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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
k_oudidi@yahoo.fr
A.Hajami
Si2M Laboratory
National School of Computer Science
and Systems Analysis, Rabat, Morocco
Abdelmajid_hajami@yahoo.fr
M.Elkoutbi
Si2M Laboratory
National School of Computer Science
and Systems Analysis, Rabat, Morocco
elkoutbi@ensias.ma
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
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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. bounded- ness 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)
If a routing metric is chosen for any network N, and if N
has maximal spanning tree with respect to the metric, then the
routing metric is monotonic
Theorem 3 (sufficiency of Boundedness and Monotonicity)
If a routing metric is chosen for any network N, and if N
has maximal spanning tree with respect to the metric, then the
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).
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.
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.
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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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