In this work, we study the freshness and significance of information in an IoT status update system in which an Energy Harvesting (EH) device samples an information source and forwards update packets to a destination node via a direct channel. We introduce and optimize a semantics-aware metric, Query Version Age of Information (QVAoI), in the system along with other metrics: Query Age of Information (QAoI), Version Age of Information (VAoI), and Age of Information (AoI). We formulate the optimization problem as a Markov Decision Process to determine the optimal transmission policy at the device, which decides the time slots for transmitting updates, subject to the device's battery energy limitations and the energy arrivals. Furthermore, we derive closed-form expressions for the average update rate and the QVAoI for a unit-capacity battery, serving as analytical benchmarks. We compare the performance of QVAoI-Optimal, QAoI-Optimal, VoI-Optimal, and AoI-Optimal policies with a baseline greedy policy. All semantics-aware policies achieve better performance than the greedy policy. The QVAoI-Optimal policy, in particular, demonstrates a significant performance improvement either by providing fresher, more relevant, and more valuable updates with the same energy arrivals or by reducing the number of transmissions in the system while maintaining the same level of freshness and information significance as the QAoI-Optimal and other policies.

翻译：本文研究了一个物联网状态更新系统中的信息新鲜度与重要性问题，其中能量采集设备采集信息源样本并通过直接信道将更新数据包转发至目的节点。引入并优化了系统中的语义感知度量——查询版本信息年龄（QVAoI），同时与其他度量（查询信息年龄QAoI、版本信息年龄VAoI和信息年龄AoI）进行协同优化。将优化问题建模为马尔可夫决策过程，以确定设备在电池能量限制与能量到达约束下的最优传输策略，即决定传输更新的时隙分配。进一步推导了单位容量电池场景下平均更新率和QVAoI的闭式表达式，作为解析基准。将QVAoI最优策略、QAoI最优策略、VoI最优策略和AoI最优策略与基准贪婪策略进行性能对比，结果显示所有语义感知策略均优于贪婪策略。特别地，QVAoI最优策略在相同能量到达条件下能提供更及时、相关和更有价值的更新，或在保持与QAoI最优及其他策略相同水平的新鲜度和信息重要性的同时减少系统传输次数，展现出显著性能提升。