This document summarizes a research paper that proposes a new geographic routing protocol called Adaptive Position Update (APU) for mobile ad hoc networks (MANETs). APU uses two rules - mobility prediction and on-demand learning - to reduce beacon update costs and improve routing performance. Simulation results show that APU significantly reduces packet loss compared to existing distance-based and speed-based beaconing schemes, improving the packet delivery ratio. The paper also introduces the Greedy Perimeter Stateless Routing protocol to find optimal paths in the location topology graph efficiently.

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
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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 [2] 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 [2], 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

3. 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.

4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
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Figure 3.4: Greedy Perimeters Stateless Routing Protocol
Greedy Perimeter Stateless Routing Protocol [6] 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

5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
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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[2] .This proposed work we have using protocol is Greedy Perimeter Stateless Routing
Protoco[6]l 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.
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