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Energy Efficient- CBRP Method based on WCA for Various Topologies in Mobile Adhoc Networks using Network Coding

Mirza Arif baig, Dr Pankaj Kawadkar

Abstract


The mobile ad hoc network, or MANET, is one of most promising networks to originate from technological world. Nodes within MANETs change locations over time, setup themselves, and transfer data from source to destination even without assistance of a router or base station. As a result, understanding the type of routing utilised by these networks is crucial for efficient data transmission. It can be a mammoth job for the nodes to properly establish a path to forward and route their packets when they are in constant motion because they lack specific routers to accomplish these duties. This research explores the performance of various mobile ad-hoc routing protocols in detail. Over 160 simulation scenarios have been run, with up to 6 performance factors studied and compared across three distinct network scales to ensure a thorough examination. The findings of prior research are compared in this research, and a thorough analysis is performed to determine the best effective routing method under different mobility conditions. In three phases, mobility, speed, and network load, all of the key proactive and reactive routing systems are compared. The dynamic source routing protocol (DSR) performs best in small networks, while the ad-hoc on demand distance vector (AODV) routing protocol performs best in medium and large networks, according to simulation data. At cluster heads, network coding has been used to limit the number of transmissions and thus the amount of energy consumed. To improve coding opportunities, we adopted a flow-based queue structure while executing coding at intermediate nodes. For varied topologies and situations in MANETs, we present a performance analysis of the energy efficient cluster based routing protocol (ECCRP) approach. This strategy is intended to improve CBRP's energy consumption performance. MATLAB/Network simulator is used to carry out the suggested work's implementation and simulation. We take into account energy use while sending, receiving, idling, and sleeping. The expected outcome assures that the best cluster head is chosen and that the cluster life time is extended by lowering energy consumption.

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References


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