A Link Quality Indicator (LQI) Based Closed-Form Localization Framework for Vehicular Ad Hoc Networks (VANETs)

Waqas Ahmad¹, Shahzad Anwar², Abid Iqbal^3,*, Abuzar Khan⁴, Saad Arif⁵, Ali S. Alzahrani³, Mohammed Al-Naeem⁶, Fatimah Alhayan⁷, Syed Hashim Raza Bukhari³, Ghassan Husnain^4,*
1 Department of Computer Science, Iqra National University, Peshawar, Pakistan
2 Department of Mechatronics Engineering, University of Engineering & Technology, Peshawar, Pakistan
3 Department of Computer Engineering, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
4 Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan
5 Department of Mechanical Engineering, College of Engineering, King Faisal University, Al Ahsa, Saudi Arabia
6 Department of Computer Networks Communications, CCSIT, King Faisal University, Al-Ahsa, Saudi Arabia
7 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
* Corresponding Author: Abid Iqbal. Email: email ; Ghassan Husnain. Email: email
(This article belongs to the Special Issue: Machine Learning and Data Fusion for Autonomous Control and Surveillance Systems)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.083950

Received 14 April 2026; Accepted 11 June 2026; Published online 03 July 2026

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Abstract

Accurate vehicle localization is essential for safety-critical vehicular ad hoc networks (VANETs), including emergency response, navigation, traffic monitoring, and cooperative driving. However, conventional GPS/GNSS positioning systems have often shown degradation in tunnels, dense urban corridors, and non-line-of-sight (NLOS) environments, where satellite visibility and signal reliability are limited. This paper proposes a calibrated Link Quality Indicator (LQI)-based closed-form localization framework for partially connected Roadside Unit (RSU)-assisted VANETs. The proposed framework first calibrates the LQI-to-range relationship using numerical regression parameters and then converts accepted LQI observations into distance estimates. The distances are processed through a variance-aware weighted least squares (WLS) estimator, after which scalar consistency refinement is applied to improve the geometric consistency of the final position estimate. Partial connectivity is explicitly modeled using communication radius, LQI-threshold, and packet-reception constraints, rather than assuming that all vehicles and anchors are fully connected. The proposed method is evaluated against least squares (LS), WLS, RSSI-WLS, Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), cooperative localization, hybrid GNSS/INS/RSS fusion, and machine-learning-based localization baselines. The evaluation includes Monte Carlo simulations under shadowing, fading, packet loss, anchor-geometry, and NLOS conditions, together with confidence interval, statistical-significance, runtime, ablation, and sensitivity analyses. In addition, trace-driven validation is performed using the NGSIM US-101 real-world vehicle trajectory dataset. The NGSIM dataset provides real vehicle mobility traces, while LQI observations are generated using the calibrated LQI-distance model because the dataset does not contain physical LQI measurements. In the 100-vehicle case, throughput improves from 0.614 to 1.169 successful localizations/s compared with LS, corresponding to a 90.39% gain and from 0.591 to 1.169 successful localizations/s compared with WLS, corresponding to a 97.80% gain. In the NGSIM trace-driven experiment, the proposed method achieves RMSE values of 2.39, 2.55, and 3.14 m for 10, 50, and 100 vehicles, respectively. These results indicate that calibrated LQI-based localization provides a low-cost, infrastructure-compatible, and computationally efficient positioning framework for ITS and safety-critical VANET applications.