Energy Consumption Reduction using Ant Colony Optimization in Manet using Machine Learning Techniques

Mobile Ad-hoc networks (MANETs) are easy to use because MANET has the set of nodes that are moved randomly within the network which is infrastructure less. The MANET uses Multi-Protocol Label Switching (MPLS) to handle the network's changeable topology. The performance of the network is hampered by the high energy consumption of the network nodes. ACO and HSA are employed to find the best path in this work. There are three key parameters that are taken into account by the fitness function: distance, residual energy, and hops. ACO-HSA approach is used to select the most energy-efficient path for network data transport. The ACO-HSA approach is compared to existing routing protocols in terms of performance. In this proposed work we have focused only on residual energy and residual energy is increased compared to existing work.