Machine Learning-based Energy Efficient Routing Protocols for VANET: Dynamic Clustering Model - Softcover

Inhaltsangabe

This book proposes k-means (KM), fuzzy c-means (FCM), and a self-organizing map neural network (SOMNN) machine learning-based Dynamic Clustering Model (DCM) to identify trustworthy vehicle nodes for energy-efficient multi-hop routing. This conserves the energy of vehicle nodes by utilizing their Euclidean distances from each other, as well as their distances from both the Cluster Head (CH) and the Roadside Unit (RSU). In DCM, the route discovery process adopts the Dynamic Source Routing (DSR) protocol within a cluster to locate relay vehicles. This approach enhances Packet Delivery Ratio (PDR) and application throughput and reduces cluster overhead and delay by mitigating the unnecessary flooding of control packets. The proposed model is evaluated in city map-based VANET scenarios for two applications: 1) vehicular data up-loading and 2) vehicular data downloading.

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