Node Clustering Communication Method With Member Data Estimation to Improve QoS of V2X Communications for Driving Assistance With Crash Warning

• Main Authors: Takeshi Hirai, Tutomu Murase
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Advanced driver assistance systems; device-to-device communication; vehicular ad hoc networks; wireless networks
• Online Access: https://ieeexplore.ieee.org/document/8672055/

This paper proposes a low-cost cluster-based method called CLASES that employs a cluster members' data estimation mechanism to improve Quality of Services (QoS) of Vehicle-to-Everything communications for driving assistance with Crash Warning Application (CWA). The idea of the proposed method is to use the estimation mechanism by cluster heads and the estimation error correction mechanism by cluster members, instead of collecting member's data by intracluster communications, which most conventional cluster-based methods have used. Intracluster communications require some additional costs or reduce the bandwidth of intercluster communications; therefore, we can use the proposed method at low costs in comparison with the conventional methods. The proposed method also enables to control the number of active nodes by an advantage of clustering; that is, the proposed method appropriately adjusts the number of nodes that are likely to transmit frames simultaneously. Thus, data frames are transmitted as parallel as possible while suppressing the probability of frame collision errors, and the QoS improves. On the other hand, the disadvantage is that the error correction mechanism yields some additional frames, and the QoS deteriorates. We evaluated the performance of the proposed method in various parameters. The results show that the proposed method accommodate more nodes by 27 % than that of the method without clustering even at the realistic occurrence frequency of the estimation errors. Thus, this paper contributes to providing the improving QoS for CWA at low costs.