A First Look at Mobile Intelligence: Architecture, Experimentation and Challenges

TL;DR

This paper explores mobile intelligence architectures, evaluates current applications, and discusses challenges, highlighting the gap between user experience expectations and current capabilities to guide future research.

Contribution

It provides a detailed analysis of existing mobile intelligence architectures, experimental evaluation of commercial applications, and identifies key challenges for future development.

Findings

Significant gap between QoE requirements and current performance

Existing architectures are limited in meeting user expectations

Identified challenges to advance mobile intelligence research

Abstract

Artificial intelligence (AI) technology makes mobile devices become intelligent objects which can learn and act automatically. Although AI will bring great opportunities for mobile applications, little work has focused on the architecture and the interaction with the cloud. In this article, we present three existing architectures of mobile intelligence in detail and introduce its broad application prospects. Furthermore, we conduct a series of experiments to evaluate the performance of the prevalent commercial applications and intelligent frameworks. Our results show that there is a big gap between Quality of Experience (QoE) requirements and the status quo. So far, we have seen only the tip of the iceberg. We pose issues and challenges to advance the area of mobile intelligence and hope to pave the way for the forthcoming.