stochastic decision making for adaptive crowd sourcing in medical big-data platforms
Post on 14-Feb-2016
10 Views
Preview:
DESCRIPTION
TRANSCRIPT
Ph: +91 9573777164 http://www.mtechprojects.com
MTECH PROJECTS 2015 - 2016:
JAVA PROJECTS DSP PROJECTS POWER SYSTEMS PROJECTS DOT NET PROJECTS DIP PROJECTS POWER ELECTRONICS PROJECTS NS2 PROJECTS VLSI PROJECTS MATLAB SIMULATION PROJECTS ANDROID PROJECTS VHDL PROJECTS ENERGY SOURCES PROJECTS CLOUD SIM PROJECTS VERILOG PROJECTS RELIABILITY PROJECTS CLOUD COMPUTING PROJECTS EMBEDDED PROJECTS PSCAD PROJECTS WIRELESS NETWORK PROJECTS ROBOTIC PROJECTS MECHANICAL PROJECTS MOBILE COMPUTING PROJECTS CADENCE PROJECTS CHEMICAL PROJECTS
Ph: +91 9573777164 www.mtechprojects.com
Stochastic Decision Making for Adaptive Crowd sourcing in Medical Big-Data Platforms
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.
top related