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.

翻译：暂无翻译