IoT based Smart Air Pollution Monitoring System

Potbhare, Piyush Devidas; Bhange, Ketan; Tembhare, Gaurav; Agrawal, Rahul; Sorte, Swati; Lokulwar, Prasad P.

doi:10.1109/icaaic53929.2022.9792743

Cited by 27 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Investigating gases in the environment will be an important factor. The MQ9 gas sensor used can detect various gases such as carbon monoxide gas (CO), liquefied petroleum gas (LPG), and methane [14], [15]. This sensor will detect all the gases, as they can be risky in a closed environment and can possess a threat to life.…”

Section: Mq9 Gas Sensormentioning

confidence: 99%

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

Khetan,

Sarkar,

Sabunwala

et al. 2024

IJRES

View full text Add to dashboard Cite

<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>

show abstract

Section: Mq9 Gas Sensormentioning

confidence: 99%

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

Khetan,

Sarkar,

Sabunwala

et al. 2024

IJRES

View full text Add to dashboard Cite

show abstract

“…Wearable devices are used to monitor patients for any abnormal health conditions [16,17] so that doctors can react quickly if a problem occurs. The development of such systems has also made its way into environmental control, where systems have been developed for monitoring air pollution [20], carbon dioxide [21], and solar power [22]. Systems are also being developed to monitor and control residential buildings, making them into smart homes [23] and monitoring grids, and controlling network congestion.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, condition monitoring systems are being developed, which are particularly targeted toward weather sensors [24] and wind turbines [25][26][27]. Researchers are also looking into a more portable approach towards condition monitoring systems with the advancement of electrical machines, which can give better mobility and less complexity [20,[28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines

et al. 2022

View full text Add to dashboard Cite

Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach.

show abstract

Improving Low-Light Images Using an Advanced Histogram Equalization Method and a Self-Calibrated Illumination Technique

Jha,

Sakhare,

Chavhan

et al. 2024

Proceedings of Fifth International Conference on Computing, Communications, and Cyber-Security

View full text Add to dashboard Cite

IoT based Smart Air Pollution Monitoring System

Cited by 27 publications

References 26 publications

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

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

Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines

Improving Low-Light Images Using an Advanced Histogram Equalization Method and a Self-Calibrated Illumination Technique

Contact Info

Product

Resources

About