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. https://dl.acm.org/doi/10.1145/3337821.3337926
August 5–8, 2019, Kyoto, Japan
Keyword: IoT, Energy-Efficient Clustering, Q-learning, Lifespan-Extended Network, High-Dimensional Space
With the emergence of Internet of Things (IoT), many battery-operated sensors are deployed in different applications to collect, process, and analyze useful information. In these applications, sensors are often grouped into different clusters to support higher scalability and better data aggregation. Clustering based on energy distribution among nodes can reduce energy consumption and prolong the network lifespan. In our paper, we propose a machine-learning-based energy-efficient clustering algorithm named QLEC to select cluster heads in high-dimensional space and help non-cluster-head nodes route packets. QLEC first selects cluster heads based on their residual energy through successive rounds. Besides, we prove the optimal cluster number in a high-dimensional wireless network and adopt it in our QLEC algorithm. Furthermore, Q-learning method is utilized to maximize residual energy of the network while routing packets from sensors to the base station (BS). The energy-efficient clustering problem in high dimensional space can be formed as an NP-Complete problem and QLEC is proved to solve it in the running time O(kX), where k is the cluster number and X is the number of updates Q-learning needs to converge. Extensive simulations and experiments based on a large-scale dataset show that the proposed scheme outperforms a newly proposed FCM-based algorithm and k-means clustering in terms of network lifespan, packet delivery rate, and transmission latency. To the best of our knowledge, this is the first work adopting Q-learning method in clustering problems in high-dimensional space.