Autonomic position-based QoS routing protocol formobile wireless networks with a cross-layer

architecture

Wafa BerrayanaSchool of computer engineering,

University of Manouba, Tunisia

LIP6, UPMC, France

wafa.berrayana@lip6.fr

Habib YoussefResearch unit PRINCE,

University of Sousse, Tunisia

habib.youssef@fsm.rnu.tn

Rami LangarLIP6, UPMC, France

rami.langar@lip6.fr

Guy PujolleLIP6, UPMC, France

guy.pujolle@lip6.fr

Abstract—The provision of Quality of Service (QoS) overwireless networks has recently been the subject of intensive re-search. The emerging solutions recommend combining the cross-layer and the autonomic paradigms. Most proposed cross-layersolutions consider purely local information in their adaptationsand do not care about the effects of such egoistic behavior onother network nodes. This non-collaborative behavior limits theoverall system performance. In this work, we develop a new QoSrouting protocol called QoS-PAR (QoS Position Aided Routingprotocol) to prove the importance of combining cross-layeringand adaptations based on network knowledge for autonomousorganization of wireless networks. QoS-PAR integrates admissioncontrol as well as resources reservation mechanisms. We considertwo QoS metrics: the minimum required bandwidth and themaximum end to end delay. The bandwidth availability test takesinto account the intra-flow interference and the interferences ofone hop as well as the carrier sensing neighbors. To evaluatethe efficiency of our proposal, extensive simulations have beendone. Results show, that our XLEngine-based QoS-PAR protocolsignificantly outperforms the AODV protocol, which is based onthe TCP/IP layered architecture.

Index Terms—Mobile wireless networks, cross-layer, auto-nomic, QoS, routing, etc.

I. INTRODUCTION

Wireless networks design are constrained by paradigms

edicted 30 years ago, such as layered conception and purely

local adaptations, which completely ignore wireless networks

specificities. To overcome such challenging design, two new

paradigms are, actually, introduced: the cross-layer design

[1] and the autonomic organization [2]. Cross-layering has

fascinated the attentions of researchers to deal with the

challenges presented by wireless environments (e.g. channel

dynamicity over time and space, its broadcast nature, nodes

mobility, etc). It refers to protocol design done by vigorously

exploiting the dependence between protocol layers to improve

performances. The autonomic paradigm is imposed to make

future networks working in an optimal way with minimum

human intervention. It becomes requisite as the network size

and the configuration complexity of networks grow. Currently,

many researchers affirm that combining both paradigms is,

until now, the best alternative for wireless networks, especially,

for better QoS [3], [4]. In wireless networks local information

might not lead to optimal results when used for network-wide

decision processes such as routing, load balancing, etc [3],

[4]. Adopted local strategies at one node should consider the

network-wide availability of resources and ’fairness’ issues

with other nodes. Also, a network knowledge is necessary to

realize the autonomic feature as it grants to nodes the ability

to perceive their network status and to react accordingly. In

[3], we proposed a new architecture called XLEngine (Cross-

Layer Engine). To our knowledge, XLEngine is, until now, the

only cross-layer autonomic architecture with local and network

wide knowledge. To construct such network knowledge, every

node of the network has to distribute its local view (a particular

selection of the local knowledge). The network knowledge

is a summarize of all received local views. To this end, we

propose the Local View Management Protocol (LVMP) [7].

LVMP differs from other dissemination techniques by the

selective broadcast it implements. It consists of preventing

some nodes from rebroadcasting a received local view to

reduce the redundancy, while assuring that this local view will

be received by every reachable node in the network. Compared

to already proposed cross-layer architectures [3], XLEngine is

characterized by its ability to support QoS applications.

To prove the importance of previously discussed paradigms for

better QoS support in wireless mobile ad hoc networks, we

develop a new QoS routing protocol called QoS-PAR (QoS

Position Based Routing Protocol). Moreover, we study the

importance of enclosing the self-properties, requisite by an

autonomic system, to develop a robust routing protocol. Before

describing QoS-PAR, we study routing protocols in ad hoc

networks and their relation with the self-properties already

described.

II. WIRELESS NETWORKS

A. Wireless networks and the cross-layer paradigm

The wireless channel presents a numerous of challenging

specificities [1]. Hence, in wireless networks, we believe

that the most significant technical challenge is the design

process itself. Indeed, whereas a layered conception, reduces

978-1-4244-8435-5/10/$26.00 ©2010 IEEE

complexity and facilitates modularity and standardization, it

is inefficient in wireless networks [1], [3]. Wireless networks

specificities require integrated protocols at all layers, from

the physical layer to the application layer to be robust and

adjustable to channel variations. Also, they require a global

design optimization to effectively exploit their poor resources.

Those goals could be achieved by a cross-layer conception

[1] which opens up a complete new set of potential in terms

of performance and adaptability. In [1], [3] and [4] authors

discuses about the various cross-layer adaptations and their

potential benefits.

In wireless networks, having a network knowledge (or

network-awareness, or network view) is mandatory to allow

nodes to take better local optimizations by considering real

network resources without degrading the overall network per-

formance [3], [4]. Moreover, such knowledge allows nodes

reacting to growing complexity of wireless networks and to

adapt their functionalities and strategies accordingly.

B. Wireless networks and the autonomic paradigm

The next-generation wireless networks will be characterized

by thousands and thousands of nodes, possibly mobile ones,

with embedded capabilities that make them sensitive to their

contexts. Installations and configurations of such networks

will be challenging, time-consuming and error-prone tasks,

even for experts. Once deployed, such networks will require

continuous management to provide a uniform service environ-

ment, recover from faults or maximize overall performance.

We believe that the autonomic management conception [2] is

not a luxury for wireless networks, but it represents a coming

step that we must prepare from now. In next section we study

the autonomic paradigm, its requirements and tools.

III. THE AUTONOMIC PARADIGM

The autonomic paradigm [2] is imposed as the size and the

complexity of networks increase. It will allow to networks to

be self-managed by carrying out tasks that have been usually

assured by computer specialists. Providing self-management

capability will augment the usability, will enable inexperienced

users to run networks with small networking talents and

knowledge and will reduce the needed configuration system

time.

According to [2], the self-management implies four aspects (or

properties): self-configuration, self-optimization, self-healing,

and self-protecting.

A. The self-properties

1) Self-configuration: A self-configurable network node

has the aptitude to configure itself dynamically in reaction

to surrounding events (for example, by installing missing

software). To achieve this goal, the network node has to

be notified, constantly, about any correlated modification of

its environment. Besides, the network node must implement

suitable configuration policies which mirror the configuration

management goals of the network.

A variety of network technologies already perform self-

configuration or ’plug and play’ configuration. For example,

Ethernet and Internet are self-configuring. Recently, there has

been considerable research in self-configuring protocols for

ad hoc wireless networks such as routing protocols [4], [5],

medium access, etc.

2) Self-optimization: Self-optimizing networks will con-

stantly look for ways to get better their operations, identifying

and seizing opportunities to make themselves more efficient

in performance and/or cost. To achieve self-optimization is

nontrivial, because of the critical and often overlooked ob-

servation: nodes are intrinsically selfish and work in a non-

cooperative way which limits their performance and the overall

network performance. This selfishness is, mainly, caused by

the fact that local operations are based on local knowledge.

This might not lead to optimal or even near-optimal results

when used for network-wide decision processes such as rout-

ing, load balancing, etc. A network knowledge is required

to implement the self-optimization property since it offers to

nodes the capability to perceive their network changes and to

react accordingly.

3) Self-protecting: The self-protecting implies to anticipate

problems based on early reports and to take steps to avoid

or mitigate them. For example, if the detected battery level is

below a threshold, then stop non critical applications to save

energy for the benefit of QoS applications.

Self-protection requires permanent awareness and knowledge,

both, local and network-wide and the flexibility to react

accordingly. Sophisticated observation, analysis techniques,

cooperation, and information sharing between network ele-

ments with learning concepts are crucial to achieve this goal.

4) Self-healing: Self-healing is the process of discovering,

diagnosing and reacting to faults. Self-healing systems

are able to recover from faults by themselves or with the

assistance of other devices of the same network. Self-healing

is one of the main challenges to growing networks. Fault

detection, notification and recovery are the main steps toward

self-healing.

The self-properties are accompanied by other properties:

awareness, self-monitoring (the process of collecting informa-

tion), self-adjustment (it enables applications to adapt them-

selves to existing resources accessibility) [1].

B. The awareness concept

Awareness is the base of autonomic networks as it offers

all the necessary information to set up the self-properties.

Indeed, Self-management requires that the computing system

knows itself and to know its network status. The awareness

component implies two parts: self-awareness (local knowl-

edge) part and network-awareness (network knowledge) part.

The first part implies intra element awareness (or cross-layer

awareness). The network-awareness implies all information

gathered from other network nodes and all network status

information. The awareness component is the input component

in autonomic systems. If this component is unable to gather

Fig. 1. Relationship between the self-properties in autonomic systems.

enough and required information, then targeted adaptation and

optimization goals could not be achieved (optimize network

performance, support QoS, optimize energy consumption,

etc.). Figure 1 describes relations between the awareness

component and the self-properties of an autonomic system.

Self-learning and self-optimization modules will primary treat

the parameters provided by self-awareness module by means

of highly-intelligent algorithms, and then determine and give

the suitable action list to self-configuration, self-protecting and

self-healing properties. The entire process does not impose any

direct human intervention.

1) Self-awareness: Those data could be gathered from

different protocols layers, hardware devices (battery, CPU,

storage disk, GPS device, etc), user context, or applications,

etc. To establish such self-awareness, a cross-layer approach

is requisite since it allows interactions between all layers and

furnishes requisite mechanisms. Based on such self-awareness,

the network node could manage its own internal state and

behavior. For example, by detecting that the battery voltage

drops under a predetermined value, this triggers a power

warning event to applications. Non critical applications could

then be turned off to save energy.

2) Network-awareness: The network-awareness is a re-

quired component in autonomic systems as it denotes the

capability of network nodes to perceive their context, par-

ticipate in context exchanges with others network elements

to perform their functions in a mutual recursive way, and,

above all, to react to the network changes. Basically, network

nodes operate in a non-collaborative way that restricts their

performance and the overall network performance, as they do

not always efficiently exploit available resources [4], [3], [6].

Indeed, a local adaptation might not lead to any significant

improvement when used for network-wide decision processes

such as routing or end-to end QoS support.

Having a network-awareness allows a node to evaluate its

local status against the average network status. This allows

the node to take right local decisions and optimizations by

considering ’fairness’ issues between nodes and by consider-

ing real network resources without negatively affect the overall

system performance. To acquire such network-awareness and

to update the configuration policies, nodes have to exchange

their local knowledge over the network. This exchange could

be done according to two modes: the Request/Response mode

and the ’inform’ mode. The first technique suffers from the

great generated overhead. The ’inform’ technique consists of

sending a message to nodes when a change happens, without

waiting for them to request information.

IV. ROUTING PROTOCOL AND THE AUTONOMIC PARADIGM

Routing is the basic service in ad hoc networks (mobile

networks, sensor networks, etc.) since it is responsible of

establishing routes to allow end to end communications. Any

other network service resides on this fundamental function-

alities. Actually, it is argued that cross-layer conception of

routing protocols is requisite [4]. Indeed, cross-layer concep-

tion permits the routing protocol to be adjustable to, both,

higher-layers and lower-layers parameters. It allows sharing

of wireless link characteristics, the node mobility, applica-

tions requirements, etc. Globally, the cross-layer conception

provides to the routing protocol required information to take

right decision. When QoS is required, the routing job becomes

more and more hard. In this case, it has to encapsulate

an essential set of mechanisms to help granting such QoS,

preventing, detecting, and responding to QoS loss, such as

available resources evaluation, admission control, resources

reservations, etc.

Moreover, routing is one of the wireless network protocols that

can not work based on a purely local knowledge. Indeed, it has

to consider network wide availability of resources and to take

into consideration fairness issues between different network

nodes. Besides, it has to react automatically and to adjust its

decisions to network changes (such as network size, traffic

density, topology, network partitioning, etc.).

A. Routing protocol components

Routing protocol needs several components to cooperate

harmoniously to achieve its functionalities. Many of these

components should be cross-layer (with a self-awareness)

and should execute some of their tasks based on a network

knowledge. Many routing components could include other

sub-components. When QoS is required, extra components

should be considered. We distinguish two categories of compo-

nents: core components which are possessed by most routing

protocols and auxiliary components which are not necessary

to all routing protocols. Table I gives for each one its matching

components.

TABLE IMAIN ROUTING PROTOCOL COMPONENTS.

Corecomponents

Route discovery

QoS Route discovery Admission controlResources evaluation

Route selectionRoute maintenance

Route recoveryAuxiliary com-ponents

QoS loss/Degradation detection

QoS loss notificationNeighbor management

Security

• Route discovery: this component is responsible of finding

routes toward the destination.

• QoS Route discovery: this component is responsible of

finding routes satisfying the requested QoS toward the

destination.

• Route selection: among the found routes, select a route

according to specific criteria. In most cases, the shortest

path is the chosen criterion. With QoS routing, the chosen

criteria could be the minimum required bandwidth, the

maximum end to end available bandwidth, etc.

• Route maintenance: it plays a vital role in ad hoc net-

works by reducing the probability of link failure (can

occur because of nodes mobility, energy depletion, hard

medium contention, etc.). Every routing protocol has its

own specification to implement the route maintenance.

• Route recovery: it aims to reestablish a (QoS) route after

a link failure or a QoS loss.

• QoS loss/degradation detection: because of nodes mobil-

ity, the network topology is continuously dynamic, which

results in the increasing of packet and QoS losses. This is

not desirable for QoS applications like video, audio, etc.

So, instantaneous QoS loss detection is crucial to ensure

rapid QoS route repairs.

• QoS loss notification: if at a specific node of the route,

the QoS requirements are no longer met for one of the

admitted flows, then a QoS loss notification should be

launched.

• Neighbor management: it aims to update neighborhood

information such as location, direction, ID, resources etc.

Usually, Hello packets are used for this purpose.

• Security: this component is responsible of managing the

security challenges of wireless channel.

B. Routing protocol and the self-properties

Self-properties (all or some of them) are inherent and joined

to the nature of mobile ad hoc networks [4]. Considering such

properties is mandatory when conceiving dedicated protocols

and particularly routing protocols in order to guarantee an

efficient data transmission. Next we present some examples

about how routing protocol could contribute to the set up of

the self-properties.

1) Self-configuration: Ad hoc networks are mainly char-

acterized by a highly dynamic topology. The routing pro-

tocol plays an important role by contributing to their self-

configuration to the physically available communication links.

This affects the performance in terms of path length, traffic

concentration, QoS requirements and resilience to failures.

2) Self-optimization: Ad hoc networks suffer from poor re-

sources (bandwidth and energy). An efficient routing protocol

should consider this drawback by avoiding establishing routes

through nodes with a low battery level and/or overloaded

nodes. This behavior is a sort of self-optimization. Choosing

shortest paths is another example.

3) Self-protection: Predict a link failure and find a sub-

stitute route, which will be functional immediately after the

effective link failure, is a sort of self-protection. This function-

ality is the responsibility of the route maintenance component.

Link failure prediction could be based on the measured power

of received signals, from neighbors, or the surveillance of their

geographic locations. In this case, the covered transmission

range area of a signal is subdivided into a safe range and a

preemptive range. When the current node detects that the next-

hop of the route is in the preemptive range, then it predicts a

link failure.

4) Self-healing: After a link failure or a node death, the

network can restore or repair routes by itself using its routing

protocol. Such routing protocol should be simple, fast failures

detector and easy to implement.

V. QOS-PAR, A QOS ROUTING PROTOCOL BASED ON

SELF-AWARENESS AND NETWORK-AWARENESS

To prove the importance of combining cross-layering and

adaptations based on network knowledge for autonomous

organization of wireless networks, we develop a new QoS rout-

ing protocol called QoS-PAR (QoS Position Aided Routing

protocol). Accordingly, QoS-PAR has sufficient information to

establish stable routes while fulfilling the QoS requirements,

taking into account fairness issues between nodes and con-

sidering real network resources without degrading the overall

system performance. Figure 2 presents QoS-PAR components

(i.e. QoS route discovery, route selection, route maintenance,

QoS loss detection, QoS loss notification, route recovery and

Neighbor management) and their communications with the

knowledge plan. To lighten the figure, we don’t represent

the ’Neighbor management component’. In this work, we

particularly focus on the QoS Route discovery component

which is launched by the emission of a QoS Route Request

(QoS-RREQ) by the source of a QoS flow ’j’.

QoS-PAR distinguishes from other QoS routing protocols by

Fig. 2. QoS-PAR components.

several characteristics. First, it exploits, both, local cross-layer

information and the network knowledge for admission control

decisions which is followed by a resources reservation for each

admitted QoS flow. Second, it exploits the nodes geographic

locations (available from the network knowledge) to optimize

the generated routing overhead by locating the destination into

a limited zone. This task is executed by the Geographic Rout-

ing Algorithm (GRA) of QoS-PAR. The admission control

algorithm (ACA) considers two QoS metrics: the minimum

required bandwidth by the QoS flow ’j’ (denoted Bjmin)

and the maximum end to end delay (denoted Djmax). ACA

corresponds to two parts: the bandwidth test and the delay

test. The bandwidth test takes into consideration the impact

of the intra-flow interference [8] as soon as the inter-flow

interferences caused by one hop and carrier sensing neighbors.

Both interferences are often ignored by QoS routing protocols.

This dramatically impacts the end to end QoS. To the best of

our knowledge, we are the first to present a complete toolbox

for QoS support with two constraints (minimum required

bandwidth and maximum delay). Figure 3 presents the main

parts of the route discovery of QoS-PAR.

Fig. 3. QoS-PAR: QoS Route discovery.

A. ACA, Admission Control Algorithm

1) The bandwidth test: It starts by evaluating the available

bandwidth (denoted Bavailable(K)) at the node K of the route

and the bandwidth (denoted Bjcons(K)) that the new QoS flow

’j’ will consume at the node K once it will be admitted (taking

into account the intra-flow interference).

• Available bandwidth evaluationThe evaluation of the available bandwidth is a challenging

task since it requires particularly the knowledge of one hop

and carrier sensing neighbors activities. Indeed, in ad hoc

networks the bandwidth availability of a node is determined

not only by the raw channel bandwidth, but also by its one hop

neighbors bandwidth usage and by the interferences caused by

its carrier sensing neighbors [8]. Bavailable(K) value is given

by next equation and is evaluated by the CLMC component

(particularly by the LocalCLE component) [9]:

Bavailable(K) = ChCap − Bself (K) − BN (K) − BCSN (K) (1)

Where ChCap is the channel capacity. Its value is read

from the MAC layer through CLMC. Bself (K) is the sum

of bandwidth consumed by already admitted flows at node

K. BN (K) and BCSN (K) denotes the amount of bandwidth

consumed by admitted flows of one hop neighbors of K and

carrier sensing neighbors of K respectively. Both values are

given by the network knowledge CLMC.

• Flow bandwidth consumed evaluationThis bandwidth is caused by the intra-flow interference which

occurs when several nodes belonging to the same route com-

pete for the channel access in the same carrier sensing zone to

transmit the packets of the same flow [8]. The underestimation

of the consumed bandwidth could dramatically decrease the

QoS. Regrettably, the majority of proposed admission control

approaches ignores the effect of intra-flow interference and

makes no difference between the bandwidth required by the

source of the flow and the bandwidth that it consumes (which

is true in wired networks). To this end, we include in QoS-

PAR a procedure to evaluate the bandwidth consumed by the

flow ’j’. This evaluation is done by the LocalCLE component

under request of QoS-PAR. Bjcons(K) depends on the position

of the current node K in the multi-hop route Rj . It is given

by next equations [8]:

Bjcons(K) = CCK ∗ Bj

req (2)

CCK = |CSNK ∩ Rj | + 1 (3)

CCK is the Contention Count at node K. It is the number

of nodes of the route Rj which are positioned inside the

carrier sensing range of K. To calculate CCK , node K has

to know the set of its carrier sensing neighbors, read from

the network knowledge, and the list of nodes participating in

the route Rj . Unfortunately, during the route exploration, the

node K only knows the hops count of the partial route from S.

So K estimates the number of nodes that separate him from

destination and localized into its carrier sensing range. The

geographic locations of K, the source and the destination (read

from the network knowledge) are exploited for this purpose.

Once Bavailable(K) and Bjcons(K) are evaluated, the next step

is to verify if there is enough bandwidth for the new QoS flow.2) The delay test: The end to end delay is subdivided

into many sub delays (i.e. one hop delays) (see Figure 4).

We denote the one hop delay as DHK , where H and K are

neighbors of the same route. Note that DHK could be defined

as the time difference between the arrival of the QoS-RREQ

packet at the MAC layer of node H (denoted as TArrival)

and the time it is received by the MAC layer of node K

(denoted as TTimestamp and marked by the MAC layer of

node K) (see Eq.(4)). Accordingly, a cross-layer cooperation

is required between MAC and QoS-PAR to evaluate DHK .

We suppose that nodes clocks to be synchronized.

DHK = TArrival − TTimestamp (4)

We note that the delay measurement is done during the route

Fig. 4. End to end delay and on hop delay decompositions.

discovery in a passive way using QoS-RREQ packets to avoid

any extra overhead. Since DHK depends on the packet size

and the delay estimation is done using QoS-RREQ packets,

hence a correction should be done on the measured DHK to

refer to data packets rather than QoS-RREQ packets. So, the

new value of DHK (denoted D′HK) is expressed as follows:

D′HK = DHK +

Data length − QoS-RREQ lengthChCp

(5)

Where, Data length and QoS-RREQ length denote packet

length of data and QoS-RREQ packets respectively. Next, we

use the term DHK and we mean by it D′HK . Furthermore,

since delays depend on many random events such as nodes

mobility, start/stop of sessions, etc, the variation of delay

between the same two neighbors may be important over time.

To address this problem, we consider the approach proposed

in [10]. It consists of calculating an average weighted by

a forgetting factor (exponential forgetting). We consider this

method for its passive aspect, since it does not introduce an

extra overhead. Then ,the delay between nodes H and K is

re-expressed as:

DHK(t) = (1 − λ) ·n

∑

�=0

(λ� · D′HK(t − �)) (6)

Where λ ∈ [0, 1) is the forgetting factor and n is the correctionorder of the variance. In what follows, and by abuse language,

we use the notation DHK instead of DHK(t).Now, once DHK is evaluated, the next step is to verify if the

delay constraint is satisfied. We note that in the QoS-RREQ,

we find the remaining maximum tolerable delay towards the

destination. Specifically, the QoS-RREQ initially sent by S

contains the maximum end to end delay. At every crossed

node, the delay constraint is considered as respected only if the

estimated DHK value is lower than the delay value contained

in the QoS-RREQ. The delay value in the QoS-RREQ packet

is decreased each time by DHK .

B. GRA, the Geographic Routing Algorithm

Most of the routing protocols utilize the flooding technique

during the route discovery phase. It consists of broadcasting,

by each node of the network, a route request to all its neighbors

until reaching the destination. The resulting overhead is very

important and can reach up to 90% of the whole network

traffic. This enormous overhead has a dramatic impact on

the network performance and, particularly, on the QoS as it

consumes the resources (energy and bandwidth) for useless

retransmissions and increases the probability of collisions. For

these reasons, we propose to exploit the nodes locations to

reduce the routing overhead and to efficiently exploit network

resources in order to provide better QoS.

1) GRA at the source node: At the source, the geographic

routing algorithm (GRA) consists of delimiting the destina-tion search zone to a rectangular zone rather than the overall

network. To determine the destination search zone borders,

considered as a rectangular zone, GRA exploits the geographic

locations of the source and the destination and the maximum

speed (denoted as maxSp) of the destination. Informations

about the destination are read from the network knowledge.

To better understand our concept, we consider the example

given by figure 5. In this figure, we denote by S, T, T’ and D’

the four tops of this zone. The first top S is the source node

with location (xS , yS). S knows the location of D (xD, yD) at

time t1 (its last update time). We note t2, the initiation time

of a route discovery from S to D. from t1 to t2, D may move.

We denote by destination moving zone, the zone where D

could be at time t2. As represented in Figure 5, it is a circular

Fig. 5. QoS-PAR: route exploration using the destination search zonedelimitation.

zone. We consider the destination search zone as the smallest

rectangle that includes current location of S and the destination

moving zone. We consider the worst case where D is at the

farthest position, denoted D’(xD′ , yD′), from S. xD′ and yD′

are given by next relations:

xD′ = xD + maxSp · (t2 − t1) (7)

yD′ = yD + maxSp · (t2 − t1) (8)

The four tops of the destination search zone are attached to

the QoS-RREQ.

C. GRA at an intermediate node receiving a QoS-RREQ

An intermediate node K receiving the QoS-RREQ begins by

verifying if it is localized into the destination search zone. If it

is, then it launches the admission control algorithm. Otherwise,

it discards this QoS-RREQ.

VI. PERFORMANCE EVALUATION

For the implementation and the performance evaluation

of QoS-PAR, we constructed a simulation based on J-Sim

[12]. We implemented the whole XLEngine architecture [3],

including QoS-PAR, and the LVMP protocol [7]. Recall that

LVMP is used to exchange local knowledge in order to allow

every node to construct its network knowledge (furnishing for

the admission control algorithm and the geographic algorithm

requisite informations). The knowledge plane of XLEngine

(CLMC) is one of its key element through which cross-layer

interactions occurs between different layers. We compare QoS-

PAR with AODV [11], a legacy routing protocol with a purely

local knowledge. Until now, only AODV is supported by J-

Sim.

A. Simulation set up

We consider ad hoc mobile nodes with a transmission range

and a carrier sensing range of 100m and 200m respectively.

The number of mobile nodes varies from 10 to 60 nodes.

They are moving into an area of 600m2 according to the

random way point mobility model with a maximum speed of

2m/s. Every node operates at IEEE 802.11b MAC layer with

a transmission rate of 11Mbps. Links are asymmetric. The

number of admitted QoS flows varies from 5 to 15. Each QoS

flow consists of a constant bit rate (CBR) source with 512byteUDP packet and 62.5ms packet generation period. Those

0 × 100

1 × 103

2 × 103

3 × 103

4 × 103

5 × 103

10 20 30 40 50 60

Delay

to es

tablish

route

s (ms

)

Network size

QoS-PAR with 5 QoS FlowsQoS-PAR with 10 QoS FlowsQoS-PAR with 15 QoS Flows

AODV with 5 QoS FlowsAODV with 10 QoS FlowsAODV with 15 QoS Flows

Fig. 6. Impact of varying the network size on the delay to establish routes.

flows are launched according to a Poisson distribution process

with exponential durations. The maximum tolerable end to end

delay and the minimum required bandwidth are set equal to

35ms and 64Kbps, respectively. Each simulation is running

for 1000s. Each with a different seed entry and different

random mobility scenarios. Each point of the resulting figures

is obtained by averaging 10 consecutive simulations. Also that

as in [13], a first order (� = 1) variance correction is applied,

and the optimum forgetting factor (λ) is fixed to 0.2 in Eq.(6).

In the next obtained figures, bars around each point indicate

95% confidence interval.

For space constraints, we report results on the following

performance metrics: (1) the Average delay to establish routes:the time between the sending of the (QoS)RREQ packet

from the source and the reception of the (QoS)RREP packet

from the destination, (2) the average end to end delay, (3)

the M-PDR (Max delay constraint satisfied Packet DeliveryRatio): defined as the ratio of the received data packets,

by all destinations, respecting the delay constraint, (4) thenormalized routing overhead: defined as the ratio between the

number of the propagated routing packets to the number of

data packets received by all destinations and respecting the

delay constraint, and (5) the Acceptance Rate: defined as the

ratio between the number of QoS established flows and the

number of flows requesting admission, which is fixed in our

case to 20 QoS flows.

B. Performance results and analysis

1) Average delay to establish routes and End to End de-lay: Figure 6 proves that QoS-PAR presents highly lower

delay (varying from 1.6ms to 4.5ms) for route establishment

compared to AODV (varying from 452ms to 5175ms) for

different network size. Figure 7 plots the end to end delay

for both QoS-PAR and AODV. Obtained delays with QoS-

PAR are lower from 10 to 45 times than those obtained with

AODV. For example, for a network with 20 nodes and 15established QoS flows, QoS-PAR achieves an average delay

of 2.68ms compared to 66.28ms with AODV. The difference

between both protocols increases when increasing network

sizes and number of established flows. Indeed, during the route

discovery, the admission control algorithm of QoS-PAR, with

the maximum delay and the minimum required bandwidth

constraints, are followed by a bandwidth reservation. Hence

admitted QoS flows have the guarantee that the bandwidth

will be available as long as the route is valid, while respecting

the delay constraint. When data packets begin to violate the

delay constraint, the QoS loss notification is triggered followed

by shutting down routes if QoS-PAR fails to rebuild routes

respecting QoS constraints. With QoS-PAR, the delay slightly

increases when increasing the number of QoS flows for the

same network size. Indeed, when the available bandwidth is

shared between more flows, more time is spent to establish and

repair routes with sufficient available resources. For AODV,

since it uses the flooding technique during the route discovery,

then the amount of sent routing packets increases as the

network size and the number of QoS flows increase. As

a result, the collision probability increases, which leads to

increasing, both, the period of time to establish routes and

the number of data retransmissions. This leads to increase the

average end to end delay.

0

20

40

60

80

100

120

140

160

10 20 30 40 50 60

End T

o End

Delay

(ms)

Network size

QoS-PAR with 5 QoS FlowsQoS-PAR with 10 QoS FlowsQoS-PAR with 15 QoS Flows

AODV with 5 QoS FlowsAODV with 10 QoS FlowsAODV with 15 QoS Flows

Fig. 7. Impact of varying the network size on the End to end delay.

2) M-PDR: Obtained results plotted in figure 8, are highly

correlated with those plotted in Figure 7, where QoS-PAR

clearly outperforms AODV. The M-PDR achieved by QoS-

PAR is consistently high, above 90% for even a network of

60 nodes with 15 QoS flows. For AODV, the M-PDR is equal

to 74% for 10 nodes and 5 QoS flows, and 36% when the

network size is set to 60 nodes. The M-PDR performance of

AODV got significantly worse when the number of QoS flows

is set to 15.

3) Normalized routing load/overhead: This metric is used

to quantify the routing load (or overhead). Figure 9 clearly

shows that QoS-PAR outperforms AODV for all network sizes

and number of established flows with a generated overhead

up to 50% lower. Specifically, with a network size of 40nodes and 5 established flows, AODV sends 4 routing packets

(on the average) to receive one data packet respecting the

delay constraint, compared to 1.77 for QoS-PAR. This value

grows when increasing the number of established flows and

the network size (that is 7 sent routing packets to receive

1 data packet). With AODV, obtained results increase much

more rapidly than for QoS-PAR, as the network size increases.

20%

30%

40%

50%

60%

70%

80%

90%

100%

10 20 30 40 50 60

M-PD

R

Network size

QoS-PAR with 5 QoS FlowsQoS-PAR with 10 QoS FlowsQoS-PAR with 15 QoS Flows

AODV with 5 QoS FlowsAODV with 10 QoS FlowsAODV with 15 QoS Flows

Fig. 8. Impact of varying the network size on the M-PDR.

Indeed, the flooding technique used by AODV consists of

broadcasting, by a node, a received route request to all its

neighbors. While for QoS-PAR, obtained results are a conse-

quence of its admission control algorithm and its geographic

aspect. Specifically, every node outside the destination search

zone, does not participate in the route discovery, reducing

hence the amount of re-broadcasted routing packets. More-

over, the bandwidth test of QoS-PAR prevents route request

packets from being propagated through heavily loaded nodes.

This reduces congestion and interference caused by redundant

routing packets transmissions. All these factors keep the QoS-

PAR overhead lower than that generated by AODV even

if the number of established QoS flows and the network

size increase. We remark also, that QoS-PAR assures load

balancing between network nodes, which is a kind of self-

optimization (optimization of the network resources).

1

2

3

4

5

6

7

8

9

10

11

12

13

10 20 30 40 50 60

Norm

alized

routing

load

Network size

QoS-PAR with 5 QoS FlowsQoS-PAR with 10 QoS FlowsQoS-PAR with 15 QoS Flows

AODV with 5 QoS FlowsAODV with 10 QoS FlowsAODV with 15 QoS Flows

Fig. 9. Impact of varying the network size on the Normalized routing load.

4) The QoS flows acceptance rate: Figure 10 plots the

mean acceptance rate. For both protocols, it increases with

network size as more alternative routes become available.

Globally, AODV accepts about 17% more QoS flows than

QoS-PAR. This difference is a consequence of its admission

control, as QoS-PAR rejects QoS flows that would exceed the

channel capacity and/or would not respect the delay constraint.

Whereas AODV only discards flows when the route request

or route reply fails to reach destination or source. Moreover

QoS-PAR verifies the accessibility of resources at its neigh-

borhood and carrier sensing neighborhood, which reduces the

probability to establish an end to end QoS route, even when

the network gets denser. Besides, the position-based routing

achieved by QoS-PAR restricts the space of possible routes

by limiting the destination search zone to a small rectangle.

Nodes outside this zone rejects route request packets.

It is important to note that QoS-PAR does not admit QoS

flows, if the network is incapable to grant them the required

QoS is a kind of: (1) self-optimization (optimization of the

network resources) with its philosophy is accept less flows, inorder to provide better QoS’, and (2) self-protection of already

admitted QoS flows from being embarrassed by new admitted

flows.

0

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60

Accep

tance

ratio (

%)

Network size

QoS-PARAODV

Fig. 10. Impact of varying the network size on the QoS flows acceptancerate.

5) Further results: We studied also the influence of QoS

traffic load on the performance of QoS-PAR. The network

size is set to 40 stations moving with a speed of 2m/s with

10 established QoS flows. The packet generation period of the

corresponding CBR sources varies from 0.0625sec to 0.35sec.

The studied metrics are the M-PDR and the normalized routing

load.

• M-PDRWith results close to 100%, figure 11 proves that QoS-PAR

is always better than AODV even for high traffic load. For

AODV, increasing the packet generation period (i.e. decreasing

the traffic load) means decreasing the amount of generated

routing overhead. This diminishes congestion and interference

caused by redundant routing packets transmissions leading to

diminish the end to end delay and, hence, to better M-PDR.

• Normalized routing loadFigure 11 shows that QoS-PAR always generates lower over-

head than AODV. This overhead gain could attain 500%.

For both protocols, the normalized overhead increases while

reducing the traffic load. This increase is justified by the

decrease of the number of sent data packets by different

sources, and, consequently, the decrease of the received data

packets by all destinations, which represents the denominator

of the relation evaluating the normalized routing overhead.

50%

60%

70%

80%

90%

100%

0.0625 0.1 0.15 0.2 0.25 0.3 0.35

M-PD

R

Packet generation period (sec)

AODV with 10 QoS FlowsQoS-PAR with 10 QoS Flows

Fig. 11. Impact of varying the QoS traffic load on the M-PDR.

2

4

6

8

10

12

14

16

0.1 0.15 0.2 0.25 0.3 0.35

Norm

alized

routing

load

Packet generation period (sec)

QoS-PARAODV

Fig. 12. Impact of varying the QoS traffic load on the routing load.

VII. CONCLUSION

This paper presents QoS-PAR, a novel QoS routing protocol

for ad hoc networks. Our proposition is based on a new cross-

layer, autonomic architecture called XLEngine and integrates

both admission control and resource reservation mechanisms.

To the best of our knowledge, it is the only geographical

routing protocol that is cross-layer, QoS aware and takes

into account the intra-flow and carrier sensing neighbors

interferences. To quantify the importance of cross-layering and

network knowledge based adaptation for better QoS support

in wireless ad hoc networks, we present an analysis based on

simulation. Obtained results show that QoS-PAR outperforms

AODV by maintaining low delays (lower up to 45 times) and

achieving a very good packet delivery ratio (better up to 90%).

The AODV overhead is up to 7 times the one generated with

QoS-PAR. The overall XLEngine overhead is lower than the

AODV overhead only. We verify, that the LVMP overhead

to set up a network knowledge is acceptable and does not

affect the overall network performance. The more challenging

part to implement the self-properties is to have such network

knowledge which is already available by XLEngine. Accord-

ingly, it remains to develop adequate techniques to help QoS-

PAR to self-protect routes by anticipating link failures and

QoS loss problems. The same remark is valid regarding the

self-healing property. In addition, we have to explore how

to benefit from XLEngine to develop other self-managing

protocols. Regarding the performance evaluation, we have to

extend it to other geographical and QoS routing protocols.

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