Title :
Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms
Author :
Joongheon Kim ; Wonjun Lee
Author_Institution :
Mobile Commun. Group, Intel Corp., Santa Clara, CA, USA
Abstract :
This paper proposes two novel algorithms for adaptive crowdsourcing in 60-GHz medical imaging big-data platforms, namely, a max-weight scheduling algorithm for medical cloud platforms and a stochastic decision-making algorithm for distributed power-and-latency-aware dynamic buffer management in medical devices. In the first algorithm, medical cloud platforms perform a joint queue-backlog and rate-aware scheduling decisions for matching deployed access points (APs) and medical users where APs are eventually connected to medical clouds. In the second algorithm, each scheduled medical device computes the amounts of power allocation to upload its own medical data to medical big-data clouds with stochastic decision making considering joint energy-efficiency and buffer stability optimization. Through extensive simulations, the proposed algorithms are shown to achieve the desired results.
Keywords :
Big Data; buffer storage; cloud computing; decision making; medical computing; medical image processing; power aware computing; processor scheduling; queueing theory; access points; adaptive crowdsourcing; buffer stability optimization; distributed power-and-latency-aware dynamic buffer management; joint energy-efficiency; joint queue-backlog; max-weight scheduling algorithm; medical Big-Data platforms; medical cloud platforms; medical clouds; medical data; medical devices; medical imaging Big Data platforms; medical users; power allocation; rate-aware scheduling decisions; stochastic decision making; stochastic decision-making algorithm; Biomedical imaging; Crowdsourcing; Decision making; Heuristic algorithms; Resource management; Scheduling; Wireless communication; 60 GHz; IEEE 802.11ad; dynamic buffering; medical big-data platforms; stochastic decision making;
Journal_Title :
Systems, Man, and Cybernetics: Systems, IEEE Transactions on
DOI :
10.1109/TSMC.2015.2415463