PERFORMANCE OF OLSR MANET ADOPTING CROSS-LAYER APPROACH UNDER CBR AND VBR TRAFFICS ENVIRONMENT

International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018
DOI: 10.5121/ijcnc.2018.10601 1
PERFORMANCE OF OLSR MANET ADOPTING
CROSS-LAYER APPROACH UNDER CBR AND VBR
TRAFFICS ENVIRONMENT
Teerapat Sanguankotchakorn1
, Sanika K.Wijayasekara2
and Sugino Nobuhiko3
1
Telecommunications Field of Study, School of Engineering and Technology,
Asian Institute of technology, Pathumthani, Thailand
2
Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
3
Interdisciplinary Graduate School of Science and Engineering,
Tokyo Institute of Technology, Japan
ABSTRACT
The routing protocols play an important role in Mobile Ad-Hoc Network (MANET) because of the
dynamically change of its topology. Optimized Link State Routing (OLSR), unawareness of Quality of
Service (QoS) and power-consumed protocol, is an example of a widely-used routing protocol in MANET.
The Multi-Point Relays (MPR) selection algorithm is very crucial in OLSR. Therefore, firstly, we propose a
heuristic method to select the best path based on two parameters; Bit Error Rate (BER) derived from the
physical layer and Weighted Connectivity Index (CI) adopted from the network layer. This can be done via
the cross-layer design scheme. This is anticipated to enhance the performance of OLSR, provide QoS
guarantee and improve the power consumption. The performances of the proposed scheme are investigated
by simulation of two types of traffics: CBR and VBR (MPEG-4), evaluated by metrics namely Throughput,
Packet Delivery Ratio (PDR), Average End-to-End Delay, Control Overhead and Average Total Power
Consumption.We compare our results with the typical OLSR and OLSR using only Weighted CI. It is
obvious that our proposed scheme provides superior performances to the typical OLSR and OLSR using
only Weighted CI, especially, at high traffic load.
KEYWORDS
Mobile Ad-hoc Network (MANET), OLSR, Bit Error Rate (BER), Weighted Connectivity Index, Quality of
Service (QoS)
1. INTRODUCTION
MANET, an infrastructure-less mobile network, is formed by a collection of mobile interfaces
without any support of the centralised administration. MANET is becoming more important in the
area of wireless communication due to its self-configured nature, infrastructure-less, and its high
penetration of mobile devices in the world. Due to the characteristic of the wireless network
including MANET, the overall performance of this type of network is quite low [1],[2]. In
addition, one of the most important constraint of mobile devices is their limited energy, the
energy efficient routing becomes a main constraint in MANET environment [3],[4].
There are two main routing protocols in MANET; Proactive and Reactive protocols. In proactive
routing protocol, each node creates its unique routing table by collecting the routing information
broadcasted by the other nodes in the network while nodes running reactive routing protocols
create their own routing table based on a request. OLSR [10] is an example of proactive
protocols. And DSR (Dynamic Source Routing) and AODV (Ad Hoc On-Demand Distance-

2
Vector) [7] are examples of reactive protocols. The performance comparison between proactive
and reactive protocols is carried out in [35]. Most of the protocols in MANET are QoS-unaware.
To provide the QoS guarantee in MANET is the challenging problem. Therefore, there is many
works proposing the algorithms to provide QoS guarantee in MANET [8]~[22].
This work focuses on OLSR, since it is flexible enough to support different kind of delay
sensitive and multimedia applications [8],[9]. In addition, OLSR proved to be outperforming the
other reactive and proactive protocols in [35]. OLSR is a QoS-unaware protocol where its
characteristic is determined in terms of routing table and network coverage. OLSR consists of
four main principles such as neighborhood sensing, message flooding, topology information and
path computation. It has three kinds of messages namely HELLO, TC(Topology Control) and
MID(Multiple Interface Declaration) [10]. HELLO messages are flooded to nearby neighbors
every 2 seconds (default value). By using the information carried in HELLO messages
exchanged, each node selects set of its Multipoint Relays (MPRs), a key concept in OLSR used to
optimize the number of packets flooded into the network. The TC messages are forwarded by
only the selected MPR nodes. The nodes create a partial topology graph based on the collected
information from the flooded TC messages. A node determines the best route from source to
destination based on the created partial topology graph and path computation algorithm [10].
To provide QoS, the bandwidth and delay are mainly considered in many previous works
proposed in OLSR [11],[12]but they are not suitable in various scenarios where multiple QoS
constraints are essential [23],[24]. The concept of Weighted Connectivity Index (CI) is firstly
introduced in [13], then used to improve the OLSR performance later on [6],[8],[14],[15]. The
detailed derivation of Weighted CI as well as the MPR selection method is described especially
[6]. In [15], the non-additive metric such as Weighted CI and multiple additive QoS metrics in
path computation method is considered. Recently, the framework called cross-layer design has
proved that it can improve the performance of a wireless network, especially MANET
[22],[25]~[27]. The cross-layer concept is applicable in various applications, namely mobile
social networks [28],[29], wireless sensor networks [26] and next generation network [22]. In
terms of providing QoS guarantee in network, many works using various parameters were
proposed based on the cross-layer concept [28],[30] as well. It is well known that the high packet
loss as well as delay is due to the high BER in communication links [9]. In [16], the BER is
considered in AODV path computation. However, the BER in proactive protocol has not been
taken into account yet.
In this work, we extend the investigation further into our proposed method called CBC-OLSR [6],
where the BER and Weighted CI are adopted in the cross-layer framework for OLSR protocol
under two traffic types: CBR (Constant Bit Rate) and VBR (Variable Bit Rate).It is anticipated
that the paths found by our proposed method have higher stability since the lowest BER and the
highest Weighted Connectivity Index (CI) in a link is adopted. This results in the efficient power
consumption in MANET as well. In conclusion, the main contribution of this work is the cross-
layer framework using BER of the link and Weighted CI, from physical and network layer,
respectively to improve the MPR-selection process and routing-table computation. This leads to
more stable path between source and destination which can enhance the overall performance of
OLSR as well as to reduce the overall power consumption of the network. Our proposed method
is compared with standard OLSR [5],[10] and the modified OLSR proposed in [8],[13]-[15] in
terms of Throughput, Packet Delivery Ratio (PDR), Average End-to-End (E2E) Delay, Control
Overhead and the Average Total Power Consumption[31],[32]. In addition, the computational
complexity of all three algorithms is also compared with each other.
This paper is structured as follows: Section 2 describes the related works. Section 3 describes the
proposed system model as well as the detailed algorithm. Section 4 provides the detailed
parameters and simulation scenarios while the results of simulation and discussion are provided in
Section 5. We conclude our work in Section 6.

2. RELATED WORKS
Several works to find the shortest path from source to destination were proposed in the literature
[21][33]. In wireless network, especially MANET,
mainly on the MPR selection process and routing computation. The routing computation in
MANET has also been developed mainly based on shortest path algorithm.
In [21], the “look-ahead” method is improved for
enhanced version of fully polynomial time approximation scheme for multi
optimal problem is proposed in [33]
parameters on each of the finding path can be guaranteed not to exceed the given constraints.
Then, the nonlinear definition of path constraints is adopted to reduce both time and space
complexity. The work that mainly handles the MPR selection method
the literature [18].
The new concept based on the new parameters is also proposed in
weighted CI and delay is proposed as QoS metrics in an algorithm called shortest
find the feasible paths. The research regarding finding the optimal paths using weighted CI is also
found in [8], where the Multipoint
[9], the MPR computation algorithm is modified to determine
between the node and the two
optimization is investigated. The proposed algorithm always chooses the energy
with some increase in normalized routing overhead.
and reactive protocols can be found in [35]
In [16], the weighted CI with BER is
framework. It is shown under its proposed method,
terms of all evaluation metrics. In [15]
routing in OLSR by considering weighted CI and multiple additive QoS. To verify that OLSR
performs well for multimedia a
routing protocols when delivering
in MANET is carried out in, such as
3. PROPOSED CROSS-LAYER
In this section, we illustrate the concep
OLSR, as depicted in figure 1. T
information i.e., QoS requirement
determined by SNR of the link. Here, for simulation purpose, a model called ErrorModel80211
was integrated into NS-2 simulator to derive th
In wireless network, especially MANET, the works on OLSR protocol have carried out
developed mainly based on shortest path algorithm.
ahead” method is improved for multi-constraint QoS routing
enhanced version of fully polynomial time approximation scheme for multi-constrained path
in [33]. It constructs the auxiliary graph through which the
The work that mainly handles the MPR selection method in OLSR is also found in
based on the new parameters is also proposed in [13]. The parameter called
weighted CI and delay is proposed as QoS metrics in an algorithm called shortest-highest path to
The research regarding finding the optimal paths using weighted CI is also
Multipoint Relaying (MPR) is modified to find the optimized paths.
utation algorithm is modified to determine the lowest BER among
he node and the two-hop neighbors. In [34], the multipath OLSR for energy
optimization is investigated. The proposed algorithm always chooses the energy-optimized path
with some increase in normalized routing overhead. The work on comparison between proactive
otocols can be found in [35].
weighted CI with BER is adopted in reactive protocol AODV under cross layer design
framework. It is shown under its proposed method, the performance of AODV is
In [15], a method called G_MCP is proposed to implement QoS
performs well for multimedia applications, the work in [36] evaluated the outcome of ad
ing MPEG-4 video traffic. The work about the energy consumption
in MANET is carried out in, such as [3].
LAYER DESIGN-BASED CONCEPT
illustrate the concept of our proposed cross-layer framework
. The Physical layer information i.e. BER, and the Application layer
information i.e., QoS requirement are provided to the Network layer. The value of BER is
of the link. Here, for simulation purpose, a model called ErrorModel80211
2 simulator to derive the BER as given in [37].
Figure 1. Proposed System Design
3
works on OLSR protocol have carried out
routing, while an
constrained path
. It constructs the auxiliary graph through which the QoS
in OLSR is also found in
. The parameter called
highest path to
The research regarding finding the optimal paths using weighted CI is also
to find the optimized paths. In
among all links
], the multipath OLSR for energy
optimized path
rison between proactive
in reactive protocol AODV under cross layer design
performance of AODV is improved in
_MCP is proposed to implement QoS
evaluated the outcome of ad-hoc
The work about the energy consumption
ayer framework called CBC-
he Physical layer information i.e. BER, and the Application layer
Network layer. The value of BER is
of the link. Here, for simulation purpose, a model called ErrorModel80211

4
Here, we modify the OLSR HELLO messages to carry the BER and one-hop weighted CI values.
The MPR heuristic selection approach and the Shortest-highest path computation adopted here
are illustrated in detail in section 3.3 and Figure 3. These proposed algorithms are modified from
[8] and [13], respectively. Based on these algorithms, a node selects MPR nodes and the best path
to a destination satisfying the lowest BER of the links. If the tie happens, then, the highest
Weighted CI of the nodes will be used instead.
3.1. Definition and Notation of Weighted CI
The definition and detailed proof of weighted CI is covered in great detail already in [13].
Therefore, for the sake of the readers, only the necessary parts are mentioned again here. The
Weighted Connectivity Index (CI) combining both node's degree and link capacity into a single
metric of any network can be defined as follows:
(1)
Where q(i, j) is normalized link capacity between node i and j, 0 q(i,j) 1. When q(i, j)= 0, it
means the link is disconnected or unavailable.
The n-hop weighted CI of node ican be defined as
(2)
WhereGi
n-hop
is a sub-graph of G originating and covering up to n-hop from node i.
The weighted CI adopted here is called 2-hop Weighted CI [13] since only the information up to
2-hops is considered.
3.2. Bit Error Rate Calculation
Firstly, we calculate SNR using the following equation:
(3)
Where Rx_power denotes signal strength of the frame at the receiver which can be calculated by the
propagation model, Rx_power(i) is the signal strength of frame ith
, Nr is the noise power calculated by
the receiver sensitivity of the data rate used by the frame and n is the total number of frames
arrived.
Then, the graph between BER vs SNR provided by Intersil HFA3861B [37], as illustrated in
figure 2.In this work, BPSK is considered as the modulation technique of the wireless link.
w
χ =
w
χ G( )=
q(i, j)
d(i)d( j)(i, j)∈E
å
≤ ≤
w
χ i
n−hop
G( )=
q(u,v)
d(u)d(v)(u,v)∈Ei
n−hop
å
SNR =10log x _ powerR
rN + x _ power(i)Ri=1
n
å
æ
è
ç
ç
ö
ø
÷
÷

Figure
3.3. Heuristic MPR Selection Approach Based on
and Bit Error Rate (BER)
The MPR selection algorithm is
nodes, which are allowed to forward the control packets over the network. Therefore, MPR
selection procedure is useful to measure the quality of the OLSR protocol in
load, quality of the link in the routing table and
OLSR, this research adopts the heuristic approach for MPR selection process based on minimum
BER and maximum Weighted CI as illustrated as foll
Heuristic MPR Selection Process
MPR (G=(V,E); N_1, N_2, MPR(x)
1. Initially, set MPR(x)={ }
2. For all nodes in N_1, calculate
3. Add 1-hop neighbouring nodes in N
4. While the nodes in N2exist, but
(a) Calculate the number of nodes in N
MPR set, for each node in N
(b) Add node in N1 that provides the lowest BER link
MPR(x)
where, MPR(x)is a set of neighbours of node
hop neighbours and 2-hop neighbours, respectively.
node y (where y N1).
In step 3 in this algorithm, the nodes will be declared
providing the reachability to their 2
weighted CI and lowest BER link to its neighbours, will be decl
selector.
3.4. Pseudo Code of the Proposed CBC_OLSR Algorithm
Actually the pseudo code of our proposed CBC
However, the detailed explanation is not sufficiently given. Therefore, the pseudo code is shown
again here for the sake of explanation.
denoted byG, V and E, respectively
neighbors, 2-hop neighbors and topology
∈
Figure 2. IEEE 802.11b BER vs. SNR [37]
Heuristic MPR Selection Approach Based on Weighted Connectivity Index (CI)
The MPR selection algorithm is a crucial part of OLSR protocol since it determines the MPR
selection procedure is useful to measure the quality of the OLSR protocol in terms of routing
routing table and network coverage [9]. To provide the QoS in
BER and maximum Weighted CI as illustrated as follows:
Heuristic MPR Selection Process
MPR (G=(V,E); N_1, N_2, MPR(x) V)
2. For all nodes in N_1, calculate d(y), {y} N1
nodes in N1 to MPR(x) to provide path to reach some nodes in N
exist, but are not covered by at least one node in the MPR(x) :
Calculate the number of nodes in N2 which are uncovered by at least one node in
h node in N1,
that provides the lowest BER link or the highest Weighted CI to
)is a set of neighbours of node x which are selected as MPR. N1 and N2
hop neighbours, respectively. d(y) is the degree of a 1-hop neighbour of
nodes will be declared as MPR nodes, if they are the only nodes
their 2-hop neighbours. While in step 4, the node having the highest
weighted CI and lowest BER link to its neighbours, will be declared as MPR node by the MPR
3.4. Pseudo Code of the Proposed CBC_OLSR Algorithm
Actually the pseudo code of our proposed CBC-OLSR algorithm is already shown in [6].
again here for the sake of explanation. In figure 3, a graph, a set of nodes and a set of links are
respectively where G=(V, E). And N1, N2and Tare the sets of 1
hop neighbors and topology, respectively.CBC-OLSR selects the best path based on
⊂
∀ ∈
5
Weighted Connectivity Index (CI)
since it determines the MPR
terms of routing
To provide the QoS in
path to reach some nodes in N2
are not covered by at least one node in the MPR(x) :
which are uncovered by at least one node in
the highest Weighted CI to
2 are set of 1-
hop neighbour of
if they are the only nodes
the node having the highest
ared as MPR node by the MPR
OLSR algorithm is already shown in [6].
es and a set of links are
are the sets of 1-hop
OLSR selects the best path based on

the lowest BER or the highest Weighted CI value of the links. Firstly,
routing table are deleted; and all nodes in
count, CI and BER of each link are computed.
added to routing table if there is no path to reach destination stored in routing table; or it provides
a better path in term of higher value of CI and lower value of BER. In step 4,
found will be computed and compared to find the minimum one. If the tie occurs, then, the
algorithm will select the path based on the highest Weighted CI and declare it in the routing table.
Figure 3. Modified Shortest
For the sake of the readers, the example illustrating the proposed algorithm is provided as
follows:
Example: Consider the network topology shown in f
and BER are known and illustrated in the form of (Weighted CI, BER), where Weighted CI and
BER in this example are in arbitrary unit. The detailed calculation of Weighted CI can be found
in [13]-[15], therefore, we skip it here.
Figure 4. Example Network Topology
Based on the algorithm illustrated
Node A to F. Firstly, link A-D is selected since it has
while both have the same BER. Next, link D
among all links connecting to node D. Similarly, the link E
chosen. That is, the higher priority is given to the Weighted CI if the tie of BER occurs.
A
(4,3)
(2,3)
the lowest BER or the highest Weighted CI value of the links. Firstly, in step 1,
routing table are deleted; and all nodes in N1are added to routing table in step 2. Then, t
count, CI and BER of each link are computed. In step 3 and 4, all nodes in N2and/or
a better path in term of higher value of CI and lower value of BER. In step 4, the BER of all paths
ted and compared to find the minimum one. If the tie occurs, then, the
Figure 3. Modified Shortest-highest Path Algorithm
the example illustrating the proposed algorithm is provided as
the network topology shown in figure 4. Assume that all links Weighted CI
[15], therefore, we skip it here.
Figure 4. Example Network Topology
Based on the algorithm illustrated in figure 3, assume that we need to find the best path from
D is selected since it has the higher Weighted CI than link A
while both have the same BER. Next, link D-E is selected since it has the highest Weighted CI
among all links connecting to node D. Similarly, the link E-D is selected. Finally, link C
priority is given to the Weighted CI if the tie of BER occurs.
B
D
C
E
F(5,4)
(4,5)
(6,4)
(8,3)(3,2)
(6,2)
(7,3)
6
in step 1, all entries in
Then, the hop-
and/or T will be
the BER of all paths
ted and compared to find the minimum one. If the tie occurs, then, the
the example illustrating the proposed algorithm is provided as
igure 4. Assume that all links Weighted CI
igure 3, assume that we need to find the best path from
the higher Weighted CI than link A-B,
E is selected since it has the highest Weighted CI
D is selected. Finally, link C-F is
priority is given to the Weighted CI if the tie of BER occurs.

7
4. SIMULATION PARAMETERS AND PERFORMANCE EVALUATION METRICS
4.1 Simulation Parameters
This section illustrates the parameters and the performance evaluation metrics used in our
simulations for both CBR and VBR (MPEG-4) services. The parameters used in our simulations
are listed in table 1, while table 2 illustrates the power consumption in each node's state [38] used
in our simulations of the energy consumption of the network.
The definition of power consumption of each state is as follows:
Transmit: node transmits a packet with this transmission power.
Receive: node consumes this amount of power when receiving a packet regardless of correct,
erroneous or garbled reception.
Idle: when no packet is received, nor transmitted, node keeps listening to the medium and
consumes this amount of power. We assume that nodes can change from "Idle" state to
"Transmit" or 'Receive" state immediately without any power consumption in transition period.
Sleep: the node becomes “Sleep” state if the residual power of node is lower than the
minimum power in "Idle" state. Within this state, the node cannot detect any signals because the
radio is switched off.
Table 1. Simulation Parameters.
Parameters CBR MPEG-4
Node
Density
Node
Mobility
Node
Density
Node
Mobility
Area (m2
) 1,000 x 1,000
Link Capacity (Mbps) 2
Number of Nodes (Number
of Connections)
10(2)-
50(10)
50(10) 10(2)-
50(10)
50(10)
Speed (m/s) 2 1-30 2 1-30
Pause Time (s) 0
Video Source - Foreman QCIF
(176x144)
Packet Size (bytes) 100 1,500
Traffic Rate/Connection
(Kbps)
100 200-400
Mobility Model Random Way Point
Node Transmission Range
(m)
250
Simulation Time (s) 300
No. of Executions/Scenario 20
Table 2. Power Consumption in each Node’s state
State Power Consumption (W)
Transmit 1.3
Receive 0.9
Idle 0.74
Sleep 0.047
Initial Energy (J) 1,000

4.2. Performance Evaluation Metrics
Our proposed CBC-OLSR algorithm
Throughput: the amount of data
Packet Delivery Ratio (PDR):
total number of packets transmitted
Average End-to-End Delay
packets under consideration.
Control overhead: the total number of
Average Total Power Consumption:
network in a period under consideration
5. SIMULATION RESULTS
In this section, the simulation results of our proposed CBC
standard OLSR and OLSR using
using NS2 simulator [39]. The traffics under consideration in this work are CBR and VBR
(MPEG-4 video).The performances are analysed using the afore
evaluation metrics. We run the simulations 20 times per one data point and all the
illustrated with 95% confidential interval to ensure the validity of the simulation.
5.1. Constant Bit Rate Service
5.1.1 CBR: Effect of Node Density
This section shows the impact of node density on the performance evaluation metrics of our
proposed CBC-OLSR, standard OLSR and OLSR using only Weighted CI. Actually, we carried
out the simulations on various speed, but, we illustrate the results at only speed = 2 m/s due to the
same trend of results for the other speeds.
Figure 5
Figure 6. CB
. Performance Evaluation Metrics
OLSR algorithm is evaluated using the following evaluation metrics:
the amount of data successfully transmitted over the network in a unit of time.
elivery Ratio (PDR): the ratio of received packets at destination with respect to the
total number of packets transmitted by sources.
(E2E):the mean value of the overall delay experienced
the total number of non-data packets transmitted within the network.
rage Total Power Consumption: the average of all energy consumed by all node
under consideration.
AND DISCUSSION
In this section, the simulation results of our proposed CBC-OLSR algorithm are compared with
standard OLSR and OLSR using only Weighted CI algorithm. All simulations are car
. The traffics under consideration in this work are CBR and VBR
4 video).The performances are analysed using the afore-mentioned performance
un the simulations 20 times per one data point and all the
% confidential interval to ensure the validity of the simulation.
Constant Bit Rate Service
5.1.1 CBR: Effect of Node Density
f node density on the performance evaluation metrics of our
OLSR, standard OLSR and OLSR using only Weighted CI. Actually, we carried
end of results for the other speeds.
re 5. CBR: Throughput vs. Number of Nodes
. CBR: Packet Delivery Ratio vs. Number of Nodes
8
evaluation metrics:
successfully transmitted over the network in a unit of time.
ratio of received packets at destination with respect to the
mean value of the overall delay experienced by all
the network.
energy consumed by all nodes in the
OLSR algorithm are compared with
ithm. All simulations are carried out
. The traffics under consideration in this work are CBR and VBR
mentioned performance
un the simulations 20 times per one data point and all the results are
f node density on the performance evaluation metrics of our
OLSR, standard OLSR and OLSR using only Weighted CI. Actually, we carried

Figure 7. CBR:
Figure 8. CBR: Control Overhead vs. Number
Figure 9. CBR: Average Total Pow
Figures 5~7 illustrates the Throughput, PDR and Average E2E
obvious that our proposed CBC-
is larger than 30. This is due to
on our proposed algorithm. The path identified is the best due to the highest stability (minimum
BER and maximum weighted CI).
When the number of nodes in the network is small (less than 30), in general, the average
transmission distance between nodes become longer as well as the node's degree becomes smaller
which results in higher BER and frequent link broken. While in standard OLSR, the MPR nodes
and best path are selected based on only the number of hop
Weighted CI, the best path is selected based on only the highest Weighted CI where the BER is
not put into account. Therefore, the path found based on minimum BER
CI is probably hard to find or, even
Packet Delivery Ratio and Throughput in our proposed algorithm comparing to the other two
algorithms. However, the path found by our prop
which can provide the lowest Average E2E
. CBR: Average End-to-End Delay vs. Number of Nodes
. CBR: Control Overhead vs. Number of Nodes
. CBR: Average Total Power Consumption vs. Number of Nodes
hroughput, PDR and Average E2E Delayers us number of nodes
-OLSR provides the best performance when the number o
This is due to the best path can be identified by the MPR nodes selected based
. The path identified is the best due to the highest stability (minimum
BER and maximum weighted CI).
nodes in the network is small (less than 30), in general, the average
and best path are selected based on only the number of hop-count whereas, in OLSR with only
not put into account. Therefore, the path found based on minimum BER and maximum Weighted
even though it is found, broken frequently. This results in lower
algorithms. However, the path found by our proposed CBC-OLSR is still the most stable path,
ovide the lowest Average E2E Delay among all algorithms.
9
number of nodes. It is
number ofnodes
selected based
. The path identified is the best due to the highest stability (minimum
nodes in the network is small (less than 30), in general, the average
count whereas, in OLSR with only
and maximum Weighted
found, broken frequently. This results in lower
OLSR is still the most stable path,

When the number of nodes increases, the average transmission distance between the nodes
decreases. Generally, this results in lower BER in
of neighbours around a node, which reduces the link loss. This results in higher thro
PDR but lower average end-to-end delay in our proposed CBC
Figure 8 illustrates the comparison of Contro
Control Overhead of all algorithms increases exponentially with the increment of number of
nodes. It is apparent that the control overhead of o
of the increasing number of MPR nodes introdu
Figure 9 illustrates Average Total Power Consumption of all algorithms. The
Power Consumption increases
proposed CBC-OLSR consumes lowest energy among three algorithms. Since our proposed
CBC-OLSR algorithm selects the most stable routes based on minimum BER and maximum
Weighted CI. Therefore, it can reduce the effect of interference and medium collisions,
leads to the link failures between the nodes in a network. This results directly in the reduction of
the total power consumption in transmitting and receiving packets in the network.
Even though the Control Overhead
algorithms, however, its size is very
CBC-OLSR algorithm issues the highest nu
consumption is lowest due to the stability of the selecte
5.1.2. CBR: Effect of Node Mobility
In this scenario, we investigate the performance of all algorithms in various node
environment.
Figure 10
Figure 11
decreases. Generally, this results in lower BER in transmission links and also increases the degree
of neighbours around a node, which reduces the link loss. This results in higher thro
end delay in our proposed CBC-OLSR.
illustrates the comparison of Control Overhead of three algorithms. It is apparent that the
It is apparent that the control overhead of our proposed CBC-OLSR is the highest, because
asing number of MPR nodes introduced by our proposed MPR heuristic algorithm
Total Power Consumption of all algorithms. The Average
Power Consumption increases with the increasing number of nodes. It is obvious that ou
OLSR consumes lowest energy among three algorithms. Since our proposed
OLSR algorithm selects the most stable routes based on minimum BER and maximum
Weighted CI. Therefore, it can reduce the effect of interference and medium collisions,
the total power consumption in transmitting and receiving packets in the network.
Control Overhead of our proposed method is the highest
is very small comparing to data packet’s size. Therefore
OLSR algorithm issues the highest number of Control Overheads; still the power
consumption is lowest due to the stability of the selected routes.
Effect of Node Mobility
In this scenario, we investigate the performance of all algorithms in various node
Figure 10. CBR: Throughput vs. Speed
Figure 11. CBR: Packet Delivery Ratio vs. Speed
10
transmission links and also increases the degree
of neighbours around a node, which reduces the link loss. This results in higher throughput and
l Overhead of three algorithms. It is apparent that the
is the highest, because
ced by our proposed MPR heuristic algorithm.
Average Total
the increasing number of nodes. It is obvious that our
OLSR consumes lowest energy among three algorithms. Since our proposed
OLSR algorithm selects the most stable routes based on minimum BER and maximum
Weighted CI. Therefore, it can reduce the effect of interference and medium collisions, which
among three
small comparing to data packet’s size. Therefore, even the
still the power
In this scenario, we investigate the performance of all algorithms in various nodes' speed

Figure 12
Figure 13
Figure 14. CBR: Average Total Power Consumption vs. Speed
As shown in figures 10~12, it is obvious that our proposed CBC
Throughput and Packet Delivery Ratio and the lowest Average End
algorithms at all speed under consideration here.
based on minimum BER and maximum Weighted CI, therefore, this path is supposed to be the
most stable path. However, the probability of link
speed, this results in performance degradation of Throughput, PDR and Average E2E Delay.
In figure 13, our proposed CBC-
while standard OLSR has the lowest one. Since the standard OSR uses native MPR selection
method to optimize the number of Control Overhead while OLSR with only weighted CI has to
consider the maximum Weighted CI of the nodes in the MPR sele
proposed CBC-OLSR, it has to consider both the minimum BER of the links and the maximum
Weighted CI.
As depicted in figure 14, when the speed increases the Average Total Power Consumption lightly
decreases. According to the energy model [32]
of data packets is mainly depending on the size of data packets. Since the size of MAC or routing
Figure 12. CBR: Average End-to-End Delay vs. Speed
Figure 13. CBR: Control Overhead vs. Speed
. CBR: Average Total Power Consumption vs. Speed
, it is obvious that our proposed CBC-OLSR provides the highest
Delivery Ratio and the lowest Average End-to-End Delay among three
algorithms at all speed under consideration here. Because the path selected by our proposed
mum BER and maximum Weighted CI, therefore, this path is supposed to be the
stable path. However, the probability of link-break increase with the increment of nodes’
mance degradation of Throughput, PDR and Average E2E Delay.
-OLSR has the highest number of Control Overhead in all speed
consider the maximum Weighted CI of the nodes in the MPR selection method, whereas
OLSR, it has to consider both the minimum BER of the links and the maximum
when the speed increases the Average Total Power Consumption lightly
energy model [32], the energy consumption in transmitting/receiving
11
OLSR provides the highest
End Delay among three
our proposed is
mum BER and maximum Weighted CI, therefore, this path is supposed to be the
break increase with the increment of nodes’
mance degradation of Throughput, PDR and Average E2E Delay.
ad in all speed
ction method, whereas, in our
OLSR, it has to consider both the minimum BER of the links and the maximum
when the speed increases the Average Total Power Consumption lightly
, the energy consumption in transmitting/receiving

12
control packet is very small comparing to the size of data packet, therefore, the power
consumption in any nodes can be approximated by the power consumption of only data packets.
Hence, when the speed of nodes increases, the amount of data packets flooded over the network
(as depicted as Throughput in figure 10) become lower due to the frequent occurrence of link
break. This results in the decrement of power consumption of all algorithms. As discussed earlier,
the path found by our proposed algorithm is more stable than those of the other ones; therefore,
the loss due to link break is small. This result in the least Total Power Consumption of our
proposed CBC-OLSR among all algorithms considered here.
5.2. Variable Bit Rate (VBR: MPEG-4) Services
The MPEG-4 video traffic is generated by integrating EvalVid toolset with NS-2 and usingthe
Foreman YUV QCIF (176X144) as the video source with 400 frames [40].
5.2.1. MPEG-4: Effect of Node Density
Figure 15. MPEG-4: Throughput vs. Number of Nodes
Figure 16. MPEG-4: Packet Delivery Ratio vs. Number of Nodes
Figure 17. MPEG-4: Average End-to-End Delay vs. Number of Nodes

Figure 18. MPEG
Figure 19. MPEG-4: Average
It is apparent, as shown in figure
among all three algorithms at a large
case.
As shown in figure 17, the proposed CBC
three algorithms. It can be noticed that
recommended in ITU G.114 [41
high node density because of the increment of control packets.
proposed CBC-OLSR generates
because a large number of Hello and TC packets are
the SNR (in order to calculate BER).
As depicted in figure 19, when the number of nodes increases, the Total Power Consumption also
increases due to the large amount of traffic flooded into the network. As mentioned previously,
our proposed CBC-OLSR provides more stable route,
failures in the network. This results i
power of mobile nodes in MANET. Therefore, the proposed CBC
power among three algorithms even though higher Control Overheads
network. As mentioned previously, the size of control packet is very small comparing to the size
of data packet; therefore, the power consumption due to the control packets does not affect the
Total Power Consumption in the network.
MPEG-4: Control Overhead vs. Number of Nodes
4: Average Total Power Consumption vs. Number of Nodes
igures 15~16, that our proposed CBC-OLSR is the most superior
large number of nodes. The reason is similar to the
, the proposed CBC-OLSR depicts the lowest Average E2E
three algorithms. It can be noticed that all algorithms comply with the E2E delay requirement
mmended in ITU G.114 [41] recommendation.The Average E2E2 Delay slightly increases at
the increment of control packets. Figure 18 illustrates that our
OLSR generates the highest number of Control Overheads into the network
of Hello and TC packets are needed to collect the nodes degree and sense
the SNR (in order to calculate BER).
, when the number of nodes increases, the Total Power Consumption also
OLSR provides more stable route, which reduces the probability
failures in the network. This results in the increment of network lifetime by saving the battery
power of mobile nodes in MANET. Therefore, the proposed CBC-OLSR consumes the lowest
power among three algorithms even though higher Control Overheads are flooded into the
therefore, the power consumption due to the control packets does not affect the
Total Power Consumption in the network.
13
of Nodes
is the most superior
similar to the CBR service
Delay among
delay requirement
Delay slightly increases at
illustrates that our
into the network
needed to collect the nodes degree and sense
, when the number of nodes increases, the Total Power Consumption also
which reduces the probability of link
time by saving the battery
OLSR consumes the lowest
are flooded into the
therefore, the power consumption due to the control packets does not affect the

5.2.2 MPEG-4: Effect of Node Mobility
Figure 20
Figure 21
Figure 22. MPEG
Figure 23
4: Effect of Node Mobility
Figure 20. MPEG-4: Throughput vs. Speed
Figure 21. MPEG-4: Packet Delivery Ratio vs. Speed
. MPEG-4: Average End-to-End Delay vs. Speed
Figure 23. MPEG-4: Control Overhead vs. Speed
14

Figure 24. MPEG
Figures 20~22 illustrate the simulation results of Throughput, PDR and Average E2E Delay
versus speed. It is apparent that
speed increases. Regarding the Throughput and PDR, it is obvious that o
is a little bit superior to the other two algorithms in all speeds.
selects the MPR nodes and the best path based on the lowest BER and the highest Weig
Therefore, the path selected is more stable than those of the other two algorithms. This results in
higher Throughput and Packet Delivery Ratio. The OLSR only with Weighted CI performs a little
better than standard OLSR, since it considers bandwid
Our proposed CBC-OLSR provides the lowest Average E
all speed, as shown in figure 22
Average E2E Delay satisfying the E2E
which requires the one-way E2E
increases, the link break occurs more frequent. This results in the increment of Average E
Delay and Control Overhead in all three algorithms.
As illustrated in figure 23, our proposed algorithm provides the highest number of Control
Overheads among three algorithms due to the need of Hello and TC messages generated to co
the nodes' degree and the SNR.
Overheads because of the optimization of
Control Overhead flooded into the network.
As shown in figure 24, due to the stable route selected based on the
Weighted CI, the proposed algorithm provides a little bit lower Total Power Consumption than
standard OLSR and OLSR only with Weighted CI algorithms i
can be explained by the same reason
5.3. Computational Complexity
To study and compare the Computational Complexity of the proposed CBC
OLSR and OLSR with only Weighted CI,
same platform and environment
used indirectly as a measurement of
result is illustrated in figure 25.
MPEG-4: Average Total Power Consumption vs. Speed
illustrate the simulation results of Throughput, PDR and Average E2E Delay
versus speed. It is apparent that the overall performance of all three metrics degrade
Regarding the Throughput and PDR, it is obvious that our proposed CBC
the other two algorithms in all speeds. Since the proposed algorithm
selects the MPR nodes and the best path based on the lowest BER and the highest Weig
ce it considers bandwidth in its path selection process.
OLSR provides the lowest Average E2E Delay among all three algorithms at
igure 22. It is obvious that only our proposed method provides the
2E Delay satisfying the E2E delay recommendation at high speed in ITU G.114 [5
E2E delay to be less than 150 ms. Moreover, when the speed
increases, the link break occurs more frequent. This results in the increment of Average E
in all three algorithms.
, our proposed algorithm provides the highest number of Control
among three algorithms due to the need of Hello and TC messages generated to co
SNR. However, the standard OLSR has the lowest number of Control
Overheads because of the optimization of the number of MPR selected as well as the number of
Control Overhead flooded into the network.
, due to the stable route selected based on the lowest BER and the highest
standard OLSR and OLSR only with Weighted CI algorithms in high mobility environment. This
can be explained by the same reason as the case of CBR service.
5.3. Computational Complexity
To study and compare the Computational Complexity of the proposed CBC-OLSR with standard
nd OLSR with only Weighted CI, we run the simulation of all algorithms on the exact
nment as shown in table 3. Then, the execution time is measured and
used indirectly as a measurement of Computation Complexity of the algorithms. The simul
15
illustrate the simulation results of Throughput, PDR and Average E2E Delay
metrics degrades when the
ur proposed CBC-OLSR
ince the proposed algorithm
selects the MPR nodes and the best path based on the lowest BER and the highest Weighted CI.
s path selection process.
Delay among all three algorithms at
. It is obvious that only our proposed method provides the
h speed in ITU G.114 [5],
delay to be less than 150 ms. Moreover, when the speed
increases, the link break occurs more frequent. This results in the increment of Average E2E
, our proposed algorithm provides the highest number of Control
among three algorithms due to the need of Hello and TC messages generated to collect
est number of Control
the number of
lowest BER and the highest
n high mobility environment. This
OLSR with standard
of all algorithms on the exactly
n, the execution time is measured and
The simulation

16
Table 3. Simulation Platform and Environment
Parameters Value
Simulator NS 2 Version 2.34
Operating System 32 bit Ubuntu 11.10 (oneiric)
Processor Intel core i3, 2.3 GHz
Memory (GB) 2
Simulation Time (s) 60
Figure 25. Execution Time vs. Number of Nodes
As shown in figure 25, it is obvious that the execution time of the proposed CBC-OLSR is the
highest among all three algorithms, especially at the high number of nodes. When the number of
nodes is small, the computation time may be negligible. However, it becomes significant when
the number of nodes is large due to the drastically increasing computation of both BER and
Weighted CI.
6. CONCLUSIONS
In this work, we extend the investigation of our proposed cross-layer framework for OLSR called
CBC-OLSR. The proposed CBC-OLSR algorithm selects the MPR nodes and best routes in
Network layer based on the BER and Weighted CI parameters.
We perform various simulations under different node density, speed and traffic types; CBR and
VBR (MPEG-4 video). The performance comparison between our proposed CBC-OLSR,
standard OLSR and OLSR with only Weighted CI is shown. We observe that the proposed CBC-
OLSR performs well in both high node density and various speed conditions for both CBR and
VBR traffics. Moreover, it is obvious that our proposed algorithm consumes the lowest power
among all algorithms studied here at all speed and node density. Since it selects the most stable
path from source to destination based on minimum BER and maximum Weighted CI. This
reduces the occurrence of link break in transmitting/receiving packets between source and
destination. Hence, it improves the overall performance including the power consumption of
OLSR. Consequently, it can be concluded that our proposed CBC-OLSR is an efficient algorithm,
which has the capability to send data over the network and also saves the overall battery power of
mobile units in MANET. However, regarding the computational complexity, it is observed that
our proposed algorithm has the highest complexity, especially at high node density due to the
computation of BER of each link as well as Weighted CI of each node.
ACKNOWLEDGEMENTS
This work is partially supported by National Institute of Information and Communication
Technology (NICT), Japan. The authors would like to express their sincere thanks here.

17
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AUTHORS
Teerapat Sanguankotchakorn was born in Bangkok, Thailand on December 8, 1965.
He received the B. Eng in Electrical Engineering from Chulalongkorn University,
Thailand in 1987, M. Eng and D. Eng in Information Processing from Tokyo Institute
of Technology, Japan in 1990 and 1993, respectively. In 1993,he joined
Telecommunication and Information Systems Research Laboratory at Sony
Corporation, Japan where he holds two patents on Signal Compression. Since October
1998, he has been with Asian Institute of Technology where he is currently an
Associate Professor at Telecommunications Field of Study, School of Engineering and Technology. He is a
Senior member of IEEE and member of IEICE, Japan.
Sanika K. Wijayasekara was born in Sri Lanka on January 14, 1986. She received her B.Sc.(Hons) in IT
specialized in Computer System and Networking degree from Sri Lanka Institute of Information
Technology, Sri Lanka in 2010 and M.Sc in Telecommunications from Asian Institute of Technology,
Thailand in 2012. Her current research interests are in the area of Cross-Layer designs, QoS assurances in
multimedia applications and wireless network protocols.
Nobuhiko Sugino was born in Yokkaichi, Mie, Japan on November 19, 1964. He received B. Eng, M. Eng,
and D.Eng. in Physical Electronics from Tokyo Institute of Technology in 1987, 1989 and 1992,
respectively. Since 1992, he has been with Tokyo Institute of Technology, where is now an Associate
Professor at Department of Information System, Interdisciplinary Graduate School of Science and
Engineering. Dr. Nobuhiko Sugino is a member of IEICE and IEEE.

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