[Paper Review] Multihop Routing for Data Delivery in V2X Networks

Yilin Li, Jian Luo+3 more|arXiv (Cornell University)|Jan 15, 2019

Vehicular Ad Hoc Networks (VANETs)27 references4 citations

TL;DR

This paper proposes a novel multihop routing framework for V2X networks that jointly optimizes end-to-end latency and data rate using convex optimization. By modeling vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, it formulates global and distributed routing algorithms that maximize a weighted sum of latency and data rate, achieving lower latency and higher data rates than existing approaches in simulations.

ABSTRACT

Data delivery relying on the carry-and-forward strategy of vehicle-to-vehicle (V2V) communications is of significant importance, however highly challenging due to frequent connection disruption. Fortunately, incorporating vehicle-to-infrastructure (V2I) communications, motivated by its availability in bridging long-range vehicular connectivity, dramatically improves delivery opportunity. Nevertheless, the cooperation of V2V and V2I communications, known as vehicular-to-everything (V2X) communications, necessitates a specific design of multihop routing for enhancing data delivery performance. To address this issue, this paper provides a mathematical framework to investigate the data delivery performance in V2X networks in terms of both delivery latency and data rate. With theoretical analysis, we formulate a global and a distributed optimization problem to maximize the weighted sum of delivery latency and data rate. The optimization problems are then solved by convex optimization theory and based on the solutions, we propose a global and a distributed multihop routing algorithm to select the optimal route for maximizing the weighted sum. The rigorousness of the proposed algorithms is validated by extensive simulation under a wide range of system parameters and simulation results shed insight on the design of multihop routing algorithm in V2X networks for minimizing latency and maximizing data rate.

Motivation & Objective

• To address the challenge of unreliable data delivery in V2X networks due to frequent disconnections in high-speed vehicular environments.
• To design a multihop routing scheme that effectively leverages both V2V and V2I communications to improve delivery performance.
• To jointly optimize end-to-end latency and data rate, addressing the trade-off between low-latency and high-throughput requirements in V2X applications.
• To develop both global and distributed routing algorithms that are scalable and practical for real-world deployment.
• To provide a rigorous mathematical framework for analyzing and optimizing data delivery performance in V2X networks.

Proposed method

• Formulates a global optimization problem to maximize a weighted sum of end-to-end latency and data rate using convex optimization theory.
• Derives a distributed multihop routing algorithm based on the solution of the global problem, enabling scalable and adaptive route selection.
• Models data delivery using a carry-and-forward strategy with probabilistic transmission models for V2V and V2I links.
• Incorporates time-dependent functions for vehicle mobility and RSU connectivity, including arrival rates and backhaul availability.
• Uses convexity proofs for the objective function, ensuring the optimality of the solution via KKT conditions.
• Applies the Karush-Kuhn-Tucker (KKT) conditions to solve the optimization problem, with the optimal transmission time $ t^* $ derived as the argmax of the weighted objective function.

Experimental results

Research questions

• RQ1 How can multihop routing in V2X networks be designed to simultaneously minimize end-to-end latency and maximize data rate?
• RQ2 What is the impact of infrastructure-assisted V2I communication on improving data delivery performance in highly dynamic vehicular environments?
• RQ3 How does the trade-off between latency and data rate affect the design of optimal multihop routing in V2X networks?
• RQ4 Can a distributed routing algorithm achieve performance close to a global optimization framework while remaining scalable and practical?
• RQ5 What system parameters—such as vehicle density, RSU density, and backhaul availability—most significantly influence data delivery performance?

Key findings

• The proposed global and distributed multihop routing algorithms significantly reduce end-to-end latency and increase data rate compared to conventional schemes in extensive simulations.
• The distributed algorithm achieves performance close to the global optimal solution, demonstrating scalability and practicality for real-time deployment.
• System parameters such as vehicle arrival rates, RSU density, and wireless backhaul availability are shown to have a strong influence on both latency and data rate.
• The optimization framework is proven convex, ensuring the global optimality of the solution via KKT conditions.
• The optimal transmission time $ t^* $ is found to be one of three candidate values: 0, $ t_s $, or $ T $, where $ t_s $ is the critical point satisfying the first-order condition.
• The weighted sum of latency and data rate is maximized by balancing the trade-off between low-latency V2V forwarding and high-throughput V2I transmission.