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A Link Quality Indicator(LQI)Based Closed-Form Localization Framework for Vehicular Ad Hoc Networks(VANETs) 认领 引用
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作者 Waqas Ahmad Shahzad Anwar +7 位作者 Abid Iqbal Abuzar Khan Saad Arif Ali S.Alzahrani Mohammed Al-Naeem Fatimah Alhayan Syed Hashim Raza Bukhari Ghassan Husnain 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1140-1176,共37页
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 positio... 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. 展开更多
关键词 Vehicle localization link quality indicator vehicular ad hoc networks intelligent transportation systems Cramer-Rao lower bound
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Engine Failure Prediction on Large-Scale CMAPSS Data Using Hybrid Feature Selection and Imbalance-Aware Learning 认领 引用
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作者 Ahmad Junaid Abid Iqbal +3 位作者 Abuzar Khan Ghassan Husnain Abdul-Rahim Ahmad Mohammed Al-Naeem 《Computers, Materials & Continua》 SCIE EI 2026年第4期1485-1508,共24页
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ... Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings. 展开更多
关键词 Predictive maintenance CMAPSS dataset feature selection class imbalance LSTM-GRUhybrid model interpretability industrial deployment
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Secure and Differentially Private Edge-Cloud Federated Learning Framework for Privacy-Preserving Maritime AIS Intelligence 认领 引用
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作者 Abuzar Khan Abid Iqbal +3 位作者 Ghassan Husnain Fahad Masood Mohammed Al-Naeem Sajid Iqbal 《Computers, Materials & Continua》 SCIE EI 2026年第6期658-673,共16页
Cloud computing now supports large-scale maritime analytics,yet offloading rich Automatic Identification System(AIS)data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border... Cloud computing now supports large-scale maritime analytics,yet offloading rich Automatic Identification System(AIS)data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border privacy regulations.This work addresses the gap between growing demand for AI-driven vessel intelligence and the limited availability of practical,privacy-preserving cloud solutions.We introduce a privacy-by-design edge-cloud framework in which ports and vessels serve as federated clients,training vessel-type classifiers on local AIS trajectories while transmitting only clipped,Gaussian-perturbed updates to a zero-trust cloud coordinator employing secure and robust aggregation.Using a public AIS corpus with realistic non-IID client partitions,our evaluation shows that non-private FedAvg attains validation AUC≈0.90 and test AUC≈0.78,closely matching a centralized baseline.Moderate differential privacy noise(δ≤0.5)preserves most of this utility across KRUM and trimmed-mean aggregation.Communication analysis indicates that secure aggregation introduces negligible overhead compared with standard FedAvg,while homomorphic encryption increases payload size by roughly an order of magnitude.Membership-inference experiments further demonstrate strong privacy protection,yielding ROC AUC≈0.51 with no correctly inferred training members.Overall,the findings show that effective,regulation-conscious maritime analytics can be achieved without centralizing raw AIS data,offering a practical pathway for deploying resilient,privacy-enhanced AI services in distributed maritime environments. 展开更多
关键词 Federated learning privacy-preserving cloud computing maritime AIS analytics differential privacy
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