计算机科学
规范化(社会学)
无线传感器网络
可再生能源
强化学习
能源管理
无线
能量收集
能量(信号处理)
算法
实时计算
数学优化
人工智能
计算机网络
电信
电气工程
工程类
数学
统计
社会学
人类学
作者
Chengrun Qiu,Yang Hu,Yan Chen,Bing Zeng
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2019-10-01
卷期号:6 (5): 8577-8588
被引量:191
标识
DOI:10.1109/jiot.2019.2921159
摘要
To overcome the difficulties of charging the wireless sensors in the wild with conventional energy supply, more and more researchers have focused on the sensor networks with renewable generations. Considering the uncertainty of the renewable generations, an effective energy management strategy is necessary for the sensors. In this paper, we propose a novel energy management algorithm based on the reinforcement learning. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm is applicable for the continuous states and realizes the continuous energy management. We also propose a state normalization algorithm to help the neural network initialize and learn. With only one day's real solar data and the simulative channel data for training, the proposed algorithm shows excellent performance in the validation with about 800 days length of real solar data. Compared with the state-of-the-art algorithms, the proposed algorithm achieves better performance in terms of long-term average net bit rate.
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