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.
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.