LAUSR

The past few years have witnessed a tremendous increase on the use of unmanned aerial vehicles (UAVs) in a wide range of civilian and commercial applications. UAVs are expected to be an important component for 5G and beyond 5G networks. However, there are many challenges associated with the development of UAV networks and applications. In this study, we focus on the heavy computation tasks of UAVs, and design a new and novel task offloading scheme for UAV networks. To achieve the best possible tradeoff between communication delay and computation cost, we adopt the basic concept of $\left ({\alpha, \beta }\right)$ -bargaining solution, and formulate a cooperative bargaining game model to solve the UAV computation offloading problem. According to the characteristics of $\left ({\alpha,\beta }\right)$ -bargaining solution, the main advantage of our approach is to provide an axiom-based strategic solution for the task offloading problem while dynamically responding to the current UAV conditions. Extensive simulations are performed in order to confirm the performance superiority of our proposed scheme compared to the existing state-of-the-art protocols. Numerical results show that our approach achieves in average about 10% and 20% better performance results in terms of system throughput, task failure probability, and energy efficiency ratio of UAVs. Finally, a few of open problems are outlined and identified as possible future research directions.