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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.

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We introduce a novel method to controllably improve the performance of photonic quantum random number generators by using minimum information entropy per bit as a standalone design parameter.