TY - GEN
T1 - Context-aware reinforcement learning-based mobile cloud computing for telemonitoring
AU - Wang, Xiaoliang
AU - Wang, Wei
AU - Jin, Zhanpeng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/6
Y1 - 2018/4/6
N2 - Mobile cloud computing (MCC) has been extensively studied to provide pervasive healthcare services in a more affordable manner. Through offloading computation-intensive tasks from mobile to cloud, a significant portion of energy can be saved to extend the mobile battery life, which is critical to maintaining continuous and uninterrupted healthcare services. However, given the ever-changing clinical severity, personal demands, and environmental conditions, it is essential to explore context-aware approach capable of dynamically determining the optimal task offloading strategies and algorithmic settings, with the goal of achieving a balanced trade-off among energy efficiency, diagnostic accuracy, and processing latency. To this aim, we propose a model-free reinforcement learning based task scheduling approach to adapt to the changing requirements.
AB - Mobile cloud computing (MCC) has been extensively studied to provide pervasive healthcare services in a more affordable manner. Through offloading computation-intensive tasks from mobile to cloud, a significant portion of energy can be saved to extend the mobile battery life, which is critical to maintaining continuous and uninterrupted healthcare services. However, given the ever-changing clinical severity, personal demands, and environmental conditions, it is essential to explore context-aware approach capable of dynamically determining the optimal task offloading strategies and algorithmic settings, with the goal of achieving a balanced trade-off among energy efficiency, diagnostic accuracy, and processing latency. To this aim, we propose a model-free reinforcement learning based task scheduling approach to adapt to the changing requirements.
UR - https://www.scopus.com/pages/publications/85050883011
U2 - 10.1109/BHI.2018.8333459
DO - 10.1109/BHI.2018.8333459
M3 - Conference contribution
AN - SCOPUS:85050883011
T3 - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
SP - 426
EP - 429
BT - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
Y2 - 4 March 2018 through 7 March 2018
ER -