International Journal of Engineering Trends and Technology- Volume4Issue1- 2013
Mr.Rakesh Shivhare
M.Tech. Scholar
CSE Deptt
RITS,Bhopal
Prof. Ashish Khare
Assistant Prof.
CSE Deptt
RITS,Bhopal
Dr R.C,Jain
Director
SATI Vidisha
Abstract
Mobile Ad-hoc Network is a temporary infrastructure less internet where node can travel across anywhere anytime without loss their connectivity with the rest of the world .For any communication operation over the MANET source node broadcast a message to all the other nodes in the network. . Network wide broadcasting in Mobile Ad Hoc Network provides important control and route establishment functionality for a number of unicast and multicast protocols. Broadcasting in MANET poses more challenges than in wired networks due to node mobility and scarce system resources. Broadcasting is categorized into deterministic and probabilistic schemes. This paper presents a probabilistic routing protocol having better performance in terms of packet delivery fraction ratio, packet loss ratio and average latency in rebroadcasting a route request message by using D-S belief method. Simulation results are presented, which shows packet loss, saved load distribution and packet delivery fraction ration of the probabilistic broadcast protocols. another end so there is a need of multiple networks’ hope. A simplest way of routing can be use the geographic information of each node. Collect the geographic information of each node and perform routing between them.
Keywords
— Bayesian approach , D-S theory, Belief Theory.
I.
I NTRODUCTION
Wireless communication is a widely adopted system in the current scenario. It needs a huge exploitation in this era with independent mobile users. A Mobile Ad-Hoc Network is collection of independent mobile users that communicate over comparatively bandwidth controlled by wireless links. This collection is temporary available and can be change at run time. All nodes of MANET act as router and there are able to work without the facility of any infrastructure and centralized administration. Dynamic topology of network is an important scheme to make MANET popular. It is necessary to be energy constraint of end user of MANET[1]. There are many applications available like military operations, disaster management, medical facility etc.
As far as the routing protocols are concert for the
MANET, it is a very difficult task due to its dynamic change of topology. Number of protocols has been implemented like
AODV and DSDV for deciding the route in the network. The mobile node of a network work on limited bandwidth so that each node should have capable of searching and sensing its adjacent node. Due to limited bandwidth or transmission range node as to generate keep alive signal to show its existence in a network [2]. If one node transmits one end to
Figure 1 Simple Ad-Hoc Network with nodes
Figure 1 shows the simple architecture of a temporary network in order to communication. This temporary network can change as per requirement of user. When user moves in the network than the protocol work in the network as per the scenario.
II.
RELATED WORK
Behavior analysis is a important task for any network. In this paper [1] the author examines the behavior of Ad Hoc networks. This analysis has done with the help of simulations, using different routing protocols and topologies. The author also calculates node mobility effects, by looking into both static and fully mobile configurations. The paper presents a systematic analysis with respect to various ad-hoc network and the topologies used at the current scenario. Author also considers node placement, node mobility and routing protocols for calculations.
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International Journal of Engineering Trends and Technology- Volume4Issue1- 2013
In the paper author throw some light on the wireless mesh network [2]. It also provides the excellent review of protocols last Node D must transmit the packet before v’s RAD timer expires. with respect to ad-Hoc Network.
In the proposed paper the author analyzed the various adhoc network protocols [3].especially for the MANET. The performance of AODV, OLSR and TORA was analyzed with the help of OPNET modeler which is easy to access. The protocols were tested using the same parameters with high
CBR traffic flow and random mobility. Performance of protocols with respect to scalability has also analyzed.
This paper [4] explains the principle of the node degree algorithm. The author selects nodes with high probability to forward data packets in the process of routing searching.
Results of simulation show: number of relay nodes is reduced significantly in the process of routing setup.
MANET use to forward route request by all node of the network which is known as flooding [5]. This paper presents the experiments with three alternative broadcast algorithms which has reduced the number of forwarders although different algorithms for node selection. Simulation results suggest that in general, any of these algorithms outperforms flooding in delivery ratio and number of retransmissions. The author highlighted the performance differences of the algorithms when experimented in different network conditions.
C Chen and Y.-C. Tseng [9, 10] proposed a distance based approach in which there is no need to rebroadcast if it receives the initial packet from a source node s that is within a threshold distance D.
IV.
PROPOSED WORK
Proposed methodology is probabilistic On–demand Routing
Protocol based on D-S belief function present a novel way of finding route from source to the destination based on highest mutually probabilities (affinity) of each node towards the particular destination, which is calculated using belief function as show in equation 2 along with that prototype also makes sure that the data travels through shortest route only, thereby minimizing the time delays between sending and receiving of data packet.
Prototype system transmits data alternately via two disjoint paths for distribute traffic and to reduce traffic through one path and also to avoid the loss of battery power that may occur a result of standby mode of nodes in second path, as they might only be waiting for the data packet to be received.
III.
P ROBABILISTIC A PPROACHES
Deterministic routing approach suffered from rebroadcasting problem whereas Probabilistic approaches overcome the problem of rebroadcasting and present best ways to reduce rebroadcast. In a probabilistic scheme, nodes rebroadcast the message with a pre determined probability p. the studies in
[6,7] shows that probabilistic broadcast incurs significantly lower overhead as compared to blind flooding. J. Cartigny ,D.
Simplot[8] and Y.-C. Tseng, S.-Y. Ni, and E.-Y. Shih[9] proposed a simple probabilistic approach of probability 1 or 0 for rebroadcasting. A node will broadcast either with probability 1 or with probability 0. That means with probability 1 it behaves like a flooding approach whereas with
0 probabilities it is not broadcasting a single packet. P = f (N,
P) Where P is the probability that a node forwards the broadcast packet and N is the number of nodes in the network.
The function f depends on the specific protocol being analysed.
C Chen, Chin-Kai Hsu and Hsien-Kang Wang [10] proposed a
Counter-Based scheme in which a transfer node S will rebroadcast if and only if it receives packet smaller than a threshold value (T ie the number of copies of a packet received before the expiry RAD). Each node aims duplicate packets with a counter, the counter is received by 1 for each duplicate packet for the RAD expires increased. For any node
S, a duplicate packet from a random node D, three things happen in chronological order initially Node D must be a neighbour of node S then Node D must transmit the packet at
In proposed methodology every node maintains a history table that maintains neighbor node’s id and the value of the attributes responsible for MAI.
MAI indexing value is calculated for each intermediate node within the path with respect to its neighbour’s node and forward the data packet to the neighbour node having Highest
MAI value
V.
MAI
INDEXING
MAI is a probabilistic indexing value based upon past experience. It is very helpful to find out how reliability of
Particular node with respect to any desired destination calculated by using the belief function [14, 15].
( ) *
(
(
) ( )
) ( ) ( ) ( )
+
As shows in equation number 1, Ni and Si is the neighbor node, source node respectively whereas A is the list of attribute responsible mainly routing overhead, packet delay, packet drop and throughput in past with successful transmission.
( ) ⌈ ⌉
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International Journal of Engineering Trends and Technology- Volume4Issue1- 2013
⌈
∑
⌉ ……….2
If (Hop count (Nib(S i
) unicast Rres(Di))<=Max Hop count for scenario )
In the example below, source S broadcasts a route request path for destination D. MAI indexing value is calculated for each intermediate node within the path with respect to its neighbor’s node and forward the data packet to the neighbor node having Highest MAI value.
S
R
G
P
Q
Highest MAI Index
Moderate MAI Index lowest MAI Index
W
U
V
X
Z M
Y
D
Figure 1: Packet Transmission Through Mutual Affinity Index
VI.
P ROPOSED A LGORITHM
Assumption
1.
T
N
=
∑
// Total Number Node in Network
2.
Nib(N i
) = Neighbor node with respect to each node N i
3.
S i
= Any node N i
that behave like source node
4.
D i
= Any node N i that behave like destination node
5.
R req
= Route Request Message
6.
R res
= Route Response Message
7.
Response[I,J]= 1D Table that maintain Route Request Response
From Any node I to node J
8.
HT[I,J]= 2D table that maintain MAI indexing value of any S i with respect to their neighbor Nib(S i
)
9.
Hop count (P)= return Number of hop count in path P
10.
Max MAI indexing Value(Select_Nib(Si)=0
11.
Select_Nib(S i
)= node having highest MAI indexing value
Algorithm (Next Node for Route)
{
For (Nib(S i
)=1;Nib(S i
)>=∑Nib(S i
);Nib(S i++
))
{
Sender S i
broadcast R req
for D i

Nib(S i
)
Nib(S i
) unicast Rres(Di)

S i
Then
Response [S i
, Nib (S i
) ]=1
Else
Response [S i
, Nib (S i
) ]=0
}
For (Nib(S i
)=1;Nib(S i
)>=∑Nib(S i
);Nib(S i++
))
{
If (Response [S i
, Nib (S i
) ]=1)
{
Else If (Max MAI indexing
Value[Select_Nib(Si)]>=MAI Indexing [S i
, Nib(S i
)])
Else
{
Max MAI indexing Value[Select_Nib(Si)]=MAI
Indexing [S i
, Nib(S i
)]
Select_Nib(S i
)= Nib(S i
)
}
}
}
Return [Select_Nib(S i
)]
}
Algorithm (MAI indexing)
{
For (Nib(S i
)=1;Nib(S i
)>=∑Nib(S i
);Nib(S i++
))
{
MAI Indexing [S i
, Nib(S i
)]
⌉
VII.
S IMULATION & R ESULTS
Proposed prototype system simulated by using the network simulator NS2 using 802.11 wireless network, route for every mobile nodes generate randomly with payload 1400 bytes. For
(
( )
) ⌈
HT[S i
,Nib(S i
)]=
(
( )
)
}
ISSN: 2231-5381 http://www.internationaljournalssrg.org

International Journal of Engineering Trends and Technology- Volume4Issue1- 2013 calculating packet delivery fraction, delivery ratio, routing load, throughput ratio and packet delay with random network size, as shown in fig 2, 3 both image show result report generate by proposed work having all the parameter for 50 node and 100 nodes scenario respectively.
Further graph 4 shows load distribution ie routing load of proposed scenario with different network size which is defiantly increase as compare to existing technique because proposed prototype system use more control packet as compare to simple Bayesian probabilistic routing approach for route discovery.
Figure 4 : Lode distribution
Figure 2: Scenario Result for 50 Nodes
But proposed mythology shows better response in packet delivery fraction ratio as show in figure 5 because proposed methodology use alternate path for simultaneous transmission to distribute channel load to any alternate channel.
Figure 5:- Packet Delivery Fraction Ratio
3 : Scenario Result for 100 nodes
Figure
Figure 6:- Packet Transmit and Loss Ratio of Proposed Work
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International Journal of Engineering Trends and Technology- Volume4Issue1- 2013
International Symposium on MANET and
Computing, 2003
[3]
C. E. Perkins, and P. Bhagwat, ―Highly dynamic
Destination-Sequenced Distance-Vector routing
(DSDV) for mobile computers,‖ ACM SIGCOMM
Computer Communication Review, London, UK. Oct
Figure 6:- Packet Transmit and Loss Ratio Of Existing Work
1994. 23:234-244.
[4] D. B. Johnson, D. A. Maltz, ―Dynamic Source
Routing in Ad HocWireless Networks,‖ Internet draft, draft-ietf-manet-dsr-00.txt, March 1998.
[5] Rusheel Jain, Murali Parameswaran, Chittaranjan
Hota , ―An Efficient On-Demand Routing Protocol forMANETs using Bayesian Approach‖ in 2011
IEEE
[6] Y. Sasson, D. Cavin, and A. Schiper, Probabilistic
Broadcast for flooding in wireless mobile ad hoc networks, In Proc. IEEE Wireless Communications
& Networking Conference (WCNC 2003), pp.
1124–1130, March 2003.
[7] S. Ni, Y. Tseng, Y. Chen, and J. Sheu., "The broadcast storm problem in a mobile ad Hoc networks," in Proceeding of theACM/IEEE
International Conference on Mobile Computing and
Networking (MOBICOM), 1999, pp. 151-162.
[8] J. Cartigny and D. Simplot, "Border node
Furthermore figure 5,6 shows packet loss ratio of proposed work and exiting work and its seem to be very clearly proposed work have low packet loss ratio even for larger transmission packet whereas existing Bayesian probabilistic approach having larger packet loss ratio for lower transmission packet. retransmission based probabilistic broadcast protocols in ad hoc networks," Telecommunication
Systems,, vol. 22, pp. 189-204, 2003.
[9] Y.-C. Tseng, S.-Y. Ni, and E.-Y. Shih, "Adaptive approaches to relieving broadcast storms in a
VIII.
C ONCLUSIONS
Geographical and time radiant historical information is very to compute highly probabilistic route from source to destination and towards reduce the probability of collision, rebroadcast at the expense of lower packet loss and enhanced scheme has with lower latency rate and better reach ability by using a D-S belief method. The simulation results show that the proposed method is better than the traditional methods. The graphs show the packet delivery ratio, routing load and packet loss in both methods.
IX.
R EFERENCES
[1] C. E. Perkins, and E. M. Royer, ―Ad-hoc on-demand distance vector routing,‖ 2nd IEEE Workshop on
Mobile Computing Systems and Applications,
Monterey, California, USA: Feb 25 – 26, 1999: 90-
100.
[2] H D-Ferriere, M Grossglauser, and M Vetterli, ―Age
Matters: Efficient Route Discovery in Mobile Ad
Hoc Networks Using Encounter Ages,‖ 4 th
ACM wireless multihop ad hoc networks," IEEE
Transaction on Computers,, vol. 52, pp. 545- 557,
2003.
[10] C Chen, Chin-Kai Hsu and Hsien-Kang Wang, ―A distance-aware counter-based broadcast scheme for wireless ad hoc networks‖, Military comm.
Conference- 2005, MILCOM 2005 IEEE 17-20 Oct
2005 pages 1052-1058 Vol-2
[11] Dasgupta, ―Immunity-based intrusion detection system: a general framework, Proceeding of the 22nd
National
[12] K.R. Chowdhary ,V.S. Bansal ―Information
Retrieval using Probability and Belief Theory‖ in ieee 2011
[13] Yan Chenghua, Chen Qixiang , ―An Improved
Algorithm for Dempster-Shafer Theory of Evidence‖ in International Conference on Electronic Commerce and Business Intelligence,IEEE 2009
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