Harvesting-Aware Energy Management for Environmental Monitoring WSN

Rodway, James; Musilek, Petr

doi:10.3390/en10050607

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…and z ∈ {1, 2, ..., ξ}, and is assumed to follow a Poisson distribution with a mean harvested energy, E h,mean . The harvesting modeling and the impact of daytime and nighttime on solar harvesting performance were well studied [44], [45]. In literature [44], the empirical measurements were conducted to model the energy harvesting for solar-powered wireless devices and the results verified that the harvested energy highly depends on properties such as harvesting time, light intensity, and deployment operating environment.…”

Section: E Energy Harvestingmentioning

confidence: 98%

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

et al. 2021

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The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, the opportunistic spectrum access capability of cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast network, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the successful transmission probability of the packets among field devices by opportunistically allocating the primary channels to the devices for data transmissions. In this paper, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process by considering the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe to maximize the received packets at the GW. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.INDEX TERMS wirelessHART, cognitive radio, markov decision process, industrial scientific medical

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Section: E Energy Harvestingmentioning

confidence: 98%

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

et al. 2021

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“…Fuzzy logic controllers are decision‐making tools [129] and can be applied as power management systems to control and manage energy harvesting output [130]. Besides controlling harvested energy, fuzzy logic controllers extend the lifetimes of energy harvesting devices [131]. Because fuzzy logic is a rule‐based algorithm, it can be combined with optimization‐based algorithms to create a two‐step energy management system [132].…”

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

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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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“…Delay in forwarding packets of data is a strategy to ensure efficient energy consumption, which can be achieved by providing the optimum solution of clustering and routing in wireless sensor networks (WSN) using DE [115]. Clustering ensures that data is transmitted in hierarchical order and reduces into distinct groups which helps to improve power utilization.…”

Section: Energy Optimisationmentioning

confidence: 99%

Differential Evolution in Wireless Communications: A Review

Okagbue¹,

Adamu

Anake

2019

Int. J. Onl. Eng.

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<p class="0abstract">Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this context.</p>

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Harvesting-Aware Energy Management for Environmental Monitoring WSN

Cited by 10 publications

References 45 publications

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

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

Differential Evolution in Wireless Communications: A Review

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