Chongqing University of Posts and Telecommunications

Yun Li

Guangfu Wu

Zhixiu Yao

Shichao Xia

Feibo Hu

Ziliang Liu

Junnan Pan

Hongcheng Zhuang

Mengmeng Liu

Zhaocheng Wang

Yunfeng Jiang

A task unloading method based on unmanned aerial vehicle cooperation is provided. The method includes: establishing an unmanned aerial vehicle cooperation task unloading system; establishing, for each of ground users, a local task unloading model of the ground user; establishing, for each of the ground users, an unmanned aerial vehicle task unloading model of the ground user; establishing a hovering model of the unmanned aerial vehicles; and calculating an optimal task unloading assignment solution by using a second price auction algorithm to perform task unloading. With the method, task unloading is performed based on unmanned aerial vehicle mobile edge computing, improving the quality of service and the service experience of the users.

Task unloading method based on unmanned aerial vehicle cooperation

A distributed task offloading and computing resources management method based on energy harvesting is provided, including: establishing a task local computing model and an edge cloud computing model; establishing a device maximum benefit objective function based on the perturbation Lyapunov optimization algorithm and a mobile edge computing server maximum benefit objective function; pre-selecting, by the device based on a pre-screening criteria, a mobile edge computing server for task offloading; calculating an optimal task size strategy for performing task offloading by the device to the selected mobile edge computing server by using a Lagrange multiplier algorithm and a KKT condition; obtaining an optimal quotation strategy of the mobile edge computing server for the device in each of time slots; and obtaining a solution of the optimal task size strategy meeting a Stackelberg equilibrium and a solution of the optimal dynamic quotation strategy meeting the Stackelberg equilibrium as a resource allocation strategy.

Distributed task offloading and computing resources management method based on energy harvesting

A distributed computation offloading method based on computation-network collaboration in a stochastic network is provided. The distributed computation offloading method includes: building a device revenue maximization problem model and a MEC server revenue maximization problem model based on a local computing model and an edge cloud computing model; building composite sets of scenarios for dwell time and waiting latency based on a random movement of a user and burst computation demands; compensating for a game strategy by using a posteriori recourse action and building a game-based stochastic programming model between the device and the MEC server; transforming a multi-stage stochastic regularization problem for both the device and the MEC server into a DEP problem by constructing a scenario tree, and solving the DEP problem to obtain an optimal task strategy for the offloading from the MEC server and an optimal offering strategy of the MEC server to the device.

Distributed computation offloading method based on computation-network collaboration in stochastic network

The present application relates to the technical field of mobile communications, and specifically relates to an evolutionary game-based multi-user switching method in a software-defined satellite network system. The multi-user switching method comprises: respectively calculating the elevation angle and the remaining coverage time of a satellite according to obtained information, and calculating the payoff according to three basic factors, i.e., the capacity, the elevation angle, and the remaining coverage time of the satellite; a controller calculating the average payoff of users in the area, and broadcasting same to the users; when it is determined that the payoff of all the users is greater than the average payoff, ending multi-user switching; and when it is determined that the payoff of all the users is not greater than the average payoff, the users selecting other switching satellites having higher payoffs.

Evolutionary game-based multi-user switching method in software-defined satellite network system

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Chongqing, China

Yun Li