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Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person’s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.

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Global climate change has led to a steep rise in natural disasters. In these times, it is essential to provide emergency last-mile delivery to disaster-affected populations using connected delivery trucks; however, this gives rise to several challenges. There is an unpredictable demand for resources and the need for fault-tolerant path planning in case the trucks are subjected to attack or breakdowns. Resources must be tracked prevent theft and maldistribution. To achieve these objectives, we use a hybrid UAV-Truck architecture for last-mile relief distribution. To increase the delivery operation's robustness, we propose a Self-Optimizing StreamChain (SOSChain) that tracks and controls the status of trucks and their onboard resources. During failure scenarios, the use of information in the SOSChain enables other vehicles to optimally re-route and redistribute resources from damaged vehicles. Extensive simulation shows that SOSChain achieves over 25% improvement in throughput and up to 50% reduction in ordering latency compared to StreamChain approach in a simulated disaster environment with up to 50% vehicle failure rate.