带方向感知功能的环境物联网设备中，通过联合部署与调度实现能量中性覆盖优化
Energy-Neutral Coverage Optimization by Joint Deployment and Scheduling in Ambient IoT Devices with Directional Sensing
AI总结：
本文针对带方向感知的环境物联网设备，研究联合部署与调度的覆盖优化，提出四种策略，实验显示混合LP+RL等方法在低中能量收集场景下覆盖更优、收敛更快。
AI中文摘要：
环境物联网（A-IoT）设备依靠能量收集和占空比循环维持运行，从根本上改变了与传统始终开启的传感器网络相比的协同感知模式。本文研究了配备方向感知功能的A-IoT设备的联合部署与感知调度，探索了四种解决方案策略：（i）带静态占空比循环的网格部署；（ii）集中式策略梯度强化学习（RL）方法，该方法以网格部署为起点，学习感知能量的设备重定位和占空比循环策略；（iii）混合整数线性规划（LP）方法，将静态部署设计与占空比分配相结合；（iv）混合LP+RL方法，将基于优化的初始化与基于学习的优化相结合。利用代表性的A-IoT用例，我们评估了覆盖范围与设备密度、视场角以及由收集的能量和设备消耗决定的最大可行占空比之间的函数关系。数值结果表明，所提出的LP+RL和RL策略始终优于网格基线和基于LP的方法，在低和中等能量收集（EH）模式下，实现了高达2倍的平均有效覆盖范围提升。相比之下，独立的LP方法在严格的EH约束下，受限于其保守的静态占空比分配。此外，LP+RL方法的结构化初始化大幅加快了收敛速度，与独立RL相比，将总离线优化时间减少了高达10倍。
英文摘要：
Ambient IoT (A-IoT) devices rely on energy harvesting and duty cycling to sustain operation, thereby fundamentally changing collaborative sensing compared with traditional always-ON sensor networks. In this paper, we study the joint deployment and sensing scheduling of A-IoT devices equipped with directional sensing. We explore four solution strategies: (i) a grid deployment with static duty cycling, (ii) a centralized policy-gradient reinforcement learning (RL) approach that begins with a grid deployment and learns energy-aware device relocation and duty-cycling policies, (iii) a mixed-integer linear programming (LP) approach that couples static deployment design with duty-cycle allocation, and (iv) a hybrid LP+RL that combines optimization-based initialization with learning-based refinement. Using representative A-IoT use cases, we evaluate coverage as a function of device density, field-of-view, and maximum feasible duty cycle, determined by harvested energy and device consumption. Numerical results indicate that the proposed LP+RL and RL policies consistently outperform both the grid baseline and the LP-based method, achieving up to 2x higher mean effective coverage in low and medium energy harvesting (EH) regimes. In contrast, the standalone LP method remains limited by its conservative static duty cycle allocation under tight EH constraints. Moreover, the structured initialization of the LP+RL method substantially accelerates convergence, reducing the total offline optimization time by up to 10x compared to the standalone RL.
