基于智能体仿真的无人机电池充电在物联网应用中的研究：精准农业、灾难恢复与登革热病媒控制
Agent-Based Simulation of UAV Battery Recharging for IoT Applications: Precision Agriculture, Disaster Recovery, and Dengue Vector Control
AI总结：
本文针对无人机电池续航不足问题，采用智能体仿真方法建模充电协调，提出无通信的充电策略，并通过6000次仿真验证了CT策略在极端条件下的可靠性，为物联网应用提供基线。
摘要：
无人机（UAVs或drones）的低电池自主性可能使智能农业（精准农业）、灾难恢复以及对抗登革热病媒的应用变得困难。本文考虑了两种方法，首先枚举了这三种物联网应用类型中观察到的特征，然后使用智能体仿真（ABS）方法对无人机电池充电协调进行建模。通过这种方式，我们提出蜂群中的每架无人机不进行关于此充电协调决策的通信，从而减少能量消耗并允许远程使用。总共运行了6000次仿真，以评估两种提出的策略，即基线（BL）和充电阈值（CT）协调充电策略，在30种情境下的表现，涉及每次仿真如何结束仿真运行以及它们在充电前工作多长时间。CT策略在极端系统使用情况下显示出更可靠的结果。本工作的结论展示了这三种物联网应用在没有无人机与地面站之间通信的情况下实现其永久服务的潜力。这项工作可以作为未来策略和仿真参数增强的基线。
英文摘要：
The low battery autonomy of Unnamed Aerial Vehicles (UAVs or drones) can make smart farming (precision agriculture), disaster recovery, and the fighting against dengue vector applications difficult. This article considers two approaches, first enumerating the characteristics observed in these three IoT application types and then modeling an UAV's battery recharge coordination using the Agent-Based Simulation (ABS) approach. In this way, we propose that each drone inside the swarm does not communicate concerning this recharge coordination decision, reducing energy usage and permitting remote usage. A total of 6000 simulations were run to evaluate how two proposed policies, the BaseLine (BL) and ChargerThershold (CT) coordination recharging policy, behave in 30 situations regarding how each simulation sets conclude the simulation runs and how much time they work until recharging results. CT policy shows more reliable results in extreme system usage. This work conclusion presents the potential of these three IoT applications to achieve their perpetual service without communication between drones and ground stations. This work can be a baseline for future policies and simulation parameter enhancements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
