面向基于蜂群的无人机服务的故障感知组合
Failure-Sentient Composition For Swarm-Based Drone Services
AI总结：
本文提出一种故障感知框架，利用加权持续联邦学习预测无人机软故障并评估严重程度，结合带前瞻优化的启发式算法生成最优服务组合，从而保障蜂群配送的可靠性。
摘要：
我们提出了一种新颖的面向蜂群无人机配送服务的故障感知框架。该框架确保那些性能出现明显下降（称为软故障）且属于蜂群一部分的无人机，不会干扰包裹成功交付给消费者。该框架组合了一个加权持续联邦学习预测模块，以准确预测单个无人机的故障时间以及故障后的正常运行时间。这些预测用于在无人机和蜂群两个层面确定故障的严重程度。我们提出了一种带前瞻优化的基于速度的启发式算法，以在考虑故障的情况下生成最优服务集合。在真实数据集上的实验结果证明了我们所提方法在预测准确率、交付时间和执行时间方面的高效性。
英文摘要：
We propose a novel failure-sentient framework for swarm-based drone delivery services. The framework ensures that those drones that experience a noticeable degradation in their performance (called soft failure) and which are part of a swarm, do not disrupt the successful delivery of packages to a consumer. The framework composes a weighted continual federated learning prediction module to accurately predict the time of failures of individual drones and uptime after failures. These predictions are used to determine the severity of failures at both the drone and swarm levels. We propose a speed-based heuristic algorithm with lookahead optimization to generate an optimal set of services considering failures. Experimental results on real datasets prove the efficiency of our proposed approach in terms of prediction accuracy, delivery times, and execution times.
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。
