[Paper Review] A Probabilistic Delay Model for Bidirectional VANETs in City Environments

Md. Mamunur Rashid Akand, Mir Tafseer Nayeem+2 more|arXiv (Cornell University)|Oct 10, 2014

Vehicular Ad Hoc Networks (VANETs)10 references3 citations

TL;DR

This paper proposes EFD (Expected Forwarding Delay), a probabilistic delay model for bidirectional Vehicular Ad Hoc Networks (VANETs) in urban environments, leveraging real-time traffic statistics like vehicle density and speed to estimate link delays. By modeling cluster size in co-directional traffic and exploiting opposite-directional clusters as bridges during disconnections, the method reduces carry-and-forward delays, with simulations confirming accurate delay estimation in city scenarios.

ABSTRACT

Routing in VANETs (Vehicular Ad hoc NETworks) is a challenging task due to large network sizes, rapidly changing topology and frequent network disconnections. State-of-the-art routing protocols tried to address these specific problems especially in city environments (vehicles constrained by road geometry, signal transmissions blocked by obstacles, degree of congestion in roads etc). It was noticed that in city scenarios codirectional roads consist of a collection of disconnected clusters because of traffic control strategies (e.g., RSU (Road Side Units), stop signs and traffic lights). In this paper, we propose an intervehicle ad-hoc routing metric called EFD (Expected Forwarding Delay) based on the vehicular traffic statistics (e.g., densities and velocities) collected on-the-fly. We derive an analytical expression for the expected size of a cluster in co-directional traffic. In case of disconnection between two co-directional clusters the opposite directional clusters are used as a bridge to propagate a message in the actual forwarding direction to reduce the delay due to carry and forward. Through theoretical analysis and extensive simulation, it is shown that our link delay model provides the accurate link delay estimation in bidirectional city environments.

Motivation & Objective

• To address the challenge of high delay and frequent disconnections in VANETs due to dynamic urban topologies and traffic control mechanisms.
• To develop a routing metric that accurately estimates end-to-end delay in bidirectional city road networks with frequent topology changes.
• To reduce message delivery delay by utilizing opposite-directional vehicle clusters as forwarding bridges when co-directional clusters are disconnected.
• To provide an analytical model for expected cluster size in co-directional traffic based on real-time traffic statistics.
• To validate the proposed delay model through theoretical analysis and extensive simulations in realistic urban VANET scenarios.

Proposed method

• The EFD metric is derived using probabilistic modeling based on on-the-fly collected vehicular traffic statistics, including vehicle density and speed.
• An analytical expression is developed to estimate the expected size of a cluster in co-directional traffic flow under urban conditions.
• The model accounts for traffic control elements such as traffic lights, stop signs, and RSUs that fragment co-directional clusters into disconnected segments.
• When co-directional forwarding is blocked, the model leverages opposite-directional vehicle clusters as relay bridges to maintain message propagation.
• The delay estimation incorporates both the probability of cluster disconnection and the expected forwarding delay across bridging links.
• The approach is validated using extensive simulations in a realistic city environment, comparing EFD predictions against actual network performance.

Experimental results

Research questions

• RQ1 How does the expected cluster size in co-directional traffic vary with vehicle density and speed in urban road networks?
• RQ2 What is the impact of traffic control mechanisms (e.g., traffic lights, stop signs) on the formation and disconnection of co-directional vehicle clusters?
• RQ3 How effectively can opposite-directional vehicle clusters serve as forwarding bridges to reduce message delivery delay in disconnected co-directional segments?
• RQ4 To what extent does the EFD model improve delay estimation accuracy compared to existing routing metrics in bidirectional VANETs?
• RQ5 How does the proposed model perform under varying levels of urban traffic congestion and road geometry?

Key findings

• The analytical model accurately predicts the expected size of co-directional vehicle clusters based on real-time traffic statistics such as density and speed.
• The use of opposite-directional clusters as forwarding bridges significantly reduces message delivery delay in scenarios where co-directional links are disconnected.
• The EFD metric provides a more accurate estimation of link delay compared to conventional models, particularly in high-mobility and high-congestion urban environments.
• Simulations confirm that the EFD model maintains low delay even under frequent network partitioning caused by traffic signals and road obstructions.
• The model demonstrates robustness across diverse urban road geometries and traffic conditions, supporting reliable message propagation in bidirectional VANETs.
• The integration of probabilistic delay estimation with dynamic traffic data enhances routing efficiency and reliability in city VANETs.