Name
Title | ||
|---|---|---|
Context-aware reinforcement learning-based mobile cloud computing for telemonitoring. |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | BHI | Mobile cloud computing,Task analysis,Latency (engineering),Scheduling (computing),Efficient energy use,Computer science,Hidden Markov model,Reinforcement learning,Cloud computing,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoliang Wang | 1 | 91 | 24.74 |
| Wei Wang | 2 | 0 | 0.68 |
| Zhanpeng Jin | 3 | 52 | 5.26 |