Abstract
With the rapid development of information technology, low-cost unmanned aerial vehicles (UAVs) appear. With advanced sensing and actuating technologies, they are being increasingly applied to a variety of scenarios. However, considering their limited computing resource and restricted battery capability, the computation-intensive tasks or data-intensive tasks will face tough challenges. With the aid of Mobile Edge Computing (MEC), moving computation-intensive tasks from resource-constrained UAVs to edge cloud servers can significantly save energy and finally achieve impressive performance.This paper proposes an evolutionary game based algorithm to solve the computation offloading problem for UAVs. By replicator dynamics, UAVs select the suitable service provider to offload the computation tasks via achieving a tradeoff between time delay, energy consumption and monetary cost when network externality exists. Simulation results show that the proposed algorithm can rapidly converge to evolutionary equilibrium and achieve desirable performance.
This work is supported by the National Natural Science Foundation of China under Grant no. 61902295.
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Gu, Q., Shen, B. (2022). An Evolutionary Game Based Computation Offloading for an UAV Network in MEC. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_48
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