AVOIDING JAMMING FOR TRAFFIC ALLOCATION USING ...

International Journal of Advanced Computer and Mathematical Sciences ISSN 2230-9624. Vol 3, Issue 4, 2012, pp563-571 http://bipublication.com

AVOIDING JAMMING FOR TRAFFIC ALLOCATION USING MULTIPLE-PATH ROUTING K. Kishore Kumar1, S.Spurthi2 and P.Praveen Kumar3 1,2

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Dept of CSE, Hasvita Institute of Science &Technology, Hyderabad, A.P, India Dept of CSE, Samskruti College of Engineering & Technology, Hyderabad, A.P, India [Received-08/12/2012, Accepted-17/12/2012]

ABSTRACT: Multipath routing is the routing technique used to multiple alternative paths through a network, which can yield a variety of benefits such as fault tolerance, increased band width, or improved security. Multiple-path routing protocols allows a data source node to distribute the total traffic among the accessible paths. In this paper, we deal with the problem of jamming-aware source routing in which traffic allocations are based on empirical jamming statistics at individual network nodes and are performed by source node. We specify this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. By using distributed algorithm based on decomposition in network utility maximization we can solve the centralized optimization problem. The impact of jamming and incorporate these estimates into the traffic allocation problem are demonstrated using the network’s ability. Keywords: Multiple-path routing, Jamming, Network utility maximization.

INTRODUCTION: A wireless mesh network can have weak effects on data transport through the network jamming point-to-point transmissions. Providing an effective denial-of-service attack on end-to-end data communication the effects of jamming are at the physical layer resonate through the protocol stack. The simplest methods to defend a network against jamming attacks consist of physical layer solutions such as spread-spectrum or beam forming, forcing the jammers to expend a greater resource to reach the same goal. However, recent study has showed that intelligent jammers can incorporate cross layer protocol information into jamming attacks, reducing resource expenditure by several orders of magnitude by targeting certain link layer and MAC implementations as well as link layer error detection and correction protocols. Hence, more sophisticated anti-jamming methods and

defensive measures must be incorporated into higher-layer protocols. The majority of anti-jamming techniques are channel surfing or routing around jammed regions of the network make use of diversity. Anti-jamming protocols may use multiple frequency bands, different MAC channels, or multiple routing paths. Here we use the multiple routing paths based on anti-jamming diversity. While considering the potential effect of jamming on the resulting data throughput, each source node must be able to make an intelligent allocation of traffic across the available paths makes effective use of this routing diversity. Each source must collect information on the impact of the jamming attack in various parts of the network in order to characterize the effect of jamming. . However, the extent of jamming at each network node depends on a number of unknown parameters, including the strategy used

AVOIDING JAMMING FOR TRAFFIC ALLOCATION

by the individual jammers and the location of the jammers with respect to each transmitterreceiver pair. Hence, the impact of jamming is probabilistic from the perspective of the network1, and the characterization of the jamming impact is further complicated by the fact that the jammers’ strategies may be dynamic and the jammers themselves may be mobile. The end-to-end throughput achieved by each sourcedestination pair will also be non-deterministic since the effect of jamming at each node is probabilistic, and, hence, must be studied using a stochastic framework. In this paper we 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. We formulate the centralized traffic allocation problem for multiple source nodes as a convex optimization problem. We propose methods which allow individual network nodes to locally characterize the jamming impact and aggregate this information for the source nodes. We show that the multisource multiple-path optimal traffic allocation can be computed at the source nodes using a distributed algorithm based on decomposition in network utility maximization. We demonstrate that the use of portfolio selection theory allows the data sources to balance the expected data throughput with the uncertainty in achievable traffic rates. Fig: 1

Existing System: To distribute the total traffic among available paths the source node must perform the traffic allocation based on empirical jamming statistics at individual network nodes. If any path to be disturbed/jammed a routing path is requested an existing routing path is not be updated, the responding nodes along the path will disconnect the routing path. Proposed System: We show that in multi-source networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization. We proposed techniques for the network nodes to estimate and characterize the impact of jamming and for a source node to incorporate these estimates into its traffic allocation. We specify this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics which allows individual network nodes to locally characterize the jamming impact and aggregate this information for the source nodes. We demonstrate that the use of portfolio selection theory allows the data sources to balance the expected data Throughput with the uncertainty in achievable traffic rates. Advantage: The main advantage of the proposed system is that each time a new routing path is requested or an existing routing path is updated, the responding nodes along the path will relay the necessary parameters to the source node as part of the reply message for the routing path. Mesh routers have minimal mobility and perform dedicated routing and configuration, which significantly decreases the load of mesh clients and other end nodes. And the goal of the paper is to efficiently allocate the traffic to maximize the overall throughput. FEASIBILITY STUDY During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements

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for the system is essential. The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates.Three key considerations involved in the feasibility analysis are • • •

Economical Feasibility Technical Feasibility Social Feasibility

The main advantage of this Jamming-Aware Traffic Allocation for Multiple-Path Routing Using Portfolio Selection Project is to overcome the problems of previous system. The previous system has time consuming process and sometimes it can disturb the wireless networks. In the present scenario every time it can update the route path or it can create a new route path for sending messages. This project was implemented on C#.Net & SQL Server software technologies and it has 5 major modules namely traffic allocation module, impact of traffic jumping module, jammer mobility module, packet success rate module and finally optimal traffic allocation module.

available paths on the basis of expected throughput. Estimating End-to-End Packet Success Rates The source needs to estimate the effective endto-end packet success rate to determine the optimal traffic allocation. Assuming the total time required to transport packets from each source to the corresponding destination is negligible compared to the update relay period Traffic Allocation Constraints We must consider the source data rate constraints, the link capacity constraints, and the reduction of traffic flow due to jamming at intermediate nodes Optimal Traffic Allocation Using Portfolio Selection Theory To determine the optimal allocation of traffic to the paths in Portfolio Selection, each source s chooses a utility function that evaluates the total data rate, or throughput, successfully delivered to the destination node Return and risk The expected performance of each 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.

MODULE DESCRIPTION Point the Effect of Jammer Mobility on Network Providing a set of parameters to be estimated by network nodes and methods for the sources to aggregate this information and characterize the Simulation Our output could be a simulation of data transfer with source to destination in form of nodes and link layer and jammers in link. Activity Diagram:

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Class Diagram:

Collaboration Diagram:

Sequence Diagram:

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Use Case Diagram:

OUT PUT SCREENS:

FIG:2 client login form

FIG:3 client checking login details and download files form

FIG:4 server login form

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FIG:5 server upload file

FIG:6 client can upload the file request to server

FIG:7 server checking the client request file

FIG:8 server can send the files to multiple clients K. Kishore Kumar, et al.

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FIG:9 send the files from server to clients by using router

FIG:10 router can choose the nodes regarding as multiple clients for sending the files

FIG:11 files are transferred to the clients successfully

FIG:12 client can download the files individually forms

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FIG:13 first clients can download the file with user credentials

FIG:14 first client can download the file with user credentials

FIG:15 first clients can download the file with user credentials

CONCLUSION: We have showed the methods for each network node to probabilistically characterize the local impact of a dynamic jamming attack and for data sources to incorporate this information into the routing algorithm. We have formulated this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. By using distributed algorithm based on decomposition in network utility maximization we have solved the centralized optimization problem. We presented the impact of jamming and incorporate these estimates into the traffic allocation problem are demonstrated using the network’s ability. Hence we deal with the problem of jammingaware source routing in which traffic allocations are based on empirical jamming statistics at individual network nodes and are performed by source node.

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REFERENCES: [1] I. F. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: A survey,” Computer Networks, vol. 47, no. 4, pp. 445–487, Mar. 2005. [2] E. M. Sozer, M. Stojanovic, and J. G. Proakis, “Underwater acoustic networks,” IEEE Journal of Oceanic Engineering, vol. 25, no. 1, pp. 72–83, Jan. 2000. [3] R. Anderson, Security Engineering: A Guide to Building Dependable Distributed Systems. JohnWiley&Sons, Inc.,2001. [4] J. Bellardo and S. Savage, “802.11 denial-ofservice attacks: Real vulnerabilities and practical solutions,” in Proc. USENIX Security Symposium, Washington, DC, Aug. 2003, pp. 15–28. [5] D. J. Thuente and M. Acharya, “Intelligent jamming in wireless networks with applications to 802.11b and other networks,” in Proc. 25th IEEE Communications Society Military Communications Conference (MILCOM’06), Washington, DC, Oct. 2006, pp. 1–7. [6] A. D. Wood and J. A. Stankovic, “Denial of service in sensor networks,” IEEE Computer, vol. 35, no. 10, pp. 54–62, Oct. 2002. [7] G. Lin and G. Noubir, “On link layer denial of service in data wireless LANs,” Wireless Communications and Mobile Computing, vol. 5, no. 3, pp. 273–284, May 2005. [8] W. Xu, K. Ma, W. Trappe, and Y. Zhang, “Jamming sensor networks: Attack and defense strategies,” IEEE Network, vol. 20, no. 3, pp. 41– 47, May/Jun. 2006. [9] D. B. Johnson, D. A. Maltz, and J. Broch, DSR: The Dynamic Source Routing Protocol for Multihop Wireless Ad Hoc Networks. AddisonWesley, 2001, ch. 5, pp. 139–172. [10] E. M. Royer and C. E. Perkins, “Ad hoc ondemand distance vector routing,” in Proc. 2nd IEEE Workshop on mobile Computing Systems and Applications (WMCSA’99), New Orleans, LA, USA, Feb. 1999, pp. 90–100. [11] R. Leung, J. Liu, E. Poon, A.-L. C. Chan, and B. Li, “MP-DSR: A QoS aware multi-path dynamic source routing protocol for wireless ad-hoc networks,” in Proc. 26th Annual IEEE Conference on Local Computer Networks (LCN’01), Tampa, FL, USA, Nov. 2001, pp. 132–141. [12] H. Markowitz, “Portfolio selection,” The Journal of Finance, vol. 7, no. 1, pp. 77–92, Mar. 1952. [13] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge,2004.

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