{"title":"Dual-Actor Critic Adaptive Energy Management Method for EH-WSN Based on Battery Energy Neutral Operation","authors":"Shuhua Yuan;Yongqi Ge;Xin Chen;Yalin Wang;Rui Liu;Jintao Gao","doi":"10.1109/JSEN.2024.3472089","DOIUrl":null,"url":null,"abstract":"Energy harvesting wireless sensor nodes collect energy in a nonlinear dynamic change, resulting in low ability to dynamically match the collected and consumed energy of the node in the process of maintaining energy neutral operation (ENO).To address this problem, the concept of battery ENO (BENO) is proposed by analyzing the battery energy buffer characteristics, and the dual-actor critic energy harvesting wireless sensor node adaptive energy management (DAC) method is proposed based on BENO. The method designs a dual-actor critic structure, senses ENO through the battery energy neutral value, and dynamically adjusts the duty cycle based on this value, in order to achieve the purpose of improving the ability of dynamically matching the collected energy with the consumed energy. The experiments are carried out on three datasets with different energy harvesting capabilities, and compared and analyzed with three classical algorithms, RLman, AQL and FQL. The experimental results show that compared with the other three classical algorithms, DAC sacrifices a small amount of duty cycle, but effectively improves the stability of battery energy, and improves the energy utilization and ENO performance. The BENO concept and the DAC methodology can provide guidance and references for the research of energy management in energy-harvesting wireless sensor nodes.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"38466-38478"},"PeriodicalIF":4.3000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10709877/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Energy harvesting wireless sensor nodes collect energy in a nonlinear dynamic change, resulting in low ability to dynamically match the collected and consumed energy of the node in the process of maintaining energy neutral operation (ENO).To address this problem, the concept of battery ENO (BENO) is proposed by analyzing the battery energy buffer characteristics, and the dual-actor critic energy harvesting wireless sensor node adaptive energy management (DAC) method is proposed based on BENO. The method designs a dual-actor critic structure, senses ENO through the battery energy neutral value, and dynamically adjusts the duty cycle based on this value, in order to achieve the purpose of improving the ability of dynamically matching the collected energy with the consumed energy. The experiments are carried out on three datasets with different energy harvesting capabilities, and compared and analyzed with three classical algorithms, RLman, AQL and FQL. The experimental results show that compared with the other three classical algorithms, DAC sacrifices a small amount of duty cycle, but effectively improves the stability of battery energy, and improves the energy utilization and ENO performance. The BENO concept and the DAC methodology can provide guidance and references for the research of energy management in energy-harvesting wireless sensor nodes.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
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-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice