Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning

The reduction of maintenance costs in agricultural wireless sensor networks (WSNs) requires reducing energy consumption. At the same time, care should be taken not to affect communication quality and network lifetime. This paper studies a joint optimization algorithm for transmitted power and channe...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiao Han, Huarui Wu, Huaji Zhu, Cheng Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	power control channel allocation DDPG mixed variable Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/8e3b6abacdcb482aa59f16f05f6ccd0f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8e3b6abacdcb482aa59f16f05f6ccd0f
record_format	dspace
spelling	oai:doaj.org-article:8e3b6abacdcb482aa59f16f05f6ccd0f2021-11-25T18:50:26ZJoint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning10.3390/pr91119192227-9717https://doaj.org/article/8e3b6abacdcb482aa59f16f05f6ccd0f2021-10-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/1919https://doaj.org/toc/2227-9717The reduction of maintenance costs in agricultural wireless sensor networks (WSNs) requires reducing energy consumption. At the same time, care should be taken not to affect communication quality and network lifetime. This paper studies a joint optimization algorithm for transmitted power and channel allocation based on deep reinforcement learning. First, an optimization model to measure network reward was established under the constraint of the signal-to-interference plus-noise-ratio (SINR) threshold, which includes continuous power variables and discrete channel variables. Secondly, considering the dynamic changes of agricultural WSNs, the network control is described as a Markov decision process with continuous state and action space. A deep deterministic policy gradient (DDPG) reinforcement learning scheme suitable for mixed variables was designed. This method could obtain a control scheme that maximizes network reward by means of black-box optimization for continuous transmitted power and discrete channel allocation. Experimental results indicated that the studied algorithm has stable convergence. Compared with traditional protocols, it can better control the transmitted power and allocate channels. The joint power and channel optimization provides a reference solution for constructing an energy-balanced network.Xiao HanHuarui WuHuaji ZhuCheng ChenMDPI AGarticlepower controlchannel allocationDDPGmixed variableChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 1919, p 1919 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	power control channel allocation DDPG mixed variable Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	power control channel allocation DDPG mixed variable Chemical technology TP1-1185 Chemistry QD1-999 Xiao Han Huarui Wu Huaji Zhu Cheng Chen Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning
description	The reduction of maintenance costs in agricultural wireless sensor networks (WSNs) requires reducing energy consumption. At the same time, care should be taken not to affect communication quality and network lifetime. This paper studies a joint optimization algorithm for transmitted power and channel allocation based on deep reinforcement learning. First, an optimization model to measure network reward was established under the constraint of the signal-to-interference plus-noise-ratio (SINR) threshold, which includes continuous power variables and discrete channel variables. Secondly, considering the dynamic changes of agricultural WSNs, the network control is described as a Markov decision process with continuous state and action space. A deep deterministic policy gradient (DDPG) reinforcement learning scheme suitable for mixed variables was designed. This method could obtain a control scheme that maximizes network reward by means of black-box optimization for continuous transmitted power and discrete channel allocation. Experimental results indicated that the studied algorithm has stable convergence. Compared with traditional protocols, it can better control the transmitted power and allocate channels. The joint power and channel optimization provides a reference solution for constructing an energy-balanced network.
format	article
author	Xiao Han Huarui Wu Huaji Zhu Cheng Chen
author_facet	Xiao Han Huarui Wu Huaji Zhu Cheng Chen
author_sort	Xiao Han
title	Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning
title_short	Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning
title_full	Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning
title_fullStr	Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning
title_full_unstemmed	Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning
title_sort	joint power and channel optimization of agricultural wireless sensor networks based on hybrid deep reinforcement learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8e3b6abacdcb482aa59f16f05f6ccd0f
work_keys_str_mv	AT xiaohan jointpowerandchanneloptimizationofagriculturalwirelesssensornetworksbasedonhybriddeepreinforcementlearning AT huaruiwu jointpowerandchanneloptimizationofagriculturalwirelesssensornetworksbasedonhybriddeepreinforcementlearning AT huajizhu jointpowerandchanneloptimizationofagriculturalwirelesssensornetworksbasedonhybriddeepreinforcementlearning AT chengchen jointpowerandchanneloptimizationofagriculturalwirelesssensornetworksbasedonhybriddeepreinforcementlearning
_version_	1718410658810167296

Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning

Ejemplares similares