Intelligent GPS-less Speed Detection and clustering in VANET

Vehicular Ad hoc NETwork (VANET) has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks (clusters) so that channel interferences and flooding messages can be limited. This research presents a novel Multi-resolution Relative Speed Detection (MRSD) model to improve the clustering algorithm in VANET without using GPS. MRSD uses the Moving Average Convergence Divergence (MACD), the Momentum of Received Signal Strength (MRSS), and Artificial Neural Networks (ANNs) to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.