Using unmanned aerial vehicles (UAVs) for data collecting in remote places is advantageous because of UAVs' low cost and extended range mobility. To facilitate the operation of time-sensitive applications, the acquired data is processed as geographically close to the end user as is practically possible. The suggested paradigm for energy-efficient UAV-assisted Internet of Things data gathering integrates trajectory and resource optimization. The three essential features of the paradigm are data collection, the optimal UAV trajectory, and data scheduling. This work utilizes an innovative data gathering technique and an optimal scheduling paradigm that the authors explicitly devised for intelligent farms. While gathering data, the sensors are arranged in a manner which is entirely random to produce the best clustering that can be generated based on multiple objectives such as distance, latency, energy, trust, and quality of service (QoS). The lion mated with cats optimization (LMCO) is a novel hybrid optimization technique proposed to discover an ideal cluster head. The lion algorithm and the regular cat-mouse-based optimization method are brought together in this model to create the LMCO model. The UAV creates an ideal straight line collision-free path for its trajectory by utilizing the LMCO model and anticipated values for the received signal strength indicator. This enables the UAV to collect data from all clusters in the region that is being investigated. The data is delivered by the UAV to the base station (B5) closest to it. In the second step, the BS will choose the cloud node that is the most easily accessible based on the best possible combination of five factors: power efficiency, response rate, availability, execution time, and QoS. After that, an existing model is contrasted with the proposed model in terms of energy consumption, distance traveled, latency, and response time, among other metrics.

中文翻译：

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基于人工智能的节能无人机辅助物联网数据收集，具有集成轨迹和资源优化


使用无人机（UAV）在偏远地区收集数据具有优势，因为无人机成本低且机动性广。为了促进对时间敏感的应用程序的操作，所获取的数据在地理上尽可能靠近最终用户的地方进行处理。建议的节能无人机辅助物联网数据收集范例集成了轨迹和资源优化。该范式的三个基本特征是数据收集、最优无人机轨迹和数据调度。这项工作利用了作者为智能农场明确设计的创新数据收集技术和最佳调度范例。在收集数据时，传感器以完全随机的方式排列，以产生可以基于多个目标（例如距离、延迟、能源、信任和服务质量 (QoS)）生成的最佳聚类。狮子与猫配对优化（LMCO）是一种新颖的混合优化技术，旨在发现理想的簇头。该模型将狮子算法和常规的基于猫鼠的优化方法结合在一起，创建了 LMCO 模型。无人机利用 LMCO 模型和接收信号强度指标的预期值为其轨迹创建理想的直线无碰撞路径。这使得无人机能够从正在调查的区域中的所有集群收集数据。数据由无人机传送到距离它最近的基站（B5）。第二步，BS将根据五个因素的最佳组合选择最容易访问的云节点：功率效率、响应率、可用性、执行时间和QoS。 之后，将现有模型与建议模型在能耗、行驶距离、延迟和响应时间等指标方面进行对比。