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Comparative analysis of AODV and DSDV using machine learning approach in MANET
Ayushree1, Sandeep Kumar Arora2.
Mobile Ad-Hoc networks possess a dynamic structure which is characterized by
the absence of central administrator. Due to such dynamic network, the
possibilities of acquisition of optimal path diminish to a great extent and hence
the durability of the optimal transmission of data packet becomes severe. Each
and every node in MANET is battery powered up and mobile in nature, hence
mobility becomes the prime reason of energy exhaustion in such network. The
main objective of presented paper is to attain the most reliable path with least
mobility for successful transmission of data packets. The algorithm used for
attainment of optimal path is knowledge based learning algorithm which is
implied over two routing protocols; AODV (Ad-Hoc On Demand Distance
Vector Routing) and DSDV (Destination Sequence Distance Vector Routing).
The performance evaluation is done by means of Relay Number which is
inversely proportional to the mobility of node. AODV and DSDV are further
employed over network systems with varying number of nodes, i.e., 12 and 24
nodes network system. The performance comparison is made on the basis of
two performance parameters such as throughput and PDR (Packet Delivery
Ratio). A proposition is made that analysis of PDR and throughput in
knowledge based learning algorithm is better in comparison with other
traditional techniques like Destination Sequence Distance Vector (DSDV). The
simulation is performed over NS-2 network simulator, which enables the
implementation of wired and wireless simulation.
Affiliation:
- K L University (India), India
- Lovely Professional University, India
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Indexation |
Indexed by |
MyJurnal (2019) |
H-Index
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0 |
Immediacy Index
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0.000 |
Rank |
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Indexed by |
Scopus (SCImago Journal Rankings 2016) |
Impact Factor
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- |
Rank |
Q3 (Engineering (miscellaneous)) |
Additional Information |
0.193 (SJR) |
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