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.