Transmission Method for Overhead Reduction in Mobile Ad-hoc Networks

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Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 2, February 2012)

251 Transmission Method for Overhead Reduction in

Mobile Ad-hoc Networks

Rahul Kundu1, Seema Rawat2, Praveen kumar3

1B.Tech- 4th Year, 1National Power Training Institute, New Delhi

2, 3

Assistant Professor , Amity University Noida

1_{rahul.kundu1989@gmail.com,} 2_{srawat1@amity.edu,}

3_{pkumar3@amity.edu}

Abstract: - The Mobile Ad-hoc Networks are wireless networks that have no fixed infrastructure. There are no fixed routers-instead each node acts as router and forwards traffic from other nodes. Since the nodes in a MANET are highly mobile, the topology changes frequently and the nodes are dynamically connected in an arbitrary manner. This work focuses on reliable data packet forwarding in ad hoc networks and proposes a new reputation-based solution to mitigate the effects of adverse situations caused by misbehaving nodes

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Keywords: MANET, packet forwarding, reputation, misbehaving nodes.

I. INTRODUCTION

The Mobile Ad-hoc Networks are wireless networks that have no fixed infrastructure. There are no fixed routers-instead each node acts as router and forwards traffic from other nodes. Since the nodes in a MANET are highly mobile, the topology changes frequently and the nodes are dynamically connected in an arbitrary manner.

The lack of infrastructure in ad hoc networks assumes that the nodes depend on each other to forward the data packets to their destinations. In addition to the fact that the packet forwarding operation is power consuming, nodes in ad hoc networks do not belong in general to the same owner. As a consequence, no node has an incentive to forward data packets for the others. It turns out that assuming a non-hostile environment when routing protocols operate is not a reasonable assumption as nodes might drop data packets received from the other nodes in order to save their resources or to disrupt the network functioning. Thus, using a security mechanism for improving the overall network performance and avoiding the malicious nodes seems to be mandatory.

The overheads met by mobile ad-hoc network protocols in particular between reactive protocols and pro-active protocols. Topology of radio networks is usually dense and varies often. The control overhead in protocols for this type of networks depends mainly on the way routes are constructed. Manet protocols propose mainly two ways for creating routes in ad-hoc networks:

1. The classical one consists in every node emitting hellos in order to learn the topology (A mechanism is then used to broadcast this information or a sufficient part of it to be able to compute routes);

2. On the other hand, a very simple way of finding a route consists in flooding, the source floods a packet and the path followed by the packet to reach the destination is used.

The increasing spread of mobile wireless devices with ad hoc or short range networking capabilities opens the path for new applications for mobile Ad-hoc Networks. Motivated by the way information spreads by word-of mouth communication between humans, we use mobile devices to form mobile information networks. In a mobile information dissemination network, users share information as they come into communication range to each other. Their devices match information they want to share and information they are looking for. If there is a match, information is passed without any further user interaction. This can be viewed as information infection.

No relation between the participating users; in the worst case they are anonymous to each other. In this situation a reputation system could improve the usefulness of information dissemination networks.

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If Tarun had acquired some good reputation from others who have already checked out Tarun‟s restaurant recommendations, Ashwani could benefit from this extra information and give it a try. Define reputation as the collected information about one entity‟s former behavior as experienced by others. Trust expresses an entity‟s willingness to proceed with an action that might be harmful based on information like the risk, benefit and reputation of involved entities. Reputation systems collect ratings, process and consolidate this information and make it available upon request. This has two effects: They provide information that allows users to predict how someone may behave in future. Simultaneously, they influence the future by giving the incentive to behave well.

At a first glance it seems unreasonable to base trust in information spread by a user on her reputation, because he may only pass on what he has learned from someone else. However, our reputation systems should motivate users to verify information before spreading it further or to mark it as uncertain. Most existing approaches to use reputation systems for mobile ad-hoc networks concentrate on solving routing issues raised by misbehaving nodes. They also neglect the dynamics of a scenario with highly mobile nodes where the chance to meet someone again is low. In the majority of cases privacy issues are completely ignored. Especially when a human user as the owner of a device is involved, reputation is sensitive personal information. Often it contains detailed information about the entity‟s former transactions including and it is critical for an entity not to lose reputation by false accusation.

II. RELATED WORK

Watchdog and path rater components to mitigate routing misbehavior have been proposed by Marti, Giuli, Lai and Baker. They observed increased throughput in mobile ad-hoc networks by complementing DSR with a watchdog for detection of denied packet forwarding and a path rater for trust management and routing policy rating every path used, which enable nodes to avoid malicious nodes in their routes as a reaction. The nodes rely on their own watchdog exclusively and do not exchange reputation information with others.

CONFIDANT stands for „Cooperation Of Nodes, Fairness In Dynamic Ad-hoc NeTworks‟ and it detects malicious nodes by means of observation or reports about several types of attacks and thus allows nodes to route around misbehaved nodes and to isolate them from the network. Nodes have a monitor for observations, reputation records for first-hand and trusted second-hand observations, trust records to control trust given to received warnings, and a path manager for nodes to adapt their behavior according to reputation.

Simulations for “no forwarding” have shown that CONFIDANT can cope well even with half of the network population acting maliciously. The protocol uses also second-hand information, i.e. observations by others, which can be vulnerability in the presence of liars.

CORE, a collaborative reputation mechanism proposed by Michiardi and Molva, also has a watchdog component; however it is complemented by a reputation mechanism that differentiates between subjective reputation (observations), indirect reputation (positive reports by others), and functional reputation (task-specific behavior), which are weighted for a combined reputation value that is used to make decisions about cooperation or gradual isolation of a node. Regarding nodes as requesters and providers, and comparing the expected result to the actually obtained result of a request obtain reputation values. Again, nodes only rely on first-hand observations and do not exchange reputation information.

III. PROPOSED REPUTATION BASED SCHEME:

In an adhoc network setting is more vulnerable to attacks that can either be in terms of refusal to cooperate in packet forwarding, providing false information to the routing protocol or even failure of intermediate nodes. Such misbehavior on the part of the participating nodes goes undetected at the time of route discovery and hence cannot be entirely addressed by the existing routing protocols. this context that MAGNET proposes a reputation based cooperative scheme to monitor node misbehavior. Any packet forwarding decision is then based on the reputation of the nodes and route discovery is reinitiated when the reputation-monitoring module detects performance problems or possible attacks. A monitoring module continuously monitors node behavior and assigns reputation values to nodes based on their packet forwarding activity. Each node maintains reputation values of its neighbors and other nodes that have had a transaction with it. The reputation value can range over [0, 1] with 0 suggesting worst behavior and 1 reflecting best behavior. A node, P, observes the packet forwarded by its neighbor, Q, and assigns a reputation value, r (P, Q), equal to the ratio of number of packets forwarded by Q

to the number of packets sent to it. As the network activity increases more reputation values are generated. Each node stores these reputation values as a weighted average () of old and new values using the following formula.

r current (P,Q) = (1-)r old(P,Q) + .r new(P,Q)

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The formula suggested for computing the composite reputation value from the reputation values obtained from a neighbor, says R, is given as

recomposite (P, Q) = r (P,R). r(R,Q) + (1-r(P,R)) .r(P,Q)

Such an only value based aggregation works well in situations where either the input values completely encapsulate the associated information or rest on similar information base. However, none of these may always be true, especially for representations from across the network. Information content relies on the interpretation of data, which varies with the change in context. Also, geographically dispersed network elements might observe different aspects of the target phenomenon resulting in a completely different information base. In such scenarios it is important to have an information conscious aggregation of network inputs. In the context of reputation computations, reputations values are computed as the ratio of packets forwarded by a node to the packets send to it. Nodes monitoring a target may send different number of packets to it. In addition to the nature of data flow, packet forwarding between any two nodes also depends on relative mobility. In an adhoc setting, nodes may move in and out of each other‟s range frequently, disrupting/ re-routing ongoing flows. As a result, a node with the longest association with it can best describe behavior of the target node. However, this information is lost while computing reputation values. As a result, value only averaging of reputation opinions of, say, 0.2 (based on 100 packet forwards) and 0.9 (based on 10 packet forwards) assigns a consolidated value of 0.55 even though sustained observations suggest a value of around 0.2.

Another important aspect while seeking opinion from geographically distributed entities in a network is to account for uncertainty of response. Such uncertainty can occur in two forms – no opinion and lack of response. A simple way to accommodate such uncertainty would be to assume lowest reputation value for any lack of response or responses reflecting no opinion. However such assumptions are not necessarily a true representation of the information and can lead to unwarranted effect on the reputation computations; in this case lowering the reputation of a node. It is, therefore, important that any such scheme remains equipped to handle uncertainty while soliciting network inputs. In the above demonstrated scenario, it is expected that a lack of response (or delayed response) should not affect the outcome of reputation computations.

IV. SIMULATION PARAMETERS

To test the performance of mechanism, a simulation scenario with the support of the network simulator ns-2 is used. Each mobile host has an omni-directional antenna having unity gain with a nominal radio range of 250 m. The random waypoint model is selected as a mobility model in a rectangular area (600 x 800meters) with a nodes‟ speed uniformly between 0 and a maximum value of 10 m.s-1.

Each node in the network is assumed to have a buffer with a capacity of 64 packets and using a FIFO interface queue. Nodes remain stationary for a specified period called the “pause time”. In the simulation work, considering the Dynamic Source Routing protocol (DSR), the total simulation time is 900 seconds.

V. SIMULATION RESULTS

Data Drop

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254 Data drops

0 2000 4000 6000 8000 10000

0 200 400 600 800 1000

pause time

d

a

ta

p

k

ts

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ro

p

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DSR

Improved DSR

Through Put:

It is the ratio of packets forwarded by the node to the number of packets received by the node.

Throughput

190 192 194 196 198 200 202 204 206 208 210

0 200 400 600 800 1000

puase time

DSR Improved DSR

Delivery Ratio

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delevry ratio

74

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82

84

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pause time

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_DSR

Improved DSR

Routing Overhead: The overhead occurred can be reduced which is shown in the simulation results.

routing overhead

0 5000 10000 15000 20000 25000 30000 35000 40000

0 200 400 600 800 1000

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VI. CONCLUSION

In this paper, propose a method on Reputation-based scheme to encourage packet forwarding and discipline selfish behavior in a no cooperative ad hoc network. The unique features of this scheme are

1) The reputation of a node is quantified by objective measures (through neighbor monitoring),

2) The reputation of a node is only propagated to its neighbors, which greatly reduces communication overhead as compared to the schemes that maintain reputation globally.

With the reputation measure obtained by the scheme, to design a punishment scheme to penalize selfish nodes. The experimental results show that the proposed scheme can successfully identify selfish nodes and punish them accordingly.

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