Mobile edge computing system content caching method assisted by unmanned aerial vehicle

Abstract

The invention relates to an unmanned aerial vehicle assisted mobile edge computing system content caching method. Comprising the following steps: S1, acquiring ground node coordinates; s2, dividing the caching process into a file caching stage and a file retrieval stage; s3, establishing a cost minimization optimization model in combination with channel capacity, unmanned aerial vehicle flight rate, unmanned aerial vehicle and ground node storage capacity constraints; s4, optimizing the unmanned aerial vehicle waypoints by using a clustering algorithm, so that each ground node is at least within the service range of one unmanned aerial vehicle waypoint; s5, solving the flight path of the unmanned aerial vehicle with the minimum time consumption by using a simulated annealing algorithm; and S6, based on the flight path of the unmanned aerial vehicle, solving the cost minimization optimization model by using a simulated annealing algorithm to obtain an optimal cache strategy. Under the conditions of unmanned aerial vehicle cruising ability, ground node storage ability, D2D transmission link channel state and the like, the balance between the file caching cost and the retrieval cost is realized by optimizing the flight path of the unmanned aerial vehicle and the file caching position, and the time delay of the system is reduced.

Description

technical field [0001] The invention belongs to the technical field of information and communication engineering, and in particular relates to a content caching method for a UAV-assisted mobile edge computing system. Background technique [0002] The existing Mobile Edge Computing (MEC) network architecture is usually composed of users (terminals) and small base stations (Mobile Edge Computing servers) at fixed locations. However, in many real-life scenarios, such as war or disaster sites, small The deployment of base stations becomes very difficult. In this case, UAVs are flexible and can be quickly deployed and dispatched. Therefore, in some mobile edge computing networks, drones replace fixed-location small base stations, deploy at the edge of mobile users, and connect to mobile edge servers through wireless backhaul to provide data distribution, storage, and auxiliary computing. In order to prolong the communication time of UAVs or maximize the communication throughput...

AI Technical Summary

Problems solved by technology

In order to prolong the communication time of UAVs or maximize the communication throughput, people have proposed an algorithm to optimize the total network overhead of UAV-assisted mobile edge computing from the perspective of the entire system, but the parameters required in the existing technology are not easy to obtain, and the algorithm is implemented more difficult

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