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Limited by the computational capabilities and battery energy of terminaldevices and network bandwidth, emotion recognition tasks fail to achieve goodinteractive experience for users. The intolerable latency for users alsoseriously restricts the popularization of emotion recognition applications inthe edge environments such as fatigue detection in auto-driving. Thedevelopment of edge computing provides a more sustainable solution for thisproblem. Based on edge computing, this article proposes a multi-modalmulti-layer emotion-aware service (MULTI-EASE) architecture that considersuser's facial expression and voice as a multi-modal data source of emotionrecognition, and employs the intelligent terminal, edge server and cloud asmulti-layer execution environment. By analyzing the average delay of each taskand the average energy consumption at the mobile device, we formulate adelay-constrained energy minimization problem and perform a task schedulingpolicy between multiple layers to reduce the end-to-end delay and energyconsumption by using an edge-based approach, further to improve the users'emotion interactive experience and achieve energy saving in edge computing.Finally, a prototype system is also implemented to validate the architecture ofMULTI-EASE, the experimental results show that MULTI-EASE is a sustainable andefficient platform for emotion analysis applications, and also provide avaluable reference for dynamic task scheduling under MULTI-EASE architecture.

A Sustainable Multi-modal Multi-layer Emotion-aware Service at the Edge