Understanding Uncertainty of Edge Computing: New Principle and Design Approach

Abstract

Due to the edge's position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately. Particularly, the edge users' locally specific requirements that change depending on time and location cause a phenomenon called dataset shift, defined as the difference between the training and test datasets' representations. It renders many of the state-of-the-art approaches for resolving uncertainty insufficient. Instead of finding ways around it, we exploit such phenomenon by utilizing a new principle: AI model diversity, which is achieved when the user is allowed to opportunistically choose from multiple AI models. To utilize AI model diversity, we propose Model Diversity Network (MoDNet), and provide design guidelines and future directions for efficient learning driven communication schemes.

Towards Semantic Communication Protocols: A Probabilistic Logic Perspective

“…For example, consider an environment where UEs have periods of bursty SDUs, UEs leave and enter, or the wireless channel deteriorates over time. To cope with these non-stationary environment, keeping multiple compact SPMs as a portfolio grant model diversity and ensemble gain [45], [46].…”