scholarly journals Sistem Kontrol Kandang Ayam Closed House Berbasis Internet Of Things

2021 ◽  
Vol 9 (3) ◽  
pp. 79
Author(s):  
Jumaidi Jamal ◽  
Thamrin Thamrin
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Broiler Chicken ◽  
Smartphone Application ◽  
Sensor Data ◽  
The Internet ◽  
Produce Water ◽  
Temperature And Humidity ◽  
System Output ◽  
Incandescent Lamps

Penelitian ini bertujuan membuat sistem kontrol suhu, kelembaban, dan kadar kualitas udara pada kandang ayam broiler dengan sistem closed house yang berbasis teknologi Internet of Things (IoT). Sistem ini dirancang untuk mengontrol suhu dan kelembaban kandang ayam broiler yang dapat dimonitoring secara real time melalui sebuah platform digital pada smartphone. Metode pembuatan alat dimulai dengan merancang diagram blok, memilih spesifikasi komponen berdasarkan diagram blok, pembuatan diagram alir, mengintegrasikan hardware, dan pemograman sistem. Output sistem dilengkapi dengan komponen yang berfungsi untuk mengendalikan kondisi di dalam kandang ayam broiler seperti lampu pijar, kipas, dan pompa air. Lampu pijar digunakan sebagai pemanas ruangan yang akan meningkatkan suhu di dalam kandang. Kipas digunakan untuk menjaga sirkulasi udara di dalam kandang. Pompa air digunakan untuk mengalirkan air pada jaring-jaring agar menghasilkan uap air untuk melembabkan udara di dalam kandang. Sistem dibuat berbasis Internet of Things (IoT), artinya sistem terhubung ke internet menggunakan modul ESP8266 dan mikrokontroler Arduino Nano. Sistem mengakses data sensor dari web Firebase yang kemudian ditampilkan dalam bentuk angka pada sebuah aplikasi smartphone sebagai sarana untuk memonitoring kandang ayam. Hasil pengujian menunjukkan bahwa sistem yang telah dibuat mampu menjaga suhu dan kelembaban di dalam kandang ayam sesuai dengan kondisi ideal ayam broiler.Kata kunci : Closed House, DHT22, MQ-135, NodeMCU ESP8266, Firebase.  This study aims to create a control system for temperature, humidity, and air quality levels in broiler chicken coops with a closed house system based on Internet of Things (IoT) technology. This system is designed to control the temperature and humidity of broiler chicken coops which can be monitored in real time via a digital platform on a smartphone. The method of making the tool begins with designing block diagrams, selecting component specifications based on block diagrams, making flow diagrams, integrating hardware, and system programming. The system output is equipped with components that function to control conditions in the broiler chicken coop such as incandescent lamps, fans, and water pumps. Incandescent lamps are used as space heaters which will increase the temperature in the cage. Fans are used to maintain air circulation in the cage. A water pump is used to circulate water in the nets to produce water vapor to humidify the air in the cage. The system is made based on the Internet of Things (IoT), meaning that the system is connected to the internet using the ESP8266 module and the Arduino Nano microcontroller. The system accesses sensor data from the Firebase web which is then displayed in the form of numbers on a smartphone application as a means to monitor chicken coops. The test results show that the system that has been made is able to maintain the temperature and humidity in the chicken coop in accordance with the ideal conditions of broiler chickens.Keywords: Closed House, DHT22, MQ-135, NodeMCU ESP8266, Firebase.

scholarly journals An Intelligent Weather Monitoring System using Internet of Things

2019 ◽  
Vol 8 (4) ◽  
pp. 4531-4536
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Weather Forecast ◽  
Sensor Data ◽  
Graphical Form ◽  
The Internet ◽  
Time Data ◽  
Data Set ◽  
The People

With a drastic change in climate continuously it is very harmful to the people who are living in the disaster-prone areas. In some areas the people are not warned for the consequences of coming specifically in their areas, they are told about the average temperature and humidity of the city while the humidity and temperature vary at different altitude and changes at short distances. The system is a very cost-effective and efficient method for controlling and monitoring the weather, and it sends the data to the cloud so that it can be visible anywhere through internet. The temperature, humidity, and pressure play a significant role in different fields like agricultural, industrial and Logistical Field. Weather forecast is necessary for the growth and development of these industries. The Internet of Things (IoT) is the technology used in developing the proposed system, which is an efficient and advanced method for connecting the sensors to the cloud which can store real-time sensor data and connect the entire world of things in a network. Here things might be anything like electronic gadgets, sensors, and automotive electronic equipment. The system deals with controlling and monitoring the environmental conditions like Temperature, Pressure, Smoke, Relative humidity level and various other gases with sensors and sends the information to the cloud and then plot the sensor data in graphical form. An Intelligent prediction is to be done using machine learning. Machine learning is a branch of Artificial Intelligence (AI) which is a compelling method of Analyzing and predicting the given data-set. The data collected will be analyzed continuously. The real-time data which has to be sent through the sensor can be accessible throughout the world using the internet

scholarly journals Distributed Immersive Participation as Crowd-Sensing in Culture Events

2014 ◽  
Vol 7 (2) ◽  
Author(s):  
Theo Kanter ◽  
Rahim Rahmani ◽  
Jamie Walters ◽  
Willmar Sauter
Keyword(s):  
Internet Of Things ◽  
Distributed Computing ◽  
Real Time ◽  
Social Innovation ◽  
The Internet ◽  
New Paradigm ◽  
Content Creation ◽  
Crowd Sensing ◽  
Information Objects

This article investigates new forms for creating and enabling massive and scalable participatory immersive experiences in live cultural events, characterized by processes, involving pervasive objects, places and people. The multi-disciplinary research outlines a new paradigm for collaborative creation and participation towards technological and social innovation, tapping into crowd-sensing. The approach promotes user-driven content-creation and offsets economic models thereby rewarding creators and performers. In response to these challenges, we propose a framework for bringing about massive and real-time presence and awareness on the Internet through an Internet-of-Things infrastructure to connect artifacts, performers, participants and places. Equally importantly, we enable the in-situ creation of collaborative experiences building on relevant existing and stored content, based on decisions leveraging multi-criteria clustering and proximity of pervasive information, objects, people and places. Finally, we investigate some new ways for immersive experiences via distributed computing but pointing forward to the necessity to do more with regard to collaborative creation.

scholarly journals AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Yu ◽  
Chun Shan ◽  
Jilong Bian ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Computing Environment ◽  
The Real ◽  
Edge Node ◽  
Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

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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