Self-learning for Day-night Mode Energy Strategy for Solar Powered Environmental WSN Nodes

Prauzek, Michal; Konecny, Jaromír; Hlavica, Jakub; Musílek, Petr

doi:10.1109/ccece47787.2020.9255790

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Table 13 lists several approaches suitable for energy management systems. Reinforcement learning based algorithms such as SARSA [121], Q‐learning [120], and Deep Reinforcement Learning [116] can use historical data to manage energy production and consumption in sensors powered by energy harvesting. In ref.…”

Section: Algorithmsmentioning

confidence: 99%

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Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

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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 approaches 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 operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.

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Section: Algorithmsmentioning

confidence: 99%

“…In ref. [120], the study experiments with Q‐learning in an environmental monitoring system which uses a wireless sensor network controller to achieve optimal data collection and transmission performance and thus minimise failure due to energy storage depletion. In ref.…”

Section: Algorithmsmentioning

confidence: 99%

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…  E t T t (9) The discharge target for the night is 20 % of the total storage capacity. This value is a reserve for the beginning of the next daylight period.…”

Section: Soes( ) Soes( ) mentioning

confidence: 99%

“…In this contribution, we are presenting the follow-up research based on the previous paper [9]. This past research presented the design of a hybrid energy management strategy with Q-learning control during daylight and a linear discharging process at night.…”

Section: Introductionmentioning

confidence: 99%

Optimizing of Q-Learning Day/Night Energy Strategy for Solar Harvesting Environmental Wireless Sensor Networks Nodes

Prauzek

Konecny

2021

ELEKTRON ELEKTROTECH

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This research article presents the application of the Q-learning algorithm in the operational duty cycle control of solar-powered environmental wireless sensor network (EWSN) nodes. Those nodes are commonly implemented as embedded devices using low-power and low-cost microcontrollers. Therefore, there is a significant need for an effective and easy way to implement a machine learning (ML) algorithm in terms of computer performance. This approach uses a Q-learning-based policy implementing a sleep/run switching algorithm driven by the state of charge. The presented algorithm is based on two modes: daylight and nighttime, which is a suitable solution for solar-powered systems. The study includes the complete process of design EWSN node strategy with an optimal reward policy. The presented algorithm was tested and verified on an EWSN node model and a 5-year data set of solar irradiance values was used for the learning process and its validation. As part of the study, we are also presenting the validation in terms of Q-learning parameters, which include the learning rate and discount factor. The result section shows that the overall performance of the presented solution is more suitable for solar-powered EWSN then state-of-the-art studies. Both day/night experiments reached 828 203 measurement/transmission cycles, which is 12.7 % more than in the previous studies using the strategy defined by the state of energy storage.

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An Analysis of Double Q-Learning-Based Energy Management Strategies for TEG-Powered IoT Devices

Prauzek

Konecny

Paterova

2023

IEEE Internet Things J.

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Self-learning for Day-night Mode Energy Strategy for Solar Powered Environmental WSN Nodes

Cited by 4 publications

References 10 publications

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Optimizing of Q-Learning Day/Night Energy Strategy for Solar Harvesting Environmental Wireless Sensor Networks Nodes

An Analysis of Double Q-Learning-Based Energy Management Strategies for TEG-Powered IoT Devices

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