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Stochastic Decision Making for Adaptive Crowd sourcing in Medical Big-Data Platforms - 2015

PROJECT TITLE:

Stochastic Decision Making for Adaptive Crowd sourcing in Medical Big-Data Platforms - 2015

ABSTRACT:

This paper proposes two novel algorithms for adaptive crowdsourcing in sixty-GHz medical imaging huge-information platforms, particularly, a max-weight scheduling algorithm for medical cloud platforms and a stochastic call-creating 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, every scheduled medical device computes the amounts of power allocation to upload its own medical data to medical big-data clouds with stochastic call creating considering joint energy-efficiency and buffer stability optimization. Through extensive simulations, the proposed algorithms are shown to achieve the required results.


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