Driving Path Predication Based Routing Protocol in Vehicular Ad hoc Networks

Abstract

The vehicular mobility is reflection and extension of the human social activity. Since human trajectories show a high degree of temporal and spatial regularity, thus vehicular driving paths are predictable to a large extent. In this paper, we firstly analyze the predictabilities of different types of vehicles and then propose a new driving path predication based routing protocol (DPPR). With hello messages to broadcast vehicles' driving path predication information to neighbor vehicles, DPPR can observably increase the successful ratio to find the proper next hop vehicles that move toward the optimal expected road in intersection areas. In roads with sparse vehicle density, DPPR utilizes vehicles to carry messages to roads with high vehicle density while the messages' forward paths partially coincide with the vehicles' driving paths. Moreover, as to messages that can tolerate long delay, they can be carried to destinations by vehicles whose driving paths will pass the messages' destination in order to optimize bandwidth utilization. Simulation results demonstrate the effectiveness of the proposed DPPR protocol.

A Software-Defined Directional Q-Learning Grid-Based Routing Platform and Its Two-Hop Trajectory-Based Routing Algorithm for Vehicular Ad Hoc Networks

“… General private cars: have regular trajectories and obvious regularity in time and space. Taxis: have a flexible and variable travel route, and often have a credible destination [ 23 ], but the travel path is not fixed. …”

“…Taxis: have a flexible and variable travel route, and often have a credible destination [ 23 ], but the travel path is not fixed.…”

“…(1) DPPR [ 23 ] assumes that each vehicle knows its own driving trajectory in advance, and the driving trajectory is a sequence formed by the number of intersections that will be passed through. Due to the traffic information, each road can be assigned a weight value, which is the transmission delay time of this road, and the shortest delay path can be calculated via the Dijkstra algorithm [ 24 ].…”

TDMP: Reliable Target Driven and Mobility Prediction based routing protocol in complex Vehicular Ad-hoc Network

“…PBR can also be divided into Non-Delay Tolerant Network routing (Non-DTN) and Delay Tolerant Network (DTN) routing. The aim of the Non-DTN is to transfer data packets to the destination node as soon as possible, which is greatly used in the effectively populated VANETs, including GPSR [30], GSR [31], A-STAR [32], GPCR [33], GyTAR [34], DPPR [35], IGR [36], RPS [37], APR [38], PAR [39], RPGR [40], HSVNs [41], IGRP [42], PDGR [43], PGRP [44] and MPBRP [45]. The DTN lack of a reliable and persistent end-to-end path will move the data packet before establishing the node connections, which includes VADD [46] and GeOpps [47].…”

A Survey of Data Distribution Mechanisms in VANET

“…An important step in assisting vehicle data distribution is to choose the optimal relay vehicle. Based on the position provided by the GPS, the future position and the mobility characteristics of the vehicle can be derived [13]. Wu et al [14] used a multi-level Markov chain to predict the next position of the vehicle and select assisted vehicles based on the vehicles' existing trajectory.…”