Emotion-Aware Mobile Edge Computing System: A Case Study

Recently, great progress has been witnessed in the application of mobile cloud computing in the field of health care such as online medical inquiries. However, due to the limitation of cognitive intelligence, QoE (Quality of Experience) is hampered by two problems, the first of which is that the traffic pressure of the core network cannot well meet the requirements of delay-sensitive emotional services, especially for users with different emergencies, while the second is that current applications cannot provide personalized service for different users. Based on the two problems, we propose an emotion-aware mobile edge computing architecture based on emotional task priority to guide the allocation of edge resources and to provide intelligent and personalized emotional services with higher QoE. Specifically, we first introduce the entities involved in the proposed architecture of emotion-aware mobile edge computing system. Next, we describe our optimal computing resource allocation strategy, including important concepts and a detailed algorithm. Finally, we build a test platform and conduct experiments, which show that the proposed architecture obtains better performance in terms of system utility compared with baseline methods.