ELDP: Extended Link Duration Prediction Model for Vehicular Networks

Abstract

Link duration between two vehicles is considered an important quality of service metric in designing a network protocol for vehicular networks. There exist many works that study the probability density functions of link duration in a vehicular network given various vehicle mobility models, for example, the random waypoint model. None of them, however, provides a practical solution to estimating the link duration between two vehicles on the road. This is in part because link duration between vehicles is affected by many factors including the distance between vehicles, their turning directions at intersections, and the impact of traffic lights. Considering these factors, we propose the extended link duration prediction (ELDP) model which allows a vehicle to accurately estimate how long it will be connected to another vehicle. The ELDP model does not assume that vehicles follow certain mobility models; instead, it assumes that a vehicle's velocity follows the Normal distribution. We validate the ELDP model in both highway and city scenarios in simulations. Our detailed simulations illustrate that relative speed between vehicles plays a vital role in accurately predicting link duration in a vehicular network. On the other hand, we find that the turning directions of a vehicle at intersections have subtle impact on the prediction results.

Prefetching of mobile devices information - a DNS perspective

“…Wang et al [5] proposed a prediction model called extended link duration prediction (ELDP), which allows the vehicle to estimate the link duration with the other vehicles. Simulations in a city and highway show that the speed of vehicles has an impact on the link duration prediction in Vanet networks.…”

“…• Antennas F to O correspond to the predictions for previous positions of the device ( f T −i (T +j), i ∈ [ [1,5]], j ∈ [ [1,4]], i − j > 0 ). If antenna A corresponds to one of these antennas, but not antennas B to E, our prediction was a failure, but the information is still present in the gateway's cache, and despite the prediction failure for this exact timestamp, we hit our gateway's cache as the information was not purged yet.…”

A Stochastic Distribution Based Methodology to Estimate Control Phase Time for Software-Defined Radios in Tactical MANETs

“…Furthermore, a routing method is proposed based on the link availability analysis to reduce the routing overhead caused by mobility. An extended link duration prediction (ELDP) model to estimate the link connectivity between vehicles is proposed in [28]. The model estimates the relative velocity distribution and computes the expected link duration between vehicles based on relative speeds, inter-vehicle distance, the impact side road units, and vehicles movement patterns.…”

Effective Prediction of V2I Link Lifetime and Vehicle's Next Cell for Software Defined Vehicular Networks: A Machine Learning Approach

“…Most assumed a predefined mobility model and simplified radio propagation models that, in fact, do not accurately match the reality especially in urban or in-door environments. For V2V wireless link prediction in Vehicular Ad-hoc NETwork (VANET), some work assumed that the speed of vehicles remains constant (as in [5] ), or that vehicles moves along a straight highway [6], [7] assuming some predefined probability distribution for vehicle speed (as in [6]- [10]). Based on similar assumptions (i.e.…”