Abstract: A method for request scheduling in an unmanned aerial vehicle-assisted mobile edge computing network: determining multiple feasible UAV deployment points based on obstruction information in a target area; randomly selecting U feasible UAV deployment points from the multiple feasible UAV deployment points as UAV deployment points; dividing each UAV into multiple virtual UAVs; allocating user requests in a central queue of a UAV-assisted MEC to the virtual UAVs; using a round-robin policy to schedule the user requests allocated to each virtual UAV; after a specified time period t, if a network pressure mitigation condition isn't met, inputting historical user request data of each feasible UAV deployment point into a trained MT-LSTM neural network model, obtaining a to-be-processed data volume of each feasible UAV deployment point in a next specified time period t; re-determining UAV deployment points; redeploying UAVs carrying small cell base stations based on the new UAV deployment points.

Abstract: A method for request scheduling in an unmanned aerial vehicle-assisted mobile edge computing network: determining multiple feasible UAV deployment points based on obstruction information in a target area; randomly selecting U feasible UAV deployment points from the multiple feasible UAV deployment points as UAV deployment points; dividing each UAV into multiple virtual UAVs; allocating user requests in a central queue of a UAV-assisted MEC to the virtual UAVs; using a round-robin policy to schedule the user requests allocated to each virtual UAV; after a specified time period t, if a network pressure mitigation condition isn't met, inputting historical user request data of each feasible UAV deployment point into a trained MT-LSTM neural network model, obtaining a to-be-processed data volume of each feasible UAV deployment point in a next specified time period t; re-determining UAV deployment points; redeploying UAVs carrying small cell base stations based on the new UAV deployment points.

Abstract: The disclosure discloses a method and system for adjusting user field of view (FoV) in a three-dimensional virtual reality (VR) scene. The method includes: acquiring two consecutive frames of scene pictures during a period of a user experiencing a VR scene by using a VR device; determining a projected scene flow vector on the basis of the two consecutive frames of scene pictures; generating a virtual scene graph of the two consecutive frames of scene pictures; determining a projected scene flow value of the virtual scene graph on the basis of the projected scene flow vector; determining a peripheral vision pixel of the virtual scene graph; calculating a projected scene flow average value of the peripheral vision pixel on the basis of the projected scene flow value of the virtual scene graph; and dynamically adjust a FoV of the user on the basis of the projected scene flow average value.

Abstract: The disclosure discloses a method and system for adjusting user field of view (FoV) in a three-dimensional virtual reality (VR) scene. The method includes: acquiring two consecutive frames of scene pictures during a period of a user experiencing a VR scene by using a VR device; determining a projected scene flow vector on the basis of the two consecutive frames of scene pictures; generating a virtual scene graph of the two consecutive frames of scene pictures; determining a projected scene flow value of the virtual scene graph on the basis of the projected scene flow vector; determining a peripheral vision pixel of the virtual scene graph; calculating a projected scene flow average value of the peripheral vision pixel on the basis of the projected scene flow value of the virtual scene graph; and dynamically adjust a FoV of the user on the basis of the projected scene flow average value.

Abstract: Uncertainty-ware federated learning methods and systems in a mobile edge computing network can include defining an average volume of a training parameter of each user equipment under an uncertainty of a mobile edge computing network based on a federated learning framework; determining an average model size factor and the minimum and maximum number of aggregators during each federated learning task request; determining the number of aggregators; constructing an auxiliary graph, and determining a location decision according to the auxiliary graph; determining a total cost during each federated learning task request according to the location decision; adjusting the number of aggregators according to the total cost with a resource capacity of the mobile edge computing network as a constraint to obtain the decision including aggregator placement, user equipment assignment and the optimal number of aggregators during each federated learning task request, and optimizing the federated learning framework.