We investigate a remote monitoring framework with multiple sensing modalities including IoT sensors on the ground, mobile UAVs in the air, and a periodically available satellite constellation. While the IoT sensors cover small areas and remain fixed, the UAVs can move between locations and cover larger areas, and the satellites can observe the entire region but have high latency and low reliability. We divide the deployment region into cells and model it as a graph, with the nodes representing individual cells and edges representing possible UAV mobility patterns. To evaluate the freshness of collected information from this graph, we adopt the Age of Information (AoI) metric, measured separately for each cell. Under a given deployment of IoT nodes and UAV mobility patterns, our objective is to ascertain whether the system should actually utilize monitoring updates from satellites - a seemingly simple yet surprisingly elusive question. For stationary randomized scheduling policies, we develop closed-form expressions and lower bounds for the weighted-sum AoI and utilize this analysis to explore performance tradeoffs as system parameters vary. We also provide a Lyapunov style max-weight policy and detailed simulations that provide crucial insights for deploying such systems in practice.

翻译：本文研究一种多模态远程监控框架，包含地面物联网传感器、空中移动无人机以及周期性可用的卫星星座。物联网传感器覆盖范围固定且面积有限，无人机可在不同位置间移动并覆盖更大区域，而卫星虽能观测整个区域但具有高延迟与低可靠性特征。我们将部署区域划分为单元网格，并将其建模为图结构：节点代表独立单元，边表示无人机的潜在移动模式。为评估从该图结构所采集信息的新鲜度，我们采用信息年龄（AoI）度量指标，并对每个单元进行独立测量。在给定物联网节点部署与无人机移动模式的条件下，本研究旨在确定系统是否应实际采用卫星监测更新——这是一个看似简单却出人意料难以解答的问题。针对静态随机调度策略，我们推导了加权和AoI的闭式表达式与下界，并利用该分析探索系统参数变化时的性能权衡关系。此外，我们提出了一种李雅普诺夫风格的最大权重策略，并通过详细仿真为实际系统部署提供了关键性见解。