to establish routes instantly.

Such a network is also expected to route traffic, possibly overmultiple hops, in distributed manner, and to adapt itself to the

highly dynamic changes of its links , mobility and residual

energy patterns of its constituent nodes.

Providing QoS in MANETs [1] is a tedious task. It’s known

that combining multiple criteria in the routing process is a

Hard problem (NP-Complet) A complete QoS model in

MANETs will span multiple layers, however the network

layer plays a vital role in providing the required support

mechanisms. The goal of QoS routing is to obtain feasible

paths that satisfy end-system performance requirements. Most

QoS routing algorithms present an extension of existing

classic best effort routing algorithms. There are three main

categories

of MANET routing protocols: Proactive (table-driven),

Reactive (on-demand) and Hybrid. Proactive protocols build

their routing tables continuously by broadcasting periodic

routing updates through the network; reactive protocols build

their routing tables on demand and have no prior knowledgeof the route they will take to get to a particular node. Hybrid

protocols create reactive routing zones interconnected by

proactive routing links and usually adapt their routing strategy

to the amount of mobility in the network.

In this paper we reiterate our proposed mobility metric.

Based on the use of this mobility metric we propose a new

composite metric, to find the optimal path given the QoS

constraints. The objective of the composite metric is to find an

optimal stable path with maximum available bandwidth and to

prolong network life time.

Using the OLSR Protocol, we show that our proposed

metric selects stable MPR Set rather than the QOLSRalgorithm which is a well known OLSR QoS algorithm for

MANETs.

This paper is organized as follows. Section 2 gives an

overview of the original OLSR protocol. Section 3 summarizes

the state of the art dealing with QoS support in MANETs and

describes the QoS routing problems Section 4 presents our

proposed composite metric based on mobility, residual energy

and bandwidth as QoS parameters. In Section 5, simulations

and results are discussed. The last part of this paper concludes

and presents some future work.

II. OPTIMIZED LINK STATE ROUTING PROTOCOL

A. Overview

OLSR (Optimized Link State Routing) protocol [2-3] is a

proactive table driven routing protocol for mobile ad hoc

networks and it is fully described on RFC 3626 (Thomas

Clausen & Philippe Jacquet, (October 2003)). As a link state

routing protocol, OLSR periodically advertises the links

building the network. However, OLSR optimizes the topology

information flooding mechanism, by reducing the amount of

links that are advertised and by reducing the number of nodes

forwarding each topology message to the set of MPRs only.

Information topology is called Topology Control (TC) message

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 8, No. 1, April 2010

138 http://sites.google.com/site/ijcsis/

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and exchanged using broadcasted into the network. TC

messages are only originated by nodes selected as Multipoint

Relays (MPRs) by some other node in the network. MPRs are

selected in such a way that a minimum amount of MPRs,

located one-hop away from the node doing the selection(called MPR Selector ), are enough to reach every single

neighbour located two-hops away of MPR selector . By

applying this selection mechanism only a reduced amount of

nodes (depending on the network topology) will be selected as

MPRs[18]. Every node in the network is aware of its one-hop

and two-hop neighbours by periodically exchanging HELLO

messages containing the list of its one-hop neighbours. On the

other hand, TC messages will only advertise the links between

the MPRs and their electors. Then, only a partial amount of

the network links (the topology) will be advertised, also MPRs

are the only nodes allowed to forward TC messages and only

if messages come from a MPR Selector node. Theseforwarding constrains considerably decrease the amount of

flooding retransmissions (Figure 1). This example shows the

efficiency of the MPR mechanism because only eight

transmissions are required to reach all the 23 nodes building

the network, which is a significant saving when compared to

traditional flooding mechanism where every node is asked to

retransmit to all neighbours.

Figure 1: Flooding with MPR mechanism

B. MPR Selection Algorithm

The computation of the MPR set with minimal size is a NP-

complet problem [14-16]. For this end, the standard MPR

selection algorithm currently used in the OLSR protocol

implementations is as follows:

Figure 2-a- Example of MRRset calculation.

For a node x, let N(x) be the neighborhood of x. N(x) is the set

of nodes which are in the range of x and share with x a

bidirectional link. We denote by N2(x) the two-neighborhood

of x, i.e, the set of nodes which are neighbors of at least one

node of N(x) but that do not belong to N(x) (see Figure 2-a).

Based on the above notations, the standard algorithm for

MPR selection is defined as follows (figure 2-b):

OLSR uses hop count to compute the shortest path to an

arbitrary destination using the topology map consisting of all

its neighbours and of MPRs of all other nodes. Number of hop

criterion as a routing metric is not suitable for QoS support as

a path selected based on the least number of hops may not

satisfy the required QoS constraints.

Figure 2-b: MPR Selection Algorithm

III. RELATED WORK

A. Qos Support in a Manet

In this section we discuss the recent work done to provideQoS functionality in Manets.

INSIGNIA, [7], is an adaptation of the IntServ Model to the

mobile ad hoc networks. QoS guarantee is done by per-flow

information in each node that is set up by the

signalling/reservation protocol. The destination statistically

measures QoS parameters (e.g. packet loss, delay, average

throughput,etc.) and periodically sends QoS reports to the

source. Based on those reports, the source node can adapt real-

time flows to avoid congestion.

SWAN, [13], Service differentiation in stateless Wireless

Ad-hoc Network, is an adaptation of the DiffServ Model to the

mobile ad-hoc networks. Nodes do not need to keep per-flowinformation in order to handle packets. QoS guarantee is

provided according to the class of the flow once it has been

accepted.

FQMM, [11], Flexible Qos Model for MANET, has been

introduced to offer a better QoS guarantee to a restricted

number of flows whereas a class guarantee is offered to the

other flows. FQMM is a hybrid approach combining per-flow

granularity of IntServ for high priority classes and perclass

granularity of DiffServ for low priority classes.

G. Ying et al [8] have proposed enhancements that allow

OLSR to find the maximum bandwidth path. The heuristics




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are based on considering only bandwidth as a QoS routing

constraint and revisions to the MPR selection criteria. They

identify that MPR selection is vital in optimal path selection.

The key concept in the revised MPR selection algorithm is

that a “good bandwidth” link should never be omitted. Basedon this three algorithms were proposed: OLSR_R1, R2 and

R1.

The research group at INRIA [9],[10] proposed a QoS

routing scheme over OLSR. Their technique used delay and

bandwidth metric for routing table computation. Such metrics

are included on each routing table entry corresponding to each

destination.

QOLSR [11] and work presented in [9] enhance OLSR with

QoS support. Both propose a solution providing a path such

that the bandwidth available at each node on the path is

higher than or equal to the requested bandwidth. Furthermore,

QOLSR considers delay as a second criterion for pathselection.

However, all of these solutions do not take into account at

all mobility and energy parameters induced by the nature of

Manet Network.

B. Qos Routing Problems

One of the key issues in providing end-to-end QoS in a

given network is how to find a feasible path that satisfies the

QoS constraints. The problem of finding a feasible path is NP-

Complete if the number of constraints is more than two, it

cannot be exactly solved in polynomial time and mostly dealt

with using heuristics and approximations. The network layer

has a critical role to play in the QoS provision process. The

approaches used by the QoS routing algorithms follow a trade-

off between the optimality of paths and the complexity of

algorithms especially in computing multiconstrained path. A

survey on such solutions can be found in [14].

The computation complexity is primarily determined by the

composition rules of the metrics [16]. The three basic

composition rules are: additive (such as delay, delay jitter,

logarithm of successful transmission, hop count and cost),

multiplicative (like reliability and probability of successful

transmission) and concave/min-max (e.g. bandwidth). The

additive and multiplicative metric of a path is the sum and

multiplication of the metric respectively for all the links

constituting the path. The concave metric of a path is the

maximum or the minimum of the metric over all the links in

the path.

Otherwise, if ji M ; is the metric for link { } j,i and P is the

path between (i, j, k,..1,m) nodes, the QoS metric M(P) is

defined as [14-15]:

• Additive : M(P) = ji M ; + k i M ; +…+ ml M ;

• Multiplicative : M(P) = ji M ;

xk i M ;

x…xml M ;

• Concave : M(P) = { } M,...,M,Mmin ml;k i; ji;

The proof of NP-Completeness relies heavily on the

correlation of the link weight metrics. QoS Routing is NP-

Complete when the QoS metrics are independent, real

numbers or unbounded integers.In general, QoS routing focuses on how to find feasible and

optimal paths that satisfy QoS requirements of various voice,

video and data applications. However, based on maximizable

routing metrics theory [16], it is shown that two or more

routing metrics can be combined to form a composite metric if

the original metrics are bounded and monotonic.

Before we proceed to the mathematical proof, we give

definitions of maximal metric tree and the properties desired

for combining metrics i.e. boundedness and monotonicity.

Definition 1: Routing Metric

A routing metric for a network N is six-tuple (W,Wf, M, mr, met,

R RR R ) where:

1. M is a set of metric values

2. Wf is a function that assigns to each edge { } j,i in N a

weight Wf( { } j,i ) in W

3. W is a set of edge weights

4. mr is a metric value in M assigned to the root.

5. met is a metric function whose domain is MxW and

whose range is M (it takes a metric value and an edge

value and returns a metric value).

6. R RR R is a binary relation over m, the set of metric values that

satisfy the following four conditions of irreflexivity,

Definition 2: Maximum Metric Tree

A spanning tree of N is called a maximum metric tree with

respect to an assigned metric iff every rooted path in T is

maximum metric with respect to the assigned metric. In

simple words every node obtains its maximum metric through

its path along a maximum metric tree.

Definition 3: Boundedness

A routing metric (W, Wf, M, mr, met, R RR R ) is bounded iff the

following condition holds for every edge weight w in W and

every metric value m in M.

met (m,w) R RR R m ∨ met(m,w) = m

Definition 4: Monotonicity

A routing metric (W,Wf, M, mr, met, R RR R ) is monotonic iff the

following condition holds hue for every edge weight w in W

and every pair of metric values m and m’ in M:

m R RR R m’ ⇒ (met (m,w) R RR R met (m’,w)

∨ met (m,w) = met (m’,w))

(W,Wf, M, mr, met, R R R R ) is called strict monotonic iff

m R RR R m’ ⇒ met (m,w) R met (m’,w)

Theorem 1 (Necessity condition of Boundedness)

If a routing metric is chosen for any network N, and if N

has maximal spanning tree with respect to the metric, then the




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routing metric is bounded

Theorem 2 (Necessity condition of Monotonicity)



routing metric is monotonic

Theorem 3 (sufficiency of Boundedness and Monotonicity)



routing metric is monotonic.

IV. OUR IMPROVEMENT

A. Presentation of the solution

Our solution can be summarized as follows. Bandwidth is

one of the most important factors required and requested by

customer’s applications. Mobility and energy are crucial

problem in MANETs, and up to now, the majority of routing

protocols have shown some weaknesses to face a high mobility

and poor energy resources in the network.

Our objective consists in positively manage the network

bandwidth taking into account the constraints of energy and

mobility, in order to adapt and improve the performance of

manet routing protocol and prolog network life time.

Initially, we start by giving the results of comparing our

approach based solely on mobility parameter. Thus we

evaluate the modified OLSR (Mob-OLSR) that uses our

proposed mobility metric [4]. Mob-OLSR is then compared to

the standard version of the OLSR protocol (without QoS

extension) and QOLSR (The well known OLSR QoS

extension for Manets).

Simulations results conduct us to think to use mobility

parameters to fulfil QoS requirements. So, we focus on

maximizing the bandwidth based on the parameters of

mobility. In this regard, two metrics are proposed. The first is

based on the sum criteria and the second is based on the

product criteria.

We have processed in the performance comparison between

the OLSR protocol using the MPR standard algorithm, and

the two modified OLSR protocols: SUM-OLSR and PRD-

OLSR protocols. The SUM-OLSR protocol is related to the

sum criteria, and the PRD-OLSR protocol is related to the

product criteria. By the end we have eliminated the sum

criteria for his hard cost in terms of PDR (comparing to

product critéria). However, it is important to mention that the

eliminated criteria (the sum) also perform well comparing to

QOLSR protocol.

In a second step, and in order to maximize bandwidth while

taking into account the constraints of energy, a new

generalized metric is presented.

The proposed metric (EN-OLSR) will be compared to

different proposed metrics so called PRD-OLSR and OLSR-

Mob QOLSR

To the best of our knowledge, this work is amongst the first

efforts to consider nodes with mobility and energy

constraints in Manets.

B. Proposed criterion

Our goal is to select the metric to maximize network

throughput taking into account taking into account the key

constraints of MANET environment (mobility, energy). The

idea behind the composite metric is that a cost function is

computed locally at each node during the topology

information dissemination during the flooding process.

Once the network converges, each node runs a shortest path

algorithm based on the calculated composite metric to find the

optimal route to the destination. An underlying implication of

this is that each node should also be able to measure or gather

the information required. Bandwidth, mobility and remaining

energy information’s are available and could simply be

gathered from lower layers. This paper is mainly focused on

solving the routing issues based on the assumption that an

underlying mechanism is there to gather the necessary

information about the individual metrics.

We suggest the simple solutions already proposed in [7] can

be used to get bandwidth. Mobility estimation will be based on

our lightweight proposed mobility measure cited [4-6] due to

its simplicity and lightweight. Energy information is derived

from the energy model used in NS2 simulator at MAC Layer

[4].

Individual metrics must be combined according to the

following dependencies:

• Nodes with no energy must be rejected in the process of

route discovery and maintenance

• Nodes with a high degree of mobility should be avoided

in the process of routes construction.

• Tolerate a slight decrease in throughput in order to

maximize other performance parameters (delay,

collisions, NRL)

• Nodes start with a maximum energy and bandwith

ressources. The residual energy decreases over time

depending on node’s states (transmitting/receiving, in

idle/transition mode, etc.).

Based on these results, the proposed relationship for the

composite metric is given below:




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Where

BW : Available Bandwidth in kilobits per second

E : residual energy of node (number in range 0 to 5; 0

refers no energy for node to perform)

The constants K0, K1, K2, will be set by the administrator

based on network nature. For example, in a very dynamic

environment, and to give more importance to the mobility of

nodes, we can fix K0 to 0, K1 to 1 and K2 to 10.

Constant k3 is not null and is used to indicate if the

environment takes into account the energy or not. Thus, an

important value of K3 indicates that energy is important in

the process of routing.

Composite metric scales Bandwidth metric with following

calculations:

BW = 106

/ available bandwidth

The proposed metric reflects a real dynamic environment

where nodes have limited energy resources, and bandwidth

constraints are crucial (streaming application). The idea

behind the proposed metric is that in Manets environments,

durable-stable link with optimal bandwidth should never be

omitted.

C. Proprieties of the proposed metrics

In this subsection we prove that each of the individual

metrics satisfies the conditions of houndness and monotonicity

conditions then we prove the proposed metric.

Node and Link Available Bandwidth:

The bandwidth metric represents the available bandwidth at

the link. A simple technique proposed in [17], which

computes available bandwidth based on throughput can be

used to measure the bandwidth on any given node (respect.

link L(i,j)).

Available bandwith “α ” for each node could be estimated

by calculating the percentage of free time LT which is then

multiplied by the maximum capacity of the medium Cmax as

follows [17]:

max* C T

L=α (4)

Let Bav (i,j) represent available bandwidth of the link then,

{ })();(min) j,( j Bi Bi B avavav = (5)

Where )(i Bav is the available bandwidth of the node i

Also let W i,j be the edge weight on the link L(i,j). W i,j can be

estimated from the following relationship given below.

jiW , =),(

1

ji Bav

(6)

The condition of boundness implies that along any path

starting from root, the metric is non-increasing. The metric

relation is given by: met {m,W(i,j)}.

Given m is the metric of the root. It is evident that this

meets the boundedness and that monotonicity conditions hold

for the selected metric. The available bandwidth is alwayspositive, hence for any node located at distance “d” from the

root W(i,j) would always be less than or equal to the metric

value at the root. Since the bandwidth is always positive and

greater than zero hence it satisfies the boundedness and

monotonicity conditions.

Mobility & energy:

The mobility metric represents the rate of changes in the

neighbouring of a node at time t compared to the previous

state at time t t ∆− . In a previous work [18], We define the

mobility degree of a mobile node i at a time t by the followingformula:

1i

NodesOut( t ) NodesIn( t ) M ( t ) ( )

Nodes( t t ) Nodes( t )

λ λ λ = + −

− ∆(7)

Where:

NodesIn(t ) : The number of nodes that joined the

communication range of i during the interval [ ]t t ,t − ∆ .

NodesOut(t ) : The number of nodes that left the

communication range of i during the interval [ ]t t ,t − ∆ .

Nodes(t ) : The number of nodes in the communication

range of i at time t.

λ : The mobility coefficient between 0 and 1 defined inadvance. For example, in an environment where the number

of entrants is large relative to the number of leavers, we can

encourage entrants taking λ = 0.25

Many simulations have been done for different values of λ

( λ =0, 0.25, 0.5, 0.75, 1). Simulation result [4] shows that for

λ =0.75 the network performs well (in term of delay, Packet

delivery fraction and throughput). For this reason, we consider

λ =0,75 in the rest of this work.

Let M ji LWij

),(= be the edge weight on the link L(i,j). The

link mobility between two nodes A and B is defined as theaverage mobility of the involved nodes (see Figure 4), as

showed in following equation:

( , )

( ) ( )

2

A B L A B

M t M t M

λ λ

λ += (8)

Figure 4. Link mobility estimation example: %45);( = B A L M

As node’s mobility reflects how likely it is to either corrupt

or drop data. It could be considered as reliability metrics [15].




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Because the reliability metric is bounded and strictly

monotonic, it may be sequenced with the partial metric while

preserving boundedness and monotonicity.

Moreover, residual energy function is monotonic and

bounded its value decreases (depending on the state of thenode: transmission / reception, transition/sleep mode,etc.)

from a maximum value (ex 200) to 0. it also reflects how

likely it is to either corrupt or drop data. Consequently, it can

be sequenced with the partial metric while preserving

boundedness and monotonicity.

Energy consumption parameters are derived from the

energy model defined in NS2 [19] as follows :

Pt_consume= 1.320 (~ 3.2W drained for packet transmission);

Pr_consume= 0.8 (2.4W drained for reception); P_idle=0.07 ,

P_sleep =06; P_transition=0.5

The edge weight ij E for the link L(i,j) (see figure 5) can be

estimated from the following relationship:

),(Min jiij E E E = .

Where i E : the remaining energy for the node i and i E =0

means that the node i have drained out its energy. Thus,

routing protocol should omit such node in the process of

learning routes.

Figure 5. Link energy estimation example: 200);(

= ji L

E

To validate the robustness and efficiency of the proposed

Metrics , we use four models: bandwidth model , mobility

model, sum_bandwidth-mobility Model, prd_bandwidth-

mobility model and bandwidth-energy-mobility model.

Figure 6: the proposed metrics for QoS

Metrics serves as Cost-to-Forward function. In OLSR,

metrics will be used as criterion in MPR selection algorithm.

By exchanging Hello messages, every node is aware of its

neighbor nodes and can simply compute its Cost-to-Forward

value (i.e. to forward packet).

The Cost-to-Forward function (F(i)) for each of the four

models can be defined as shown in figure 6.To motivate the nodes to reveal their exact Cost-to-Forward

value during the cluster head election and the MPR selection,

a reputation-based incentive mechanism using the VCG

mechanism could be used [20]. The nodes participating

truthfully to these processes see their reputation to increase.

Since the network services are offered according to the

reputation of the nodes, they would benefit to participate

honestly.

V. SIMULATIONS AND RESULTS

In this section we have compared the performance of the

original OLSR protocol based on the MPR selection standard

algorithm, and the two modified OLSR protocols related to

different proposed model: : bandwidth model (QOLSR) ,

mobility model (MobOLSR), sum_bandwidth-mobility Model

(Sum-OLSR), prd_bandwidth-mobility model (prd-OLSR)

and bandwidth-energy-mobility model(EN-OLSR).

A. Performance metrics

For comparison process, we have used the most important

metrics for evaluating performance of MANET routing

protocols during simulation. These considered metrics are:

Normalized Routing Overhead (NRL): It represents the ratio

of the control packets number propagated by every node in thenetwork to the data packets number received by the

destination nodes. This metric reflect the efficiency of the

implemented routing protocols in the network.

Packet Delivery Fraction (PDF): This is a total number of

delivered data packets divided by total number of data packets

transmitted by all nodes. This performance metric will give us

an idea of how well the protocol is performing in terms of

packet delivery by using different traffic models.

Average End-to-End delay (Avg-End-to-End):This is the

average time delay for data packets from the source node to

the destination node. This metric is calculated by subtracting

”time at which first packet was transmitted by source” from”time at which first data packet arrived to destination”. This

includes all possible delays caused by buffering during route

discovery latency, queuing at the interface queue,

retransmission delays at the MAC layer, propagation and

transfer times.

Collision: It represents the number of interfered packets

during simulation time. It occurs when two or more stations

attempt to transmit a packet across the network at the same

time. This is a common phenomenon in a shared medium .

Packet collisions can result in the loss of packet integrity or

can impede the performance of a network

(9)

(10)

(11)




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Avg_throughput: is the average rate of successful message

delivery over a communication channel. Throughput and

quality-of-service (QoS) over multi-cell environments are two

of the most challenging issues that must be addressed when

developing next generation wireless network standards

B. Simulation environment

For simulating the original OLSR protocol and the modified

OLSR protocols related to our proposed criterions, we have

used the OLSR protocol implementation which runs in version

2.33 of Network Simulator NS2 [19-22].

We use a network consisting of 50 mobile nodes to simulate

a high-density network. These nodes are randomly moved in

an area of 800m by 600m according to the Random Waypoint

(RWP) mobility model [21]. Moreover, to simulate a high

dynamic environment (the worst case), we have consider the

RWP mobility model with a pause time equal to 0. nodes can

move arbitrarily with a maximum velocity of 140km/h. All

simulations run for 100s.

A random distributed CBR (Constant Bit Rate) traffic

model is used which allows every node in the network to be a

potential traffic source and destination. The CBR packet size

is fixed at 512 bytes. The application agent is sending at a rate

of 10 packets per second whenever a connection is made. All

peer to peer connections are started at times uniformly

distributed between 5s and 90s seconds. The total number of

connections and simulation time are 8 and 100s, respectively.

For each presented sample point, 40 random mobility

scenarios are generated. The simulation results are thereafter

statistically presented by the mean of the performance metrics.

This reduces the chances that the observations are dominated

by a certain scenario which favors one protocol over another.

As we are interested in the case of high mobility (i.e. high link

status and topology changes) we have reduced the HELLO

interval and TC interval at 0.5s and 3s, respectively, for quick

updates of the neighbors and topology data bases.

C. Results and discussion

To show how the modified versions of the OLSR protocol

are more adapted to the link status and topology changescomparing to the original OLSR protocol, we have made

several performance comparison based on the five

performance metrics cited in Section 5-A. Moreover, with the

supposed configuration cited above, we have run simulations

in different mobility levels by varying maximum speed of

nodes between 0km/h (no mobility) to 140km/h (very high

mobility) in steps of 10km/h. To maximize performances we

have chosen the mobility coefficient equal to λ =0.75.

a) Comparing MobOLSR to OLSR and QOLSR

Figure 7-a shows that Mob-OLSR and QOLSR protocols

ensure a good enhancement in terms of delay when compared

to the original OLSR protocol for all maximum speeds.

Precisely, the QOLSR and MobOLSR protocols delay is

around 1.25 seconds (enhancement by 0.4s comparing to he

original OLSR) with higher mobility rate (maximum speedequal to 140km/h) and decreases to almost 1.25 seconds

(enhancement by 0.1sec comparing to he original OLSR) with

static topology conditions.

For the original OLSR protocol the delay gets more than

twice as large being almost 2.1 sec for high mobility and

surprisingly increasing to over 1.4 seconds when the mobility

is decreased.

For the intermediate speed (from 40m/s to 100m/s) a

lightweight difference between MobLSR and QOLSR is

noticed (enhancement by 0.1sec for MobOLSR when

compared to QOLSR for maximum speeds (0m/s and 30m/s))

. This allows us to conclude that MobOLSR performs wellthan QOLSR for intermediate speed. According to the

Figure7-b, the original OLSR and MobOLSR protocols ensure

in the whole the same packet delivery fraction for all

maximum speeds with a slight improvement for the original

OLSR for all maximum speed.

Delay

1

1.5

2

2.5

0 20 40 60 80 100

pause time(s)

d e l a y ( s )

OLSR

QOLSR

MOBOLSR

Figure 7-a. Comparison of the three versions of the OLSR

protocol in term of delay.

Indeed, it can be seen that the number of packets dropped

along the path is quite similar for all maximum speed being

approximately 45% at worst for the original OLSR andMobOLSR and 35% for QOLSR.

Moreover, the ratio is worse for a continuously changing

network (i.e. high maximum speed) than for the static path

conditions, because the number of link failures grows along

with the mobility. However, it is interesting to notice that even

with static topology conditions, sending nodes do not achieve

100% packet delivery but only 85%-89%. This clearly shows

the impact of the network congestion and packet interference

as the load on the network increases. Moreover, when

comparing MobOLSR and original OLSR to QOLSR, QOLSR

protocol presents a remarkable degradation in PDF for all




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maximum speeds. This is because QOLSR does not take into

account the state of links in MPR selection process. In

summary, we can say that the MobOLSR protocol is more

adapted to all levels of mobility from 0m/s (no mobility) to

40m/s (very high mobility).

Pdf

30

45

60

75

90

0 20 40 60 80 100

pause time

r a t e ( % )

OLSRQOLSRMOBOLSR

Figure 7-b. Comparison of the three versions of the OLSR

protocol in terms of packet delivery fraction.

Figure7-c, shows the average throughput for the three

version of protocols. The original OLSR and MobOLSR

protocols ensure in the whole the same average throughput

for all maximum speeds being approximately 125 kbps at

worst.

The ratio is worse for a continuously changing network

than for the static conditions. Moreover, it is interesting to

notice that even with static topology conditions, the network

average throughput does not reach the channel capacity

(5Mbps) but only 230 kbps. This clearly shows the impact of

the network congestion and packet interference as the load on

the network increases.

Avg Throughput

110

140

170

200

230

0 20 40 60 80 100

pause time (s)

OLSR

QOLSR

MOBOLSR

Figure 7-c. Comparison of the three versions of the OLSR

protocol in term of throughput.

Thus, QOLSR ensures an enhancement by 10kbps

comparing to MobOLSR and the original OLSR for all

maximum speed.

Figure7-d illustrates the normalized routing load (NRL)

introduced into the network for the three versions of OLSR

protocol, where the number of routing packets is normalized

against sent data packets. A fairly stable normalized controlmessage overhead would be a desirable property when

considering the performance as it would indicate that the

actual control overhead increases linearly with maximum

speed of nodes due to the number of messages needed to

establish and maintain connection. The original OLSR

protocol and MobOLSR protocol produces the lowest amount

of NRL when compared to QOLSR protocol during all

maximum speed values. Moreover, original OLSR and

MobOLSR protocol produce the sme routing load for all the

maximum speed.

In the worst case (at the maximum speed value equal to

40m/s), the NRL increases to 2.1% for QOLSR protocol and

1.3% for the original OLSR. Precisely, comparing to QOLSR

protocol, the MobOLSR and original OLSR protocols produce

twice less routing packets. This explains that our proposed

criterion based on mobility parameter request less routing

packets to establish and maintain routes in the network.

NRL

0

0.5

1

1.5

2

2.5

0 20 40 60 80 100pause time (s)

r a t e ( % )

OLSR

QOLSR

MOBOLSR

Figure 7-d. Comparison of the three versions of the OLSR protocol in term of

NRL

Collision is a common phenomenon in a shared medium.

Packet collisions can result in the loss of packet integrity or

can impede the performance of a network especially qualtity of

service sensed by the end user. Interference and quality-of-

service (QoS) over multi-cell environments are two

challenging issues that must be addressed when developing

next generation wireless network standards.

Figure 7-e, shows that the original OLSR produces the

lowest amount of collision packet comparing to MobOLSR

and QOLSR. However, we can see that our proposed protocol

MobOLSR ensures an enhancement by 56% comparing to

QOLSR, The well known OLSR QoS extension for Manets.

The average number of collision packet for QOLSR (respct




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MobOLSR and the Original OLSR) is 30000 (respect 17000,

and 11000) for all maximum speed.

Collision

0

5000

10000

15000

20000

25000

30000

35000

0 20 40 60 80 100pause time

N b r e ( # )

OLSR

QOLSR

MOBOLSR

Figure 7-e. Comparison of the three versions of the OLSR

protocol in term of collision.

b) Comparing Sum-OLSR and Prd-OLSR to QOLSR

Figure8-a illustrates the average end to end delay for the

proposed protocols (Sum-OLSR and OLSR Met2 MOBOLSR

and QOLSR). Comparing to QOLSR, it is interesting to notice

that our proposed protocols MOBOLSR and Prd-OLSR

perform well in a dynamic topology (enhancement by 0.5 sec

for maximum speed 40m/s to 80m/s).

Furthermore, we can see that (figure8-b) Prd-OLSR

performs well in term of PDF, when compared to the otherproposed protocol (Sum-OLSR MOBOLSR and QOLSR) for

all maximum speed. Precisely, Prd-OLSR gets more than

twice as large comparing to QOLSR.

Delay

1

1,5

2

2,5

0 20 40 60 80 100

pause time

d e l a y

OLSR

QOLSRSum-OLSR

Prd-OLSR

Figure 8-a. Comparison of the proposed versions of the OLSR protocol in terms

of delay.

A lightweight degradation in average throughput for Prd-

OLSR protocol is shown in figure8-c comparing to QOLSR.

Pdf

30

45

60

75

90

0 20 40 60 80 100

pause time

OLSR

QOLSR

Sum-OLSR

Prd-MET

Figure 8-b. Comparison of the proposed versions of the OLSR protocol in terms

of delay

However , we notice that Prd-OLSR performs well comparingto MomOLSR and Sum-OLSR. This is because Prd-OLSR

select stable routes offering an optimal bandwidth. This is

confirmed by the improvement seen in the PDF parameter.

MobOLSR protocol produces the lowest amount of NRL

when compared to Sum-OLSR and Prd-OLSR protocols

during all maximum speed values (figure8-d). Moreover, we

notice that Sum-OLSR and Prd-OLSR protocols produce less

amount of NRL compared to QOLSR for all the maximum

speed.

Avg Throughput

110

140

170

200

230

0 20 40 60 80 100

pause time

OLSRQOLSR

Sum-OLSRPrd-OLSR

Figure 8-c. Comparison of the proposed versions of the OLSR protocol in terms

of throughput

This explains that our proposed criterion based on mobility

parameter request less routing packets to establish and

maintain routes in the network.




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NRL

0

0,5

1

1,5

2

2,5

0 20 40 60 80 100

pause time

OLSRQOLSRSum-OLSRPrd-OLSR

Figure 8-d. Comparison of the proposed versions of the OLSR protocol in terms

of NRL

Figure8-e, shows that the original QOLSR produces thehighest amount of collision packet comparing to the othersproposed protocols.

The average number of collision packet for QOLSR (respct

Sum-OLSR and Prd-OLSR) is 33000 (respect 25000) for all

maximum speed.

QOLSR produces an interfered environment in comparison

with our proposed protocols.

ENOLSR uses the composite metric with energy

constraints, from figures 9-a we notice that the proposed

ENOLSR find a compromise between bandwidth, energy and

mobility. Our proposed protocol selects stable routes providingan optimum bandwidth while prolonging the lifetime of the

network.

Collision

0

5000

10000

15000

20000

25000

30000

35000

0 20 40 60 80 100

pause tim e

OLSR

QOLSR

Sum-OLSR

Prd-OLSR

Figure 8-e. Comparison of the proposed versions: Collision

OLSR provides the worst delay when compared to the

proposed protocols. Precisely, the QOLSR and ENOLSR

protocols delay is around 1.65 seconds (enhancement by

0.3sec comparing to the original OLSR) with higher mobilityrate (maximum speed equal to 140km/h) and decreases to

almost 1.25 seconds (enhancement by 0.1sec comparing to he

original OLSR) with static topology conditions. This allows us

to conclude that ENOLSR and QOLSR protocols ensure in

the whole the same delay. However, it is interesting to notice

that all of the proposed protocols perform well than the

original OLSR protocol in term of delay.

A tolerable degradation in throughput is shown for our

QOLSR protocol provides the worst amount of RNL whencompared to the others protocols. ENOLSR ensures an

optimal NRL. It exceeds the NRL induced by OLSR and

MobOLSR and performs QQLSR . In the worst case (at the

maximum speed value equal to 40m/s), the NRL increases to

2.1% for QOLSR protocol, 1.3% for the original OLSR and

1.6% for ENOLSR,MOBOLSR and Sum-OLSR&2.

In addition, QOLSR ensures the worst PDF when compared

to the proposed protocols. An enhancement of 10% (resp

65%) when comparing to the Original OLSR protocol (resp

MobOLSR) is noticed.




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Figure 9-a. Performance comparison of the proposed versions of the OLSR protocol

Collision

0

5000

10000

15000

20000

25000

30000

35000

0 20 40 60 80 100

pause time

OLSRQOLSRMOBOLSRPrd-METEN_OLSR

Figure 9-b. Comparison of the ENOLSR protocol : collision

c) Prolonging network life time

Selfish nodes can have a major impact on the performance

of the solutions presented in Section VI. In some extreme

cases, these malicious nodes can cause serious denials of

service. The main problem comes from the fact that the MPRs

and the optimal network paths are selected based some

private information revealed by the nodes. Selfish nodes can

misbehave and reveal false information if this behavior can

save their energy and mobility degree. Moreover, one of the

main drawback of the classical OLSR is the malicious use of

the broadcast TC messages A malicious compromised node

can flood the network with fake TC messages.

In order to increase the network lifetime, ENOLSR

protocol use the node residual energy for the routing process

(equation 1).

In particular, for the following sub-sections, simulation run

for 70 sec. Nodes are nodes moves randomly according to theRandom Waypoint (RWP) mobility model [22]. Nodes

velocity can reach 40m/s and the pause time is equal to 10sec.

We choose the energy model defined in NS to model nodes

energy consumption with (Pt_consume= 3 ; Pr_consume= 2;

P_idle=0.07, P_sleep =06; P_transition=0.5). Nodes

initial energy is fixed to 160. For comparison, we measure the

average energy for nodes in MPRSet for both OLSR and

ENOLSR protocols.

Figure10 shows that energy consumption for original

OLSR is linear. For the ENOLSR protocol network life time




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is prolonged. Indeed, for the original OLSR (resp ENOLSR)

the first node dies after 17sec (resp 43s). This is because

during the normal process of selecting the MPR used by

standard OLSR protocol, the MPRs are selected based on the

parameters of reachebality (number of node they can reach in

level 2 : 2 hop neighbors). This induces a loss of energy for

the nodes that have been elected several times as MPRs.

MPR_Set Avg_Residual_energy

0

30

60

90

120

150

180

0

2

7

5

. 7

2

1

9 .

9

1

2 .

7

1

7 .

2

2

4 .

5

2

3 .

9

2

8 .

4

3

2 .

1

3

9 .

7

3

8 .

5

4

2 .

6

4

7 .

7

4

9 .

5

5

1 .

1

5

6 .

3

6

0

6

4 .

3

time

e n

e r g

y

OLSR

EN_OLSR

Figure 10 : MPRSet average residual energy for OLSR & ENOLSR

Contribution appears clearly in an environment where the

nodes are intelligent and therefore does not revel true

information during the process of MPR selection for fear they

lose their energy. So, the generalized criterion is designed to

cope with selfish nodes.

VI. CONCLUSION AND FUTURE WORK

Satisfying QoS requirements of the traffic in MANETs are

the key functions for transmission required for multimedia

applications. In this paper we have discussed the different

approaches used to provide QoS functionality in OLSR. Our

proposed metric is an attempt to make use of the available

resources and find the most optimal path based on multiple

metrics taking into account mobility and energy parameters.

The proposed metric selects the most stable path based on

mobility and energy information and QoS requirements on

bandwidth. Our proposed approaches are , totally or partially,

based on a mobility degree, residual energy and available

bandwidth that is quantified and evaluated in time by each

mobile node in the network.

The proposed metric is expected to efficiently support real-

time multimedia traffic with different QoS requirements.

Simulation results show that the proposed protocols perform

well when compared to the QOLSR protocol which is a well

known OLSR QoS extension.

The next step is to extend the proposed approaches to

Wireless Sensor Network routing protocols.

REFERENCES

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DRAFT, IETF MANET Working Group

[3] T. Clausen (ed) and P. Jacquet (ed). “Optimized Link State Routing

protocol (OLSR)”. RFC 3626 Experimental, October 2003.

[4] K.oudidi, N.enneya, A. Loutfi, and M.Elkoutbi ’Mobilité et Routage

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Networks workshop (Med-Hoc-Net), Sardegna, Italy, 2002.

[6] Nourddine Enneya, Kamal Oudidi, and Mohammed El Koutbi, "Enhancing

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[8] Ying Ge Kunz, T. Lamont, L. ‘’Quality of service routing in ad-hoc

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[9] H. Badis, A. Munareto, K. A1 Agba, “QoS for Ad Hoc Networking Based

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[10] A. Munareto H. Badis, , K. AI Agba, “A Linl-state QoS Routing Protocol

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[12] Nauman Aslam', William Phillips', William Robertson' composite metric

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F.A. Kuipers, T. Korkmaz, M. Krunz, P. Van Mieghem, “Performance

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[14] S. Yilmaz, I. Matta, “Unicast Rotuing: Cost- Performance Tradeoffs”

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[15] G. Mohammad, S. Marco, “Maximizable Routing Metrics” IEEE/ACM

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[17] A. Qayyum, L. Viennot and A. Laouiti. “Multipoint relaying for flooding

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[18] P. Anelli & E. Horlait «NS-2: Principes de conception et d'utilisation»

[19] Luzi Anderegg, Stephan Eidenbenz, “Ad hoc-VCG: a truthful and cost-

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[21] http://isi.edu/nsnam/ns

AUTHORS PROFILE

K.Oudidi Born in 1976 at Marrakech Morocco. Completed his B. Tech and M.

Tech. from Faculty of Sciences, My Ismail University - Errachidia in 1995 and

1999, respectively.

He is a Ph.D. Student at the University of Mohammed –V- National School of

Computer Science and Systems Analysis, His present field of interest is the

mobility and Qos routing in mobile ad hoc networks.



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qos routing for mobile adhoc networks and performance analysis using olsr protocol

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