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.

翻译：我们提出了一种新颖的故障感知框架，用于蜂群无人机投递服务。该框架确保在蜂群出现性能显著下降（称为软故障）的无人机，不会中断包裹成功送达客户的过程。该框架通过构建加权持续联邦学习预测模块，精确预测单个无人机的故障时间以及故障后的持续运行时间。基于这些预测，可在无人机和蜂群两个层面评估故障严重程度。我们提出了一种结合前瞻优化的速度启发式算法，可在考虑故障的情况下生成最优服务组合。基于真实数据集的实验结果表明，该方法在预测精度、投递时间和执行时间方面均具有高效性。