Scope of Improvements in Aerospace Sector by Implementation of IoT

2022 ◽  
Author(s):  
Bastin Francis
Keyword(s):  
Machine Learning ◽  
Aerospace Industry ◽  
Theoretical Aspect ◽  
Sensor Data ◽  
The Internet ◽  
Aerospace Sector ◽  
New Development ◽  
Cloud Server ◽  
Optimum Solution ◽  
The Internet Of Things

While considering the aerospace domain, the internet of things (IoT) provides the way for new development and this IoT technology allows many possibilities in the aerospace domain. This study aims to examine the theoretical aspect of IoT in the aerospace industry. And propose a system that enhances the flight journey experience from the flight booking of each customer. This will also improve the manufacturing end-to-end process in the aerospace industry with the help of IoT sensors. These can be achieved with help of the data collection (Previous sensor data), cloud computing, and machine learning. As per the proposing system, all IoT sensor data will be collected and saved the data in the cloud server. These data will be used for training the algorithm to achieve the optimum solution in the future.

scholarly journals Streaming Data Fusion for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1955 ◽  
Author(s):  
Klemen Kenda ◽  
Blaž Kažič ◽  
Erik Novak ◽  
Dunja Mladenić
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Data Fusion ◽  
Predictive Analytics ◽  
Heterogeneous Data ◽  
Streaming Data ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
The Internet ◽  
The Internet Of Things

To achieve the full analytical potential of the streaming data from the internet of things, the interconnection of various data sources is needed. By definition, those sources are heterogeneous and their integration is not a trivial task. A common approach to exploit streaming sensor data potential is to use machine learning techniques for predictive analytics in a way that is agnostic to the domain knowledge. Such an approach can be easily integrated in various use cases. In this paper, we propose a novel framework for data fusion of a set of heterogeneous data streams. The proposed framework enriches streaming sensor data with the contextual and historical information relevant for describing the underlying processes. The final result of the framework is a feature vector, ready to be used in a machine learning algorithm. The framework has been applied to a cloud and to an edge device. In the latter case, incremental learning capabilities have been demonstrated. The reported results illustrate a significant improvement of data-driven models, applied to sensor streams. Beside higher accuracy of the models the platform offers easy setup and thus fast prototyping capabilities in real-world applications.

scholarly journals Micro climate monitoring system in closed broiler cages based on the internet of things

2020 ◽  
Vol 8 (2) ◽  
pp. 100-105
Author(s):  
Damar Wicaksono ◽  
Taufiq Kamal
Keyword(s):  
Wind Speed ◽  
Monitoring System ◽  
Effective Temperature ◽  
Sensor Data ◽  
The Internet ◽  
Climate Monitoring ◽  
Cloud Server ◽  
Microclimate Monitoring ◽  
Smart Agriculture ◽  
The Internet Of Things

Smart agriculture has an emerged concept by using IoT sensors capable of providing various information about their field condition and conducting environmental monitoring to improve the yield of efficient crops. This research aims to develop a microclimate monitoring system in a closed house. The microclimate being monitored is the effective temperature, which is the temperature felt by broilers at that time in a fast area. In this research, IoT has been implemented using WeMos D1 R32 by sending sensor data to observe the effective temperature parameters as actual temperature, humidity, and wind speed into an MQTT cloud server. Microclimate control in the cage is based on effective temperature. The data can be displayed on a 16x4 LCD screen and accessed via an Android smartphone from anywhere and at any time.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

Sistem Keamanan Gedung Menggunakan Kinect Xbox 360 Dengan Metode Skeletal Tracking

2021 ◽  
Vol 5 (6) ◽  
pp. 1137-1142
Author(s):  
Hamdi Alchudri ◽  
Zaini
Keyword(s):  
Optical Data ◽  
Visual Images ◽  
The Internet ◽  
Transmission Method ◽  
Sensor Reading ◽  
Loss Of Life ◽  
Analog To Digital ◽  
Cloud Server ◽  
User Data ◽  
The Internet Of Things

The incidence of fire and theft is very threatening and causes disruption to people's lifestyles, both due to natural and human factors resulting in loss of life, damage to the environment, loss of property and property, and psychological impacts. The purpose of this study is to create a building security system using Kinect Xbox 360 which can be used to detect fires and loss of valuable objects. The data transmission method uses the Internet of Things (IoT) and skeletal tracking. Skeletal detection uses Arduino Uno which is connected to a fire sensor and Kinect to detect suspicious movements connected to a PC. Kinect uses biometric authentication to automatically enter user data by recognizing objects and detecting skeletons including height, facial features and shoulder length. The ADC (Analog to Digital Converter) value of the fire sensor reading has a range between 200-300. The fire sensor detects the presence of fire through optical data analysis containing ultraviolet, infrared or visual images of fire. The data generated by Kinect by detecting the recognition of the skeleton of the main point of the human body known as the skeleton, where the reading point is authenticated by Kinect from a range of 1.5-3 meters which is declared the optimal measurement, and if a fire occurs, the pump motor will spray water randomly. to extinguish the fire that is connected to the internet via the wifi module. The data displayed is in the form of a graph on the Thingspeak cloud server service. Notification of fire and theft information using the delivery system from input to database

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

scholarly journals Machine Learning for Internet of Things

2019 ◽  
Vol 8 (07) ◽  
pp. 24680-24782
Author(s):  
Manisha Bagri ◽  
Neha Aggarwal
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Support Vector ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Patterns ◽  
New Development ◽  
Share Data

By 2020 around 25-50 billion devices are likely to be connected to the internet. Due to this new development, it gives rise to something called Internet of Things (IoT). The interconnected devices can generate and share data over a network. Machine Learning plays a key role in IoT to handle the vast amount of data. It gives IoT and devices a brain to think, which is often called as intelligence. The data can be feed to machines for learning patterns, based on training the machines can identify to predict for the future. This paper gives a brief explanation of IoT. This paper gives a crisp explanation of machine learning algorithm and its types. However, Support Vector Machine (SVM) is explained in details along with its merits and demerits. An algorithm is also proposed for weather prediction using SVM for IoT.

scholarly journals An Improved Unsupervised Single-Channel Speech Separation Algorithm for Processing Speech Sensor Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dazhi Jiang ◽  
Zhihui He ◽  
Yingqing Lin ◽  
Yifei Chen ◽  
Linyan Xu
Keyword(s):  
Internet Of Things ◽  
Speech Signal ◽  
Single Channel ◽  
Low Noise ◽  
Sensor Data ◽  
The Internet ◽  
Speech Separation ◽  
Adaptive Wavelet ◽  
Enhancement Technique ◽  
The Internet Of Things

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

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