发表机构
Indian Institute of Technology Tirupati（蒂鲁帕蒂印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FLoRa提出多级优化架构，结合SA路径规划、CMA-ES悬停定位和POMDP探测决策，在能量约束下高效收集占空比LoRa节点数据，显著提升信息价值和覆盖率。
AI 中文摘要
使用无人机（UAV）进行数据收集在LoRa物联网设备（IoTDs）为节省电池而进行占空比循环时具有挑战性。在能量约束下，无人机必须决定访问哪些IoTD、以何种顺序访问、在何处悬停以及探测每个节点的次数，同时基于时间的数据新鲜度会衰减。可处理地解决该问题需要多级优化架构：用于路由的离散组合优化、用于空间定位的连续全局优化以及不确定性下的序贯决策。我们提出FLoRa，一种飞行辅助LoRa数据收集架构，使用模拟退火（SA）进行路径规划，协方差矩阵自适应进化策略（CMA-ES）进行悬停定位，以及部分可观测马尔可夫决策过程（POMDP）来探测IoTD。为了量化从占空比循环节点收集的效用，我们引入了基于拉取系统的信息价值（VIP）指标，该指标奖励新鲜数据并惩罚失败的探测，确保问题适定性并防止在IoTD关闭时无限探测。在每条POMDP样本路径上跟踪硬电池约束需要状态增广，加剧了维度灾难。为可处理性，SA和CMA-ES处理硬电池约束，而在POMDP层我们通过拉格朗日松弛将其放松为软平均约束。由于求解网络级POMDP计算复杂，我们通过前向分解技术近似节点间时间依赖性，将其分解为节点级POMDP。评估显示，FLoRa在总预期VIP上分别优于元启发式、贪婪和深度强化学习基线30.6%、27.8%和15.2%，同时节点覆盖率分别提高24.5%、29.2%和8.8%，成功收集分别提高24.3%、25.6%和15.2%。
英文摘要
Data collection using Unmanned Aerial Vehicles (UAVs) is challenging when LoRa IoT Devices (IoTDs) duty-cycle to conserve battery. Under energy constraints, a UAV must decide which IoTDs to visit, in what order, where to hover, and how many times to probe each node, while time-based data freshness decays. Tractably solving this problem requires a multi-level optimization architecture: discrete combinatorial optimization for routing, continuous global optimization for spatial positioning, and sequential decision-making under uncertainty. We propose FLoRa, a Flight-assisted LoRa data collection architecture using Simulated Annealing (SA) for path planning, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for hover positioning, and Partially Observable Markov Decision Processes (POMDPs) for probing IoTDs. To quantify collection utility from duty-cycling nodes, we introduce the Value of Information for Pull-based systems (VIP), a metric that rewards fresh data and penalizes failed probes, imposing well-posedness and preventing indefinite probing when an IoTD is off. Tracking hard battery constraints on every POMDP sample path requires state augmentation, worsening the curse of dimensionality. For tractability, SA and CMA-ES work on the hard battery constraints, while at the POMDP layer we relax them into soft average constraints via Lagrangian relaxation. Since solving the network-wide POMDP is computationally complex, we decompose it into node-level POMDPs by approximating inter-node time dependency using a forward-decomposition technique. Evaluation shows FLoRa outperforms metaheuristic, greedy, and deep reinforcement learning baselines by 30.6%, 27.8%, and 15.2% in total expected VIP, while increasing node coverage by 24.5%, 29.2%, and 8.8%, and successful collections by 24.3%, 25.6%, and 15.2%, respectively.
Comments20 pages
