By Topic

A Reinforcement Learning Framework for Dynamic Power Management of a Portable, Multi-camera Traffic Monitoring System

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Khan, U.A. ; Inst. of Networked & Embedded Syst., Alpen-Adria Univ., Klagenfurt, Austria ; Rinner, B.

Dynamic Power Management (DPM) refers to a set of strategies that achieves efficient power consumption by selectively turning off (or reducing the performance of) a system components when they are idle or are serving light workloads. This paper presents a Reinforcement Learning (RL) based DPM technique for a portable, multi-camera traffic monitoring system. We target the computing hardware of the sensing platform which is the major contributor to the entire power consumption. The RL technique used for the DPM of the sensing platform uses a model-free learning algorithm that does not require a priori model of the system. In addition, a robust workload estimator based on an online, Multi-Layer Artificial Neural Network (ML-ANN) is incorporated to the learning algorithm to provide partial information about the workload and to take better decisions according to the changing workload. Based on the estimated workload and a selected power-latency tradeoff parameter, the algorithm learns to use optimal time-out values in sleep and idle modes of the computing hardware. Our results show that the learning algorithm learns an optimal DPM policy for the non-stationary workload, while significantly reducing the power consumption and keeping the system response to a desired level.

Published in:

Green Computing and Communications (GreenCom), 2012 IEEE International Conference on

Date of Conference:

20-23 Nov. 2012