Signal reconstruction frameworks for intelligent transportation systems: from nonuniform sampling to energy-efficient sensing

IntroductionUrban-scale intelligent transportation systems rely on heterogeneous sensor networks that often operate under irregular sampling schedules and strict energy constraints. These conditions challenge the stable and accurate reconstruction of sensed signals in large-scale mobility and environmental monitoring applications. This paper proposes a stability-aware framework for adaptive and energy-efficient signal reconstruction in such resource-constrained urban sensing environments.MethodsThe proposed framework builds on generalized and nonuniform sampling theory and extends B-spline and Riesz-basis formulations to irregularly sampled signals acquired by urban sensor networks. It derives explicit quantitative trade-offs among sampling density, energy consumption, and reconstruction accuracy, and establishes theoretical stability conditions under sparse and jittered acquisition. The approach is assessed through comprehensive simulations using synthetic traffic-density signals across different sampling densities, jitter levels, and additive noise conditions.ResultsThe results show that the Riesz-basis iterative reconstruction achieves fidelity comparable to spline-based reconstruction once the sampling density exceeds the predicted stability threshold. Under these conditions, sensing energy consumption is reduced by up to 60% while maintaining high reconstruction quality. The framework also exhibits graceful degradation under additive noise and preserves structural similarity above 0.8 at 10 dB SNR, demonstrating robust performance for edge-deployed sensors.DiscussionThe findings indicate that stability-aware sampling design can effectively balance reconstruction accuracy and energy efficiency in heterogeneous urban sensor networks. By integrating rigorous signal-processing theory with practical green-sensing requirements, the proposed framework provides a mathematically grounded basis for resilient and low-power data acquisition in future intelligent transportation infrastructures.