🤖 AI Summary
This study addresses the energy inefficiency of roadside units (RSUs) in vehicle-to-infrastructure (V2I) networks caused by continuous operation during off-peak hours, as well as the risk of excessive communication latency due to wake-up delays under independent sleep scheduling. To overcome these challenges, the authors propose a spatially correlated cooperative sleep scheduling framework that leverages upstream traffic detection signals shared across infrastructure links and exploits spatiotemporal correlations in vehicular traffic at adjacent intersections to enable predictive wake-up. The problem is formulated as a constrained Markov decision process and efficiently solved by decomposition into single-RSU subproblems. Evaluated on real-world traffic data from four intersections in Kuwait City, the approach achieves a 59.5% reduction in energy consumption while maintaining a 99% latency compliance rate. Extrapolated to a 200-RSU deployment scenario, it yields an annual carbon reduction of 5.25 metric tons, representing a 7.7% additional energy saving over independent optimization.
📝 Abstract
As vehicle-to-infrastructure (V2I) deployments scale, roadside units (RSUs) that consume 10-25W continuously yet serve negligible traffic during off-peak hours represent a growing source of energy waste. Sleep scheduling can exploit the pronounced diurnal variation in urban traffic, but the WAVE service restoration overhead of up to 100ms nearly exhausts the 3GPPTS~22.185 latency budget, making independent sleep decisions risky. This paper proposes a cooperative framework in which upstream RSUs share traffic detection signals with downstream neighbors via infrastructure-to-infrastructure links, enabling predictive wake-up that exploits spatial correlation between adjacent intersections. The framework is formulated as a constrained Markov decision process and decomposed into per-RSU subproblems solvable by value iteration. Four algorithms of increasing sophistication are evaluated on real hourly traffic data from four consecutive signalized intersections in Kuwait City, comprising a total of 762,050 vehicles over five days. The cooperative algorithm reduces corridor energy consumption by 59.5% relative to always-on operation while maintaining 99% latency compliance, and provides 7.7 percentage points of additional savings over independent per-RSU optimization at downstream RSUs with spatial correlation \r{ho} >= 0.97. Extrapolated to a 200-RSU urban deployment, the cooperative approach yields an estimated 5.25 tonnes of CO2 reduction per year.