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In 5G, multi-access edge computing enables the applications to be offloaded to near-end edge servers for faster response. According to the 3GPP standards, users in 5G are separated into many types, e.g., vehicles, AR/VR, IoT devices, etc. Specifically, the high-priority traffic can preempt edge resources to guarantee the service quality. However, even if a traffic is transmitted with low priority, its latency requirement in 5G is much lower than that in 4G. Too strict latency requirement and priority-based service make resource configuration difficult on the edge side. Therefore, we propose the edge-cloud offloading mechanism, in which each edge server can offload tasks to back-end cloud server to ensure service quality of both high-and low-priority traffic. In this paper, we establish a priority-based queuing system to model the edgecloud offloading behaviors. Based on the formulation of our system model, we propose Knapsack Potential Game (KPG) to derive an optimal offloading ratio for each edge server to balance the cost-effectiveness of the overall system. We demonstrate that KPG has low computational complexity and outperforms two baseline algorithms. The results indicate that KPG's performance is optimal and provides a theoretical guideline to operators while designing their edge-cloud offloading strategies without large-scale implementation.

This paper proposes a robot peer reciprocal learning system in which robot peers can not only cooperatively accomplish a difficult task but also help each other to learn better. In this system, each robot is an independent individual and has the ability to make individual decisions. They can communicate about image information, individual decisions, and current state to formulate mutual decisions and motions. For learning a new concept, we propose a mutual learning method, which allows the robots to learn from each other by exchanging weights in their neural network concept learning system. The simulation results show that the robots can learn from each other to build general concepts from limited training, while improving both of their performances at the same time. Finally, we design two cooperative tasks, which require the robots to formulate mutual sequential motions and keep communicating to manage their motions. The robotic experiments demonstrate that the proposed robot peer reciprocal learning system can help robots achieve difficult tasks in appropriate and cooperative ways, just as humans do.INDEX TERMS Mutual concept learning, reciprocal learning, robot cooperation, robot peer.