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[01035] Reinforcement learning-based routing strategy in IoT applications using MDC

  • Session Time & Room : 2E (Aug.22, 17:40-19:20) @E803
  • Type : Contributed Talk
  • Abstract : WSNs and IoT devices consume more power for data transmission. To reduce energy consumption, most of the traditional learning methodologies need enormous volumes of data and feature engineering, thus raising the learning complexity. A reliable reinforcement learning-based MDC model for effective routing is proposed to lower the learning complexity. Furthermore, the Q-Learning methodology is used to enhance learning along the shortest path. Combining these techniques can improve network stability while also enhancing routing performance significantly.
  • Classification : 68T01, 68T07, 68T35, Reinforcement learning, Machine Learning
  • Format : Talk at Waseda University
  • Author(s) :
    • Muralitharan Krishnan (Sungkyunkwan University)
    • Yongdo Lim (Sungkyunkwan University)