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

Joongheon Kim, Wonjun Lee

Research output: Contribution to journalArticlepeer-review

27 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: Systems
Issue number11
Publication statusPublished - 2015 Nov


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

ASJC Scopus subject areas

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


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