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

Abstract

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.

Efficient Message Dissemination on Curve Road in Vehicular Networks

“…With vehicles' location and mobile information, Ryu and Cha proposed the relay selection method based on the predicted link lifetime. The candidate node with the longest lifetime was selected as the relay node to forward messages [23]. Jiang et al proposed the UAV-assisted geographic routing in VANETs based on the Q-Learning, the aerial component calculates the global routing path for the ground component, and then, the vehicle on the ground forwards the routing request to the optimal node [24].…”

A hybrid optimization with ensemble learning to ensure VANET network stability based on performance analysis

“…The possible relay nodes, on the other hand, wait for the longest before retransmission, depending on the congestion resolution mechanism. This method may cause a significant delay, particularly in high-mobility circumstances 15 . Vector-based Tracking Detection (V-TRADE) and History-enhanced V-TRADE, are position-based broadcast protocols are also suggested (HV-TRADE).…”