PMID- 34319812
OWN - NLM
STAT- MEDLINE
DCOM- 20220418
LR  - 20220613
IS  - 2167-647X (Electronic)
IS  - 2167-6461 (Linking)
VI  - 10
IP  - 2
DP  - 2022 Apr
TI  - A Reinforcement Learning-Based Framework for Crowdsourcing in Massive Health Care 
      Internet of Things.
PG  - 161-170
LID - 10.1089/big.2021.0058 [doi]
AB  - Rapid advancements in the internet of things (IoT) are driving massive 
      transformations of health care, which is one of the largest and critical global 
      industries. Recent pandemics, such as coronavirus 2019 (COVID-19), include 
      increasing demands for ubiquitous, preventive, and personalized health care to be 
      provided to the public at reduced risks and costs with rapid care. Mobile 
      crowdsourcing could potentially meet the future massive health care IoT (mH-IoT) 
      demands by enabling anytime, anywhere sense and analyses of health-related data 
      to tackle such a pandemic situation. However, data reliability and availability 
      are among the many challenges for the realization of next-generation mH-IoT, 
      especially in COVID-19 epidemics. Therefore, more intelligent and robust health 
      care frameworks are required to tackle such pandemics. Recently, reinforcement 
      learning (RL) has proven its strengths to provide intelligent data reliability 
      and availability. The action-state learning procedure of RL-based frameworks 
      enables the learning system to enhance the optimal use of the information as the 
      time passes and data increases. In this article, we propose an RL-based 
      crowd-to-machine (RLC2M) framework for mH-IoT, which leverages crowdsourcing and 
      an RL model (Q-learning) to address the health care information processing 
      challenges. The simulation results show that the proposed framework rapidly 
      converges with accumulated rewards to reveal the sensing environment situation.
FAU - Almagrabi, Alaa Omran
AU  - Almagrabi AO
AD  - Department of Information Systems, Faculty of Computing and Information 
      Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
FAU - Ali, Rashid
AU  - Ali R
AUID- ORCID: 0000-0002-9756-1909
AD  - Department of Smart Device Engineering, School of Intelligent Mechatronics 
      Engineering, Sejong University, Seoul, Republic of Korea.
FAU - Alghazzawi, Daniyal
AU  - Alghazzawi D
AUID- ORCID: 0000-0002-5533-3203
AD  - Department of Information Systems, Faculty of Computing and Information 
      Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
FAU - AlBarakati, Abdullah
AU  - AlBarakati A
AD  - Department of Information Systems, Faculty of Computing and Information 
      Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
FAU - Khurshaid, Tahir
AU  - Khurshaid T
AD  - Department of Electrical Engineering, Yeungnam University, Gyeongsan, Republic of 
      Korea.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210728
PL  - United States
TA  - Big Data
JT  - Big data
JID - 101631218
SB  - IM
MH  - *COVID-19/epidemiology
MH  - *Crowdsourcing
MH  - Delivery of Health Care
MH  - Humans
MH  - *Internet of Things
MH  - Reproducibility of Results
OTO - NOTNLM
OT  - big data analytic
OT  - crowdsouring
OT  - health care IoT
OT  - internet of medical things
OT  - massive data
OT  - reinforcement learning
EDAT- 2021/07/29 06:00
MHDA- 2022/04/19 06:00
CRDT- 2021/07/28 17:15
PHST- 2021/07/29 06:00 [pubmed]
PHST- 2022/04/19 06:00 [medline]
PHST- 2021/07/28 17:15 [entrez]
AID - 10.1089/big.2021.0058 [doi]
PST - ppublish
SO  - Big Data. 2022 Apr;10(2):161-170. doi: 10.1089/big.2021.0058. Epub 2021 Jul 28.