TDMP-Reliable Target Driven and Mobility Prediction based Routing Protocol in Complex VANET

Vehicle-to-everything (V2X) communication in the vehicular ad hoc network (VANET), an infrastructure-free mechanism, has emerged as a crucial component in the advanced Intelligent Transport System (ITS) for special information transmission and inter-vehicular communications. One of the main research challenges in VANET is the design and implementation of network routing protocols which manage to trigger V2X communication with the reliable end-to-end connectivity and efficient packet transmission. The organically changing nature of road transport vehicles poses a significant threat to VANET with respect to the accuracy and reliability of packet delivery. Therefore, a position-based routing protocol tends to be the predominant method in VANET as they overcome rapid changes in vehicle movements effectively. However, existing routing protocols have some limitations such as (i) inaccurate in high dynamic network topology, (ii) defective link-state estimation (iii) poor movement prediction in heterogeneous road layouts. In this paper, a target-driven and mobility prediction (TDMP) based routing protocol is therefore developed for high-speed mobility and dynamic topology of vehicles, fluctuant traffic flow and diverse road layouts in VANET. The primary idea in TDMP is that the destination target of a driver is included in the mobility prediction to assist the implementation of the routing protocol. Compared to existing geographic routing protocols which mainly greedily forward the packet to the next-hop based on its current position and partial road layout, TDMP is developed to enhance the packet transmission with the consideration of the estimation of inter-vehicles link status, and the prediction of vehicle positions dynamically in fluctuant mobility and global road layout.Comment: 35 pages,16 Figure

A probability-based multimetric routing protocol for vehicular ad hoc networks in urban scenarios

Vehicular Ad hoc Networks have received considerable attention in recent years and are considered as one of the most promising ad-hoc network technologies for intelligent transport systems. Vehicular Ad hoc Networks have special requirements and unique characteristics (e.g., special mobility patterns, short life links, rapid topology changes) which make the design of suitable routing protocols, a challenge. Consequently, an efficient routing protocol that fits with VANETs’ requirements and characteristics is a crucial task to obtain a good performance in terms of average percentage of packet losses and average end-to-end packet delay. To attain this goal, we propose a novel probabilistic multimetric routing protocol (ProMRP) that is specially designed for VANETs. ProMRP estimates the probability for each neighbor of the node currently carrying the packet, to successfully deliver a packet to destination. This probability is computed based on four designed metrics: distance to destination, node’s position, available bandwidth and nodes’ density. Furthermore, an improved version of ProMRP called EProMRP is also proposed. EProMRP includes an algorithm that accurately estimates the current position of nodes in the moment of sending the packet instead of using the last updated position obtained from the previous beacon message. Simulations are carried out in a realistic urban scenario using OMNeT++/VEINS/SUMO, including real maps from the OpenStreetMaps platform. Simulation results show a better performance of ProMRP and EProMRP compared to recent similar proposals found in the literature in terms of packet losses and end-to-end packet delay, for different vehicles’ densities.Peer ReviewedPostprint (published version

Reliability and capability based computation offloading strategy for vehicular ad hoc clouds

In the Internet of Vehicles (IoV), with the increasing demand for intelligent technologies such as driverless driving, more and more in-vehicle applications have been put into autonomous driving. For the computationally intensive task, the vehicle self-organizing network uses other high-performance nodes in the vehicle driving environment to hand over tasks to these nodes for execution. In this way, the computational load of the cloud alleviated. However, due to the unreliability of the communication link and the dynamic changes of the vehicle environment, lengthy task completion time may lead to the increase of task failure rate. Although the flooding algorithm can improve the success rate of task completion, the offloading expend will be large. Aiming at this problem, we design the partial flooding algorithm, which is a comprehensive evaluation method based on system reliability in the vehicle computing environment without infrastructure. Using V2V link to select some nodes with better performance for partial flooding offloading to reduce the task complete time, improve system reliability and cut down the impact of vehicle mobility on offloading. The results show that the proposed offloading strategy can not only improve the utilization of computing resources, but also promote the offloading performance of the system

MPBRP- mobility prediction based routing protocol in VANETs

Vehicular Ad-hoc Networks (VANETs) technology has been emerged as an important research topic in recent years. This is because the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are becoming more popular in the field of Intelligent Transportation System (ITS) so as to further enhance traffic efficiency, safety, emissions reduction and infotainment applications. Over the last few years, a number of routing protocols in VANETs have been developed. In particular, position-based routing protocols have attracted the most interest in VANETs as they are suitable for a frequently changeable network topology and highly dynamic nature of vehicular nodes. This paper develops a new Mobility Prediction Based Routing Protocol (MPBRP) for neighborhood detection, packet transmission and path recovery in VANETs by using driver's intention collected from the positioning systems. Several major contributions are made in the paper: (1) Combining both predictive forwarding strategy and recovery strategy to detect neighbors and transfer packets. (2) Utilizing the predicted position and angles in a pre-defined time with considering driver's intention to select the neighboring nodes and discover the transmission path. (3) Validating the effectiveness and feasibility of the proposed protocol by creating a unified simulation platform (Veins) and implementing it in real world scenarios. (4) Enhancing the overall performance as the proposed routing protocol achieved competitive improvement over existing protocols in terms of packet delivery ratio, end-to-end delay and average hops about 26.22%, 21.89% and 20.79% by average in grid-based scenario, and about 26.04%,23.14% and 18.51% by average in urban scenario respectively