Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms

Joongheon Kim, Wonjun Lee

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


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.

Original languageEnglish
Article number7079536
Pages (from-to)1471-1476
Number of pages6
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Issue number11
Publication statusPublished - 2015 Nov 1


  • 60 GHz
  • dynamic buffering
  • IEEE 802.11ad
  • medical big-data platforms
  • Stochastic decision making

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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