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A decentralized sort of network that can allow the nodes to communicate with them lacking any central controller is Wireless Ad hoc Networks (WANET). Network Congestions can befall on account of nodes' restricted Bandwidth (BW) together with dynamic topology. Network Congestions brings about data loss as it makes the Data Packets (DP) be dropped on the network. Therefore, in order to lessen Network Congestions, it is necessary to model Congestion Control (CC) systems aimed at the WANET. Thus, this paper offers an optimal routing centered CC scheme utilizing the Modified Ad hoc on-demand Distances Vector (MAODV) Routing Protocol (RP) aimed at the WANET. Here, primarily, the Source Node (SN) together with Destination Nodes (DN) is initialized, and after that, the MAODV discovers the multiple routing paths. Subsequently, Stochastic Gradients Descent Deep Learning Neural Network (SGD-DLNN) identifies the Congestion Status (CS) of every node in the discovered paths. In addition, the MAODV allocates the traffic over the optimum congestion-free routing path if congestion befalls. The Levy Flight Based Black Widow Optimization (LF-BWO) algorithm chooses the optimal routing paths as of congestion-free paths. Centered upon path lifetime, residual energy, link cost, together with path distance, this algorithm enhances the Data Transmission (DT) performance by means of discovering a path. The experimentation’s outcomes are rendered to exhibit the proposed RP’s effectiveness.

Optimal Routing based Load Balanced Congestion Control using MAODV in WANET Environment