A scalable and power efficient solution for routing in mobile ad hoc network (manet)

International Journal of Mobile Network Communications & Telematics ( IJMNCT) Vol. 4, No.2, April 2014
DOI : 10.5121/ijmnct.2014.4201 1
A SCALABLE AND POWER-EFFICIENT SOLUTION
FOR ROUTING IN MOBILE AD HOC NETWORKS
(MANET)
Kirtikumar K. Patel1
Dhadesugoor.R.Vaman2
Prairie View A & M University
Prairie View, TX-77446
1
– Doctoral Candidate; Electrical and Computer Engineering Department
2
– Texas A&M University System Regents Professor, Electrical and Computer
Engineering Department
ABSTRACT
Mobile Ad Hoc Network (MANET) is a very dynamic and infrastructure-less ad hoc network. The actual
network size depends on the application and the protocols developed for the routing for this kind of
networks should be scalable. MANET is a resource limited network and therefore the developed routing
algorithm for packet transmission should be power and bandwidth efficient. These kinds of dynamic
networks should operate with minimal management overhead. The management functionality of the
network increases with number of nodes and reduces the performance of the network. Here, in this paper,
we have designed all identical nodes in the cluster except the cluster head and this criterion reduces the
management burden on the network. Graph theoretic routing algorithm is used to develop route for packet
transmission by using the minimum resources. In this paper, we developed routing algorithm for cluster
based MANET and finds a path from source to destination using minimum cumulative degree path. Our
simulation results show that this routing algorithm provide efficient routing path with the increasing
number of nodes and uses multi-hop connectivity for intra-cluster to utilize minimum power for packet
transmission irrespective of number of nodes in the network.
KEYWORDS
MANET, routing, mobility, Graph Theory, Scalable, Power efficient, wireless network
1. INTRODUCTION
Mobile Ad Hoc Network (MANET) [1] is often characterized as a system of dynamic nodes that
communicate over wireless links. It does not use towers or base stations to perform packet
transmission [2, 3]. It has all mobile nodes and they move freely in the network independent of
each other. Also, each node is able to send and receive data from other nodes in the network as
well as it can work as a router. Also, MANET is both power and bandwidth constrained and yet it
is expected to provide multi-service provisioning with end-to-end Quality of Service (QoS)
provisioning to end users. QoS in MANET is defined as the collective effect of service
performance with constraints on delay, jitter, system buffer, network bandwidth, number of hops,
power at each node, node mobility in MANET, and packet loss. When a source node is unable to
send packet to destination node, it uses another intermediate node as relay to forward packet to
the destination node. Using this multi-hop connectivity for the packet transmission, we can save
power at each intermediate node [4, 5]. However, since the nodes move freely, maintaining
continuous path connectivity imposes additional complexity. MANETs rely on all participating

2
nodes to share the task of routing and forwarding peer traffic. Thus, it is very necessary to
develop a routing algorithm which can be efficient and resource saving as well in terms of power
and bandwidth usage. It can improve the overall efficiency of the network to provide quality of
service (QoS) assurance for the required application. Also, the performance efficiency achived
with a small set of nodes must be scalable for large set of nodes. Furthermore, in MANET, fast
and unpredictable topology changes due to nodes mobility, and channel capacity vary due to
environmental effects. Thus, it is more prone to errors compared with that of wired networks and
reduces the overall network throughput than equivalent wired network.
This paper is organized as follows. Section II describes literature review. The basic system design
is described in Section III followed by acceptable performance of graph theoretic routing
algorithm in section IV. Section V concludes the paper.
2. BACKGROUND
A communication system requires packet transmission process. This packet transmission can be
done after finding a path from source to destination. Thus, a routing path is required to deliver a
packet from source to destination in the network. There are many routing algorithms have been
developed by researchers for different kind of networks [6, 7, 8, 9, 10, 11, 12]. We had discussed
many routing protocols with their advantages and limitations in our previous research [4, 5] and
these limitations prohibit them to be useful for deployment in a scalable MANET. Most of these
algorithms which have evolved over years for wired internet are not suitable for scalable MANET
application. MANET is wireless ad hoc network which consists of mobile nodes. Also, wireless
channel is more prone to error than wired channel. It can be influenced by obstacles, weather
conditions and different kind of disturbance occurred in wireless channel. Reactive routing
protocols use flooding technique to find the new route if an existing route breaks and thus it will
direct to more packet loss in deciding the on-demand routing algorithm. It uses much network
overhead in finding the new route and ad-hoc networks have very limited resources therefore it’s
not adequate idea to use more resources to find new route even if there is no guarantee that new
selected route will be more effective than previous one. In comparison, proactive routing
protocols provide higher routing efficiency in scattered traffic pattern and high mobility network.
It maintains all the routes periodically thus it is very feasible to change the route at any point. It
avoids finding of new route on demand. This technique does not use flooding technique and
therefore it doesn’t add any extra overhead to packet and saves available bandwidth usage
accordingly. In our previous papers, we demonstrated a reason to develop a scalable and power
efficient routing algorithm which can provides preemptive action and handles mobility by using
multi-hop connectivity and send packets through less contended path. Many researchers provide
their multi-hop connectivity based on shortest path and minimum power. But this shortest path is
not always efficient in terms of provide efficient transmission which utilize less amount of
resources and minimize the total transmission delay.
As per author’s knowledge, there is no research published which can help to find a route for
packet transmission over minimum contended path. Here we are presenting an idea to route
packets through minimum cumulative degree path using the graph theoretic routing algorithm for
cluster-based MANET where degree is calculated as number of incoming links to particular node
[13, 14, 15, 16].

3
3. SYSTEM MODEL
We have considered a cluster based MANET architecture and it is explained in detail in our
previous research paper [4] as well as it is shown in Fig.1 as below. The proposed scheme
develops a route for packet transmission from the source node to a destination node based on the
available degree and congestion at each node. This newly developed routing algorithm reduces
the scheduling time at each node by selecting the least congested node first in routing path;
consequently this reduces the overall delay and accomplishes the targeted QoS for the application.
In addition, it is proactive routing and it saves bandwidth and power at each intermediate node
consequently to increase efficiency of the MANET. In this section, we will show that our
designed algorithm is scalable and it is power efficient. Here we used multi-hop connectivity for
the packet transmission and our simulation results show that the multi-hop connectivity is more
power efficient than the direct transmission. It also proves that the developed routing algorithm is
scalable. The newly developed route will always be efficient in terms of transmission
delay and network throughput despite of the increasing nodes in the network.
Figure 1. MANET Architecture
3.1. Power Efficiency
In packet transmission from a source to the destination, we should consider the processing power
as well as the transmission power for the packet. Processing power is the power which is used to
calculate the route as well as to generate a new packet to send. The main aim of power control is
to reduce the total power consumption at each node and increase the network lifetime by
increasing the residual power of battery. However we can achieve multiple benefits by controlling
the transmission power in an ad hoc network and alleviate the impact of interference by using less
amount of power at each node using multi-hop connectivity to satisfy QoS requirement for the
appropriate application. Optimal power consumption in ad hoc network can be achieved by
following techniques.

4
Power required to transmit a packet from source to destination is calculated based on the distance
between them. For a transmission from source node k to destination node j at data rate R,
separated by a distance dkj, the transmitter power at k is modeled by eq. 1 as shown below.
* kj
P R dα
=
(1)
Where α is the channel loss exponent (typically between 2 and 4, depending on the channel
medium) [17]. Throughout this research, we assume α = 3. We are working in wireless medium
and channel conditions are not fixed in wireless medium. Sometimes there may some interference
available in channel thus at that time our = 4 and in case of absence of interference α = 2. There is
always zero probability of having interference, Therefore, for the better safe side, we considered α
= 3. It is obvious that the direct communication requires higher transmission power from source
node than Multihop communication.
1 2 3 1 2 3( ... ...n nd d d d d d d dα α α α α
+ + + + ) >> + + + +
(2)
From equation 2, we can say that the long range transmission consume more power in
transmission than total sum of several shorter range transmission [18, 19]. Therefore, each node
can save power and increase the network lifetime.
3.2. Scalability
A network size can be very depending on the application. Ad hoc network is mainly used in
Battle field theatre applications and emergency disaster management. In this kind of applications,
network size can’t be predicted in advance and therefore, all solutions related to mobile ad hoc
network should be scalable as well as power efficient. Routing in mobile ad hoc network has been
an interesting topic for research since long time. Also, graph theory has been applied to lot of
static problems. Here, we developed a graph theoretic based routing algorithm for dynamic ad hoc
network. Routing algorithm should be designed in such a way that it doesn’t affect the network
performance even if the number of nodes increases as per the desired application. In our previous
research [5], we have designed an algorithm to determine the number of intermediate nodes based
on the location of destination node and source node. Using the method of power circle, we can
find the total number of intermediate nodes which can relay the information from the source node
to the destination node. We also showed that the algorithm is capable of handling the mobility of
the nodes. Our routing scheme is applicable to the inter-cluster as well as intra-cluster
connectivity.

5
4. Simulation Results
We had simulated the MANET architecture on MATLAB software. In order to perform this
simulation, we developed a MANET where all nodes are randomly distributed and its area 200 x
200 meters. We have also allowed each node to move randomly and independent. Figure 2
demonstrate the random distribution of 50 nodes in the whole network. To prove the scalability of
the network, we vary network size till 200 nodes. We have selected a random source and a
destination node which can communicate by selecting a path from our developed routing
algorithm using minimum cumulative degree.
Figure 2. Random distribution of nodes in the network
4.1. Power Efficiency
MANET is combination of mobile nodes which has limited power. Thus, in order to prove the
power efficiency of the graph theoretic routing algorithm, we have simulated different number of
source-destination pairs and measure the power of each transmission. Figure 3 shows that the
multi-hop connectivity gives better power utilization at each node. Using multi-hop connectivity
we can save power at each intermediate node and increase the network life consequently. For the
case shown in Fig.4, source node sends packets to destination node using one intermediate node
and the power calculation for this case is calculated using the equation. 1 as indicated in table 1.
Also, for the same source and destination pair, the source node sends packet to the destination
node using two intermediate nodes as shown in Fig. 5 and its power is calculated in table 2. By
comparing table 1 and 2, we can conclude that using multi-hop connectivity, each node has to
utilize less amount of power rather than direct transmission. In addition, in this case, the total
power required to send packet from the source node to the destination node is lesser when source
node utilize two intermediate nodes.
0 20 40 60 80 100 120 140 160 180 200
0
20
40
60
80
100
120
140
160
180
200
Node Location in MANET at initialization
Node location in X axis
NodelocationinYaxis
1
2
3
4
5
6
7
8
9
10
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12
13
14
15
16
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31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

6
Figure 3. Direct Transmission Vs. Multi-hop Transmission
Figure 4. Source to Destination Connectivity using Single Intermediate Hop
Table 1. Power Calculation for Case shown in Fig. 4
0 10 20 30 40 50 60 70 80 90 100
1.04
1.06
1.08
1.1
1.12
1.14
1.16
1.18
1.2
Required Power for Packet Transmission
Number of Source-Destination Pair
Requiredpower(mw)
Direct Transmission (mean - 1.1435)
multi-hop transmission (mean - 1.1049)
0 50 100 150 200 250 300 350
-50
0
50
100
150
200
250
300
1
2
3
4
56
7
8
9
10
11
12 13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
2829
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Node Location in Different Circles at Initialization
Node location on X axis
NodelocationonYaxis

7
From Node 41
to node 7
From Node 7
to node 16
Total
Required Transmission
power (mw)
0.497 4.518 5.0162
Figure 5. Source to Destination Connectivity using Two Intermediate Nodes
Table 2.Power Calculation for Case shown in Fig. 5
From Node
41 to node 7
From Node 7
to node 4
From node 4
to node 16
Total
Required
Transmission power
(mw)
0.186 2.164 2.627 4.978
We also measure the end-to-end power for 50 and 100 source destination pairs and it is illustrated
in Fig. 6 and Fig. 7 respectively. These simulation results shows that the overall transmission
power for different source-destination pair for two separate algorithms. It proves that the power
required by graph theoretic routing algorithm is lesser than the shortest path algorithm. We also
increase number of nodes in the network to verify the power efficiency of the designed algorithm
and proved that the graph theoretic routing algorithm performs better than the shortest degree path
algorithm. It is shown in Fig. 8.
0 50 100 150 200 250 300 350
-50
0
50
100
150
200
250
300
1
23
4
5
6
7
8
9
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14 15
16
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2829
30
31
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3435
36
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41
42
43
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45
46
47
48
49
50
Node Location in Different Circles After Movement of Nodes
Node location on X axis
NodelocationonYaxis

8
Figure 6. Total Required power for each source-destination pair
Figure 7. Total required power for each source-destination pair
0 5 10 15 20 25 30 35 40 45 50
1
1.5
2
2.5
3
3.5
4
4.5
5
Total Required Transmission Power
Processingpower(mw)
Minimum Cumulative Degree Algorithm (mean - 3.0983)
Shortest Distance Path (mean - 3.1197)
0 10 20 30 40 50 60 70 80 90 100
1
1.5
2
2.5
3
3.5
4
4.5
5
Total Required Transmission Power
Processingpower(mw)
Minimum Cumulative Degree Algorithm (2.8730)

9
Figure 8. Total required power for different size of networks
4.2. Scalability
To demonstrate the scalability for the MANET using minimum degree based routing algorithm,
we have only considered intra-cluster connectivity in this research paper. Here we have simulated
a MANET for 100 different source-destination pairs and each time source-destination pairs
selected randomly. For the first case as shown in Fig. 9 we have simulated 50 pairs and we can
see that minimum degree based routing path has less number of degree with compared to shortest
distance path. Shortest distance path routing algorithm derives a path without knowledge of
congestion at each node based on its degree and graph theoretic routing develops route based on
node’s available congestion. Thus, degree is directly proportional to delay of packet transmission
and therefore, minimum degree algorithm provides lesser delay in packet transmission. In the
second case, we have increased our simulation for 100 different random source-destination node
pairs and we got the same result as we got in Fig. 10. We also extended our research to prove
scalability of the algorithm, we varies network size from initial condition to 200 nodes. And each
time we have some source-destination pairs. Fig. 11 shows the mean value of total cumulative
degree in each case. It also shows that as number of nodes increases, our newly developed
algorithm gives better performance and reduces overall delay and increase throughput of the
network.
0 20 40 60 80 100 120 140 160 180 200
2.2
2.4
2.6
2.8
3
3.2
3.4
3.6
3.8
Required power with increasing number of nodes
Processingpower(mw)
Nodes
Minimum Cumulative Degree Algorithm (mean - 3.1775)

10
Figure 9. Cumulative degree for each source-destination pair
Figure 10. Cumulative Degree for each source-destination pair
0 5 10 15 20 25 30 35 40 45 50
0
5
10
15
20
25
30
Cumulative Degree for Different Algorithms
MinimumCumulativeDegree
Number of Source-Destination pair
Minimum Cumulative Degree Path
Shortest Distance Path
0 10 20 30 40 50 60 70 80 90 100
0
5
10
15
20
25
X: 47
Y: 13
Cumulative Degree for Different Algorithms
MinimumCumulativeDegree
Number of Source-Destination pair
X: 47
Y: 19
X: 68
Y: 9
X: 68
Y: 20X: 24
Y: 15
X: 24
Y: 22
Minimum Cumulative Degree Path

11
Figure 11. Cumulative Degree for different size of networks
5. Conclusion
MANET is a dynamic network and therefore, it is a critical to route packet in this kind of
network. Here, we developed a graph theoretic routing algorithm for inter-cluster as well as intra-
cluster network. We designed all identical nodes except cluster head and thus we reduced
management overhead in order to provide highly efficient routing for the packets to deliver at
destination. By simplifying and using the minimum cumulative degree path as a preferred route to
destination, we can minimize the overall delay and increase the throughput as well as network life
by using multi-hop connectivity to save power at each intermediate node. In addition, our
simulation results proves that the average processing power required at nodes with buffer is
higher as compared to node without buffer in ad hoc network. Also, this routing algorithm is
scalable and provides preemptive action which reduces the overall packet loss and able to provide
efficient packet transmission.
ACKNOWLEDGEMENTS
This research work is supported in part by the National Science Foundation NSF 0931679. The
views and conclusions contained in this document are those of the authors and should not be
interpreted as representing the official policies, either expressed or implied, of the National
Science Foundation or the U. S. Government.
0 20 40 60 80 100 120 140 160 180 200
0
5
10
15
20
25
30
Cumulative Degree with increasing number of nodes
CumulativeDegree
Nodes
Minimum Cumulative Degree Algorithm

12
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Authors
Kirtikumar K. Patel received the B.S. degree in Electronics and Communication Engineering from
Hemchandracharya North Gujarat University, India, and M.S. degree in Electrical Engineering from Lamar
University, United States of America in 2006 and 2008, respectively. He is currently working towards his
PhD degree in the Department of Electrical and Computer Engineering at the Prairie View A&M
University, a member of the Texas A&M University System. His current research interests include mobile
ad hoc network, routing algorithms, Communication and signal processing as well as graph theory
applications and contention resolution algorithms.

13
Dhadesugoor R. Vaman is Texas Instrument Endowed Chair Professor and Founding Director of ARO
Center for Battlefield Communications (CeBCom) Research, ECE Department, Prairie View A&M
University (PVAMU). He has more than 38 years of research experience in telecommunications and
networking area. Currently, he has been working on the control based mobile ad hoc and sensor networks
with emphasis on achieving bandwidth efficiency using KV transform coding; integrated power control,
scheduling and routing in cluster based network architecture; QoS assurance for multi-service applications;
and efficient network management. Prior to joining PVAMU, Dr. Vaman was the CEO of Megaxess (now
restructured as MXC) which developed a business ISP product to offer differentiated QoS assured multi-
services with dynamic bandwidth management and successfully deployed in several ISPs. Prior to being a
CEO, Dr. Vaman was a Professor of EECS and founding Director of Advanced Telecommunications
Institute, Stevens Institute of Technology (1984-1998); Member, Technology Staff in COMSAT (Currently
Lockheed Martin) Laboratories (1981-84) and Network Analysis Corporation (CONTEL)(1979-81);
Research Associate in Communications Laboratory, The City College of New York (1974-79); and
Systems Engineer in Space Applications Center (Indian Space Research Organization) (1971-1974). He
was also the Chairman of IEEE 802.9 ISLAN Standards Committee and made numerous technical
contributions and produced 4 standards. Dr. Vaman has published over 200 papers in journals and
conferences; widely lectured nationally and internationally; has been a key note speaker in many IEEE and
other conferences, and industry forums. He has received numerous awards and patents, and many of his
innovations have been successfully transferred to industry for developing commercial products.

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