Position Update with Machine Learning in Mobile Adhoc Network Using Modified Gpsr
Nithiya.S1, Priyadharshini.A2, Srividhya.S3, Rajalakshmi.R4

1Nithiya.S, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Priyadharshini.A, Podhigai College of Engineering and Technology, Pallipattu (Tamil Nadu), India.
3Srividhya.S, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
5Rajalakshmi.R, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 01 May 2019 | Manuscript Published on 07 May 2019 | PP: 61-64 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1013376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In geographic routing node needs to send their information to destination node based on the geographic location here location information is used like address of the node. In this way of geographic routing every node knows its own position and its nearby node location. The frequent beaconing update reduce the network performance by increasing traffic pattern it will increase the updates cost and decrease the routing performance. To avoiding of this one in this paper we are projecting the (EAPU) Enhanced adaptive position update with machine learning algorithm. EAPU follow two main principle (i) nodes moments are frequent nature in adhoc network, it is hard to predict the moment and update their position frequently (ii) nodes which is moving towards the destination can update their position more frequently. By implementing machine learning algorithm various node moments frequencies are analyzed. Based on the prediction ML algorithm the optimal path can be chosen. By implementing these scenarios we need to use ns2 simulation using GPSR (Greedy Perimeter Stateless Routing) protocol. This scenario is mathematically compared with GPSR and periodic beaconing schemes shows that EAPU will increase the network performance by reducing update cost, effective delivers of packets and average end to end delay.
Keywords: Adhoc Network, Periodic Beaconing, GPSR.
Scope of the Article: Machine Learning