Self-Correcting Localization Scheme for Vehicle to Vehicle Communication

Abstract

-Vehicle to Vehicle communication (V2V) has taken place in research interest for many purposes such as road safety and traffic management. An accurate estimation for vehicular node position is important for such type of communication. A vehicular node can be equipped with Global Positioning Systems (GPS) to estimate its position. In practice, many vehicular nodes may lose GPS signals in rural regions due to dense foliage, or in urban regions due to compact high buildings. In this paper, the received signal strength indication (RSSI) is exploited to assist vehicular nodes to estimate their locations using inter-vehicle communication. High dynamic network topology in V2V is expected due to high node mobility. As a result, the localization error due to signal strength measurements clearly increases compared to low dynamic network topology. The proposed scheme is self-correcting solution which studies the network topology scenarios that increase localization errors and introduces optimal techniques to minimize such errors. Performance evaluation and simulation results show that this work improves localization accuracy and increases the number of vehicular nodes that estimate their locations compared to existing localization schemes.Index Terms -Vehicular ad-hoc networks, Vehicle to Vehicle communication, Localization, Radio ranging, Path loss, Shadowing.

A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks

“…The simulation experiments of the proposed HCSGWOA-NLOS-PS and the benchmarked approaches are conducted and validated using EstiNet 8.1 Simulator. [37][38][39][40][41][42][43][44] The simulation parameters considered for implementing the proposed HCSGWOA-NLOS-PS and the benchmarked WI-CLOSPS-DVBA, 23 CS-NLOSSL-CIC, 25 WDHP-NLOS-PS, 22 and CVN-NLOS-PS 21 schemes are kept constant throughout the simulation. 17,[45][46][47][48][49] They are implemented with the same environment, traffic condition and mobility model.…”

Connectivity Based Positioning System for Underground Vehicular Ad Hoc Networks

“…Though three RNs are discovered, a small measurement error due to rapid change in the network topology leads to poor localization accuracy. The work in [15] has studied the effect of vehicular ad-hoc network topology in free space environments on node localization. Connectivity based localization (i.e., Monte Carlo localization) can solve those problems.…”

“…The centroid of filtered samples that represents the estimate position of VN(j) at the current t is determined. Two values are stored for the next time slot, the value of estimated location and the current speed reading as shown in Lines (15,16). In what follows, simulation results are introduced which the performance of COPO system is compared to existing Monte Carlo schemes such as MCL [16] and VMCB [21].…”

Mobility Management Scheme Based on Smart Buffering for Vehicular Networks

“…Nowadays, vehicular ad-hoc networks (VANET) take place in research topics [1][2][3][4][5][6]. Mobility management is an important issue in VANET.…”

“…While handoff latency in HI-NEMO includes the time from notifying MR at AR 1 till recognition of MR in SW, that is suitable for the vehicle path. HO_NEMO = T l2 +T l3(1) As shown in Figure3, L3 handover delay consists of: Movement Detection delay T MD ; which equal 2 , Duplicate Address Detection delay T DAD and Registration delay T REG[15,16].…”