Overcoming Energy and Reliability Challenges for IoT and Mobile Devices with Data Analytics

摘要

A very large amount of data is produced by mobile and Internet-of-Thing (IoT) devices today. Increasing computational abilities and more sophisticated operating systems (OS) on these devices have allowed us to create applications that are able to leverage this data to deliver better services. But today's mobile and IoT solutions are heavily limited by low battery capacity and limited cooling capabilities. This motivates a search for new ways to optimize for energy-efficiency. Advanced data analytics and machine-learning techniques are becoming increasingly popular to analyze and extract meaning from Big Data. In this paper, we make the case for designing and deploying data analytics and learning mechanisms to improve energy-efficiency in IoT and mobile devices with minimal overheads. We focus on middleware for inserting energy-efficient data analytics-driven solutions and optimizations in a robust manner, without altering the OS or application code. We discuss several case studies of powerful and promising developments in deploying data analytics middleware for energy-efficient and robust execution of a variety of applications on commodity mobile devices.