UBICC Journal
Ubiquitous Computing and Communication Journal
2008 Volume 3 . 2008-04-30 . ISSN 1992-8424
Special Issue on
Mobile Adhoc Networks
UBICC Publishers © 2008
Ubiquitous Computing and Communication Journal
Edited by Usman Tariq.
Special Co-Editor Dr. Shafique Ahmad Chaudhry
Ubiquitous Computing and
Communication Journal
Book: 2008 Volume 3
Publishing Date: 2008-04-30
Proceedings
ISSN 1992-8424
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Table of Contents
Papers
1.
Performance Evaluation of Deadline Monotonic Policy over 802.11 protocol
Ines el Korbi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2.
Impact of Node density on Cross Layer Design for Reliable Route Discovery in Mobile Ad-hoc
Networks
Ramachandran B, Shanmugavel S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.
A Framework for Aggregated Quality of Service in Mobile Ad hoc Networks
Ash Mohammad Abbas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Performance Evaluation of Deadline Monotonic Policy
over 802.11 protocol
Ines El Korbi and Leila Azouz Saidane
National School of Computer Science
University of Manouba, 2010 Tunisia
Emails: ines.korbi@gmail.com Leila.saidane@ensi.rnu.tn
ABSTRACT
Real time applications are characterized by their delay bounds. To satisfy the
Quality of Service (QoS) requirements of such flows over wireless
communications, we enhance the 802.11 protocol to support the Deadline
Monotonic (DM) scheduling policy. Then, we propose to evaluate the performance
of DM in terms of throughput, average medium access delay and medium access
delay distrbution. To evaluate the performance of the DM policy, we develop a
Markov chain based analytical model and derive expressions of the throughput,
average MAC layer service time and service time distribution. Therefore, we
validate the mathematical model and extend analytial results to a multi-hop
network by simulation using the ns-2 network simulator.
Keywords: Deadline Monotonic, 802.11 protocol, Performance evaluation,
Medium access delay, Throughput, Probabilistic medium access delay bounds.
1
INTRODUCTION
Supporting applications with QoS requirements
has become an important challenge for all
communications networks. In wireless LANs, the
IEEE 802.11 protocol [5] has been enhanced and the
IEEE 802.11e protocol [6] was proposed to support
quality of service over wireless communications.
In the absence of a coordination point, the IEEE
802.11 defines the Distributed Coordination
Function (DCF) based on the Carrier Sense Multiple
Access with Collision Avoidance (CSMA/CA)
protocol. The IEEE 802.11e proposes the Enhanced
Distributed Channel Access (EDCA) as an extension
for DCF. With EDCA, each station maintains four
priorities called Access Categories (ACs). The
quality of service offered to each flow depends on
the AC to which it belongs.
Nevertheless, the granularity of service offered
by 802.11e (4 priorities at most) can not satisfy the
real time flows requirements (where each flow is
characterized by its own delay bound).
Therefore, we propose in this paper a new
medium access mechanism based on the Deadline
Monotonic (DM) policy [9] to schedule real time
flows over 802.11. Indeed DM is a real time
scheduling policy that assigns static priorities to flow
packets according to their deadlines; the packet with
the shortest deadline being assigned the highest
priority. To support the DM policy over 802.11, we
use a distributed scheduling and introduce a new
medium access backoff policy. Therefore, we focus
on performance evaluation of the DM policy in terms
of achievable throughput, average MAC layer
service time and MAC layer service time
distribution. Hence, we follow these steps:
− First, we propose a Markov Chain
framework modeling the backoff process of
n contending stations within the same
broadcast region [1].
Due to the complexity of the mathematical
model, we restrict the analysis to n
contending stations belonging to two traffic
categories (each traffic category is
characterized by its own delay bound).
−
From the analytical model, we derive the
throughput achieved by each traffic
category.
− Then, we use the generalized Z-transforms
[3] to derive expressions of the average
MAC layer service time and service time
distribution.
− As the analytical model was restricted to
two traffic categories, analytical results are
extended by simulation to different traffic
categories.
− Finally, we consider a simple multi-hop
scenario to deduce the behavior of the DM
policy in a multi hop environment.
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
1
The rest of this paper is organized as follows. In
section 2, we review the state of the art of the IEEE
802.11 DCF, QoS support over 802.11 mainly the
IEEE 80.211e EDCA and real time scheduling over
802.11. In section 3, we present the distributed
scheduling and introduce the new medium access
backoff policy to support DM over 802.11. In section
4, we present our mathematical model based on
Markov chain analysis. Section 5 and 6 present
respectively throughput and the service time
analysis. Analytical results are validated by
simulation using the ns-2 network simulator [16]. In
section 7, we extend our study by simulation, first to
take into consideration different traffic categories,
second, to study the behavior of the DM algorithm in
a multi-hop environment where factors like
interferences or routing protocols exist. Finally, we
conclude in Section 8.
2
LITTERATURE REVIEWS
2.1 The 802.11 protocol
2.1.1 Description of the IEEE 802.11 DCF
Using DCF, a station shall ensure that the
channel is idle when it attempts to transmit. Then it
selects a random backoff in the contention window
[0,CW-1], where CW is the current window size and
varies between the minimum and the maximum
contention window sizes. If the channel is sensed
busy, the station suspends its backoff until the
channel becomes idle for a Distributed Inter Frame
Space (DIFS) after a successful transmission or an
Extended Inter Frame Space (EIFS) after a collision.
The packet is transmitted when the backoff reaches
zero. A packet is dropped if it collides after
maximum retransmission attempts.
The above described two way handshaking
packet transmission procedure is called basic access
mechanism. DCF defines a four way handshaking
technique called Request To Send/ Clear To Send
(RTS/CTS) to prevent the hidden station problem. A
station S j is said to be hidden from S i if S j is
within the transmission range of the receiver of S i
and out of the transmission range of S i .
2.1.2 Performance evaluation of the 802.11
DCF
Different works have been proposed to evaluate
the performance of the 802.11 protocol based on
Bianchi’s work [1]. Indeed, Bianchi proposed a
Markov chain based analytical model to evaluate the
saturation throughput of the 802.11 protocol. By
saturation conditions, it’s meant that contending have
always packets to transmit.
Several works extended the Bianchi model either
to suit more realistic scenarios or to evaluate other
performance parameters. Indeed, the authors of [2]
incorporate the frame retry limits in the Bianchi’s
model and show that Bianchi overestimates the
maximum achievable throughput. The native model
is also extended in [10] to a non saturated
environment. In [12], the authors derive the average
packet service time at a 802.11 node. A new
generalized Z-transform based framework has been
proposed in [3] to derive probabilistic bounds on
MAC layer service time. Therefore, it would be
possible to provide probabilistic end to end delay
bounds in a wireless network.
2.2 Supporting QoS over 802.11
2.2.1 Differentiation mechanisms over 802.11
Emerging applications like audio and video
applications require quality of service guarantees in
terms of throughput delay, jitter, loss rate, etc.
Transmitting
such
flows
over
wireless
communications
require
supporting
service
differentiation mechanisms over wireless networks.
Many medium access schemes have been
proposed to provide some QoS enhancements over
the IEEE 802.11 WLAN. Indeed, [4] assigns
different priorities to the incoming flows. Priority
classes are differentiated according to one of three
802.11 parameters: the backoff increase function,
Inter Frame Spacing (IFS) and the maximum frame
length. Experiments show that all the three
differentiation schemes offer better guarantees for
the highest priority flow. But the backoff increase
function mechanism doesn’t perform well with TCP
flows because ACKs affect the differentiation
mechanism.
In [7], an algorithm is proposed to provide
service differentiation using two parameters of IEEE
802.11, the backoff interval and the IFS. With this
scheme high priority stations are more likely to
access the medium than low priority ones. The above
described researches led to the standardization of a
new protocol that supports QoS over 802.11, the
IEEE 802.11e protocol [6].
2.2.2 The IEEE 802.11e EDCA
The IEEE 802.11e proposes a new medium
access mechanism called the Enhanced Distributed
Channel Access (EDCA), that enhances the IEEE
802.11 DCF. With EDCA, each station maintains
four priorities called Access Categories (ACs). Each
access category is characterized by a minimum and a
maximum contention window sizes and an
Arbitration Inter Frame Spacing (AIFS).
Different analytical models have been proposed
to evaluate the performance of 802.11e EDCA. In
[17], Xiao extends Bianchi’s model to the prioritized
schemes provided by 802.11e by introducing
multiple ACs with distinct minimum and maximum
contention window sizes. But the AIFS
differentiation parameter is lacking in Xiao’s model.
Recently Osterbo and Al. have proposed
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
2
different works to evaluate the performance of the
IEEE 802.11e EDCA [13], [14], [15]. They propose
a model that takes into consideration all the
differentiation parameters of the EDFA especially
the AIFS one. Moreover different parameters of QoS
have been evaluated such as throughput, average
service time, service time distribution and
probabilistic response time bounds for both saturated
and non saturated cases.
Although the IEEE 802.11e EDCA classifies the
traffic into four prioritized ACs, there is still no
guarantee of real time transmission service. This is
due to the lack of a satisfactory scheduling method
for various delay-sensitive flows. Hence, we need a
scheduling policy dedicated to such delay sensitive
flows.
2.3 Real time scheduling over 802.11
A distributed solution for the support of realtime sources over IEEE 802.11, called Blackburst, is
discussed in [8]. This scheme modifies the MAC
protocol to send short transmissions in order to gain
priority for real-time service. It is shown that this
approach is able to support bounded delays. The
main drawback of this scheme is that it requires
constant intervals for high priority traffic; otherwise
the performance degrades very much.
In [18], the authors proposed a distributed
priority scheduling over 802.11 to support a class of
dynamic priority schedulers such as Earliest
Deadline First (EDF) or Virtual Clock (VC). Indeed,
the EDF policy is used to schedule real time flows
according to their absolute deadlines, where the
absolute deadline is the node arrival time plus the
delay bound.
To realize a distributed scheduling over 802.11,
the authors of [18] used a priority broadcast
mechanism where each station maintains an entry for
the highest priority packet of all other stations. Thus,
stations can adjust their backoff according to other
stations priorities.
The overhead introduced by the broadcast
priority mechanism is negligible. This is due to the
fact that priorities are exchanged using native DATA
and ACK packets. Nevertheless, the authors of [18]
propose a generic backoff policy which can be used
by a class dynamic priority schedulers no matter if
this scheduler targets delay sensitive flows or rate
sensitive flows.
In this paper, we focus on delay sensitive flows
and propose to support the fixed priority deadline
monotonic scheduler over 802.11 to schedule delay
sensitive flows. For instance, we use a priority
broadcast mechanism similar to [5] and propose a
new medium access backoff policy where the
backoff value is inferred from the deadline
information.
3
SUPPORTING DEADLINE MONOTONIC
(DM) POLICY OVER 802.11
With DCF all the stations share the same
transmission medium. Then, the HOL (Head of Line)
packets of all the stations (highest priority packets)
will contend for the channel with the same priority
even if they have different deadlines.
Introducing DM over 802.11 allows stations
having packets with short deadlines to access the
channel with higher priority than those having
packets with long deadlines. Providing such a QoS
requires distributed scheduling and a new medium
access policy.
3.1 Distributed Scheduling over 802.11
To realize a distributed scheduling over 802.11,
we introduce a priority broadcast mechanism similar
to [18]. Indeed each station maintains a local
scheduling table with entries for HOL packets of all
other stations. Each entry in the scheduling table of
node S i comprises two fields S j , D j where S j is
the source node MAC address and D j is the
(
)
deadline of the HOL packet of node S j . To
broadcast the HOL packet deadlines, we propose to
use the DATA/ACK access mode.
When a node S i transmits a DATA packet, it
piggybacks the deadline of its HOL packet. The
nodes hearing the DATA packet add an entry for S i
in their local scheduling tables by filling the
corresponding fields. The receiver of the DATA
packet copies the priority of the HOL packet in ACK
before sending the ACK frame. All the stations that
did not hear the DATA packet add an entry for S i
using the information in the ACK packet.
3.2 DM medium access backoff policy
Let’s consider two stations S 1 and S 2
transmitting two flows with the same deadline D1 (
D1 is expressed as a number of 802.11 slots). The
two stations having the same delay bound can access
the channel with the same priority using the native
802.11 DCF.
Now, we suppose that S 1 and S 2 transmit flows
with different delay bounds D1 and D 2 such as
D1 < D 2 , and generate two packets at time instants
t 1 and t 2 . If S 2 had the same delay bound as S 1 ,
its packet would have been generated at time t '2 such
as t '2 = t 2 + D 21 , where D21 = ( D2 − D1 ) .
At that time, S 1 and S 2 would have the same
priority and transmit their packets according to the
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
3
802.11 protocol.
Thus, to support DM over 802.11, each station
uses a new backoff policy where the backoff is given
by:
• The random backoff selected in [ 0 , CW − 1]
according to 802.11 DCF, referred as BAsic
Backoff (BAB).
• The DM Shifting Backoff (DMSB):
corresponds to the additional backoff slots that
a station with low priority (the HOL packet
having a large deadline) adds to its BAB to
have the same priority as the station with the
highest priority (the HOL packet having the
shortest deadline).
Whenever a station S i sends an ACK or hears
an ACK on the channel its DMSB is revaluated as
follows:
DMSB( S i ) = Deadline( HOL( S i ) ) − DTmin ( S i )
(1)
Where DTmin ( S i ) is the minimum of the HOL
packet deadlines present in S i scheduling table and
Deadline( HOL( S i ) ) is the HOL packet deadline of
node S i .
Hence, when S i has to transmit its HOL packet
with a delay bound Di , it selects a BAB in the
contention window [ 0 , CW min − 1] and computes the
WHole Backoff (WHB) value as follows:
WHB( S i ) = DMSB( S i ) + BAB( S i )
(2)
The station S i decrements its BAB when it
senses an idle slot. Now, we suppose that S i senses
the channel busy. If a successful transmission is
heard, then S i  revaluates its DMSB when a correct
ACK is heard. Then the station S i adds the new
DMSB value to its current BAB as in equation (2).
Whereas, if a collision is heard, S i reinitializes its
DMSB and adds it to its current BAB to allow
colliding stations contending with the same priority
as for their first transmission attempt. S i transmits
when its WHB reaches 0. If the transmission fails, S i
doubles its contention window size and repeats the
above procedure until the packet is successfully
transmitted
or
dropped
after
maximum
retransmission attempts.
4
In this section, we propose a mathematical
model to evaluate the performance of the DM policy
using Markov chain analysis [1]. We consider the
following assumptions:
Assumption 1:
The system under study comprises n contending
stations hearing each other transmissions.
Assumption 2:
Each station S i transmits a flow Fi with a delay
bound Di . The n stations are divided into two traffic
categories C1 and C 2 such as:
− C1 represents n1 nodes transmitting flows
with delay bound D1 .
− C 2 represents n 2 nodes transmitting flows
with delay bound D 2 , such as D1 < D 2 ,
D21 = ( D 2 − D1 ) and ( n1 + n 2 ) = n .
Assumption 3:
We operate in saturation conditions: each station has
immediately a packet available for transmission after
the service completion of the previous packet [1].
Assumption 4:
A station selects a BAB in a constant contention
window [0 ,W − 1] independently of the transmission
attempt. This is a simplifying assumption to limit the
complexity of the mathematical model.
Assumption 5:
We are in stationary conditions, i.e. the n stations
have already sent one packet at least.
Depending on the traffic category to which it
belongs, each station S i will be modeled by a
Markov Chain representing its whole backoff (WHB)
process.
4.1 Markov chain modeling a station of category
C1
Figure 1 illustrates the Markov chain modeling a
station S 1 of category C1 . The states of this Markov
chain are described by the following quadruplet
( R , i , i − j , D21 ) where:
R : takes two values denoted by C 2 and
•
~ C 2 . When R = ~ C 2 , the n 2 stations of
category C 2 are decrementing their shifting
backoff (DMSB) during D21 slots and
wouldn’t contend for the channel. When
R = C 2 , the D 21 slots had already been
elapsed and stations of category C 2 will
contend for the channel..
MATHEMATICAL MODEL OF THE DM
POLICY OVER 802.11
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
4
Figure 1: Markov chain modeling a category C1 Station
•
•
•
i : the value of the BAB selected by S 1 in
[0 ,W − 1] .
( i − j ) : corresponds to the current backoff of
the station S 1 .
D 21 : corresponds to ( D2 − D1 ) . We choose
the negative notation − D 21 for stations of
C1 to express the fact that only stations of
category C 2 have a positive DMSB equal to
D 21 .
Initially S 1 selects a random BAB and is in
one of the states ( ~ C2 , i , i ,− D21 ) , i = 0..W − 1 .
During ( D 21 − 1) slots, S 1 decrements its backoff if
none of the ( n1 − 1) remaining stations of category
C1 transmits. Indeed, during these slots, the n 2
stations of category C 2 are decrementing their
DMSB and wouldn’t contend for the channel.
When S 1 is in one of the states
( ~ C 2 , i , i − ( D21 − 1) ,− D21 ) ,
i = D 21 ..W − 1 and
th
senses the channel idle, it decrements its D 21
slot.
But S 1 knows that henceforth the n 2 stations of
category C 2 can contend for the channel (the D 21
slots had been elapsed). Hence, S 1 moves to one of
the states ( C 2 , i , i − D21 ,− D 21 ) , i = D 21 ..W − 1 .
However, when the station S 1 is in one of the
states ( ~ C 2 , i , i − j ,− D 21 ) , i = 1..W − 1 ,
j = 0.. min( D 21 − 1, i − 1) and at least one of the
( n1 − 1) remaining stations of category C1
transmits, then the stations of category C 2 will
reinitialize their DMSB and wouldn’t contend for
channel during additional D21 slots. Therefore, S 1
moves
to
the
state ( ~ C 2 , i − j , i − j ,− D 21 ) ,
i = 1..W − 1 , j = 0.. min( D21 − 1, i − 1) .
Now, If S 1 is in one of the states
( C 2 , i , i − D21 ,− D21 ) , i = ( D21 + 1) ..W − 1 and at
least one of the ( n − 1) remaining stations (either a
category C1 or a category C 2 station) transmits,
then S 1 moves to one of the states
( ~ C 2 , i − D21 , i − D21 ,− D21 ) , i = ( D21 + 1) ..W − 1 .
4.2 Markov chain modeling a station of
category C2
Figure 2 illustrates the Markov chain modeling
a station S 2 of category C 2 . Each state of S 2
Markov chain is represented by the quadruplet
( i , k , D21 − j , D21 ) where:
• i : refers to the BAB value selected by S 2 in
[0 ,W − 1] .
k : refers to the current BAB value of S 2 .
•
•
•
D21 − j : refers to the current DMSB of S 2 ,
j ∈ [ 0 , D21 ] .
D21 : corresponds to ( D 2 − D1 ) .
When S 2 selects a BAB, its DMSB equals D21 
and is in one of the states ( i , i , D 21 , D 21 ) ,
i = 0..W − 1 . During D21 slots, only the n1
stations of category C1 contend for the channel.
If S 2 senses the channel idle during D21 slots, it
moves to one of the states ( i , i ,0 , D 21 ) , i = 0..W − 1 ,
where it ends its shifting backoff.
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
5
Figure 2: Markov chain modeling a category C 2 Station
When S 2 is in one of the states ( i , i ,0 , D 21 ) ,
i = 0..W − 1 , the ( n 2 − 1) other stations of category
C 2 have also decremented their DMSB and can
contend for the channel. Thus, S 2 decrements its
BAB and moves to the state ( i , i − 1,0 , D 21 ) ,
i = 2..W − 1 , only if none of ( n − 1) remaining
stations transmits.
If S 2 is in one of the states ( i , i − 1,0 , D 21 ) ,
i = 2..W − 1 , and at least one of the ( n − 1)
remaining stations transmits, the n 2 stations of
category C 2 will reinitialize their DMSB and S 2
( i − 1, i − 1, D21 , D21 ) ,
moves to the state
i = 2..W − 1 .
4.3 Blocking probabilities in the Markov chains
According to the explanations given in
paragraphs 4.1 and 4.2, the states of the Markov
chains modeling stations S 1 and S 2 can be divided
into the following groups:
•
•
•
•
γ
: the set of states of S 2 , where stations
of category C 2 contend for the channel
(pink states in figure 2).
γ 2 = { ( i , i ,0 , D21 ) , i = 0..W − 1
2
∪ ( i , i − 1,0 , D 21 ) , i = 2..W − 1}
Therefore, when stations of category C 1 are in
one the states of ξ 1 , stations of category C 2 are in
one of the states of ξ 2 . Similarly, when stations of
category C 1 are is in one of the states of γ 1 ,
stations of category C 2 are in one of the states of
γ 2.
Hence, we derive the expressions of S 1
blocking probabilities p11 and p12 shown in
figure 1 as follows:
−
p11 : the probability that S 1 is blocked given
that S 1 is in one of the states of ξ 1 . p11 is
ξ 1 : the set of states of S 1 where none of the
n 2 stations of category C 2 contends for the
channel (blue states in figure 1).
ξ 1 = { ( ~ C 2 , i , i − j ,− D 21 ) , i = 0..W − 1,
j = 0.. min( max( 0 , i − 1) , D 21 − 1)}
the probability that at least a station S 1' of
the other ( n1 − 1) stations of C 1 transmits
given that S 1' is in one of the states of ξ 1 .
γ
of C1 transmits given that S 1' is in one of
the states of ξ 1 :
: the set of states of S 1 where stations of
category C 2 can contend for the channel
(pink states in figure 1).
γ 1 = { ( C 2 , i , i − D 21 ,− D 21 ) , i = D 21 ..W − 1}
ξ
1
: the set of states of S 2 where stations of
category C 2 do not contend for the channel
(blue states in figure 2).
ξ 2 = { ( i , i , D 21 − j , D 21 ) , i = 0..W − 1,
p 11 = 1 − ( 1 − τ
where τ
τ
11
[
=
π
(3)
]
( ~ C2 ,0 ,0 ,− D21 )
1
W − 1  min ( max ( 0 ,i − 1) ,D21 − 1)

π 1( ~C2 ,i ,i − j ,− D21 )

i= 0 
j= 0
∑
π 1( R ,i ,i −
) n1 − 1
is the probability that a station S 1'
= Pr S 1' transmits ξ 1
2
j = 0..( D 21 − 1)}
11
11
j ,− D21 )
∑




(4)
is defined as the probability of
the state ( R , i , i − j ,− D21 ) , in the stationary
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
6
conditions and Π
{
= π
1
( R ,i ,i −
j ,− D21 )
1
}
is the
probability vector of a category C 1 station.
−
p12 : the probability that S 1 is blocked given
that S 1 is in one of the states of γ 1 . p12 is
the probability that at least a station S 1' of
the other ( n1 − 1) stations of C 1 transmits
given that S 1' is in one of the states of γ 1 or
at least a station S 2' of the n 2 stations of
−
p 22 : the probability that S 2 is blocked
given that S 2 is in one of the states of γ 2 .
p 22 = 1 − ( 1 − τ
 Π i Pi = Π i


π ij = 1
 j

∑
) n1 − 1 ( 1 − τ 22 ) n2
12
(5)
where τ 12 is the probability that a station S 1'
of C 1 transmits given that S 1' is in one of
the states of γ 1 .
τ
[
= Pr S 1' transmits γ
12
=
π 1( C2 ,D21 ,0 ,− D21 )
W−1
∑
π
1
]
(6)
( C2 ,i ,i − D21 ,− D21 )
1
22
the probability that a station S 2' of
C 2 transmits given that S 2' is in one of the
states of γ 2 .
τ
12
= Pr
=
[
S '2
transmits γ
π
W−1
∑
π
π
2
2
]
2
+
i= 0
( i ,k ,D21 −
W−1
∑
π
( i ,i − 1,0 ,D21 )
2
(7)
i= 2
j ,D21 )
of the state
(10)
4.4 Transition probability matrices
4.4.1 Transition probability matrix of a
category C1 station
Let P1 be the transition probability matrix of
the station S 1 of category C1 . P1 { i , j} is the
probability to transit from state i to state j . We
have:
P1 { ( ~ C 2 , i , i − j ,− D 21 ) , ( ~ C 2 , i , i − ( j + 1) ,− D 21 )}
stationary condition. Π
2
j , D 21 ) ,
{
= π
in the
( i ,k ,D21 −
2
j ,D21 )
}
is the probability vector of a category C 2
station.
In the same way, we evaluate p 21 and p 22 the
blocking probabilities of station S 2 as shown in
figure 2:
p 21 : the probability that S 2 is blocked
−
given that S 2 is in one of the states of ξ 2 .
p 21 = 1 − ( 1 − τ
11
(11)
i = 1.. min(W − 1, D 21 − 1)
(12)
P1 { ( ~ C 2 , i , i − D 21 + 1,− D 21 ) , ( C 2 , i , i − D 21 ,− D 21 )}
P1{ ( ~ C2 , i , i − j ,− D21 ) , ( ~ C2 , i − j , i − j ,− D21 )}
= p11 , i = 2..W − 1, j = 1.. min( i − 1, D21 − 1)
P1 { ( ~ C2 , i , i ,− D21 ) , ( ~ C2 , i , i ,− D21 )} = p11 ,
is defined as the probability
( i , k , D21 −
P1 { ( ~ C 2 , i ,1,− D 21 ) , ( ~ C 2 ,0 ,0 ,− D 21 )} = 1 − p11 ,
= 1 − p11 , i = D 21 ..W − 1
( 0 ,0 ,0 ,D21 )
( i ,i ,0 ,D21 )
2
(9)
= 1 − p11 , i = 2..W − 1, j = 0.. min( i − 2 , D 21 − 2 )
i = D21
and τ
) n1 ( 1 − τ 22 ) n2 − 1
The blocking probabilities described above
allow deducing the transition state probabilities and
having the transition probability matrix Pi , for a
station of traffic category C i .
Therefore, we can evaluate the state
probabilities by solving the following system [11]:
C 2 transmits given that S 2' is in one of the
states of γ 2 .
p 12 = 1 − ( 1 − τ
12
)
n1
(8)
i = 1..W − 1
(13)
(14)
(15)
P1{ ( C2 , i , i − D21 ,− D21 ) , ( ~ C2 , i − D21 , i − D21 ,− D21 )}
= p12 , i = ( D21 + 1) ..W − 1
(16)
P1{ ( C2 ,i ,i − D21 ,− D21 ) ,( C2 ,( i − 1) ,( i − 1 − D21 ) ,− D21 )}
= 1 − p12 ,i = ( D21 + 1) ..W − 1
(17)
P1{ ( ~ C2 ,0 ,0 ,− D21 ) , ( ~ C2 , i , i ,− D21 )} =
1
,
W
(18)
i = 0..W − 1
If ( D 21 < W ) then:
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
7
1
,
W
P1{ ( C2 , D21 ,0 ,− D21 ) , ( ~ C2 , i , i ,− D21 )} =
 τ 11 = f (τ 11 ,τ 12 ,τ 22 )

 τ 12 = f (τ 11 ,τ 12 ,τ 22 )

 τ 22 = f (τ 11 ,τ 12 ,τ 22 )
 under the constraint

 τ 11 > 0 ,τ 12 > 0 ,τ 22 > 0 ,τ
(19)
i = 0..W − 1
By replacing p11 and p 12 by their values in
equations (3) and (5) and by replacing P1 and Π 1
in (10) and solving the resulting system, we can
( R ,i ,i − j ,− D21 )
express π 1
as a function of τ 11 , τ 12 and
τ 22 given respectively by equations (4), (6) and
(7).
Transition probability matrix of a
category C2 station
Let P2 be the transition probability matrix of
the station S 2 belonging to the traffic category C 2 .
The transition probabilities of S 2 are:
4.4.2
P2 { ( i , i , D21 − j , D21 ) , ( i , i , D21 − ( j + 1) , D21 )}
(20)
= 1 − p21 , i = 0..W − 1, j = 0..( D21 − 1)
P2 { ( i , i ,0 , D21 ) , ( i , i − 1,0 , D21 )} = 1 − p22 ,
P2 { ( 1,1,0 , D21 ) , ( 0 ,0 ,0 , D21 )} = 1 − p22
(23)
P2 { ( i , i ,0 , D21 ) , ( i , i , D21 , D21 )} = p22 ,
(24)
i = 1..W − 1
P2 { ( i , i − 1,0 , D21 ) , ( i − 1, i − 1, D21 , D21 )} = p22 ,
i = 2..W − 1
(25)
P2 { ( i , i − 1,0 , D21 ) , ( i − 1, i − 2 ,0 , D21 )} = 1 − p22 ,
i = 3..W − 1
P2 { ( 0 ,0 ,0 , D21 ) , ( i , i , D21 , D21 )} =
(26)
1
, i = 0..W − 1 (27)
W
22
given respectively by equations (4), (6)
and (7). Moreover, by replacing π
π
( i ,k ,D21 −
2
j ,D21 )
( R ,i ,i −
1
j ,− D21 )
and
22
< 1
THROUGHPUT ANALYSIS
Pi ,s : the probability that a station S i belonging
to the traffic category C i transmits a packet
successfully. Let S 1 and S 2 be two stations
belonging respectively to traffic categories C 1
and C 2 . We have:
P1,s = Pr [ S1 transmits successfully ξ 1 ] Pr [ξ 1 ]
+ Pr [ S1 transmits successfully γ 1 ] Pr [γ 1 ]
= τ 11 ( 1 − p11 ) Pr [ξ 1 ] + τ 12 ( 1 − p12 ) Pr [γ 1 ]
(29)
P2 ,s = Pr [ S 2 transmits successfully ξ
+ Pr [ S 2 transmits successfully γ
−
22 ( 1 −
p 22 ) Pr [γ
2]
2
] Pr[ξ 2 ]
] Pr[γ 2 ]
2
(30)
Pidle : the probability that the channel is idle.
The channel is idle if the n1 stations of
category C 1 don’t transmit given that these stations
are in one of the states of ξ 1 or if the n stations
(both category C 1 and category C2 stations) don’t
transmit given that stations of category C 1 are in
one of the states of γ 1 . Thus:
Pidle = ( 1 − τ
by their values, in equations (4), (6)
and (7), we obtain a system of non linear equations
as follows:
< 1,τ
In this section, we propose to evaluate Bi , the
normalized throughput achieved by a station of
traffic category C i [1]. Hence, we define:
=τ
By replacing p 21 and p22 by their values in
equations (8) and (9) and by replacing P2 and Π 2
in (10) and solving the resulting system, we can
( i ,k ,D − j ,D21 )
express π 2 21
as a function of τ 11 , τ 12
and τ
5
(22)
i = 2..W − 1
12
Solving the above system (28), allows deducing
the expressions of τ 11 , τ 12 and τ 22 , and deriving
the state probabilities of Markov chains modeling
category C 1 and category C 2 stations.
(21)
i = 0..W − 1, j = 0..( D21 − 1)
< 1,τ
(28)
−
P2 { ( i , i , D21 − j , D21 ) , ( i , i , D21 , D21 )} = p21 ,
11
11
) n1
Pr [ξ 1 ] + ( 1 − τ
12
) n1 ( 1 − τ 22 ) n2
Pr [γ 1 ]
(31)
Hence, the expression of the throughput of a
category C i station is given by:
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
8
Bi =
Pi ,s T P

PIdle Te + Ps Ts +  1 − PIdle −


2
∑
i= 1

ni Pi ,s  Tc


(32)
Where Te denotes the duration of an empty
slot, Ts and Tc denote respectively the duration of
a successful transmission and a collision.
2


 1 − PIdle −
ni Pi ,s 
corresponds
to
the


i= 1


probability of collision. Finally T p denotes the
∑
average time required to transmit the packet data
payload. We have:
(
)
Ts = T PHY + TMAC + T p + T D + SIFS +
( TPHY
+ T ACK + T D ) + DIFS
(
)
Tc = TPHY + TMAC + T p + TD + EIFS
For all the scenarios, we consider that we are in
n
presence of n contending stations with
stations
2
for each traffic category. In figure 3, n is fixed to
8 and we depict the throughput achieved by the
different stations present in the network as a
function of the contention window size W ,
( D21 = 1) . We notice that the throughput achieved
by category C1 stations (stations numbered from
S 11 to S 14 ) is greater than the one achieved by
category C 2 stations (stations numbered from S 21
to S 24 ).
(33)
(34)
Where T PHY , TMAC and T ACK are the
durations of the PHY header, the MAC header and
the ACK packet [1], [13]. T D is the time required to
transmit the two bytes deadline information.
Stations hearing a collision wait during EIFS before
resuming their backoff.
For numerical results stations transmit 512
bytes data packets using 802.11.b MAC and PHY
layers parameters (given in table 1) with a data rate
equal to 11Mbps. For simulation scenarios, the
propagation model is a two ray ground model. The
transmission range of each node is 250m. The
distance between two neighbors is 5m. The EIFS
parameter is set to ACKTimeout as in ns-2, where:
ACKTimeout = DIFS + ( T PHY + T ACK + T D ) + SIFS
(35)
Table 1: 802.11 b parameters.
Data Rate
Slot
SIFS
DIFS
PHY Header
MAC Header
ACK
Short Retry Limit
11 Mb/s
20 µs
10 µs
50 µs
192 µs
272 µs
112 µs
7
Figure 3: Normalized throughput as a function of
the contention window size ( D 21 = 1, n = 8 )
Analytically, stations belonging to the same
traffic category have the same throughput given by
equation (31). Simulation results validate analytical
results and show that stations belonging to the same
traffic category (either category C1 or category C 2
) have nearly the same throughput. Thus, we
conclude the fairness of DM between stations of the
same category.
For subsequent throughput scenarios, we focus
on one representative station of each traffic
category. Figure 4, compares category C1 and
category C 2 stations throughputs to the one
obtained with 802.11.
Curves are represented as a function of W and
for different values of D21 . Indeed as D21
increases, the category C1 station throughput
increases, whereas the category C 2 station
throughput decreases. Moreover as W increases,
the difference between stations throughputs is
reduced. This is due to the fact that the shifting
backoff becomes negligible compared to the
contention window size.
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
9
Finally, we notice that the category C1 station
obtains better throughput with DM than with
802.11, but the opposite scenario happens to the
category C 2 station.
We propose to evaluate the Z-Transform of the
MAC layer service time [3], [14], [15] to derive an
expression of the average service time. The service
time depends on the duration of an idle slot Te , the
duration of a successful transmission Ts and the
duration of a collision Tc [1], [3],[14]. As Te is the
smallest duration event, the duration of all events
 Tevent 
will be given by 
.
 Te 
6.1 Z-Transform of the MAC layer service time
6.1.1 Service time Z-transform of a category
C1 station:
Let TS 1 ( Z ) be the service time Z-transform of
a station S1 belonging to traffic category C 1 . We
define:
Figure 4: Normalized throughput as a function of
the contention window size (different D21 values)
In figure 5, we generalize the results for
different numbers of contending stations and fix the
contention window size W to 32.
H 1( R ,i ,i −
j ,− D21 )
(Z) :
The Z-transform of the
time already elapsed from the instant S 1 selects a
basic backoff in [ 0 ,W − 1] (i.e. being in one of the
states ( ~ C 2 , i , i ,− D 21 ) ) to the time it is found in the
state ( R ,i ,i − j ,− D21 ) .
Moreover, we define:
11
Psuc
: the probability that S 1 observes a
successful transmission on the channel,
while S 1 is in one of the states of ξ 1 .
•
11
Psuc
= ( n1 − 1)τ 11 ( 1 − τ 11 ) n1 − 2
(36)
12
Psuc
: the probability that S 1 observes a
successful transmission on the channel,
while S 1 is in one of the states of γ 1 .
•
12
Psuc
= ( n1 − 1)τ 12 ( 1 − τ 12 ) n1 − 2 ( 1 − τ 22 ) n2
Figure 5: Normalized throughput as a function of
the number of contending stations
All the curves show that DM performs service
differentiation over 802.11 and offers better
throughput for category C1 stations independently
of the number of contending stations.
6
SERVICE TIME ANALYSIS
In this section, we evaluate the average MAC
layer service time of category C 1 and category C 2
stations using the DM policy. The service time is
the time interval from the time instant that a packet
becomes at the head of the queue and starts to
contend for transmission to the time instant that
either the packet is acknowledged for a successful
transmission or dropped.
+ n2τ 22 ( 1 − τ 22 ) n2 − 1 ( 1 − τ 12 ) n1 − 1
(37)
We evaluate H 1( R ,i ,i − j ,− D21 ) ( Z ) for each state
of S1 Markov chain as follows:
H 1( ~ C2 ,i ,i ,− D21 ) ( Z ) =
(p
11
−
11
Psuc
)
 Ts 

1  11  Te 
+ Psuc Z
+
W 

 Tc  
  min ( i + D21 − 1,W − 1)
T 
Z e  
H 1( ~ C 2 ,k ,i ,− D21 )
k = i+ 1


∑
(Z)
 Ts 
 Tc 

 
 12  Te 
T
12
+ Ĥ 1( C 2 ,i + D21 ,i ,− D21 ) ( Z )  Psuc Z
+ p11 − Psuc Z  e 


(
)





(38)
Where:
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
10
 Ĥ 1( C ,i + D ,i ,− D ) ( Z ) = H 1( C ,i + D ,i ,− D ) ( Z )
2
21
21
2
21
21

 if ( i + D 21 ) ≤ W − 1

 Ĥ 1( C2 ,i + D21 ,i ,− D21 ) ( Z ) = 0 Otherwise
(39)
We also have:
H 1( ~ C2 ,i ,i −
( (1 −
j ,− D21 ) ( Z ) =
p11 ) Z ) j H 1( ~ C2 ,i ,i ,− D21 ) ( Z )
 Ts 
 
Te 
11 
1 − Psuc
Z
i = 2..W − 1, j = 1..min( i − 1, D21 − 1)
(
 Tc 
 
Te 
)
( (1 −
1−
p11 ) Z ) D21 H 1( ~ C2 ,i ,i ,− D21 ) ( Z )
 Ts 
 
11  Te 
Psuc Z
(
− p11 −
11
Psuc
)
 Tc 
 
T
Z e 
+ ( 1 − p12 ) ZH 1( C 2 ,i + 1,i + 1− D21 ,− D21 ) ( Z ) ,i = D21 ..W − 2
(41)
H 1( C 2 ,W − 1,W − 1− D21 ,− D21 ) ( Z )
=
( (1 −
p11 ) Z ) D21 H 1( ~ C2 ,W − 1,W − 1,− D21 ) ( Z )
1−
 Ts 
 
11  Te 
Psuc Z
(
− p11 −
11
Psuc
)
TS1 ( Z ) =
  Tc 
  Te 
+ ( 1 − p12 ) H 1( C2 ,D21 ,0 ,− D21 ) ( Z )
p11 H 1( ~ C2 ,0 ,0 ,− D21 ) ( Z )
Z
i= 0 

)∑
+ p12 H 1( C2 ,D21 ,0 ,− D21 ) ( Z )
1−
+ ( 1 − p11 ) Z
min ( W − 1,D21 − 1)
∑
i= 2
i
(44)
6.1.2 Service time Z-transform of a category
C2 station:
In the same way, let TS2 (Z) be the service
time Z-transform of a station S 2 of category C 2 .
We define:
H 2( i ,k ,D21 − j ,D21 ) ( Z ) : The Z-transform of the
time already elapsed from the instant S 2 selects a
basic backoff in [0 ,W − 1] (i.e. being in one of the
states ( i , i , D21 , D 21 ) ) to the time it is found in the
state ( i , k , D21 − j , D 21 ) .
Moreover, we define:
•
21
Psuc
: the probability that S 2 observes a
successful transmission on the channel,
while S 2 is in one of the states of ξ 2 .
11
Psuc
= ( n1 − 1)τ 12 ( 1 − τ 12 ) n1 − 1
p11 ) ZH 1( ~ C2 ,1,1,− D21 ) ( Z )
(
− p11 −
H 1( ~ C2 ,i ,1,− D21 ) ( Z ) +
11
Psuc
)Z
 Tc 
 
 Te 
•
1
W
If S 1 transmission state is ( ~ C 2 ,0 ,0 ,− D 21 ) ,
the transmission will be successful only if none of
the ( n1 − 1) remaining stations of C 1 transmits.
Whereas when the station S 1 transmission state is
( C 2 , D21 ,0 ,− D21 ) , the transmission occurs
successfully only if none of ( n − 1) remaining
stations (either a category C 1 or a category C 2
station) transmits.
If the transmission fails, S 1 tries another
transmission. After m retransmissions, if the
packet is not acknowledged, it will be dropped.
(45)
22
Psuc
: the probability that S 2 observes a
successful transmission on the channel,
while S 2 is in one of the states of γ 2 .
22
Psuc
= n1τ 12 ( 1 − τ 12 ) n1 − 1 ( 1 − τ 22 ) n2 − 1
(43)
Thus:
))
(
 Tc 
 
T
Z e 
 Ts 
 
11  Te 
Psuc Z
(
  Tc 
 T
+  Z  e  p11H 1( ~ C2 ,0 ,0 ,− D21 ) ( Z ) + p12 H 1( C 2 ,D21 ,0 ,− D21 ) ( Z )


(42)
H 1( ~ C 2 ,0 ,0 ,− D21 ) ( Z ) =
p11 ) H 1( ~ C 2 ,0 ,0 ,− D21 ) ( Z )
m
+ ( 1 − p12 ) ZH 1( C 2 ,i + 1,i + 1− D21 ,− D21 ) ( Z ) ,i = D21 ..W − 2
(1 −
(( 1 −
11
− p11 − Psuc
Z
(40)
H 1( C2 ,i ,i − D21 ,− D21 ) ( Z ) =
 Ts 
 
T
Z e 
+ ( n2 − 1)τ 22 ( 1 − τ 22 ) n2 − 2 ( 1 − τ 12 ) n1
(46)
We evaluate H 2( i ,i ,D21 − j ,− D21 ) ( Z ) for each state
of S1 Markov chain as follows:
1
H 2( i ,i ,D21 − j ,D21 ) ( Z ) =
, i = 0 and i = W − 1 (47)
W
 Ts 

1  22  Te 
H 2( i ,i ,D21 ,D21 ) ( Z ) =
+  Psuc Z
+
W 

 Tc  
  
T
22
p 22 − Psuc
Z  e   H 2( i + 1,i ,0 ,D21 ) ( Z ) , i = 1..W − 2


(48)
(
)
To compute H 2( i ,i ,D21 −
j ,D21 )
(Z) ,
we define
j
( Z ) , such as:
Tdec
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
11
)





m+ 1
0
Tdec
(Z) =
1
(49)
(1 −
j
Tdec
(Z) =

 21
1 −  Psuc
Z


for j = 1..D 21
 Ts 
 
 Te 
p 21 ) Z
(
)
21
+ p 21 − Psuc
Z
 Tc 
 
 Te 

 j− 1
 Tdec ( Z )


 Tc 


 


T
TS 2 ( Z ) =  p 22 Z  e  H 2( 0 ,0 ,0 ,D21 ) ( Z ) 




(1 −
p 22 ) Z
 Ts 
 
 Te 
H 2( 0 ,0 ,0 ,D21 ) ( Z )
m
∑
i= 0
m+ 1
+
 Tc 


 


Te 

H 2( 0 ,0 ,0 ,D21 ) ( Z ) 
 p 22 Z




(54)
(50)
So:
H 2( i ,i ,D21 −
j ,D21 )
(Z) =
H 2( i ,i ,D21 −
j + 1,D21 )
i = 0..W − 1, j = 1..D 21 , ( i , j ) ≠ ( 0 , D 21 )
( Z )Tdecj ( Z ) ,
(51)
And:
H 2( i ,i − 1,0 ,D21 ) ( Z ) = ( 1 − p 22 ) ZH 2( i + 1,i ,0 ,D21 ) ( Z )
+
(1 −
p 22 ) ZH 2( i ,i ,0 ,D21 ) ( Z )
 Ts 
 Tc 

 
 22  Te 
T
22
1 −  Psuc Z
+ p 22 − Psuc Z  e 


i = 2..W − 2
(
)
Where TS i( 1) ( Z ) , is the derivate of the service
time Z-transform of station S i [11].

 D21
 Tdec ( Z )


(51)
H 2( W − 1,W − 2 ,0 ,D21 ) ( Z )
=
(1 −
p 22 ) ZH 2( W − 1,W − 1,0 ,D21 ) ( Z )
 Ts 
 Tc 

 
 22  Te 
T
22
1 −  Psuc Z
+ p 22 − Psuc Z  e 


(
)
6.2 Average Service Time
From equations (43) (respectively equation
(54)), we derive the average service time of a
category C 1 station ( respectively a category C 2
station). The average service time of a category C i
station is given by:
X i = TS i( 1) ( 1)
(55)
(52)

 D21
 Tdec ( Z )


By considering the same configuration as in
figure 3, we depict in figure 5, the average service
time of category C 1 and category C2 stations as a
function of W . As for the throughput analysis,
stations belonging to the same traffic category have
nearly the same average service value. Simulation
service time values coincide with analytical values
given by equation (55). These results confirm the
fairness of DM in serving stations of the same
category.
According to figure 2 and using equations (44),
we have:
H 2( 0 ,0 ,0 ,D21 ) ( Z ) = H 2( 0 ,1,0 ,D21 ) ( Z )Tdec21 ( Z )
( 1 − p 22 ) ZH 2( 1,1,0 ,D21 ) ( Z )
+
(53)
 Ts 
 Tc  

  
 22  Te 
Te 
D21
22

1 −  Psuc Z
+ p 22 − Psuc Z
 Tdec ( Z )




D
(
)
Therefore, we can derive an expression of S 2
Z-transform service time as follows:
Figure 6: Average service time as a function of the
contention window size (D21=1, n=8)
In figure 8, we show that category C 1 stations
obtain better average service time than the one
obtained with 802.11 protocol. Whereas, the
opposite scenario happens for category C 2 stations
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
12
i
independently of n , the number of contending
stations within the network.
D 21 = 4 , the probability that
S 1 service time
exceeds 0.005s equals 0.28%. Whereas, station S 2
service time exceeds 0.005s with the probability
5.67%. Thus, DM offers better service time
guarantees for the stations with the highest priority.
In figure 9, we double the size of the contention
window size and set it to 64. We notice that
category C 1 and category C 2 stations service time
curves become closer. Indeed, when W becomes
large, the BAB values increase and the (DMSB)
becomes negligible compared to the basic backoff.
The whole backoff values of S 1 and S 2 become
near and their service time accordingly.
Figure 7: Average service time as a function of the
number of contending stations
6.3 Service Time Distribution
Service time distribution is obtained by
inverting the service time Z transforms given by
equations (43) and (54). But we are most interested
in probabilistic service time bounds derived by
inverting the complementary service time Z
transform given by [11]:
1 − TS i ( Z )
~
Xi (Z) =
1− Z
(55)
Figure 9: Complementary service time distribution
for different values of D21 (W=64)
In figure 8, we depict analytical and simulation
values of the complementary service time
distribution of both category C 1 and category C 2
station (W = 32 ) .
In figure 10, we depict the complementary
service time distribution for both category C 1 and
category C 2 stations and for values of n , the
number of contending nodes.
Figure 8: Complementary service time distribution
for different values of D21 , (W = 32 )
Figure 10: Complementary service time
distribution for different values of the contending
stations
All the curves drop gradually to 0 as the delay
increases. Category C 1 stations curves drop to 0
faster than category C 2 curves. Indeed, when
Analytical and simulation results show that
complementary service time curves drop faster
when the number of contending stations is small for
both category C 1 and category C 2 stations. This
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
13
means that all stations service time increases as the
number of contending nodes increases.
7 EXTENTIONS OF THE ANAYTICAL
RESULTS BY SIMULATION
The mathematical analysis undertaken above
show that DM performs service differentiation over
802.11 protocol and offers better QoS guarantees
for highest priority stations
Nevertheless, the analysis was restricted to two
traffic categories. In this section, we first generalize
the results by simulation for different traffic
categories. Therefore, we consider a simple multihop and evaluate the performance of the DM policy
when the stations belong to different broadcast
regions.
CW max < 1024 and K =1.
Analytical and simulation results show that
throughput values increase with stations priority.
Indeed, the station with the lowest delay bound has
the maximum throughput.
Moreover, figure 12 shows that stations
belonging to the same traffic category have the
same throughput. For instance, when n is set to 15
(i.e. m = 3 ), the three stations of the same traffic
category have almost the same throughput.
7.1 Extension of the analytical results
In this section, we consider n stations
contending for the channel in the same broadcast
region. The n stations belong to 5 traffic categories
where n = 5 m and m is the number of stations of
the same traffic category. A traffic category C i is
characterized by a delay bound Di , and
Dij = Di − D j is the difference between the
deadline values of category C i and category C j
stations. We have:
Dij = ( i − j ) K
(53)
Where K is the deadline multiplicity factor
and is given by:
Di + 1,i = Di + 1 − Di = K
(53)
Indeed, when K varies, the deadline values of
all other stations also vary. Stations belonging to
the traffic category C i are numbered from S i1 to
S im .
Figure 11: Normalized throughput for different
traffic category stations
In figure 11, we depict the throughput achieved
by different traffic categories stations as a function
of the minimum contention window size CW min
such as CW min is always smaller than CW max ,
Figure 12: Normalized throughput: different
stations belonging to the same traffic category
In figure 13, we depict the average service
time of the different traffic category stations as a
function of K , the deadline multiplicity factor. We
notice that the highest priority station average
service time decreases as the deadline multiplicity
factor increases. Whereas, the lowest priority
station average service time increases with K .
Figure 13: Average service time as a function of
the deadline multiplicity factor K
In the same way, the probabilistic service time
bounds offered to S 11 (the highest priority station)
are better than those offered to station S 51 (the
lowest priority station). Indeed, the probability that
S 11 service time exceeds 0.01s=0.3%. But, station
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
14
S 51 service time exceeds 0.01s with the probability
of 36%.
and D21 = D 2 − D1 =5 slots. Flows F3 and F4 are
transmitted respectively by S 12 and S 4 and have
the same delay bound. Finally, F5 and F6 are
transmitted respectively by S 5 and S 6 with delay
bounds D1 and D2 and D 2 ,1 = D2 − D1 = 5 slots.
Figure 16 shows that the throughput achieved
by F1 is smaller than the one achieved by F2 .
Figure 14: Complementary
distribution (W=32, n=8)
service
time
The above results generalize the analytical
model results and show once again that DM
performs service differentiation over 802.11 and
offer better guarantees in terms of throughput,
average service time and probabilistic service time
bounds for flows with short deadlines.
7.2 Simple Multi hop scenario
In the above study, we considered that
contending stations belong to the same broadcast
region. In reality, stations may not be within one
hop from each other. Thus a packet can go through
several hops before reaching its destination. Hence,
factors like routing protocols or interferences may
preclude the DM policy from working correctly.
In the following paragraph, we evaluate the
performance of the DM policy in a multi-hop
environment. Hence, we consider a 13 node simple
mtlti-hop scenario described in figure 15.
Figure 15: Simple multi hop scenario
Six flows are transmitted over the network. Flows
packets are routed using the AODV protocol.
Flows F1 and F2 are respectively transmitted by
stations S 1 and S 2 with delay bounds D1 and D2
Figure 16: Normalized throughput using DM
policy
Indeed, both flows cross nodes 6 and 7, where
F1 got a higher priority to access the medium than
F2 when the DM policy is used. We obtain the
same results for flows F5 and F6 . Flows F3 and
F4 have almost the same throughput since they
have equal deadlines.
Figure 17 show that the complementary service
time distribution curves drop to 0 faster for flow F1
than for flow F2 .
Figure 17: End to end complementary service time
distribution
The same behavior is obtained for flow F5 and F6,
where F5 has the shortest delay bound.
Hence, we conclude that even in a multi-hop
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
15
environment, the DM policy performs service
differentiation over 802.11 and provides better QoS
guarantees for flows with short deadlines.
8
CONCLUSION
In this paper we first proposed to support the
DM policy over 802.11 protocol. Therefore, we
used a distributed backoff scheduling algorithm and
introduced a new medium access backoff policy.
Then we proposed a mathematical model to
evaluate the performance of the DM policy. Indeed,
we considered n contending stations belonging to
two traffic categories characterized by different
delay bounds. Analytical and simulation results
show that DM performs service differentiation over
802.11 and offers better guarantees in terms of
throughput, average service time and probabilistic
service time bounds for the flows having small
deadlines. Moreover, DM achieves fairness
between stations belonging to the same traffic
category.
Then, we extended by simulation the analytical
results obtained for two traffic categories to
different traffic categories. Simulation results
showed that even if contending stations belong to
K traffic categories, K > 2 , the DM policy offers
better QoS guarantees for highest priority stations.
Finally, we considered a simple multi-hop scenario
and concluded that factors like routing messages or
interferences don’t impact the behavior of the DM
policy and DM still provides better QoS guarantees
for stations with short deadlines.
9
REFERENCES
[1] G. Bianchi: Performance Analysis of the IEEE
802.11 Distributed Coordination Function,
IEEE J-SAC Vol. 18 N. 3, (March 2000).
[2] H. Wu1, Y. Peng, K. Long, S. Cheng, J. Ma:
Performance of Reliable Transport Protocol
over IEEE 802.11 Wireless LAN: Analysis and
Enhancement, In Proceedings of the IEEE
INFOCOM`02, June 2002.
[3] H. Zhai, Y. Kwon, Y., Fang: Performance
Analysis of IEEE 802.11 MAC protocol in
wireless LANs”, Wireless Computer and
Mobile Computing, (2004).
[4] I. Aad and C. Castelluccia, “Differentiation
mechanisms for IEEE 802.11”, In Proc. of
IEEE Infocom 2001, (April 2001).
[5] IEEE 802.11 WG: Part 11: Wireless LAN
Medium Access Control (MAC) and Physical
Layer (PHY) specification”, IEEE (1999).
[6] IEEE 802.11 WG, ”Draft Supplement to Part
11: Wireless Medium Access Control (MAC)
and physical layer (PHY) specifications:
Medium Access Control (MAC) Enhancements
for Quality of Service (QoS)”, IEEE
802.11e/D13.0, (January 2005).
[7] J. Deng, R. S. Chang: A priority Scheme for
IEEE 802.11 DCF Access Method, IEICE
Transactions in Communications, vol. 82-B,
no. 1, (January 1999).
[8] J.L. Sobrinho, A.S. Krishnakumar: Real-time
traffic over the IEEE 802.11 medium access
control layer, Bell Labs Technical Journal, pp.
172-187, (1996).
[9] J. Y. T. Leung, J. Whitehead: On the
Complexity of Fixed-Priority Scheduling of
Periodic, Real-Time Tasks, Performance
Evaluation (Netherlands), pp. 237-250, (1982).
[10]K. Duffy, D. Malone, D. J. Leith: Modeling
the 802.11 Distributed Coordination Function
in Non-saturated Conditions, IEEE/ACM
Transactions
on
Networking
(TON),
Vol. 15 , pp. 159-172 (February 2007)
[11]L. Kleinrock: Queuing Systems,Vol. 1: Theory,
Wiley Interscience, 1976.
[12]P. Chatzimisios, V. Vitsas, A. C. Boucouvalas:
Throughput and delay analysis of IEEE 802.11
protocol, in Proceedings of 2002 IEEE 5th
International Workshop on
Networked
Appliances, (2002).
[13]P.E. Engelstad, O.N. Osterbo: Delay and
Throughput Analysis of IEEE 802.11e EDCA
with Starvation Prediction, In proceedings of
the The IEEE Conference on Local Computer
Networks , LCN’05 (2005).
[14]P.E. Engelstad, O.N. Osterbo: Queueing Delay
Analysis of 802.11e EDCA, Proceedings of
The Third Annual Conference on Wireless On
demand Network Systems and Services
(WONS 2006), France, (January 2006).
[15]P.E. Engelstad, O.N. Osterbo: The Delay
Distribution of IEEE 802.11e EDCA and
802.11 DCF, in the proceeding of 25th IEEE
International Performance Computing and
Communications Conference (IPCCC’06),
(April 2006), USA.
[16]The
network
simulator
ns-2,
http://www.isi.edu/nsnam/ns/.
[17]Y. Xiao: Performance analysis of IEEE
802.11e EDCF under saturation conditions,
Proceedings of ICC, Paris, France, (June 2004).
[18]V. Kanodia, C. Li: Distribted Priority
Scheduling and Medium Access in Ad-hoc
Networks”, ACM Wireless Networks, Volume
8, (November 2002).
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
16
Impact of Node Density on Cross Layer Design for Reliable Route Discovery
in Mobile Ad-hoc Networks
B.Ramachandran
S.Shanmugavel
Dept. of Electronics & Communication Engg.
S.R.M. University
Chennai – 603 203
profbram@yahoo.com
Dept. of Electronics & Communication Engg.
Anna University
Chennai – 600 025
ssv@annauniv.edu
Abstract :
The mobile nature of nodes and dynamic
topology of Mobile Ad-hoc Networks (MANETs) lead
to route failures and requiring the transmission of
control packets. It is important to reduce the number of
control packets to save resources and to improve the
overall performance of the network. Ad-hoc Ondemand Distance Vector (AODV) is appealing as an
efficient on demand routing protocol because of low
routing overhead and high performance. However,
AODV is not robust against topology variations as it
uses weak links due to long hops introduced by shortest
path metric. In this paper we propose a mobility
adaptive cross layer design to enhance the performance
of AODV routing protocol by establishing stable
routes. The adaptive decision making according to the
speed of mobile nodes on Route Request (RREQ)
packet forwarding results in stable routes. We also test
the impact of node density in the network on our
algorithm, to tell, when to invoke the our cross layer
design in mobile ad-hoc networks. To demonstrate the
efficiency of our protocol and its impact on network
connectivity, we present simulations using network
simulator, GloMoSim.
Keywords: Mobile Ad-hoc Networks, AODV, Routing
Overhead, Stable Route, and Cross Layer Design.
AODV and DSR send control packets only when route
discovery or route maintenance is done. When a route
is created or repaired, the control packets, particularly
RREQ packets flooded by source is network wide
broadcast. Moreover, the number of control packets
increased rapidly with network size and topology
changes.
The primary goal of an ad-hoc network
routing protocol is correct and efficient route
establishment between a pair of nodes so that messages
may be delivered in a timely manner.
Route
construction should be done with a minimum of
overhead and bandwidth consumption. The on-demand
routing protocols create route only when desired by the
source node. When a node requires a route to a
destination, it initiates a route discovery process within
the network. This process is completed once a route is
found or all possible route permutations have been
examined. Once a route has been established, it is
maintained by a route maintenance procedure or until
the route is no longer desired. The Ad-hoc On-Demand
Distance Vector routing protocol builds on the
Destination Sequenced Distance Vector (DSDV)
algorithm. It is an improvement on DSDV because it
typically minimizes the routing load by creating routes
on a demand basis.
AODV [2] is a pure on-demand route
acquisition system, since node that are not on a
selected path do not maintain routing information or
participate in routing table exchanges. When a source
node desires to send a message to some destination and
does not already have a valid route to that destination,
it initiates a “route discovery” process to locate the
destination. It broadcasts a route request packet to its
neighbours, which then forward to their neighbours and
so on, until either the destination or an intermediate
node with a “fresh enough” route to the destination is
located. During the process of forwarding the RREQ,
the intermediate nodes record in their route tables the
address of the neighbor from which the first copy of
the broadcast packet is received thereby establishing a
reverse path. If additional copies of the same RREQ
are later received, these packets are discarded. Once
the RREQ reaches the destination or an intermediate
node with a fresh enough route, the destination /
I. Introduction
Recent growing interest on potential
commercial usage of MANETs has led to the serious
research in this energy and bandwidth constrained
network. It is essential to reduce control packet
overhead as they consume resources. Routing in
MANETs is non trivial. Since mobile nodes have
limited
transmission
capacity,
they
mostly
intercommunicate by multi-hop relay. Multi-hop
routing is challenged by limited wireless bandwidth,
low device power, dynamically changing network
topology, and high vulnerability to failure and many
more. To meet those challengeous, many routing
protocols have been proposed for MANET [1]. They
are categorized as proactive and reactive protocols.
Proactive protocols such as DSDV periodically send
routing control packets to neighbors for updating
routing tables. Reactive routing protocols such as
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
17
intermediate node responds by a unicast route reply
(RREP) packet back to the neighbor from which it first
received the RREQ. (The route maintenance process
and other details of AODV are not considered here as
they are out of scope of this paper).
AODV prefers longer hops to form shortest
path, which in turn makes route with weaker links. The
presence of node mobility may induce route failures
(link failures) frequently. Many studies have shown
that the on demand approach is relatively quite
efficient under a wide range of scenarios. But when
seen in isolation, route discovery component is the
major bottleneck in on demand protocols. Since route
discovery is done via network wide flooding, it incurs
significant routing overhead and eats greater network
resources. Actually, the longer distance between
intermediate nodes on the route rises route maintenance
cost, reduces the packet transmission rate (due to
increased packet loss), and induces frequent route
failures [3].
In our previous work, we proposed a cross
layer design extension to AODV in order to form stable
routes. It reduces route failures and hence, keeps
routing overheads as low as possible, at the cost of
lengthy routes with more hops. In this paper, we go
further in enhancing AODV performance, by using
mobility based adaptive cross layer design to optimize
the trade off between route stability and number of
hops. Our objective is to form reliable routes in order
to reduce number of routing control packets, and thus
conserving network resources.
The proposed mobility adaptive cross layer
design couples the route discovery process with
physical layer related received signal strength
information and speed of mobile nodes to built stable
and optimum routes. As these constraints on received
signal strength and node speed will certainly have an
impact on network connectivity, we also study the
suitability of our algorithm under various node density
levels. The remainder of this paper is organized as
follows. In section II, we present the related work and
emphasize the need for cross layer design. Section III
describes the proposed mobility adaptive cross layer
algorithm. The simulation model, results and analysis
are presented in section IV. Finally we conclude our
discussion in section V.
II. Related Work
As an optimization for the current basic
AODV, in [4], a novel stable adaptive enhancement for
AODV routing protocol is proposed, which considers
joint route hop count, node stability and route traffic
load as a route selection metric. A QoS routing
protocol based on AODV to provide higher packet
delivery ratio and lower routing overheads using a
local repair mechanism is proposed in [5]. The received
signal strength changing rate is used to predict the link
available time between two nodes to find out a
satisfying routing path in [6], which reports
improvement in route connection time. In [7], route
fragility coefficient (RFC) is used as routing metric, to
cause AODV to find a stable route. Mobility aware
agents are introduced in ad-hoc networks and Hello
packets of AODV protocol is modified in [8] to
enhance mobility awareness of node to force it to avoid
highly mobile neighbor nodes to be part of routes and
ultimately to reduce the re-route discovery. On
receiving the Hello Packet with GPS co-ordinates of
the originator, mobility agent compares them with
previous ones and hence has awareness about the
mobility of the originator with references to itself.
In [9], an AODV based protocol which uses a
backbone network to reduce control overhead is
proposed. The destination location is given by GPS and
transmitted to source by the backbone network to limit
the route search zone. But formation of an additional
backbone network and GPS enabled service are extra
burden for infrastructure-less ad-hoc network
implementation. In order to cope with problems such as
the poor performance of wireless links and mobile
terminals including high error rate, power saving
requirements and quality of service, a protocol stack
that considers cross layer interaction is required [10].
Multi-hop routing, random movement of the
nodes and other features unique to ad-hoc networks
results in lots of control signal overhead for route
discovery and maintenance. This is highly
unacceptable in bandwidth-constrained ad-hoc
networks. Usually the mobile devices have limited
computing resources and severe energy constraints.
Currently ad hoc routing protocols are researched to
work mainly on the network layer. It guarantees the
independency of the network layer. However each
layer needs to do redundant processing and
unnecessary packet exchange to get information that is
easily available to other layers. This increases control
signals resulting in wastage of resources such as
bandwidth and energy. Due to these characteristics,
there is lot of research work happening in the
performance optimization of ad-hoc networks.
However, most of the research works are based on
optimization at individual layer. But optimizing a
particular layer might improve the performance of that
layer locally but might produce non-intuitive side
effects that will degrade the overall system
performance. Hence optimization across the layers is
required through interaction among layers by sharing
interlayer interaction metrics [11]. By using cross layer
interaction, different layers can share locally available
information. This is useful to design and standardize an
adaptive architecture that can exploit the interdependencies among link, medium access, networking
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
18
and application protocols. The architecture where each
layer of the protocol stack responds to the local
variations as well as to the information from other
layers is a major challenge [12].
Cross layer interaction schemes that can
support adaptability and optimization of the routing
protocols can discover and maintain the routes based
on current link status, traffic congestion, signal strength
etc. Usually routing layer is not concerned with signal
strength related information handling. Lower layer
takes care of signal strength related issues. Signal
strength can be useful to know the quality of link to
select for best effort packet forwarding and to achieve
power conservation [13]. Only the link with signal
strength above the threshold value can forward the
packet. Routing algorithm can exploit signal
characteristics related information for such benefits.
In the previous work on Reliable AODV [14],
we used signal strength information as interlayer
interaction parameter. The strength (received power) of
RREQ broadcast packet is passed to the routing layer
by the physical layer. In the routing layer the signal
strength is compared with a pre-defined threshold
value. If the signal strength is greater than the
threshold, the routing layer continues the route
discovery process. Otherwise the Reliable AODV
drops the RREQ packet. This leads to formation of
routes with strong links where adjacent nodes are well
within the transmission range of each other. So, even
when the nodes are moving, the probability of route
failure due to link breakages would be less with
Reliable AODV, compared to the existing Basic
AODV. The threshold value is set suitably with
reference to the nodes’ transmission power which
dictates the transmission range. The essence of
Reliable AODV is illustrated in Fig.1 where the node
A sends a RREQ which is received by its neighbors B
and C. As the received signal strength at node B
exceeds the threshold, it forwards the RREQ but the
node C drops the RREQ because it is close to the
transmission range boundary of node A and hence has
a weak link to node A.
The fixed threshold value used is independent
of speed of mobile nodes and it may not be justified to
low speed nodes. Hence, in this new adaptive cross
layer design, we propose adaptive decision making of
RREQ forwarding in accordance with speed of mobile
nodes which is discussed in the following section.
III. Mobility Adaptive Cross Layer Design
Routing protocol may let route / link failure
happen which is detected at MAC layer by
retransmission limits, but dealing with route failure in
this reactive manner results in longer delay,
unnecessary packets loss and significant overhead
when an alternate new route is discovered. This
problem becomes more visible especially when mobile
nodes move at high speed where route failure is more
probable due to dynamic topology changes and
negative impact of control packet overhead on network
resources utilization is of more significance. We
emphasize that routing should not only be aware of, but
also be adaptive to node mobility. Hence we propose
mobility adaptive cross layer design.
In this cross layer design a node receiving
signal, measures its strength and passes it from
physical layer to routing layer. We also assumed that
information about speed of the node is available to it.
Hence the signal strength, when receiving RREQ
packet which is a MAC broadcast, is passed to routing
layer along with the speed information of the node. The
AODV routing protocol’s route discovery mechanism
is modified to use the above two parameters in making
a decision on forwarding / discarding the RREQ
packet.
The received signal strength is measured and
used to calculate the distance between the transmitting
and receiving nodes. The two ray propagation model is
considered, where the loss coefficient value used is 2
as the maximum transmission range (dmax) of nodes is
350 meters which corresponds to 10dBm transmission
power. Hence the received signal strength can be
expressed as
Pr = Pt (λ/ 4πd)2
(1)
Where, Pt - Transmission Power
λ - Wavelength in meters
and d - Distance between transmitting and
receiving nodes
Also the unity gain omni directional
transmitting and receiving antennas are considered.
When the RREQ packet is presented with
received signal strength information to the AODV
implementation of the node, it calculates its distance
from transmitting node using,
d = Sqrt (Pt / Pr) * (λ / 4π)
(2)
Next, the receiving node calculates its
distance to the transmission range boundary of the
Fig. 1 Reliable AODV
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
19
transmitting node using the known maximum
transmission range (dmax) as,
db=dmax– d
(3)
The minimum time needed for a node to go
out of the transmission range boundary of the
transmitting node depends its distance from the
boundary and the speed as given below.
tb = db / Speed
(4)
If the source specifies a minimum route lifetime (tl), in its RREQ packet, any intermediate node
receiving that packet can calculate its safe distance
from transmission range boundary using its speed
information as
ds = tl * Speed
(5)
It is now possible to the node to make a
decision on forwarding the RREQ. That is, the decision
rule inserted in AODV route discovery mechanism is,
{If (db ≥ ds),
then forward RREQ
else drop RREQ }.
(6)
Hence the route discovery mechanism of
AODV routing protocol is made adaptive to the node
speed, which leads to the formation of more stable
(reliable) routes. The parameter tl, the minimum route
life-time, is application specific.
This adaptive algorithm will certainly reduce
the hop count and hence the average end-to-end delay
of data packets than those incurred with fixed signal
strength threshold based RREQ processing. To show
the efficiency of our new adaptive algorithm,
simulation results are presented in the next section.
standard with RTS / CTS extension and provide link
layer feedback to routing layer. The CBR traffic of 4
packets per sec, with 512 bytes packet size is used.
There are two randomly chosen source-destination
pairs and each source generates 4200 packets.
Simulations are run for 1200 seconds and each data
point represents an average of at least four runs with
different seed values.
Identical mobility and traffic scenarios are
used across the three protocol variants. The fixed signal
strength threshold used in AODV-Fixed variant is 78dBm whereas AODV-Adaptive used received signal
strength and speed of mobile nodes passed from
physical layer through cross layer interaction. The
minimum route life-time requirement is set as 4
seconds. We used the following five parameters to
evaluate the performance of the protocol variants: 1)
Number of routes selected (implies route failures), 2)
Number of RREQ packets transmitted (counted hopby-hop basis), 3) Packet delivery ratio, 4) Number of
Hops and 5) Average end-to-end delay.
IV. Simulation Model and Result Analysis
The simulation for evaluating the problem is
implemented within the GloMoSim library [15].
GloMoSim provides a scalable simulation environment
for wireless network systems. It is designed using the
parallel discrete event simulation capability provided
by PARSEC, a C based simulation language developed
by parallel computing laboratory at University
California at Los Angels, for sequential and parallel
execution of discrete event simulation models. The
simulation area is 1000 x 1000 square meters size,
where nodes are placed uniformly. The transmission
power and receiver threshold level of nodes are 10dBm
and -81dBm respectively. The random way point
mobility model is used. In this model, each node
chooses a random destination and move towards that
destination with a random speed chosen between the
minimum and maximum values specified. The node
then waits there for the specified pause time and
continues it movement as described above. The
bandwidth of shared wireless channel is assumed to be
2 MHz. The physical layer employs two ray
propagation model. The nodes use the distributed coordination function of IEEE 802.11 WLAN [16]
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
Fig 2. Route Failure Frequency
Fig 3. Routing Overhead
20
Fig 4. Reliable Packet Delivery
Fig 5. Average Path Length
In the experiment to study the effect of
mobility, the maximum speed of nodes is varied
between 0-25 m/sec, where 49 nodes are used in the
simulation. Fig 2 shows the number of routes used by
three protocol variants. The Fixed and Adaptive
AODVs result in reduced number of routes selected,
i.e. reduced number of route failures that reflect the
formation of reliable (more stable) routes. Hence the
number RREQ sent by nodes also got reduced as
shown Fig 3. We could also infer that usage of fixed
threshold value leads to reduced connectivity,
particularly at very low speed ranges, which make
AODV-fixed to suffer with increased RREQ broadcast
during route search process. The improvement in
packet delivery ratio is reflected in Fig 4.
Both AODV-Fixed and AODV-Adaptive
variants outperform AODV-Basic, because of stable
route formation. But this improvement is at the cost of
increased number of hops, which is shown in fig 5.
This figure also highlights the need of mobility
adaptive route discovery which optimizes routes with
speed information and helps in reducing the average
end-to-end delay of data packets significantly than
those incurred with fixed threshold usage. Fig 6 shows
the delay performance of three protocol variants.
Further, in order to explore the impact of node
density on the proposed new cross layer algorithm, we
conducted another experiment, in which the node
density is varied between 16 and 64 nodes in 1000 x
1000 sqm area. The maximum speed of mobile nodes
is set as 25 m/sec. The imposed signal strength
threshold and minimum route life-time constraints
reduce network connectivity, which is shown in Fig 7.
The number of routes used by AODV-Fixed variant is
relatively low at very low node density, which does not
imply formation of stable routes but reflects scarcity of
network connectivity. The repeated search for
connectivity increases the RREQ broadcasts in AODVFixed variant which is presented Fig 8. But, the
performance of AODV-Adaptive excels in this regard.
Hence, the control packet overhead is under control
even in lightly densed network with our adaptive
algorithm.
The packet delivery ratio suffers when these
constraints are enforced in lightly densed networks.
The improvement is visible only when network
density increases beyond a particular level as shown in
Fig 9. So, it is clear that cross layer design using signal
strength threshold is useful and improves network
performance in highly densed networks where
redundantly available links ensure required network
connectivity. Where as the new adaptive algorithm
makes a trade off in this regard between the basic and
fixed AODV variants. Hence when to invoke cross
layer algorithm is also an important design issue.
Fig 6. Delay Performance
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
21
V. Conclusion
Fig 7. Node density vs Route Failures
Fig 8. Node Density vs Routing Overhead
Fig 9. Node Density vs PDR
We observe that the cross layer
AODV with fixed threshold reduces the number of
route failures and routing overheads, at the cost of
increased hop counts and average end-to-end delay.
Certainly the proposed mobility adaptive algorithm
for route discovery optimizes the above trade off. The
AODV-Adaptive variant reduces number of hops and
delay to a greater extent and brings them closer to
those of AODV-Basic variant. It is important to note
that both cross layer AODV variants improve the
packet delivery ratio, but at the cost of slightly
increased end-to-end delay. However, the reduced
route failures and routing overheads obtained are very
attractive for mobile ad-hoc networks which are highly
resources constrained. Finally, it is worth to note that
impact on network connectivity due to signal strength
threshold enforcement is serious in lightly densed
networks and hence, the proposed cross layer design is
well suited for highly densed networks.
References:
[1] Mohammad Ilyas, “The Hand Book of Ad-hoc
Wireless Networks”, CRC Press, 2003.
[2] C E Perkins, E M Royer and S R Das, “
Adhoc On-demand Distance Vector Routing
Protocol”, IETF RFC 3651, July 2003.
[3] B.Awerbuch, D.Holmer and H.Rubens, “High
Throughput Route Selection in Multi-rate Ad-hoc
networks”, in Proc. of First working Conf. on
Wireless On-demand Network Systems, 2004.
[4] X.Zhong et al., “Stable Enhancement for AODV
Routing Protocol”, in proc. of 14th IEEE Conf. on
Personal,
Indoor
and
Mobile
Radio
Communication, vol 1, pp 201-205,2003.
[5] Y.Zhang and T.A. Gulliver, “Quality of Service for
Ad-hoc On-demand Distance Vector Routing”, in
proc.of IEEE International Conf. on Wireless
Mobile
Computing,
Networking
and
Communications, vol 3, pp 192-193, 2005.
[6] R.S.Chang and S.J.Leu, “Long-lived Path Routing
with Received Signal strength for Ad-hoc
Networks”, in proc. of 1st International
Symposium on Wireless Pervasive Computing,
2006.
[7] G.Quddus et al., “Finding A Stable Route Through
AODV by Using Route Fragility Coefficient as
Metric”, In proc. of International Conf. on
Networking and Services, pp 107- 113, 2006.
[8] M.Idrees et al., “Enhancement in AODV Routing
Using Mobility Agents”, in proc. of IEEE
Symposium on Emerging Technologies, pp 98102, 2005.
[9] D.Espes and C.Teyssie, “Approach for Reducing
Control Packets in AODV-based MANETs”, in
proc. of 4th European Conf. on Universal Multi-
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
22
service Networks, pp 93-104, Toulouse, France,
2007.
[10] G Carneiro et al., Cross Layer Design in 4G
Wireless Terminals, IEEE Wireless Commn., vol
11, no 2, pp 7-13, April 2004.
[11] T S Rappaport, et al., Wireless Commn: Past
event and a future perspective,
IEEE
Communication Magazine, vol 40,
no 5 (50th
Anniversary ), pp 148-161, 2002.
[12] V.Srivastava and M.Motani, “Cross Layer design
: A Survey and The road Ahead”, IEEE
Communication Magazine, vol. 43, no.12, pp 112119, dec2005.
[13] B. Ramachandran and S. Shanmugavel, “A
Power
Conservative Cross Layer Design for
Mobile
Ad-hoc
Networks” , Information
Technology Journal, vol 4, no 2, pp 125-131,
2005.
[14] B.Ramahandran and S.Shanmugavel, “Reliable
Route Discovery for Mobile Ad-hoc Networks”,
in proc of IETE International Conf. on Next
Generation Networks, pp CP 26.1-26.6, Mumbai,
India, 2006.
[15] GloMoSim User Manual,
http://pcl.cs.ucla.edu/project/glomosim
[16] LAN IEEE Std 802.11, Part 11:
Wireless
MAC & PHY Layer Specifications, 1999.
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
23
A FRAMEWORK FOR AN AGGREGATED QUALITY OF SERVICE IN
MOBILE AD HOC NETWORKS
Ash Mohammad Abbas, Khaled Mohd Abdullah Al Soufy
Department of Computer Engineering
Zakir Husain College of Engineering and Technology
Aligarh Muslim University
Aligarh – 202002, India
am.abbas@amu.ac.in
ABSTRACT
Providing quality of service in an ad hoc network is a challenging task. In this
paper, we discuss a framework for user perceived quality of service in mobile ad
hoc networks. In our framework, we try to aggregate the impact of various quality
of service parameters. Our framework is flexible and has a provision of providing
dynamic quality of service. Further, an application may adapt from the required
quality of service to that which can readily be provided by the network under a
stressful environment. Our framework may adapt to the QoS desired by a source
based on user satisfaction.
Keywords: User perceived quality of service, quality of service aggregation,
dynamic and adaptive, ad hoc networks.
1
INTRODUCTION
An ad hoc network is a cooperative engagement
of a collection of mobile devices without the
required intervention of a centralized infrastructure
or a centralized access point. In the absence of
centralized infrastructure, an ad hoc network may
provide a cost-effective and a cheaper way of
communication. Applications of an ad hoc network
include battlefield communications, disaster recovery
missions, convention centers, online classrooms,
online conferences, etc.
In an ad hoc network, there are no separate
routers. As a result, the devices need to forward
packets of one another towards their ultimate
destinations. The devices possess limited radio
transmission ranges, therefore, routes between any
two hosts are often multihop. The devices are often
operated through batteries whose power depletion
may cause the device failure and/or associated link
failures. Further, nodes may move about randomly
and thus the topology of the network varies
dynamically.
There can be applications of an ad hoc network,
where users expect a given level of quality of service
(QoS) to be provided by the network. These
applications may include multimedia streaming,
exchanging geographical maps, etc. However, the
requirements and the expectations of users about the
level of the QoS to be provided by an ad hoc network
may not be as high as those in case of a wired
network or a wireless network that possesses a
centralized infrastructure.
Provision of QoS in an ad hoc network is a
challenging problem. The challenge is posed by the
characteristics of ad hoc networks. In an ad hoc
network, one cannot have a solution that either relies
on extensive amount of computations or consumes a
significant amount of power and energy because the
resources of the devices used to form such a network
are scarce. Note that an ad hoc network is an on the
fly network and should be self organizing in nature.
Frequent node and link failures together with
mobility of nodes give rise to a highly dynamic
topology of the network. The dynamically varying
topology of the network makes it difficult to provide
any hard QoS guarantees. However, as the users are
aware that they are part of an ad hoc network,
therefore, instead of expecting hard QoS guarantees,
users may expect a soft QoS.
In situations, when an end-user can tolerate
variations in the QoS, the user should have a
flexibility to change the specifications of QoS
parameters depending upon the extent of satisfaction
with the QoS provided by the network. However, not
much work is done in this area. In [6], a framework
for QoS aware service location is presented in the
context of an ad hoc web server system. Therein, the
authors assign the priorities in integers to QoS
parameters. In [11], an adaptive QoS routing
protocol is presented by rerouting the packets that
faced QoS violations.
The rest of this paper is organized as follows. In
Section 2, we discuss the framework for an
aggregated quality of service. In Section 3, we
discuss how to adapt quality of service based on user
feedback. In Section 4, we describe methods of QoS
aggregation. Section 5 contains results
and
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
24
Table 1: Symbols used for QoS parameters in AQS.
Weight
Min/Max
Value
Tolerance
Limit (%)
Parameter
Weight
Min
Max
End-to-End
Delay
Delay Jitter
wD
Dmax
δD
0.40
3.0
-
wJ
J max
δJ
Bandwidth
wB
Bmin
δB
0.20
0.25
-
1.0
3.0
wR
Rmin
δR
0.2
2.0
-
Packet Delivery
Ratio
Route Lifetime
0.80
2.0
wL
Lmin
δL
End-to-End
Delay
Delay Jitter
Bandwidth
Packet
Delivery
Ratio
Route
Lifetime
Tolerance
Limit (%)
1.0
10
2.0
discussions. Finally, the last section is for conclusion
and future directions.
2
Table 2: Example 1 – Values of QoS parameters.
Parameter
A FRAMEWORK FOR AN AGGREGATED
QOS
In this section, we describe a framework for an
aggregated QoS. We call our framework as an
aggregated QoS (AQS) framework because it
aggregates the effect of many QoS parameters or
metrics. In our framework, we consider a set of QoS
parameters such as end-to-end delay, delay jitter,
bandwidth, packet delivery ratio, route lifetime 1 .
Our aggregation mechanism consists of
assigning importance or weights to each of the
parameters discussed in the previous subsection, and
then computing a factor of aggregation. Let us first
consider assignment of importance or weights 2 . To
each of these parameters, we assign a weight
wi , 0 ≤ wi ≤ 1 , in such a fashion so that
n
∑ w =1
i
(1)
0.1
0.05
-
In what follows, we define aggregated QoS to
incorporate the effect of the parameters mentioned
above.
Definition 1: Let there be n QoS parameters
P1 , P2 ,..., Pn . Let Pk ,1 ≤ k ≤ n for bandwidth be
defined as follows.
PBW =
FileSize
BW
(2)
where, FileSize denotes the size of file that is sent
using the particular bandwidth.
Let Pk ,1 ≤ k ≤ n for packet delivery ratio be
defined as follows.
PPDR = RΔ
(3)
where, Δ is the duration of time for which the
particular packet delivery ratio 3 is desired.
Having defined the constituent parameters in
time units, we now define a parameter called
Weighted Aggregate QoS (WAQ) as follows.
i =1
where wi represents the relative importance (or the
weight) of parameter i. If there are n parameters, and
each parameter i is assigned a weight 1/n then all
QoS parameters are equally important. If
wi > w j , i ≠ j , then parameter i is said to be
relatively more important than parameter j.
The notation used to represent information
related to QoS parameters is as follows. We use the
following symbols to represent QoS parameters− D:
end-to-end delays, J: delay jitter, B: bandwidth, R:
packet delivery ratio, L: route lifetime. For each of
these parameters, the symbol w is used to represent
relative importance or weight, prefix Δ is used to
represent tolerance limit, and subscripts max/min are
used to represent either the maximum or minimum
value of the parameter. This notation is summarized
in Table 1.
_________________________
1
There can be long list of QoS parameters, however, we consider
only the parameters defined above.
2
Note that it is a different issue that who assigns the weights to
the parameters and shall be discussed later in this paper.
{( P
+∑ {( P
WAQ = ∑
min
i
max
j
}
)w }
+ TLi ) wi
i
+ TL j
(4)
j
j
where 1 ≤ i, j ≤ n, i ≠ j , and Pi min is the value of ith
parameter whose minimum value is specified, and
Pjmax is the value of jth parameter whose maximum
values is specified. Further, TLi and TL j are values
of the corresponding tolerance limits for ith and jth
parameters.
Note that the unit of aggregation parameter,
WAQ, is time. Further, it will have positive values
and its values may come out to be greater than 1
depending the values of its constituent QoS
parameters. In what follows, we define an
aggregation factor, whose value lies between 0 and
1.
_________________________
3
In this way, we have converted all parameters to a single unit
i.e. time. This is done so that we are able to aggregate the effect of
different parameters on the QoS.
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
25
Table 3: Example 2 – Values of QoS parameters.
Figure 1: Adapting QoS through user feedback.
Definition 2: Let the QoS parameters,
Pi , Pjmax , and their tolerance limits, TLi , and TL j ,
min
be defined as in Definition 1. We define a factor that
we call Weighted Aggregate QoS Factor (WAQF) as
follows.
WAQ
WAQF =
min
∑ ( Pi + TLi ) + ∑ ( Pjmax + TL j )
i
j
(5)
where, WAQ is given by (4) as part of Definition 1.
It is worth mentioning that WAQF has no unit as it is
simply a ratio bearing the units of time in both the
numerator as well as the denominator of the
expression defining it.
In what follows, we discuss an example
incorporating different weights and the values of the
QoS parameters mentioned above.
Example 1: Let the weights, min/max values,
and tolerance limits assigned by the source for
different QoS parameters be as given in Table 2.
In Example 1, the end user has given the highest
relative importance by assigning a weight 0.40 to the
end-to-end delay. The maximum value of the delay is
specified to be 3.0 milliseconds, however, the user
may accept upto a tolerance limit 1.0%. This means
that user may accept a value of the end-to-end delay
upto 3.01 milliseconds. The second parameter is
delay jitter. For that the user has specified a weight
of 0.20, the maximum value to be 0.2 milliseconds,
and a tolerance limit of 1.0%. This means that the
user can accept the value of delay jitter upto 0.202
milliseconds. The third parameter is bandwidth
reserved for the flow. The user has specified a
weight of 0.25, the minimum value of bandwidth to
be 2.0 Mbps, and a tolerance limit of 3.0%. In other
words, the user may accept the bandwidth upto 1.96
Mbps. The fourth parameter, packet delivery ratio is
assigned a weight of 0.10, the minimum value 80%
and a tolerance limit of 2.0%. This means that the
user may accept the packet delivery ratio upto 78%.
The last parameter is route lifetime 4 that is assigned
a weight of 0.05, the minimum value 10
milliseconds, and a tolerance limit of 2.0%. In other
words, the user may accept the value of the route
failure time upto 9.8 milliseconds.
____________________________
4
There can be a debate whether one should consider the
minimum value or the maximum value of route failure time. The
user would prefer to specify the minimum value of route failure
time, so that there are no route failures during packet
transmissions. However, from the point of view of network, one
would like to maximize the value of route failure time.
Parameter
Weight
Min
Max
End-to-End
Delay
Delay Jitter
Bandwidth
Packet
Delivery
Ratio
Route
Lifetime
0.30
2.0
-
Tolerance
Limit (%)
1.0
0.10
0.45
0.1
0.3
2.0
-
0.80
1.0
3.0
2.0
0.05
-
10
2.0
Table 4: Default values of QoS parameters.
Parameter
Value
End-to-End Delay
Delay Jitter
Bandwidth
Packet
Delivery
Ratio
Route Lifetime
5.00
0.01
10.0
0.90
Tolerance
Limit (%)
2.0
2.0
2.0
2.0
10.0
2.0
Table 5: Sets of weights assigned to QoS
parameters.
Set No.
S1
S2
S3
S4
S5
Set of Weights
0.6 0.1 0.1 0.1
0.1 0.6 0.1 0.1
0.1 0.1 0.6 0.1
0.1 0.1 0.1 0.6
0.1 0.1 0.1 0.1
0.1
0.1
0.1
0.1
0.6
Table 6: The QoS parameter WAQ and the factor
WAQF as a function of bandwidth.
Bandwidth
1
2
3
4
5
6
7
8
9
10
WAQ
3.40964
3.30760
3.27359
3.25658
3.24638
3.23957
3.23471
3.23107
3.22824
3.22597
WAQF
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Table 7: The QoS parameter WAQ and the factor
WAQF as a function of FileSize.
FileSize
1
2
3
4
5
6
7
8
9
10
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
WAQ
3.22957
3.24638
3.26678
3.28719
3.30760
3.32801
3.34842
3.36883
3.38923
3.40964
WAQF
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
26
Table 8: The QoS parameter WAQ and the factor
WAQF as a function of Δ .
WAQ
WAQF
Δ
1
2
3
4
5
6
7
8
9
10
3.22597
3.40957
3.59317
3.77677
3.96037
4.14397
4.32757
4.51170
4.69477
4.87837
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Table 9: The QoS parameter WAQ and the factor
WAQF as a function of sets of weights.
Set of Weights
S1
S2
S3
S4
S5
WAQ
4.06298
1.61788
1.66400
2.07198
6.71298
WAQF
0.251892
0.100304
0.103163
0.128457
0.416184
Let the file size be 1 Mb, and the maximum
bandwidth be Bmax = X Mbps. As a result, 1/X
seconds is consumed in sending a file of the
specified size over the specified bandwidth. For a
variation or tolerance limit of Δ in a given
bandwidth X, the value of the parameter Pk for
bandwidth is
1
PBW =
(6)
.
X − ( X ×Δ)
Therefore, for a tolerance limit of 3.0% in the
value of bandwidth which is 2.0 Mbps (as shown in
Table 2 for Example 1), the value of PBW is
1
= 0.515463917. Further, in case of
2 − (2× 0.03)
packet delivery ratio which is given by (3), assume
that Δ be 1 time unit, so that PPDR is affected by the
packet delivery ratio only and that is R. For Example
1, the value of aggregation parameter, WAQ, comes
out to be 1.948065979. The value of denominator is
14.69946392. The value of the aggregation factor,
WAQF, comes out to be 0.132526328.
Note that in Example 1, the end-to-end delay is
assigned the highest weight or priority. This is an
example of delay-sensitive application. Depending
upon the weight assigned to a QoS parameter, there
can be other types of applications as well. For that
consider another example with different weights and
different values of corresponding QoS parameters.
Example 2: Let the weights, min/max values,
and tolerance limits assigned by the source for
different QoS parameters be as given in Table 3.
In Example 2, the bandwidth is assigned the
highest relative importance. This is an example of
bandwidth-sensitive application. The next relatively
important parameter is end-to-end delay. Other
parameters may not be so important for a particular
application, therefore, those are assigned relatively
low weights. For rest of the parameters, we assume a
similar scenario as in Example 1.
In Example 2, PBW =0.515463917. The value of
aggregation parameter, WAQ, comes out to be
1.447258763. The value of denominator is 15.233.
The value of the aggregation factor, WAQF, comes
out to be 0.095008124. Note that the aggregation
factor is 28.31% smaller as compared to that in
Example 1. The reason is that in case of WAQ, the
contribution of end-to-end delay component has
become half of that in Example 1, and the
contribution of delay jitter has also been decreased.
The total decrease of these two parameters is
approximately 0.6. The contribution due to
bandwidth has increased by a value of approximately
0.103. As a result, the net effect is a decrease by a
value of approximately 0.5 in the value of WAQ.
The aggregation factor, WAQF, has changed
accordingly.
In what follows, we discuss a framework for
adapting QoS through user feedback.
3
ADAPTIVE QOS THROUGH USER FEEDBACK
Fig. 1 shows a framework for adapting QoS
using user feedback. The steps in our framework are
as follows.
• The source or the user specifies the values
of different QoS parameters with their
minimum/maximum values and tolerance
limits.
• The QoS parameters alongwith their
respective values are given to the QoS
Manager.
• If QoS Manager receives QoS parameters
for a packet of the flow for the first time, it
sorts the parameters according to their
relative importance or weights. After, that
the QoS Manager calls a protocol or a
method to take care of the parameter that is
relatively the most important. After that it
takes measures to take care of the next
relatively important parameter (if possible),
and so on.
• The packet is then delivered to the
destination according to its QoS
specifications and the source is informed
accordingly.
• If the source or the user is satisfied with the
QoS of the packet delivered, the next packet
is sent to the destination.
• If the source is not satisfied with the QoS of
the packet delivered, it informs the QoS
Manager about the change it wishes to have
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
27
in the QoS. The QoS Manager tries to
adjust the QoS parameters accordingly.
Note that the source specifies the values of QoS
parameters, their minimum and/or maximum values,
relative importance, and tolerance limit for each
parameter. As mentioned above, QoS Manager sorts
the parameters according to their relative importance.
The QoS Manager calls appropriate methods or
protocols for providing the QoS. The fact that which
protocol has to be called first depends upon the
relative importance of the parameters. Depending
upon the relative importance of QoS parameters,
different methods are required to be called.
Further, the functionality of QoS Manager 5
resides at every node in the network. As mentioned
earlier, we confine to wireless networks using 802.11
and the nodes in that are operating in ad hoc mode.
However, the same can be extended with some
modifications to other types of wireless networks as
well. There might be a question that in ad hoc
networks, nodes have limited resources so why do
we have QoS with user feedback. It should be noted
that provision of user feedback in our approach
should require little more computations. In general,
the energy and power consumption during
transmissions is significantly larger than that of
computations. We believe that the provision of user
feedback shall not consume a significant amount of
energy rather it will add few more computations and
would be feasible with current technological trends.
In what follows, we describe what are the
methods and protocols that may need to be called for
a QoS specification.
4
METHODS FOR AGGREGATED QOS
In this section, we present methods and protocols
that the QoS Manager needs to call for providing the
desired QoS. Note that the input to the QoS Manager
is a set of parameters with their respective values,
tolerance limits, and relative importance. The first
and the foremost task that the QoS Manager needs to
perform is sorting of the QoS parameters according
to their relative importance or weight. Another set of
input is the user feedback, in case when some form
of QoS has been provided to the flow but the user is
not satisfied with the QoS. Once the QoS parameters
have been sorted, appropriate methods and/or
protocols are required to be called, to provide the
given level of QoS.
Algorithm 1 describes what are the actions that
are taken by the QoS Manager. When a sources
needs to send packets of a flow, it sends a
qosEnquiry packet to the QoS Manager along with
the specification of QoS parameters. The QoS
________________________
5
Manager extracts the values of QoS parameters,
tolerance limit, and relative importance. If the sum of
all relative importance or weight is greater than 1,
the QoS specifications are referred back to the
source. Otherwise, the QoS Manager marks whether
a parameter is “insignificant” or “significant”.
We assume that if the weight assigned to a
particular parameter is less than 1/(2k), where k is the
number of QoS parameters, then the parameter is
insignificant, otherwise it is significant. If a
parameter is insignificant, the QoS Manager need
not bother about it. For all significant parameters, the
weights of the parameters are arranged in descending
order. The first parameter in this order is the most
significant parameter. An appropriate protocol is
called to provide QoS for the most significant
parameter so obtained. A qosReply packet is sent to
the source by the QoS Manager. Upon receiving a
qosReply packet, the source sends a TestQoS
packet. The TestQoS packet is delivered to the
destination and a flag named statusPi is set to be
“done” for the QoS parameter, Pi . The source, if
satisfied sends the extent upto that he/she is satisfied.
If the satisfaction of the source falls below 50%, the
source shall specify his/her desired QoS parameters
again, and the above process shall continue. When
the user is satisfied by the QoS provided to TestQoS
packets, he/she will start sending actual packets.
The component QoS Manager is not only manages the QoS,
however, it is also responsible for other functions like resource
reservation, call admission control, and negotiating the QoS.
Figure 2: The QoS parameter WAQ and the factor
WAQF as functions of bandwidth.
Figure 3: The QoS parameter WAQ and the factor
WAQF as functions of FileSize.
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
28
__________________________________________________________________________________________
Algorithm 1: Marking of significant parameters by QoS Manager
__________________________________________________________________________________________
Let i ∈ enum P: {Delay, Jitter, Bandwidth, Delivery Ratio, Lifetime}, 1 ≤ i ≤ k , k =| P | , where |.| denotes the
cardinality. For parameter i, let wi : weight, Vi : value, δi : tolerance limit.
1: if UserSatisfactionPi ≤ 0.5 then
2:
statusPi = "notdone"
3:
Get wi , Vi , δi for all i such that statusPi = "notdone"
4:
if
∑ w > 1 then
i
i
5:
6:
7:
8:
9:
10:
11:
12:
13:
14:
15:
Refer back the QoS parameters to the source
1
else if 0 ≤ wi ≤
then
2k
Mark Pi : "insignificant"
else
Mark Pi : "significant"
end if
For all Pi that are marked "significant", arrange wi in descending order
For max( wi ) , call a protocol to provide the QoS for parameter Pi
Deliver the packet to destination
set stausPi = "done"
end if
___________________________________________________________________________
There is an issue about how long should one be
allowed so as not to waste time in setting up of the
desired QoS. This depends upon how many attempts
are being made for a QoS parameter. We limit these
attempt to 3 irrespective of the user satisfaction.
After third attempt, we assumed that the most
significant parameter is taken care of.
In what follows, we discuss some empirical
results.
5
Figure 4: The QoS parameter WAQ and the factor
WAQF as functions of Δ .
Figure 5: The QoS parameter WAQ and the factor
WAQF versus sets of weights.
RESULTS AND DISCUSSION
Let us first discuss some results pertaining to the
effect of aggregation of QoS parameters.
5.1 Effect of Aggregation
We have defined the weighted aggregation QoS
parameter, WAQ, and weighted aggregation QoS
factor, WAQF, in Section 2. For the results in this
subsection, the values of different QoS parameters
and their tolerance limits are shown in Table 4. The
default weight assigned to each QoS parameter is
equal and is 0.20. The default value of FileSize is 1
bandwidth unit (e.g. 1 Mbps, if the bandwidth is
expressed in Mbps). The default value of time
duration for which a desired packet delivery ratio is
needed, Δ is 1 time unit.
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
29
Table 6 shows the empirical values of the QoS
aggregation parameter, WAQ, and that of the QoS
aggregation factor, WAQF, as functions of
bandwidth. It is observed from Table 6 that the QoS
aggregation parameter, WAQ, decreases with the
increase in the bandwidth. The decrease in WAQ is
not linear (see Figure 2). The values of the QoS
factor remain constant. The reason is that for equal
weights assigned to individual QoS parameters, the
amount of increase in numerator and the
denominator of the QoS aggregation factor, WAQF
(as defined by (5), is almost the same. Hence,
WAQF remains the same and is equal to the weight
assigned to each individual QoS parameter.
Table 7 shows the empirical values of the QoS
aggregation parameter, WAQ, and that of the QoS
aggregation factor, WAQF, as functions of FileSize.
It is observed from Table 7 that the QoS aggregation
parameter, WAQ, increases with the increase in the
FileSize. The decrease in WAQ is linear (see Figure
3).
Table 8 shows the empirical values of the QoS
aggregation parameter, WAQ, and that of the QoS
aggregation factor, WAQF, as functions of Δ . We
mentioned earlier that Δ is the time duration for
which the specified packet delivery ratio is required.
It is observed from Table 8 that the QoS aggregation
parameter, WAQ, increases with the increase in Δ .
The decrease in WAQ is linear (see Figure 4). The
values of the QoS factor, WAQF, remain constant
for the reason mentioned above.
The sets of weights assigned to the QoS
parameters in the following order <end-to-end delay,
delay jitter, bandwidth, packet delivery ratio, route
lifetime> are shown in Table 5. It is observed that the
value of the QoS aggregation parameter, WAQ, and
that of aggregation factor, WAQF, are the largest for
the set of weights, S6, and is the smallest for the set
of weights, S2 (see Figure 5). The reason being that
in case of S6, the contribution of the largest valued
QoS parameter i.e. route lifetime is multiplied by the
largest weight among S6. However, the situation in
case of S2 is just reverse of that in S6. Note that the
next largest values of WAQ and WAQF are for the
set of weights, S2. In that case the contribution of the
next large valued parameter i.e. end-to-end delay is
multiplied by the largest weight among the set of
weights S1.
5.2 Effect of User Feedback
Recall that when the significant parameters are
found. The QoS Manager selects and calls
appropriate protocols depending upon the QoS
parameters. A qosReply packet is sent to the source
by the QoS Manager. Upon receiving a qosReply
packet, the source sends a TestQoS packet. The
TestQoS packet is delivered to the destination and a
flag named statusPi is set to “done” for the QoS
parameter, Pi . The source, if satisfied sends the
extent upto which he/she is satisfied. If the
satisfaction of the source falls below 50%, the source
shall specify his/her desired QoS parameters again,
and the above process shall continue. When the user
is satisfied by the QoS provided to TestQoS packets,
he/she will start sending actual packets.
The TestQoS packets are simply overheads 6 .
The number of these packets depends upon the
extent of user satisfaction 7 . However, since there is
a cost associated with user perceived QoS, therefore,
the number of attempts made by the user for a
desired level of QoS is restricted to 3. The user may
select any one level of QoS that suits to his/her needs
out of that provided by these attempts.
Note that we mentioned it earlier that nodes are
operating in ad hoc mode and the type of network we
are interested in, is supposed to use 802.11 standards.
A consequence of having user feedback is that some
of the energy is consumed by the TestQoS packets
which would have been used for some other useful
task. However, we would like to remind that these
packets are very small in size and that we have
limited these packets to only a few (say 3). Although,
there is some additional energy consumption,
however, that is the price to be paid to have user
feedback about the QoS. However, we believe that it
would not be too large to be afforded.
5.3 Probability of User Satisfaction
In order to evaluate the probability of user
satisfaction, let us assume a simple scenario in which
ps is the probability that the user is satisfied after an
attempt has been made, i.e. after sending a TestQoS
packet. The probability that the user is not satisfied
after sending a TestQoS packet will then be 1− ps .
Let us assume that each attempt is made
independently. Then, the probability that the user is
satisfied in k such attempts out of n attempts have
been made, will then be governed by
⎛ n⎞
n− k
PkUS = ⎜⎜ ⎟⎟⎟ psk (1− ps ) .
⎜⎝k ⎠⎟
(7)
The above equation is an expression of the
binomial distribution for Bernoulli trials. The
probability getting exactly one success (i.e. user is
________________________
6
There will be overheads for a protocol that is used to get the
desired level of QoS. However, those overheads will depend upon
the specific protocol.
7
Although, the level of user satisfaction is not a measurable
quantity, however, depending upon the feedback received by
letting them to answer a set of questions, one may be able to get a
feel of the level of user satisfaction. Either the number of
questions successfully answered by an end-user, or by some other
measure, may be taken as the user satisfaction. Therefore, let us
assume that a user satisfaction of 50% means that 50% of the
questions have successfully answered by the end-user.
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
30
satisfied in exactly one of the attempts) is given by
⎛3⎞
2
P1US = ⎜⎜ ⎟⎟⎟ p1s (1− ps ) .
⎜⎝1⎠⎟
(8)
We want that the user to be satisfied in at least
one of the attempt out of three attempts have been
made. The probability that the user is satisfied in
atleast one of the three attempts is given by
⎛ 3⎞
3− k
ψ = ⎜⎜ ⎟⎟⎟ psk (1− ps ) .
⎜⎝k ⎠⎟
(9)
For ps = 0.5 , ψ = 0.875 . It means that if the
probability of success (i.e. the probability that the
user is satisfied after an attempt) is assumed to be
0.5, then the probability of satisfaction of the user in
at least one of these attempts is 0.875. As mentioned
earlier, the user may select the QoS that fits best to
his/her needs, if he wishes to do so.
Note that, in this paper, wherever we referred to
the aggregation of QoS parameters, we mean the
aggregation of only those parameters whose
combined effect may be computed in a reasonable
amount of time (i.e. polynomial time). The
parameters that we considered in this paper are only
for the purpose of example. One has to see which
parameters can be computationally combined before
actually aggregating their effect. In case, one wishes
to see the effect of the parameters that cannot be
combined computationally, one may use a technique
called QoS filtering. In that, if one wishes to seek
QoS based on parameters ( A1 , A2 ,..., An ) . One should
first seek the QoS based on one of these parameters,
say A1 , and then on the resulting set of A1 , one
should seek QoS based on A2 , and so on. Further,
which parameter should be considered first or what
should be the order of parameters in the QoS filtering
will depend upon what is the relative importance of
the parameters considered.
6
CONCLUSION
Providing QoS in a mobile ad hoc network is a
challenging task due to inherent characteristics of
such a network. In this paper, we proposed a
framework for provision of QoS in a mobile ad hoc
network. Our contributions are as follows.
• We proposed that one can aggregate the
effect of QoS parameters depending upon
the importance or weights assigned to each
parameter.
• In our framework, we tried to incorporate
the level of user satisfaction about the QoS
provided by the network.
• We proposed that there can be a trade-off
between the QoS expected by the end-user
and the QoS that may be provided by the
network. However, we left it on to the enduser to decide about the trade-off depending
upon his/her requirements.
• We discussed overheads incurred in
adapting the QoS to the level of expectance
of the user.
In summary, we discussed a framework for an
aggregated and dynamic QoS based on user
satisfaction. Further validation of the framework
forms our future work.
7
REFERENCES
[1] S.C. Lo, G. Lee, W.T. Chen, J.C. Liu,
“Architecture for Mobility and QoS Support in
All-IP Wireless Networks”, IEEE Journal on
Selected Areas in Communications (JSAC), vol.
22, no. 4, May 2004.
[2] B. Li, L. Li, B. Li, K.M. Sivalingam, X.R. Cao,
“Call Admission Control for Voice/Data
Integrated Cellular Networks: Performance
Analysis and Comparative Study”, IEEE Journal
on Selected Areas in Communications (JSAC),
vol. 22, no. 4, May 2004.
[3] N. Passas, E. Zervas, G. Hortopan, and L.
Merakos, “A Flow Rejection Algorithm for QoS
Maintenance in a Variable Bandwidth Wireless
IP Environment”, IEEE Journal on Selected
Areas in Communications (JSAC), vol. 22, no. 4,
May 2004.
[4] Dutta, W. Chen, O. Altintas, H. Schulzrinne,
“Mobility Approaches for All-IP Wireless
Networks”, Proceedings of 6th World Multi
Conference on Systematics, Cybernetics and
Informatics (SCI), July 2002.
[5] M. Ghaderi, R. Boutaba, “Towards All-IP
Wireless Networks: Architectures and Resource
Management
Mechanism”,
InderScience
International Journal on Wireless and Mobile
Computing (IJWMC), 2005.
[6] J. Liu, V. Issarny, “QoS-Aware Service Location
in Mobile Ad hoc Networks”, Proceedings of
IEEE International Conference on Mobile Data
Management (MDM), pp. 224-235, 2004.
[7] H. Zhai, X. Chen, Y. Fang, “How Well Can the
IEEE 802.11 Wireless LAN Support Quality of
Service?”, IEEE Transactions on Wireless
Communications, vol. 4, no. 6, November 2005.
[8] C.S.R. Murthy, B.S. Manoj, Ad Hoc Wireless
Networks: Architectures and Protocols, Pearson
Education, New Delhi, 2005.
[9] H.J. Chao, X. Guo, Quality of Service Control in
High-Speed Networks, John Wiley, New York,
2002.
[10]Zanella, D. Miorandi, S. Pupolin, P. Raimondi,
“On Providing Soft-QoS in Wireless Ad hoc
Networks”, Proceedings of International
UbiCC Journal, Volume 3, Special issue on Mobile Ad hoc Networks
31
Symposium on Wireless Personal Multimedia
Computing (WPMC), October 2003.
[11]V. Kone, S. Nandi, “QoS Constrained Adaptive
Routing Protocol for Mobile Ad hoc Networks”,
Proceedings of 9th IEEE International
Conference on Information Technology (ICIT),
pp. 40-45, December 2006.
[12]M. Mirhakkak, N. Schult, D. Thomson,
“Dynamic Quality-of-Service for Mobile Ad hoc
Networks”,
Technical
Report,
Mitre
Corporation, http://www.mitrecorporation.net/
work/tech_papers/tech_papers_00/thomson\mp_
adhoc/thomson_adhoc.pdf, 2000.
[13]B. Li, “QoS-Aware Adaptive Services in Mobile
Ad hoc Networks”, Proceedings of IEEE
International Workshop on Quality of Sevice
(IWQoS), pp. 251-268, 2001.
[14]A.M. Abbas, K.A.M. Soufi, “LANM: Lifetime
Aware Node-Disjoint Multipath Routing for
Mobile Ad hoc Networks”, Proccedings of IET
International Conference on Information and
Communication Technology in Electrical
Sciences (ICTES), 2007.
[15] S. Nelakudity, Z.L. Zhang, R.P. Tsang, D.H.C.
Du, “Adaptive Proportional Routing: A
Localized QoS Routing Approach”, IEEE/ACM
Transactions on Networking, vol. 10, no. 6,
December 2002.
[16] Y.S. Chen, Y.C. Tseng, J.P. Sheu, P.H. Quo,
“An On-demand, Link-State, Multipath QoS
Routing in A Wireless Mobile Ad hoc Network”,
Elsevier Journal on Computer Communications,
2003.
[17] S. Wu, K.Y.M. Wong, B. Li, “A Dynamic Call
Admission Policy With Precision QoS
Guarantee Using Stochastic Control for Mobile
Wireless Networks”, IEEE Transaction on
Networking, vol. 10, no. 2, pp. 257-271, April
2002.
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