An intelligent machine learning-driven vehicular network framework for adaptive channel congestion management and connectivity improvement
In Vehicular Ad-Hoc Networks (VANETs), each vehicle continuously broadcasts real-time information messages, such as speed, location, acceleration, and atmospheric conditions, to surrounding vehicles. However, in the current scenario, vehicle density increases continually on the road day by day, which is responsible for excessive amount of messages generation, leads to message congestion on channel and degrades the performance of VANETs. To mitigate this issue, proposed work investigates a novel technique named as Congestion Control with Enhancing Vehicle Connectivity (CC-EVC), empowered by machine learning (ML). The CC-EVC technique works with the principle of K-Mean algorithm for grouping the neighbouring vehicles to enhance connectivity. It restricts the communication of safety messages to non-emergency (outlier) vehicles to reduce the channel congestion and monitors the real-time channel load. The proposed technique also manages the channel congestion by grouping the neighbouring vehicles according to transmission range and using adaptive message transmission rate. The performance of the proposed CC-EVC technique is measured on SUMO tool by designing a dense vehicular network. The K-Mean technique is implemented using MATLAB, and performance parameters such as Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Throughput, and End-to-End (E2E) delay are evaluated using the NS2 simulator. Simulation results demonstrate that the proposed CC-EVC technique improves the communication among vehicles by reducing channel congestion in a significant way compared to the Decentralized Congestion Control (DCC) technique. Proposed CC-EVC technique achieves 89.7298% PDR, 111.73ms E2E delay, and 0.32 NRL with Ad-Hoc On-Demand Distance Vector (AODV) routing protocol and 82.447% PDR, 93.45ms E2E delay, and 0.67 NRL with Destination-Sequenced Distance Vector (DSDV) routing protocol at 500mWatt Transmission Power (TP) and 100bytes Packet Size (PS). Further, operational efficiency of the proposed work is compared with the existing AODV and DSDV protocols with varying TP and PS. It is demonstrated that the proposed technique results in enhanced throughput and PDR with AODV protocol whereas lowering the E2E delay and NRL with DSDV protocols.