IoT Based Home Automation System using Raspberry Pi 4

Maragatham, T; Balasubramanie, P.; Vivekanandhan, M

doi:10.1088/1757-899x/1055/1/012081

Cited by 16 publications

(3 citation statements)

References 2 publications

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“…The MQ03 sensor, NodeMCU, and Arduino implementation entail wiring up the hardware parts, creating code to read the sensor's output, calibrating the sensor, and testing the implementation to make sure everything is operating as it should. The development of low cost alcohol detection systems for uses like breathalyzer tests or alcohol ignition interlocks may benefit from this kind of implementation [23], [24]. This system's technology automatically recognizes drunk driving and traffic incidents.…”

Section: Hardware Implementationmentioning

confidence: 99%

Design and implementation of an alcohol detection driver system

Alabi,

Adeaga,

Ajagbe

et al. 2024

IJRES

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A technology called an alcohol detection driver system is used to stop drunk driving by identifying alcohol in a motorist's breath or blood. This technology correctly measures the amount of alcohol a driver has in their system using sensors and algorithms, and it stops the car from starting if the amount is more than the legal limit. The number of fatal accidents and traffic fatalities caused by drinking could be greatly decreased thanks to this technology. The main focus of this project is to carry out the experiment in lowering the number of alcohol-related incidents on the road. Alcohol detection devices come in a variety of forms right now, including ignition interlocks, passive alcohol sensors, and in-car breathalyzers. Although these systems have reduced the number of drunk driving accidents, there remain questions about their efficiency, dependability, and cost. According to the sensor's specs, the output voltage of the MQ-3 sensor reduces by 69% during the sensor's recovery period of 30 seconds at 69% of baseline resistance. To assess the long-term viability and efficiency of these systems in lowering alcohol-related accidents and enhancing traffic safety, more research is required.

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Section: Hardware Implementationmentioning

confidence: 99%

Design and implementation of an alcohol detection driver system

Alabi,

Adeaga,

Ajagbe

et al. 2024

IJRES

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“…For this reason, it is necessary to incorporate an intermediary between the wristband and the smartphone to collect the data and incorporate them into the database of the system. Therefore, since the main controller of this system (NVIDIA Jetson Nano) does not include Bluetooth connectivity, a Raspberry Pi 4 was used as a secondary controller that does support this type of communication [ 54 ]. Moreover, it emits a BLE message, which orders the wristband to start emitting the information described above, and is also responsible for storing the gyroscope data locally and publishing the rest of the information to the main controller in real time via MQTT messages.…”

Section: The Sdhar-home Databasementioning

confidence: 99%

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

Ramos

Domingo

Zalama

et al. 2022

Sensors

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Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especially those who live alone. These people may experience undesired or dangerous situations in their daily life at home due to physical, sensorial or cognitive limitations, such as forgetting their medication or wrong eating habits. This work focuses on the development of a database in a home, through non-intrusive technology, where several users are residing by combining: a set of non-intrusive sensors which captures events that occur in the house, a positioning system through triangulation using beacons and a system for monitoring the user’s state through activity wristbands. Two months of uninterrupted measurements were obtained on the daily habits of 2 people who live with a pet and receive sporadic visits, in which 18 different types of activities were labelled. In order to validate the data, a system for the real-time recognition of the activities carried out by these residents was developed using different current Deep Learning (DL) techniques based on neural networks, such as Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM) or Gated Recurrent Unit networks (GRU). A personalised prediction model was developed for each user, resulting in hit rates ranging from 88.29% to 90.91%. Finally, a data sharing algorithm has been developed to improve the generalisability of the model and to avoid overtraining the neural network.

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“…The rover can be used for both indoor and outdoor applications. On comparison with the existing work studied, some of the systems didn't have remote monitoring [1], some didn't use rocker bogie mechanism [2], and some had noisy data from sensors making them less efficient [3]. This rover stands unique in such terms and uses a NGRok server [4] to remote access the Raspberry Pi.…”

Section: Introductionmentioning

confidence: 99%

Remote surveillance of enclosed and open architectures using unmanned vehicle with advanced security

Khetan,

Sarkar,

Sabunwala

et al. 2024

IJRES

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<p>Monitoring behavior, numerous actions, or any such information is considered as surveillance and is done for information gathering, influencing, managing, or directing purposes. Citizens employ surveillance to safeguard their communities. Governments do this for the purposes of intelligence collection, including espionage, crime prevention, the defense of a method, a person, a group, or an item; or the investigation of criminal activity. Using an internet of things (IoT) rover, the area will be secured with better secrecy and efficiency instead of humans, will provide an additional safety step. In this paper, there is a discussion about an IoT rover for remote surveillance based around a Raspberry Pi microprocessor which will be able to monitor a closed/open space. This rover will allow safer survey operations and would help to reduce the risks involved with it.</p>

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IoT Based Home Automation System using Raspberry Pi 4

Cited by 16 publications

References 2 publications

Design and implementation of an alcohol detection driver system

Design and implementation of an alcohol detection driver system

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

Remote surveillance of enclosed and open architectures using unmanned vehicle with advanced security

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