A Fuzzy-Rule Based Data Delivery Scheme in VANETs with Intelligent Speed Prediction and Relay Selection

Abstract

Data delivery in vehicular networks (VANETs) is a challenging task due to the high mobility and constant topological changes. In common routing protocols, multihop V2V communications suffer from higher network delay and lower packet delivery ratio (PDR), and excessive dependence on GPS may pose threat on individual privacy. In this paper, we propose a novel data delivery scheme for vehicular networks in urban environments, which can improve the routing performance without relying on GPS. A fuzzy-rule-based wireless transmission approach is designed to optimize the relay selection considering multiple factors comprehensively, including vehicle speed, driving direction, hop count, and connection time. Wireless V2V transmission and wired transmissions among RSUs are both utilized, since wired transmissions can reduce the delay and improve the reliability. Each RSU is equipped with a machine learning system (MLS) to make the selected relay link more reliably without GPS through predicting vehicle speed at next moment. Experiments show the validity and rationality of the proposed method.