Recently, unmanned aerial vehicles (UAVs) have emerged to enhance data processing, network monitoring, disaster management and other useful applications in many different networks. Due to their flexibility, cost efficiency and powerful capabilities, combining these UAVs with the existing wireless sensor networks (WSNs) could improve network performance and enhance the network lifetime in such networks. In this research, we propose a task offloading mechanism in UAV-aided WSN by implementing a utility-based learning collaborative algorithm that will enhance the service satisfaction rate, taking into account the delay requirements of the submitted tasks. The proposed learning algorithm predicts the queuing delays of all UAVs instead of having a global overview of the system, which reduces the communication overhead in the network. The simulation results showed the effectiveness of our proposed work in terms of service satisfaction ratio compared with the non-collaborative algorithm that only processes the task locally in the WSN cluster.

中文翻译：

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无人机辅助无线传感器网络中基于协作学习的任务卸载算法

最近，出现了无人驾驶飞行器 (UAV)，以增强许多不同网络中的数据处理、网络监控、灾害管理和其他有用的应用。由于它们的灵活性、成本效率和强大的功能，将这些无人机与现有的无线传感器网络 (WSN) 相结合可以提高网络性能并延长此类网络中的网络寿命。在这项研究中，我们提出了一种无人机辅助 WSN 中的任务卸载机制，通过实施一种基于效用的学习协作算法，该算法将提高服务满意度，同时考虑到提交任务的延迟要求。所提出的学习算法预测所有无人机的排队延迟，而不是对系统进行全局概览，从而减少了网络中的通信开销。