scholarly journals Deep Learning Model for Air Quality Prediction Based on Big Data

2021 ◽  
pp. 170-175
Author(s):  
P. Parkavi ◽  
S. Rathi
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Air Quality ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Pollution Monitoring ◽  
The Internet ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
The Internet Of Things

Air pollution and its harm to human health has become a serious problem in many cities around the world. In recent years, research interests in measuring and predicting the quality of air around people has spiked. Since the Internet of things has been widely used in different domains to improve the quality for people by connecting multiple sensors. In this work an IOT based air pollution monitoring with prediction system is proposed. The internet of Things is a action interrelated computing devices that are given unique identifiers and the capability of exchange information over a system without anticipating that human to human or human to machine communication. The deep learning algorithm approach is to evaluate the accuracy for the prediction of air pollution. The main objective of the project is used to predict the air Quality. The large dataset works with LSTM for better air quality prediction. The prediction accuracy of air quality with LSTM, the evaluation indicator Root means square error is chosen to measure performance.

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

Application of Internet of Things in Air Pollution Monitoring

2014 ◽  
Vol 1073-1076 ◽  
pp. 554-557
Author(s):  
Jing Li
Keyword(s):  
Air Pollution ◽  
Internet Of Things ◽  
Pollution Monitoring ◽  
The Internet ◽  
Environment Monitoring ◽  
Environment Protection ◽  
Air Pollution Monitoring ◽  
Networking Technology ◽  
Internet Of Things Technology ◽  
The Internet Of Things

This article analyses how to combine the internet of things (IOT) with air pollution monitoring. It believes that application of internet of things can resolve air pollution monitoring problems. What the paper analyzes is how the Internet of Things technology effectively applied to the field of environment protection, to improve management of environment monitoring and protection. This paper mainly introduces concepts and architecture system of IOT used in environment monitoring and protection, through analyzing the problems in environment monitoring and protection in China, on the basis of summarizing the experience of IOT used in the field of environment protection in China, further proposes how to promote the development and application of networking technology in monitoring and protection of the environment in China, and focuses on the difficulties, obstacles and solutions which exist in.

scholarly journals Next Generation Air Quality Platform: Openness and Interoperability for the Internet of Things

Sensors ◽  
2016 ◽  
Vol 16 (3) ◽  
pp. 403 ◽  
Author(s):  
Alexander Kotsev ◽  
Sven Schade ◽  
Massimo Craglia ◽  
Michel Gerboles ◽  
Laurent Spinelle ◽  
...  
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
The Internet ◽  
Next Generation ◽  
The Internet Of Things

scholarly journals INCORPOARATION OF GEOSENSOR NETWORKS INTO INTERNET OF THINGS FOR ENVIRONMENTAL MONITORING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 259-261 ◽  
Author(s):  
R. Habibi ◽  
A. A. Alesheikh
Keyword(s):  
Internet Of Things ◽  
Environmental Monitoring ◽  
Communication Technologies ◽  
Environmental Data ◽  
Pollution Monitoring ◽  
The Internet ◽  
Air Pollution Monitoring ◽  
Sensor Web ◽  
The Internet Of Things ◽  
Resolution Data

Thanks to the recent advances of miniaturization and the falling costs for sensors and also communication technologies, Internet specially, the number of internet-connected things growth tremendously. Moreover, geosensors with capability of generating high spatial and temporal resolution data, measuring a vast diversity of environmental data and automated operations provide powerful abilities to environmental monitoring tasks. Geosensor nodes are intuitively heterogeneous in terms of the hardware capabilities and communication protocols to take part in the Internet of Things scenarios. Therefore, ensuring interoperability is an important step. With this respect, the focus of this paper is particularly on incorporation of geosensor networks into Internet of things through an architecture for monitoring real-time environmental data with use of OGC Sensor Web Enablement standards. This approach and its applicability is discussed in the context of an air pollution monitoring scenario.

Role of the Internet of Things and deep learning for the growth of healthcare technology

2021 ◽  
pp. 113-127
Author(s):  
Dinesh Bhatia ◽  
S. Bagyaraj ◽  
S. Arun Karthick ◽  
Animesh Mishra ◽  
Amit Malviya
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
The Internet ◽  
Healthcare Technology ◽  
The Internet Of Things
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