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

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Title: A Link Quality Indicator (LQI) Based Closed-Form Localization Framework for Vehicular Ad Hoc Networks (VANETs).
Authors: Ahmad, Waqas, Anwar, Shahzad, Iqbal, Abid, Khan, Abuzar, Arif, Saad, Alzahrani, Ali S., Al-Naeem, Mohammed, Alhayan, Fatimah, Bukhari, Syed Hashim Raza, Husnain, Ghassan
Source: Computer Modeling in Engineering & Sciences (CMES); 2026, Vol. 148 Issue 1, p1-37, 37p
Subject Terms: Vehicular ad hoc networks, Wireless geolocation systems, Kalman filtering, Wireless communications, Monte Carlo method, Traffic monitoring
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. [ABSTRACT FROM AUTHOR]
Copyright of Computer Modeling in Engineering & Sciences (CMES) is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Link Quality Indicator (LQI) Based Closed-Form Localization Framework for Vehicular Ad Hoc Networks (VANETs).
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  Data: <searchLink fieldCode="AR" term="%22Ahmad%2C+Waqas%22">Ahmad, Waqas</searchLink><br /><searchLink fieldCode="AR" term="%22Anwar%2C+Shahzad%22">Anwar, Shahzad</searchLink><br /><searchLink fieldCode="AR" term="%22Iqbal%2C+Abid%22">Iqbal, Abid</searchLink><br /><searchLink fieldCode="AR" term="%22Khan%2C+Abuzar%22">Khan, Abuzar</searchLink><br /><searchLink fieldCode="AR" term="%22Arif%2C+Saad%22">Arif, Saad</searchLink><br /><searchLink fieldCode="AR" term="%22Alzahrani%2C+Ali+S%2E%22">Alzahrani, Ali S.</searchLink><br /><searchLink fieldCode="AR" term="%22Al-Naeem%2C+Mohammed%22">Al-Naeem, Mohammed</searchLink><br /><searchLink fieldCode="AR" term="%22Alhayan%2C+Fatimah%22">Alhayan, Fatimah</searchLink><br /><searchLink fieldCode="AR" term="%22Bukhari%2C+Syed+Hashim+Raza%22">Bukhari, Syed Hashim Raza</searchLink><br /><searchLink fieldCode="AR" term="%22Husnain%2C+Ghassan%22">Husnain, Ghassan</searchLink>
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  Data: Computer Modeling in Engineering & Sciences (CMES); 2026, Vol. 148 Issue 1, p1-37, 37p
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  Data: <searchLink fieldCode="DE" term="%22Vehicular+ad+hoc+networks%22">Vehicular ad hoc networks</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+geolocation+systems%22">Wireless geolocation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+communications%22">Wireless communications</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+monitoring%22">Traffic monitoring</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Modeling in Engineering & Sciences (CMES) is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.32604/cmes.2026.083950
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 37
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    Subjects:
      – SubjectFull: Vehicular ad hoc networks
        Type: general
      – SubjectFull: Wireless geolocation systems
        Type: general
      – SubjectFull: Kalman filtering
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      – SubjectFull: Wireless communications
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      – SubjectFull: Monte Carlo method
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      – SubjectFull: Traffic monitoring
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      – TitleFull: A Link Quality Indicator (LQI) Based Closed-Form Localization Framework for Vehicular Ad Hoc Networks (VANETs).
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              Text: 2026
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