Initiatives to minimise battery use,address sustainability,and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things(IoT)netwo...Initiatives to minimise battery use,address sustainability,and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things(IoT)networks.As a key pillar of fifth generation(5G)and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025.Thermoelectric generators(TEGs)are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy.These devices are able to recover lost thermal energy,produce energy in extreme environments,generate electric power in remote areas,and power micro‐sensors.Applying the state of the art,the authorspresent a comprehensive review of machine learning(ML)approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy.The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment,biological structures,machines,and other technologies are summarised.Based on detailed research of the state of the art in TEG‐powered devices,the authors investigated the research challenges,applied algorithms and application areas of this technology.The aims of the research were to devise new energy prediction and energy management systems based on ML methods,create supervised algorithms which better estimate incoming energy,and develop unsupervised and semi‐supervised ap-proaches which provide adaptive and dynamic operation.The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability,usability in ubiquitous temperature difference scenarios,and long oper-ating lifetime.However,TEGs also have low energy efficiency(around 10%)and require a relatively constant heat source.展开更多
Nowadays,there is a significant need for maintenance free modern Internet of things(IoT)devices which can monitor an environment.IoT devices such as these are mobile embedded devices which provide data to the internet...Nowadays,there is a significant need for maintenance free modern Internet of things(IoT)devices which can monitor an environment.IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network(LPWAN).LPWAN is a promising communications technology which allows machine to machine(M2M)communication and is suitable for smallmobile embedded devices.The paper presents a novel data-driven self-learning(DDSL)controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices.The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices.The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values,leading to effective operation of power-aware systems.The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm.The root mean square error(RMSE)and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria(the performance score parameter and normalized geometric distance)were used for overall evaluation and comparison.The experiments showed that the novel DDSL method reaches significantly lowerRMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm.The overall criteria performance score is 40%higher than the reference algorithm base on static confirmation settings.展开更多
基金supported by the project SP2023/009“Development of algorithms and systems for control,mea-surement and safety applications IX”of the Student Grant System,VSB‐TU Ostrava.This work was also supproted by the project FW03010194“Development of a System for Monitoring and Evaluation of Selected Risk Factors of Physical Workload in the Context of Industry 4.0″of the Technology Agency of the Czech Republicfunding from the European Union's Horizon 2020 research and innovation programme under grant agreement No.856670.This research received no external funding.
文摘Initiatives to minimise battery use,address sustainability,and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things(IoT)networks.As a key pillar of fifth generation(5G)and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025.Thermoelectric generators(TEGs)are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy.These devices are able to recover lost thermal energy,produce energy in extreme environments,generate electric power in remote areas,and power micro‐sensors.Applying the state of the art,the authorspresent a comprehensive review of machine learning(ML)approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy.The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment,biological structures,machines,and other technologies are summarised.Based on detailed research of the state of the art in TEG‐powered devices,the authors investigated the research challenges,applied algorithms and application areas of this technology.The aims of the research were to devise new energy prediction and energy management systems based on ML methods,create supervised algorithms which better estimate incoming energy,and develop unsupervised and semi‐supervised ap-proaches which provide adaptive and dynamic operation.The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability,usability in ubiquitous temperature difference scenarios,and long oper-ating lifetime.However,TEGs also have low energy efficiency(around 10%)and require a relatively constant heat source.
基金This work was supported by the project SP2021/29,“Development of algorithms and systems for control,measurement and safety applications VII”of the Student Grant System,VSB-TU Ostrava.This work was also supported by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project,Project Number CZ.02.1.01/0.0/0.0/16_019/0000867 under the Operational Programme for ResearchDevelopment and Education.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N◦856670.
文摘Nowadays,there is a significant need for maintenance free modern Internet of things(IoT)devices which can monitor an environment.IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network(LPWAN).LPWAN is a promising communications technology which allows machine to machine(M2M)communication and is suitable for smallmobile embedded devices.The paper presents a novel data-driven self-learning(DDSL)controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices.The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices.The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values,leading to effective operation of power-aware systems.The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm.The root mean square error(RMSE)and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria(the performance score parameter and normalized geometric distance)were used for overall evaluation and comparison.The experiments showed that the novel DDSL method reaches significantly lowerRMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm.The overall criteria performance score is 40%higher than the reference algorithm base on static confirmation settings.
