论文标题
无人用的智能农业的最佳多动物部署模型
An Optimal Multi-UAV Deployment Model for UAV-assisted Smart Farming
论文作者
论文摘要
下一代无线网络将以空中基站（UAV-BSS）的形式动态部署无人机，以提高到达区域中的无线网络覆盖范围。为了在随机环境中提供有效的服务，必须适当指定无人机，其位置和轨迹的最佳数量，以适当地指定不同情况。这种部署需要一种智能的决策机制，可以在不同时间处理各种变量。本文提出了一种用于智能农业的多用UAV-BS部署模型，该模型被制定为多标准决策（MCDM）方法，以找到最佳的无人机数量来监视动物的行为。该模型考虑了由UAV-BSS信号干扰和用户移动性最大化系统效率的效果。为了避免在UAV-BSS之间发生碰撞，我们将所考虑的区域分为几个簇，每个集群都被UAV-BS覆盖。我们的仿真结果表明，与基准聚类算法相比，部署效率高出11倍。
Next-generation wireless networks will deploy UAVs dynamically as aerial base stations (UAV-BSs) to boost the wireless network coverage in the out of reach areas. To provide an efficient service in stochastic environments, the optimal number of UAV-BSs, their locations, and trajectories must be specified appropriately for different scenarios. Such deployment requires an intelligent decision-making mechanism that can deal with various variables at different times. This paper proposes a multi UAV-BS deployment model for smart farming, formulated as a Multi-Criteria Decision Making (MCDM) method to find the optimal number of UAV-BSs to monitor animals' behavior. This model considers the effect of UAV-BSs' signal interference and path loss changes caused by users' mobility to maximize the system's efficiency. To avoid collision among UAV-BSs, we split the considered area into several clusters, each covered by a UAV-BS. Our simulation results suggest up to 11x higher deployment efficiency than the benchmark clustering algorithm.
