QLEC: A Machine-Learning-Based Energy-Efficient Clustering Algorithm to Prolong Network Lifespan for IoT in High-Dimensional Space

Published in The 48th International Conference on Parallel Processing (ICPP), 2019

Recommended citation: Ke Li, Haowei Huang, Xiaofeng Gao, Fan Wu and Guihai Chen. (2019). "QLEC: A Machine-Learning-Based Energy-Efficient Clustering Algorithm to Prolong Network Lifespan for IoT in High-Dimensional Space." The 48th International Conference on Parallel Processing (ICPP). Article No. 105. Pages 1–10. http://keli97.github.io/files/QLEC-A-Machine-Learning-Based-Energy-Efficient-Clustering-Algorithm-to-Prolong-Network-Lifespan-for-IoT-in-High-Dimensional-Space.pdf

August 5–8, 2019, Kyoto, Japan
Keyword: IoT, Energy-Efficient Clustering, Q-learning, Lifespan-Extended Network, High-Dimensional Space

  • Improved Distributed Energy Efficient Clustering (DEEC) algorithm with energy constraints and cluster coverage ranges of sensors in 3-dimensional WSNs taken into consideration.
  • Adopted Q-learning scheme to choose cluster heads for routing packets of each sensor.
  • Solved the Energy-Efficient Clustering Problem (EECP), which is an NP-Complete problem in the running time O(kX), where k is the cluster number and X is the number of updates that Q-learning needs to converge.
  • Conducted experiments with the algorithm and outperformed k-means clustering and an FCM-based algorithm in terms of network lifespan, packet delivery rate, and transmission latency.

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Recommended citation: Ke Li, Haowei Huang, Xiaofeng Gao, Fan Wu and Guihai Chen. (2019). "QLEC: A Machine-Learning-Based Energy-Efficient Clustering Algorithm to Prolong Network Lifespan for IoT in High-Dimensional Space." The 48th International Conference on Parallel Processing (ICPP). Article No. 105. Pages 1–10.