Age-Energy Tradeoff in Vehicular MEC: Sensing, Transmission, and Computation Co-Optimization
TL;DR
This work derives novel analytical expressions for the average PAoI and average EC under all three strategies, explicitly accounting for the energy and delay costs across the entire data processing chain, and demonstrates that the partial computation offloading strategy is superior.
Abstract
: Peak age of information (PAoI) and energy consumption (EC) are conflicting yet critical metrics in mobile edge computing (MEC)-assisted vehicular networks. Most existing studies overlook the joint effects of sensing, transmission, and computation. The main contributions of this work are threefold. First, we derive novel analytical expressions for the average PAoI and average EC under all three strategies, explicitly accounting for the energy and delay costs across the entire data processing chain. Second, we demonstrate that the partial computation offloading strategy is superior, effectively balancing the low latency of local processing with the high power of edge computing. Third, we formulate a weighted optimization problem to navigate the PAoI-EC tradeoff and identify an optimal offloading ratio that dynamically adapts to specific freshness and efficiency requirements. Numerical results demonstrate that jointly optimizing the offloading ratio, edge computing capability, and transmission power significantly improves performance. Our findings offer practical guidelines for designing timely and energy-efficient vehicular telematics systems.