Online TCP Throughput Map Maintenance Under Budget-Constrained Vehicular Sensing

Abstract

A Transmission Control Protocol (TCP) throughput map represents communication quality over road networks and supports communication-aware applications in intelligent transportation systems. Maintaining such a map online is challenging because vehicular measurements are sparse and unevenly distributed, network conditions vary rapidly, and sensing-budget constraints limit the number of vehicles from which onboard communication measurements can be uploaded at each time step. This work addresses online TCP throughput map maintenance under sparse vehicular observations and sensing-budget constraints. To support budget-constrained sensing, we combine discoverability-guided vehicle selection and probabilistic map updating within a digital twin (DT)-assisted vehicular sensing architecture. The resulting sensing-and-mapping method, referred to as Discoverability-aware and Statistical Mapping (DISMAP), maintains a spatio-temporal discoverability map to characterize historical sensing coverage and select vehicles that improve the coverage of under-represented regions. It then uses Gaussian Process Regression (GPR) as a probabilistic mapping engine to estimate the mean TCP throughput and predictive standard deviation, where the standard deviation is adjusted using local vehicle density. Simulation results show that DISMAP reduces the mean absolute error (MAE) and mean standard deviation (MSTD) by up to 23.7% and 37.5%, respectively, and achieves a prediction-interval miss rate (PIMR) of 0.048, which is close to the nominal value of 0.05. These results indicate a favorable balance among prediction accuracy, interval sharpness, calibration, and spatial representativeness across different traffic-density conditions.