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Available ONLINE www.ijart.org

IJART, Vol. 2 Issue 1, 2012,58-62

ISSN NO: 6602 3127 www.ijart.org Page | 58

ISSN NO: 6602 3127RRREEE SSS EEE AAA RRRCCC HHH AAA RRRTTT III CCC LLL EEE

Jamming Aware Energy Efficient Multicast Routing

In Mobile ADHOC NetworksD.Jayachandran,II ME CSE,The Kavery Engineering College.

A.Prabhu,AP/CSE,The Kavery Engineering College.

ABSTRACT

Multiple-path source routing protocols allow a data source node to distribute the total traffic among available

paths. In this article, we consider the problem of jamming-aware source routing in which the source nodeperforms traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this

traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial

statistics. We show that in multi-source networks, this centralized optimization problem can be solved using adistributed algorithm based on decomposition in network utility maximization (NUM). We demonstrate the

networks ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation

problem. Finally, we simulate the achievable throughput using our proposed traffic allocation method in severalscenarios.

Index terms: Wireless network, security, routing, node capture attack, HTTPS

1. INTRODUCTIONA mobile Ad hoc network (MANET) is a collection of

autonomous mobile nodes capable of communicatingwith each other via wireless links. Nodes in a

MANET have limited transmission range;

communication is achieved by making use of nodes to

forward packets to other nodes, which thereby have to

operate as routers. Finding a path between two

communication end points in an ad hoc network is

non trivial: node mobility results in highly dynamic

network topologies. These networks are rapidlydeployable, as they do not require any infrastructure

in place. MANETs are highly desirable in a variety of

scenarios: disaster recovery-where the entire

communication infrastructure might have been

destroyed, business meetings- where a group of

people have to share resources and communicate with

each other, communication over rugged terrain

where establishing infrastructure is not cost effective.Ad hoc networks can also be used to deploy

multimedia services; however efficient routing

protocols have to be developed before this can be

realized.The high node mobility, low bandwidth

wireless interfaces, limited battery power and

contention for a shared wireless medium makesdesigning routing protocols for ad hoc networks

difficult; any new routing protocol must take note of

these factors critically.

Several routing protocols have been proposed in ad

hoc networks. But none are proposed with security.

Our project addresses location aided routing with

security.

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International Journal of Advanced Research in Technology Vol. 2 Issue 1, March 2012


2. RELATED WORKPAMAS protocol that uses two different channels to

separate data and signaling. The Suresh Singh, Mike

Woo and C.S. Raghavendra presented several power-aware metrics that do result in energy-efficient routes.

The Minimum Total Transmission Power Routing

(MTPR) was initially developed to minimize the total

transmission power consumption of nodes

participating in the acquired route. The Min-Max

Battery Cost Routing (MMBCR) considers the

remaining power of nodes as the metric for acquiring

routes in order to prolong the lifetime of network.

C.K.Toh presented the Conditional Max-Min Battery

Capacity Routing (CMMBCR) protocol, which is a

hybrid protocol that tries to arbitrate between the

MTPR and the MMBCR. The several multipath

proactive routing protocols were developed. These

protocols use table-driven algorithms (link state or

distance vector) to compute multiple routes. But they

do not consider the power aware metrics and these

protocols generate excessive routing overhead andperform poorly because of their proactive nature. The

on-demand routing is the most popular approach in

the MANET. Instead of periodically exchanging route

messages to maintain a permanent route table of the

full topology, the on- demand routing protocols build

routes only when a node needs to send the data

packets to a destination. The standard protocols of this

type are the Dynamic Source Routing (DSR) routing.

However, these protocols do not support multipath.The several multipath on- demand routing protocols

were proposed. Some of the standard protocols are the

Ad hoc On- demand Multipath Distance Vector(AOMDV), the Split Multipath Routing (SMR), the

Multipath Source Routing (MSR) [13], the Ad hoc

On-demand Distance Vector Multipath Routing

(AODVM) and the Node- Disjoint Multipath Routing

(NDMR). These protocols build multiple routes based

on demand but they did not consider the power awaremetrics.

3. MY CONTRIBUTIONSThe allocation of traffic across multiple routing paths.

My contributions to this problem are as follow:

Formulate the problem of allocating traffic

across multiple routing paths in the presence

of jamming as a lossy network flow

optimization problem. We map the

optimization problem to that of asset

allocation using portfolio selection theory[12], [13].

Formulate the centralized traffic allocation

problem for multiple source nodes as a

convex optimization problem.

Show that the multi-source multiple-pathoptimal traffic allocation can be computed at

the source nodes using a distributed

algorithm based on decomposition in

network utility maximization (NUM) [14].

Propose methods which allow individual

network nodes to locally characterize the

jamming impact and aggregate this

information for the source nodes.Demonstrate that the use of portfolio

selection theory allows the data sources tobalance the expected data throughput with

the uncertainty in achievable traffic rates.

4. SYSTEM MODEL AND ASSUMPTIONS4.1 Network Model

The wireless network of interest can be represented by

a directed graph G = (N, E). The vertex set N

represents the network nodes, and an ordered pair (i,

j) of nodes is in the edge set E if and only if node j

can receive packets directly from node i. We assume

that all communication is unicast over the directededges in E, i.e. each packet transmitted by node i ! N

is intended for a unique node j ! N with (i, j) ! E. The

maximum achievable data rate, or capacity, of each

unicast link (i, j) ! E in the absence of jamming is

denoted by the pre predetermined constant rate cij in

units of packets per second. Each source node s in asubset S " N generates data for a single destination

node ds ! N. We assume that each source node s

constructs multiple routing paths to ds using a route

request process similar to those of the DSR [9] or

AODV [10] protocols. We let Ps = {ps1, . . . , ps Ls}

denote the collection of Ls loop-free routing paths for

source s, noting that these paths need not be disjoint

as in MP-DSR [11]. Representing each path ps! by a

subset of directed link set E, the sub-network of

interest to source s is given by the directed subgraph.

5. OPTIMAL JAMMING-AWARETRAFFIC ALLOCATION

In this section, we present an optimization framework

for jamming-aware traffic allocation to multiple

routing paths in Ps for each source node s ! S. Wedevelop a set of constraints imposed on traffic

allocation solutions and then formulate a utilityfunction for optimal traffic allocation by mapping the

problem to that of portfolio selection in finance.

Letting 's! denote the traffic rate allocated to path ps!

by the source node s, the problem of interest is thus

for each source s to determine the optimal Ls1 rate

allocation vector "s subject to network flow capacity

constraints using the available statistics !s and !s of

the end-to-end packet success rates under jamming.

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5.1 Traffic Allocation Constraints

In order to define a set of constraints for the multiple-

path traffic allocation problem, we must consider the

source data rate constraints, the link capacity

constraints, and the reduction of traffic flow due to

jamming at intermediate nodes. The traffic rateallocation vector "s is trivially constrained to the

nonnegative orthant, i.e. "s * 0, as traffic rates are

non-negative.

5.2 Optimal Traffic Allocation Using Portfolio

Selection Theory

In order to determine the optimal allocation of traffic

to the paths in Ps, each source s chooses a utility

function Us("s) that evaluates the total data rate, or

throughput, successfully delivered to the destination

node ds. In defining our utility function Us("s), we

present an analogy between traffic allocation torouting paths and allocation of funds to correlated

assets in finance. In Markowitzs portfolio selection

theory [12], [13], an investor is interested in allocating

funds to a set of financial assets that have uncertain

future performance. The expected performance ofeach investment at the time of the initial allocation is

expressed in terms of return and risk. The return on

the asset corresponds to the value of the asset and

measures the growth of the investment. The risk of the

asset corresponds to the variance in the value of the

asset and measures the degree of variation or

uncertainty in the investments growth. Describe the

desired analogy by mapping this allocation of funds tofinancial assets to the allocation of traffic to routing

paths.We relate the expected investment return on the

financial portfolio to the estimated end-to-end successrates !s and the investment risk of the portfolio to the

estimated success rate covariance matrix !s. We note

that the correlation between related assets in the

financial portfolio corresponds to the correlation

between non-disjoint routing paths. The analogy

between financial portfolio selection and theallocation of traffic to routing paths is summarized

below.

6. AODV AND DSDV PROTOCOLIMPLEMENTATION

Wireless networks are characterized by a lack of

infrastructure, and by a random and quickly changing

network topology; thus the need for a robust dynamic

routing protocol that can accommodate such an

environment. To improve the packet delivery ratio of

Destination-Sequenced Distance Vector (DSDV)

routing protocol in mobile ad hoc networks with highmobility, a message exchange scheme for its invalid

route reconstruction is being used. Two protocols

AODV and DSDV simulated using Java simulationpackage and were compared in terms throughput, end

to end delay and packet faction delivery varying

number of nodes, speed and time. Simulation results

show that DSDV compared with AODV, DSDV

routing protocol consumes more bandwidth, because

of the frequent broadcasting of routing updates. While

the AODV is better than DSDV as it doesnt maintain

any routing tables at nodes which results in lessoverhead and more bandwidth. AODV perform better

under high mobility simulations than DSDV. Highmobility results in frequent link failures and the

overhead involved in updating all the nodes with the

new routing information as in DSDV is much more

than that involved AODV, where the routes are

created as and when required. AODV use on -demand

route discovery, but with different routingmechanisms. AODV uses routing tables, one route per

destination, and destination sequence numbers, a

mechanism to prevent loops and to determine

freshness of routes.When a source node wants to send

packets to a destination to which it does not have aroute, it initiates a Route Discovery by broadcasting a

ROUTE REQUEST. The node receiving a ROUTE

REQUEST checks whether it has a route to the

destination in its cache and also check if it is

misbehavior node or not. If it has, it sends a ROUTE

REPLY to the source including a source route, whichis the concatenation of the source route in the ROUTE

REQUEST and the cached route. If the node does not

have a cached route to the destination, it adds its

address to the source route and rebroadcasts the

ROUTE REQUEST. When the destination receives

the ROUTE REQUEST, it sends a ROUTE REPLY

containing the source route to the source. Each nodeforwarding a ROUTE REPLY stores the route starting

from itself to the destination. When the source

receives the ROUTE REPLY, it caches the source

route. If any node not sends acknowledgement then

we easily identified that is misbehavior node. So findout the alternative path and forwarding the data to the

destination.The Message transfer relates with that the

sender node wants to send a message to the

destination node after the path is selected also find out

that node is not a misbehavior node and status of the

destination node through is true. The receiver node

receives the message completely and then it send the

acknowledgement to the sender node also nearbynodes through the router nodes where it is received

the message.

7. Simulation Result and Simulation SetupPlatform Windows XP

Java Sim Jist

Pause time 0, 20, 40, 80, 120, 160,

200

Simulation time 200 s

Number of nodes 50 wireless nodes

Traffic CBR(Constant Bit

Rate)

Simulation Area size 500 x 500 m

Transmission Range 250 m

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The following metrics are used in this paper for the

analysis of AODV, DSR and DSDV routing

protocols.

i) Packet Delivery Ratio

ii) Average End to End Delayiii) Throughout

Packet delivery ratio The packet delivery ratio in

this simulation is defined as the ratio between the

number of packets sent by constant bit rate sources

(CBR, application layer) and the number of

received packets by the CBR sink at destination.

Routing Overhead It is the number of packet

generated by routing protocol during the simulation.

Average end-to-end delay of data packets

There are possible delays caused by buffering duringroute discovery latency, queuing at the interface

queue, retransmission delays at the MAC, and

propagation and transfer times. Once the time

difference between every CBR packet sent and

received was recorded, dividing the total time

difference over the total number of CBR packetsreceived gave the average end-to-end delay for the

received packets. This metric describes the packet

delivery time: the lower the end-to-end delay the

better the application performance.

Figure1. Packet delivery ratio versus pause time

for AODV, DSR and DSDV(Number of node = 50,

Area space = 500m x 500m)

Figure2. Routing overhead versus pause time for

AODV, DSR and DSDV (Number of node = 50,


Figure3. Avg. end to end delay versus pause time

for AODV, DSR and DSDV (Number of node = 50,


7. CONCLUSION AND FUTURE WORK

In this paper the analysis of adhoc routing protocol is

done in the above mentioned mobility and trafficpattern on different pause time. We analyzed that

when pause time set to 0 each of the routing protocols

obtained around 97% to 99% for packet delivery ratio

except DSDV which obtained 77%. DSR and AODV

reached approx 100% packet delivery ratio when

pause time equal to 200 while DSDV obtained only

approx 94% packet delivery ratio.

DSR and DSDV has low and stable routing overhead

as comparison to AODV that varies a lot. Avg. End to

End delay of DSDV is very high for pause time 0 but

it starts decreasing as pause time increases. DSR

performs well as having low end to end delay. Whenwe compare the three protocols in the analyzed

scenario we found that overall performance of DSR is

better than other two routing protocols.

DSDV routing protocol consumes more bandwidth,

because of the frequent broadcasting of routing

updates. While the AODV is better than DSDV as it

doesnt maintain any routing tables at nodes which

results in less overhead and more bandwidth. From

the above, chapters, it can be assumed that DSDV

routing protocols works better for smaller networksbut not for larger networks. So, my conclusion is that,

AODV routing protocol is best suited for generalmobile ad-hoc networks as it consumes less

bandwidth and lower overhead when compared with

DSDV routing protocol. AODV perform better under

high mobility simulations than DSDV. High mobility

results in frequent link failures and the overhead

involved in updating all the nodes with the new

routing information as in DSDV is much more than

that involved AODV, where the routes are created as

and when required. AODV use on - demand route

discovery, but with different routing mechanics.AODV uses routing tables, one route per destination,

and destination sequence numbers, a mechanism to

prevent loops and to determine freshness of routes.

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Future work of this project We present a family of

energy-conserving flooding protocols capable of

supporting both reactive and proactive routing

approaches, as well as network applications that rely

on flooding. Based on realistic simulation models,

these protocols show significant energy-conserving

potential. Future work will focus on methods forbalancing the protocols overhead and relay

optimality to further enhance their efficiency.

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