Fuzzy-Based Hybrid Location Algorithm for Vehicle Position in VANETs via Fuzzy Kalman Filtering Approach

Location information is very critical to VANETs such as navigation, routing, network management, and road congestion. In this paper, the vehicle location problem under urban road conditions is investigated by employing the GPS, WiFi, and Cellular Network (CN) positioning systems and by developing neighbor vehicle utilization in VANETs. Since GPS is possibly affected by satellite signal in real urban environment, while WiFi is only suitable for urban and CN is affected by the number of Base Stations (BSs) and signal strength, then a fuzzy-based hybrid location algorithm is developed. The algorithm has some advantages that it can enhance these positioning features by establishing a new fuzzy-weighting location mechanism (FLM) and also can adjust dynamically the measurement noise covariance by making use of a novel fuzzy Kalman filtering method. Finally, experiment results are given to show effectiveness and merit of the proposed approach.