Routing management for mobile ad hoc networks

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME
464
ROUTING MANAGEMENT FOR MOBILE AD-HOC NETWORKS
Poonam Pahuja1
and Dr. Tarun Shrimali2
1
Research Scholar, Mewar University, Chittorgarh India
2
Research Supervisor, Mewar University, Chittorgarh India
ABSTRACT
A mobile ad hoc network (MANETs) is a collection of distributed nodes which
communicate using multi-hop wireless links with frequent node mobility. The frequent
mobility of nodes leads network partition and futile communication. To alleviate various
network connectivity and routing issues, we have proposed Cluster Based Distributed
Spanning Tree (CBDST) interconnection would be the better solution. CBDST structure is
organized into a hierarchy of groups. The nodes are put together in groups and groups are
gathered in groups of higher level, recursively. This organization, built on top of routing
tables allows the instantaneous creation of spanning trees rooted by any nodes and keeps the
load balanced between the nodes.
.
KEYWORDS: CBDST, MANET, Cluster Head, Mobile Node, NS-2
1. INTRODUCTION
A Mobile Ad-Hoc Network (MANET) is an autonomous collection of distributed
mobile users. Every host in a MANET works as a source and a sink, and also relays packets
for other hosts and is thus a router as well. This type of network can be used in fire, safety,
rescue, disaster recovery operations, conference and campus settings, car networks, personal
area networking, etc. MANETs have similar characteristics to other wireless communication
networks, which are mainly attributed to the wireless channel’s properties.
2. CLUSTER BASED DISTRIBUTED SPANNING TREE (CBDST)
CBDST structure is organized into a hierarchy of groups. The nodes are put together
in groups and groups are gathered in groups of higher level, recursively. This organization,
built on top of routing tables allows the instantaneous creation of spanning trees rooted by
any nodes and keeps the load balanced between the nodes.
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 3, May-June (2013), pp. 464-468
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465
2.1 Formation of Cluster
Initially each node is assigned a unique ID value. It broadcasts its ID value to its
neighbors and builds its neighborhood table. Each node calculates its own weight based on
the following factors:
i) Node connectivity: The number of nodes that can communicate directly with the given node
i.e. that are in its transmission range.
ii) Battery Power: The power currently left in each node. The energy is consumed by sending
and receiving of messages.
iii) Mobility: Running average of speed of each node. If mobility is less, the node is more
suitable to become cluster head CH.
iv) Distance: Sum of distance of the node from all its neighbors.
Each node broadcasts a beacon message to notify its presence to the neighbors. A
beacon message contains the state of the node. Each node builds its neighbor list based on the
beacon messages received. The cluster-heads election is based on the weight values of the
nodes and the node having the lowest weight is chosen as CH. Each node computes its weight
value based on the following algorithm:
2.2 Node Weight Computational Algorithm
Step 1: The coefficients used in weight calculation are assumed the following values w1=0.7,
w2=0.2, w3=0.05, w4=0.05; The sum of these co-efficient is 1.This is actually used to
normalize the factors such as spreading degree, distance with its neighbors, mobility of the
node, and power consumed used in the calculation of weight of a node. The factors node
connectivity and distance with its neighbors are given more importance and assumed higher
co-efficient values 0.7 and 0.2 respectively
Step 2: Compute the difference between the optimal cluster’s size ‘α’ and the real number of
neighbors ‘R (V)’as node connectivity ,nc= 1-(| α -R (V)| / α)
Step 3: For every node the sum of the distances, Dv, with all its neighbors is calculated.
Dv= Σdist(v,v’) where v’ €N(v)
Step 4: Calculate the average speed for every node until the current time T. This gives the
measure of the mobility Mv based on the X co-ordinate and Y co-ordinate i.e. position of the
node v at all previous time instance t.
Step 5: Determine how much battery power has been consumed as Pv. This is assumed to be
more for a Cluster-Head when compared to an ordinary node. Because Cluster-Head has
taken care of all the members of the cluster by continuously sending the signal.
Step 6: The weight Wv for each node is calculated based on
Wν = (w1× nc) + (w2× Dν ) + (w3×Mν ) + (w4× Pν )
Where nc is the node connectivity, Dv is the distance with its neighbors, Mv is the mobility
of the node, and power consumed is represented by, Pv
Step 7: The node with the smallest Wv is elected as a cluster-head. All the neighbors of the
chosen cluster-head are no more allowed to participate in the election procedure.
Step 8: All the above steps are repeated for remaining nodes which is not yet elected as a
cluster-head or assigned to a cluster.
2.3 Cluster Maintenance
The second phase is the clustering maintenance. There are defined two distinct types
of operations for cluster maintenance: the battery power threshold property and the node
movement to the outside of its cluster boundary.

466
2.3.1 Node Movements
The node movements can be in the form of node joining or node leaving a cluster.
These operations will have only local effects on the clustered topology if the moving node is
a MN node. If the leaving node is CH node, the cluster reorganization has to be performed for
the nodes in the cluster by evoking the clustering algorithm.
Step1: Broadcast a beacon signal to all its neighbor nodes in the transmission range;
Process the signals received from the neighbor nodes in the network and form the connection
matrix, A;
Step2: Calculate the Degree R (U) using A, Spreading degree, ∆sp, Sum of the distances, Du,
with all its neighbors, Average speed, Mu, Amount of battery power that has been consumed,
Pu, and Weight of node U as Wu;
Step3: If(A Cluster head already exist in the neighborhood)
Send request to join the Cluster;
Else
Form a new cluster and declare itself as the cluster-head;
2.3.2 Battery Power Threshold
The battery power of the nodes participating in the Clustering changes continuously.
The Cluster-Heads Power decreases more rapidly when compared to the Cluster Members.
When the Cluster-Heads Battery Power falls below a threshold then the node is no longer
able to perform its activates and a New Head from the members available need to be chosen.
Step1: Verify the threshold on the Cluster -Head’s Battery power;
If (Battery power < Threshold)
Cluster-Head sends a LIFE_DOWN message to all its Neighbors;
All the Member nodes participate in the Re-Election Procedure using
Modified Weighted Clustering Algorithm and the Node with least weight is
selected as the New Cluster-Head;
Else
Re-election is not needed;
3. RESULTS AND DISCUSSION
The mobile network model assumed in this project consists of random number of
mobile nodes with each node having fixed energy and random mobility. Further each node
starts with some energy and its energy decreases each time it passes a message. A node fails
if its whole energy has been consumed. A failed node is represented as a black node. The
measured performance of the proposed algorithm was compared with AODV using NS2
simulation tool.
3.1 Packet Delivery Ratio (PDR)
The Packet Delivery ratio is based on the number of packets delivered and number of
packets sent. Higher the pause time better is the performance of the Cluster. The CBDST
technique approaches a ratio of one or 100% delivery as the network becomes more static in
figure 1. Only under dynamic conditions the packets collide or the packets are lost. As the
pause time increases the static nature is enhanced. CBDST also delivered good PDR against
the scalability.

467
3.2 Connectivity
The connectivity of nodes gives path for data packets to be forwarded without loss
and comparison results are shown in the above graph. NS2 simulation with varying node
count ranging from 10 to 100 is used to study the percentage of connectivity in wireless
environment. Maximum possible connectivity is 100% where each node is connected with
every other node. CBDST perform well over AODV while increasing number of nodes and
pause time as shown in figure 2. CBDST has good scalability and robustness.
4. CONCLUSION
We have proposed new routing management for better connectivity, network lifetime,
less energy consumption and equally load balancing of the cluster head by re-elected the
cluster head after a threshold. The CBDST perform well over AODV for packet delivery
ratio and scalability
0
0.2
0.4
0.6
0.8
1
1.2
50
150
250
350
450
Pause Time in Sec
PacketDeliveryRatio
AODV
CBDST
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
50
150
250
350
450
Pause Time in sec
connectivityrate(%)
AODV
CBDST
0
0.2
0.4
0.6
0.8
1
1.2
10
30
50
70
90
Number of Mobile Nodes
PacketDeliveryRatio
AODV
CBDST
0
0.2
0.4
0.6
0.8
1
1.2
10
30
50
70
90
Number of Mobile Nodes
Cnnectivityratein(%)
AODV
CBDST
Fig 1. Packet Delivery Ratio Fig 2. Route Connectivity

468
REFERENCES
[1] S. Lee, J. Hsu, R. Hayashida, M. Gerla, and R. Bagrodia, “Selecting a Routing
Strategy for Your Ad hoc Network,” Special issue on Advances in Computer
Communications and Networks: Algorithms and Applications, vol. 26, no. 7, pp. 723-
733, May 2003.
[2] C. Perkins, E. Belding-Royer, and S. Das, “Ad Hoc On Demand Distance Vector
(AODV) Yu .J.P. & Chong
[3] P.H.J. (2005).A Survey of Clustering Schemes for Mobile Ad Hoc Networks. IEEE
communications Surveys and Tutorials, Vol. 7, No. 1, pp. 32-48. Routing,” IETF
RFC3561, July 2003.
[4] Yu .J.P. & Chong P.H.J. (2005).A Survey of Clustering Schemes for Mobile Ad Hoc
Networks. IEEE Communications Surveys and Tutorials, Vol. 7, No. 1, pp. 32-48.
[5] Deniz Cokuslu Kayhan Erciyes and Orhan Dagdeviren A Dominating Set Based
Clustering Algorithm for Mobile Ad hoc Networks, Springer Computational Science –
ICCS (2006).
[6] P. Victer Paul, T. Vengattaraman, P. Dhavachelvan & R. Baskaran, Improved Data
Cache Scheme Using Distributed Spanning Tree in Mobile Adhoc Network,
International Journal of Computer Science & CommunicationVol. 1, No. 2, July-
December (2010).
[7] M. Ahmed, S. Yousef and Sattar J Aboud, “Bidirectional Search Routing Protocol for
Mobile Ad Hoc Networks”, International Journal of Computer Engineering &
Technology (IJCET), Volume 4, Issue 1, 2013, pp. 229 - 243, ISSN Print: 0976 – 6367,
ISSN Online: 0976 – 6375.
[8] V. Bapuji, R. Naveen Kumar, Dr. A. Govardhan and Prof. S.S.V.N. Sarma,
“Maximizing Lifespan of Mobile Ad Hoc Networks with Qos Provision Routing
Protocol”, International Journal of Computer Engineering & Technology (IJCET),
Volume 3, Issue 2, 2012, pp. 150 - 156, ISSN Print: 0976 – 6367, ISSN Online:
0976 – 6375.

Routing management for mobile ad hoc networks

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Routing management for mobile ad hoc networks