Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

TL;DR

This survey reviews the advancements in mobile sensing and context prediction, emphasizing the evolution towards anticipatory mobile computing that predicts and acts on user context, highlighting current techniques, challenges, and future opportunities.

Contribution

It provides a comprehensive overview of the state of the art in mobile sensing, context prediction, and anticipatory computing, including machine learning methods and decision-making processes.

Findings

Mobile phones can infer and predict user context using advanced sensors.

Machine learning techniques are central to context prediction in mobile devices.

Challenges include data privacy, energy efficiency, and real-time processing.

Abstract

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.