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1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 158-163 © IAEME 158 GEOGRAPHIC ROUTING IN MOBILE PREDICTION AND ON DEMAND LEARNING IN MANET Bemin Kumar A and Vasantha kumar V PG Scholar and Assistant Professor, Dept of Computer Science and Engineering, KCG College of Technology, Karapakkam, Chennai ABSTRACT In Adaptive Position Update (APU) scenarios is a geographic routing protocol which automatically updates the position based on the moving mobile nodes and forwarding patterns in the network. Each node broadcasts its update information about location to all its neighbors’ mobile node is beacons. We propose a protocol called Greedy Perimeter Stateless Protocol which reduce the update cost and improve the performance of routing. This protocol will reduce the frequent packet loss and collision of load. In our proposed work, all source nodes will simultaneously connected to their own destination node with multiple period beacon and advantages is widest network forming. INTRODUCTION Mobile Adhoc Networks are infrastructure less networks where each node in the network is mobile in nature. Whenever the nodes want to communicate with each other they will form temporary network .The connectivity between nodes in the MANET can change frequently, that leads to multi-hop communication, which allow communication without the use of base station. The main issue in wireless network is routing the data packets .Every mobile node exchanges its own information about location to forwarding its neighboring mobile nodes. It allows each mobile node to build a location topology. However, in situations where nodes are mobile nodes frequent switch off and on, the local topology occasionally remains static. It is necessary that each mobile node broadcasts its updated information about location to all of its neighbors. These update location packets are known as beacons. In most geographic routing protocols, beacons are broadcast message periodically for maintaining an neighbor list accurate at each mobile node. In this paper, geographic routing protocols for beaconing scheme called Adaptive Position Updates strategy. APU have mutual INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 3, March (2014), pp. 158-163 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2014): 8.5328 (Calculated by GISI) www.jifactor.com IJCET © I A E M E
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 158-163 © IAEME 159 exclusive two rule .The Mobility Prediction (MP) is mobile node scenarios; The On-Demand Learning (ODL) is mobile communication scenarios. In the first set of simulations, we evaluate the mobility and traffic congestion on the performance of APU and two recently based beacon updating schemes: distance-based and speed-based beaconing (SB). The accuracy of local topology is measured by two ratio, unknown neighbor ratio and false neighbor ratio. The unknown neighbor ratio is within coverage range but the false neighbor ratio is without converge range. RELATED WORK In APU, Beacon is each mobile node broadcasts its update lo information about location all its neighbors’ mobile node. Adaptive beaconing schemes two beacon such as (i) Distance-Based Beacon (ii) Speed-Based Beacon. Distance-Based Beacon  Source node send data to Destination node within coverage managed by distance based, a mobile node transmits a beacon the mobile node removes an old neighbor if the mobile node does not hear any beacons from the neighbor node e.g., the dynamics mobile node sends beacons more frequently. When source node send long distance destination node at the time frequent packet loss and longer delay will occur .Speed-Based Beaconing , the beacon interval is dependent on the mobile node speed based. In slow node can have short time-out interval to fast neighbor and eliminated the first solution presented in the distance-based beaconing. In speed- based beaconing fast node may not detect the slow nodes. Drawback of Speed based beacon dependent on the mobile node speed. In slow mobile node communicate fast mobile node. Slow mobile node short delay fast neighbor. PROPOSED SYSTEM The APU scheme two exclusive rules. The Mobility Prediction (MP) is mobile node scenarios; it uses a simple mobility prediction scheme to estimate the location estimate accuracy and adapts the beacon update location interval using periodic beaconing. The On-Demand Learning (ODL) is mobile communication scenarios, it allows mobile nodes along the data packet forwarding path to maintain an accurate location topology by exchanging position of beacons in response to data packets it is overheard from new neighbor’s mobile node. The Mobile Prediction rule is mobile node scenarios which mean mobile within range and without range to be calculated. The On Demand Learning rule is mobile communication behaviors which mean mobile to mobile communication for long internet access. Figure 3.1: Mobile Prediction Rule
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 158-163 © IAEME 160 In Fig. 3.1, where mobile node A moving dynamically from P1 to P2 at a constant velocity. Assume that node A has just send a beacon node while at P1. In Fig 3.2 Source node A send data packet to B, B node send to closest neighbor C node and D node .incase C node close to destination node P. The optimal path is A-B-C-Path E and F nodes are Beacon outside range nodes. These nodes receive data from C and D node .In future destination node p outside range. In fig 3.3, the optimal data path will be A-B-C-E-P node. The Unknown neighbor node will be within beacon coverage area that is C and D. False Neighbor node will be outside of beacon that is E and F. The B send data to C. C does not response because C is selfish. Then B node send Alternative data forwarding path D. D send F. The Optimal data path is A-B-D-F-P. Figure 3.2: Basic on Demand Learning Rule The On Demand Learning rule is the initial possible routing forwarding path from A to P is A- B-P. Now, when source node A sends a data packets to B node, both C and D receive the data packet from A. As A is a new neighbor of C and D to the ODL rule, both C and D will send back beacons to A. when result, the links AC and AD must be discovered. Figure 3.3: On Demand Learning Rule Greedy Perimeter Stateless Routing (GPSR), a geographic routing protocol in wireless datagram networks that used the routers position and a data packet’s destination to make packet forwarding decisions making. GPSR can use location topology information to find correct optimal new routes path quickly. GPSR is to find the neighbor node who are the closer to the destination node and the forward the packet to the neighbor to the destination. GPSR is one of Geographic routing protocol and every node to its location information. A source can get the location of the destination node and link are bidirectional connected.
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 158-163 © IAEME 161 Figure 3.4: Greedy Perimeters Stateless Routing Protocol Greedy Perimeter Stateless Routing Protocol  that APU can important will reduce the update cost and improve the routing protocol performance in using of packet delivery ratio and average end-to-end delay .In Figure 3.4 X mobile node forward the packet to Y mobile node, as the distance between Y and D mobile node is less than between D node and then any of X’s other neighbors mobile node. In this greedy perimeter forwarding process respects, until the data forwarding packet reached “D” node. When the periodic beaconing schemes to estimate accuracy of local topology which existing only using single number of periodic beaconing but now we have using multiple number of Periodic beaconing so simultaneously all source connect own their destination with multiple periodic beaconing schemes. RESULT AND ANALYSIS The simulation result is based on the number of packet loss count vs number of Time .The x axis is based on number of time and y axis is based on number of data packet count is given in Figure 3.5. In our existing SB and DB data packet loss is very high but APU is reduced dasta packet delivery ratio. Figure 3.5: APU Packet Loss Ratio
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 158-163 © IAEME 162 In Figure 3.6 comparision between the Speed based beacon and Distance based beacon vs Adaptive Position Update, which simulation graph result is APU is reduced data packet loss ratio and time count must be less. Figure 3.6: comparison between SB_DB vs APU CONCLUSION In our result analysis APU is reduced packet loss compared than Speed Based and Distance Based .This proposed work we have using protocol is Greedy Perimeter Stateless Routing Protocol shows that APU can significantly reduce the update cost and improve the routing performance in terms of packet delivery ratio. We mathematically analyzed the beacon over-head and local topology accuracy of APU and validated the analytical model with the simulation results. Our results indicate that the APU strategy generates less or similar amount of beacon overhead as other beaconing schemes but achieve better packet delivery ratio and energy consumption. In addition feature, the work includes simultaneously all source connect their own destination with multiple periodic beacon and maintain the load balance. The main advantages is widest network forming. REFERENCES 1. Chen.Q, Kanhere.S.S, Hassan.M, and Lan.K.M, 2006, “Adaptive Position Update in Geographic Routing,” Proc. Int’l Conf. Comm. (ICC ’06), pp. 4046-4051. 2. Heissenbuttel.M, Braun.T, Walchli.M and Bernoulli.T, 2007, “Evaluating of the Limitations and Alternatives in Beaconing “Ad Hoc Networks. 3. Heissenbuttel.M et al., “BLR: Beacon-Less Routing Algorithms for Mobile Ad-Hoc Networks, 2004” Computer Comm., vol. 27, pp. 1076- 1086. 4. High tower.J and Borriello .G, 2001 “Location Systems for Ubiquitous Computing,” Computer, vol. 34, no. 8, pp. 57-66. 5. S. A. Nagtilak and Prof. U.A. Mande, “A Survey of Mitigating Routing Misbehavior in Mobile Ad Hoc Networks”, International journal of Computer Engineering & Technology (IJCET), Volume 1, Issue 2, 2010, pp. 106 - 117, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 6. 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.
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 158-163 © IAEME 163 7. Venkatesh Kumar.P, Vallikannu A.L and Kavitha B.C,, “Effective Broadcasting in Mobile Ad Hoc Networks using Grid Based Mechanism”, International journal of Computer Engineering & Technology (IJCET), Volume 2, Issue 1, 2011, pp. 39 - 46, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 8. Ivan stojmenovic, 2002, “Position-Based Routing in Ad Hoc Networks”, IEEE Communications Magazine. 9. Shiva Prakash, J. P. Saini, S.C. Gupta and Sandip Vijay, “Design and Implementation of Variable Range Energy Aware Dynamic Source Routing Protocol for Mobile Ad Hoc Networks”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 105 - 123, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 10. Poonam Pahuja and Dr. Tarun Shrimali, “Routing Management for Mobile Ad-Hoc Networks”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 3, 2013, pp. 464 - 468, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 11. Karp.H and Kung.H.T, 2000, “GPSR: Greedy Perimeter Stateless Routing for Wireless Networks”, Proc. ACM MobiCom, pp. 243- 254. 12. Ljubica Blazevic and Jean-Yves Le Boudec, 2006, “Location-Based Routing Method for Mobile Ad Hoc Networks”. 13. Perkins.H, Belding-Royer.E and Das.S, 2003, Ad Hoc On-Demand Distance Vector (AODV) Routing, IETF RFC 3561. 14. Quanjun Chen, 2013, “Adaptive Position Update for Geographic Routing in Mobile Ad Hoc Networks”, IEEE Transaction on Mobile Computing Vol. 12, NO. 3.