Unmanned aerial vehicles (UAVs) are beginning to make a splash in emergency disaster scenarios owing to its excellent air mobility and flexibility. Considering that large base stations often cannot be deployed to disaster areas in the first place and the variation of communication links between UAVs, we formulate the task scheduling problem for disaster scenarios as a two-stage Lyapunov optimization problem and propose a dispersed computing network consisting of UAVs and ground mobile devices, which is used for collaborative computing. We decouple the long-term stability of the task queues of the nodes in the system in terms of time slots as a deterministic optimization problem by Lyapunov techniques. By jointly optimizing the task size transmitted from the control center to the UAVs, the task size computed locally and offloaded by the UAVs and mobile devices, the energy consumption of the dispersed computation system is minimized while ensuring the stability of the computation queues. The simulation results verify that our proposed algorithm is close to the optimal case in terms of queue stability, and our algorithm is able to reduce the system energy consumption by more than 50% compared to the local computation of UAVs.

Decentralized Applications (DApps) have garnered significant attention due to their decentralization, anonymity, and data autonomy. However, these systems face potential privacy challenge. The privacy challenge arises from the necessity for external service providers to collect and process user interaction data. The untrustworthiness of these providers may lead to privacy breaches, compromising the overall security of such DApp environments. To address this challenge, we model the interaction data in the DApp environments as dynamic graphs and propose a dynamic graph publication method named HMG (Hidden Markov Model for Dynamic Graphs). HMG estimates the interaction probabilities between users by extracting the temporal information from historically collected data and constructs an optimized model to generate synthetic graphs. The synthetic graphs can preserve the dynamic topological characteristics of the interaction processes within DApp environments while effectively protecting user privacy, thus assisting external service providers in performing effective analyses. Finally, we evaluate the performance of HMG using real-world datasets and benchmark it against commonly used graph metrics. The results demonstrate that the synthetic graphs preserve essential features, making them suitable for analysis by service providers.