Harnessing Energy Consumption in a Smarthome IoT Framework

Cendana, Desiree I.; Bustillo, Neriza V.; Palaoag, Thelma D.; Marcial, Mark Anthony; Perreras, Arjay E.

doi:10.1109/cybermatics_2018.2018.00048

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Paper [3] found out that up to 30% of total energy may be saved by informing the user regarding the consumption of the appliances using multiple MARCHome devices.…”

Section: State Of the Artmentioning

confidence: 99%

Low-cost wireless home automation solution using multiple ESP32 SoCs

ENE¹,

Bîră²

2023

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI

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This paper presents a low-cost smart home system where two types on nodes were developed: the wireless sensor node where the analog or digital sensor is augmented by an ESP32 SoC, and the wired sensor nodes, where the high-power analog or digital sensors are enhanced by the addition of similar ESP32 and an USB or 230VAC power connection. The proposed solution provides features to improve security and quality of life. In addition, we implemented the prediction of the sensor data timeslots which allows energy savings and preventive maintenance (by informing the used when expected communication was skipped)

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“…Paper [3] found out that up to 30% of total energy may be saved by informing the user regarding the consumption of the appliances using multiple MARCHome devices.…”

Section: State Of the Artmentioning

confidence: 99%

Low-cost wireless home automation solution using multiple ESP32 SoCs

ENE¹,

Bîră²

2023

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI

View full text Add to dashboard Cite

show abstract

“…Today, "smart" house systems have occupied one of the key niches in development in the field of intelligent information technology [1][2][3][4][5][6]. Such systems continuously generate a stream of data, that is used to solve a number of important tasks, namely, the automation of home appliances control [7,8], providing comfortable living conditions [9,10], reducing energy consumption [11,12], etc. Similar systems can also be used to monitor the elderly, children and pets [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Teslyuk

Kazarian

Kryvinska

et al. 2020

Sensors

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In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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“…However, the limited operating time of the WSN nodes is a problem derived from the limited capacity of their batteries, for which various studies have been carried out to reduce their operational energy consumption. For automation of homes and houses in general, several publications have generated solutions [6,7] implementing sensor networks to control household appliances such as lights, fans, doors, and gas levels and measure energy consumption [8] using various sensors such as the LM35, infrared, LDR modules, and processing devices like the ESP8266 MCU node and Arduino UNO.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

J GRAPH-HOC

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In Smart Home (SH) the devices and appliances can be controlled automatically and remotely via the internet. Sensors in a smart home detect changes, which are processed and used to make intelligent decisions, such as automatically turning on the lights. To be effective, a smart home's sensors must be connected, establishing a wireless sensor network as the backbone for the smart home's functionalities. For many researchers, the performance of WSN faces several challenges. Power consumption is the most crucial factor since nodes require power to collect, process, and send data but have limited energy. As a result we propose an improved protocol for WSN in smart homes called EEP-LEACH (Energy Efficient Protocol -Low Energy Adaptive Clustering Hierarchy) based on the LEACH protocol. Matlab was used for the algorithm's performance analysis, and the simulation result showed that, compared to others protocols the death time of the nodes was longer, thus managing to balance the consumption of energy of the network nodes. Python and Arduino are used in this research to perform some simulations.

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Harnessing Energy Consumption in a Smarthome IoT Framework

Cited by 7 publications

References 16 publications

Low-cost wireless home automation solution using multiple ESP32 SoCs

Low-cost wireless home automation solution using multiple ESP32 SoCs

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

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