Energy-Neutral Coverage Optimization by Joint Deployment and Scheduling in Ambient IoT Devices with Directional Sensing

2026-08-27

AI Summary

本文研究了通过联合部署和调度方向性感知的环境物联网设备来优化能量中立覆盖的问题，提出了结合线性规划与强化学习的方法以提高覆盖率。

Abstract

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.

Problem

• Energy-Neutral
• Deployment
• Scheduling
• Directional Sensing
• Ambient IoT

Innovation

• Energy-Neutral
• Directional Sensing
• Reinforcement Learning
• Linear Programming
• Coverage Optimization