Publication Type

Journal Article

Version

submittedVersion

Publication Date

7-2017

Abstract

With the proliferation of sensors, such as accelerometers,in mobile devices, activity and motion tracking has become a viable technologyto understand and create an engaging user experience. This paper proposes afast adaptation and learning scheme of activity tracking policies when userstatistics are unknown a priori, varying with time, and inconsistent for differentusers. In our stochastic optimization, user activities are required to besynchronized with a backend under a cellular data limit to avoid overchargesfrom cellular operators. The mobile device is charged intermittently usingwireless or wired charging for receiving the required energy for transmission andsensing operations. Firstly, we propose an activity tracking policy byformulating a stochastic optimization as a constrained Markov decision process(CMDP). Secondly, we prove that the optimal policy of the CMDP has a thresholdstructure using a Lagrangian relaxation approach and the submodularity concept.We accordingly present a fast Q-learning algorithm by considering the policystructure to improve the convergence speed over that of conventionalQ-learning. Finally, simulation examples are presented to support thetheoretical findings of this paper.

Keywords

Activity tracking, fast adaptation, Internet of Things, Markov decision processes, wireless charging

Discipline

Computer Sciences | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Vehicular Technology

Volume

66

Issue

7

First Page

1

Last Page

14

ISSN

0018-9545

Identifier

10.1109/TVT.2016.2628966

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional URL

https://doi.org/10.1109/TVT.2016.2628966

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