Energy-efficient algorithm for classification of states of wireless sensor network using machine learning methods

Yuldashev, M. N.; Vlasov, Andrey I.; Novikov, A. N.

doi:10.1088/1742-6596/1015/3/032153

Cited by 11 publications

(3 citation statements)

References 5 publications

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“…During the flight, the probe will rotate around its axis, and not be in a stationary state like a satellite. It is possible to assume that the solar array generation will be slightly higher than the generation in the case of illumination of one side, since at certain moments of time the spacecraft will turn to the sun with its edge and 2 sides will be illuminated, albeit not at an optimal angle [14,15].…”

Section: Methodsmentioning

confidence: 99%

Use of Solar Panels in the Design of Small CubeSat Spacecraft

2021

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The article discusses organization of power supply for small spacecraft of CubeSat type. Various methods of power supply of CubeSat onboard equipment are worked out. The use of solar panels (SP) for power supply of CubeSat is proposed. The developed technology for the production of solar arrays of the required size from solar panels has been demonstrated. The organization of incoming control of solar panels to improve reliability and achieve maximum efficiency of produced solar panels is considered. A developed stand for diagnostics of solar cells, which allows detecting latent defects, is presented. The issue of determining power characteristics of the developed solar panels and the necessary optimal load for their maximum efficiency is discussed. The installation of solar panels on the CubeSat aircraft is described. The efficiency of solar panels is tested in space by launching CubeSat on a stratospheric probe. The parameters of the aircraft current supply and current generation by solar panels obtained as a result of the experiment are collected and processed, the results are presented in the article in the form of graphs. On basis of the data obtained, effectiveness of the use of solar panels on small aircraft of the CubeSat type is shown.

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Section: Methodsmentioning

confidence: 99%

Use of Solar Panels in the Design of Small CubeSat Spacecraft

2021

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“…At the beginning of development, the RFID-IoT architecture prevailed, unambiguously identifying automation objects connected through RFID radio frequency identification technology [19]- [21]. New IoT functionality has become achievable with the introduction of architectures based on wireless sensor networks (WSNs) [17], [22]. The subsequent development of the IoT concept was characterized by the emergence of a multi-layered intelligence of the IoT (artificial intelligence of things (AIoT)) and the formation of an intelligent cyber-physical environment [23].…”

Section: Figure 1 Classification Of Basic Iot Architecturesmentioning

confidence: 99%

Designing smart pulse flow meters using diversion analysis

Micheev

Muraviev

Rezchikova

et al. 2023

IJECE

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<span lang="EN-US">The operation of modern housing infrastructure is characterized by a constant increase in the cost of the limited resources used. This necessitates the priority implementation in the concept of a smart home of elements aimed at resource saving and their rational management. The study provides an overview of the implementation architectures of the internet of things (IoT) concept in the construction of home automation systems and the requirements they impose on the implementation of smart primary meters of controlled physical quantities. Based on a diversion analysis, a promising smart water meter was developed. The prototype is ergonomic and has a structural form factor convenient for further integration. The designed model of the electronic module of the water flow monitoring system implements, in addition to typical tasks, additional functionality: transfer of recorded indicators and technical information to the cloud storage, warning the user about an emergency situation, accumulation of current data in non-volatile memory. It is possible to use the accumulated statistics for training the predictive analysis module. The proposed architecture option will allow creating energy-efficient elements of home automation systems in the future.</span>

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“…It is used to monitor and control physical processes and monitoring remote locations [5]. IWSNs allow users to access, monitor, and control various industrial resources, from environmental and climactic conditions to manufacturing processes and equipment [6]. Typical uses of IWSNS include manufacturing and process management, hazardous material detection, energy distribution and usage monitoring, and security monitoring [7].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning

Bagwari,

Logeshwaran,

Usha

et al. 2023

IEEE Access

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Industrial Wireless Sensor Networks (WSNs) are becoming increasingly popular due to their enhanced scalability and low cost of deployment. However, they also present new challenges, such as energy optimization and network maintenance, which industrial users must address. In order to meet the challenges, Machine Learning techniques have been used to create an enhanced energy optimization model for Industrial WSNs. This model utilizes knowledge-based learning to identify and optimize the energy consumption of the nodes, allowing Industrial WSNs to consume the least amount of energy for the given tasks. In addition, the model also evaluates the effectiveness of feedback control schemes and predicts the best possible outcomes for its application in Industrial WSNs to ensure higher efficiency and longer network lifetime. The model also enables the exploration of potential trade-offs between power consumption and communication performance to ensure a better energy-efficient solution. The proposed EEOM obtained 64.72% transmission energy consumption, 35.28% transmission energy saving, 67.27% received energy consumption, 32.73% received energy storage, 52.16% idle-mode energy consumption, 47.84% idle-mode energy storage, 66.31% sleep-mode energy consumption, and 33.69% sleep-mode energy storage. It also obtained 90.44% prevalence threshold, 90.33% critical success index, 93.93% Delta-P, 90.06% MCC and 92.17% FMI rates. It also provides the ability to identify the best selection of nodes and paths for data transmission to reduce network traffic. When applied in conjunction with manual intervention, these automated knowledge-based techniques will make Industrial WSNs more reliable, efficient, and energy-cost effective.

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Energy-efficient algorithm for classification of states of wireless sensor network using machine learning methods

Cited by 11 publications

References 5 publications

Use of Solar Panels in the Design of Small CubeSat Spacecraft

Use of Solar Panels in the Design of Small CubeSat Spacecraft

Designing smart pulse flow meters using diversion analysis

An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning

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