Mobility Prediction Based Multi-Directional Broadcasting for Both Highway and Urban Vehicular Sensor Networks

• Main Authors: Minwoo Ryu, Si-Ho Cha
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Emergency message dissemination; mobility prediction; multi-directional broadcasting; vehicular sensor networks; intelligent transportation
• Online Access: https://ieeexplore.ieee.org/document/9166499/

This paper proposes a mobility prediction based multi-directional broadcasting (MPMB) protocol for both highway and urban Vehicular Sensor Networks (VSNs). VSNs have to ensure the prompt dissemination of critical sensing data to all vehicles within the Region of Interest (ROI) to avoid various road dangers. Several research studies have proposed various broadcasting protocols, but most of them just focus on either highway or urban scenarios. MPMB protocol is derived from the mobility prediction based directed broadcasting (MPDB) protocol which is a single directional broadcasting protocol for highways. The main purpose of MPMB is to improve broadcasting efficiency in both highway and urban scenarios. To do this, MPMB adopts the adaptive directional sectors and the Store-Carry-Forward (SCF) scheme to select the next broadcasting (rebroadcasting) vehicles based on the mobility prediction. MPMB protocol consists of (i) mobility prediction stage and (ii) broadcasting stage. In the mobility prediction stage, each vehicle predicts the Link Available Time (LAT) of its all neighbor vehicles through periodical beaconing. In the broadcasting stage, each broadcasting vehicle adaptively selects a vehicle with the largest LAT value predicted in the previous mobility prediction stage as a rebroadcasting vehicle in each possible directional sector. The simulation results show that MPMB protocol has better performance in terms of packet delivery rate and packet delay in comparison with three well-known reference protocols in both highway and urban scenarios with various traffic density.