Research and Application of the IOT Gateway Based on the Real-Time Specification for Java

Haikun, Teng; Liu, Xinsheng; Lv, Haitao; Yue, Xiao‐Guang

doi:10.3991/ijoe.v14i03.8419

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Several IoT applications (Bali et al, 2018) have been studied in present researches. It is observed that the IoT real-time specification (Haikun et al, 2018) is found challenging. This research is making use of IoT with surveillance cameras (Alexandrie et al, 2017) to minimize crime.…”

Section: Health Monitoringmentioning

confidence: 99%

Improving Performance During Camera Surveillance by Integration of Edge Detection in IoT System

Beniwal¹,

Saini²,

Garg³

et al. 2021

International Journal of E-Health and Medical Communications

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This paper is proposing an IoT-based camera surveillance system. The objective of research is to detect suspicious activities by camera automatically and take decision by comparing current frame to previous frame. Major motivation behind research work is to enhance the performance of IoT-based system by integration of edge detection mechanism. Research is making use of numerous cameras, canny edge detection-based compression module, picture database, picture comparator. Canny edge detection has been used to minimize size of graphical content to enhancing the performance system. Simulation of output of this work is made in MATLAB simulation tool. Moreover, MATLAB has been used to give comparative analysis among IoT-based camera surveillance system and traditional system. Such system requires less space, and it takes less time to inform regarding any suspicious activities.

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Section: Health Monitoringmentioning

confidence: 99%

Improving Performance During Camera Surveillance by Integration of Edge Detection in IoT System

Beniwal¹,

Saini²,

Garg³

et al. 2021

International Journal of E-Health and Medical Communications

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“…It interacts directly with the end user by providing services and determining a set of protocols for message passing at the application level (Yassein et al, 2016). According to Haikun et al (2018), connection to IoT management system platform by users is achieved using browser or client software through Ethernet/3G network.…”

Section: Iot Application Layermentioning

confidence: 99%

“…Due to the diversity of the IoT, it is often tied with heterogeneous protocols to support Machine-to-Machine (M2M) networks and their applications. These networks can be in the form of a private, public or hybrid models and are built to support the communication requirements for latency, bandwidth or security (Haikun et al, 2018). According to Chihana et al (2018), the network layer is responsible for ensuring that the transmission of transducer data to the next layer is achieved in a scalable and flexible manner.…”

Section: Iot Network Layermentioning

confidence: 99%

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

Chiwamba¹,

Phiri²,

Nkunika³

et al. 2019

Journal of Computer Science

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Maize is the main food crop that meets the nutritional needs of both humans and livestock in the sub-Saharan African region. Maize crop has in the recent past been threatened by the fall armyworm (Spodoptera frugiperda, J.E Smith) which has caused considerable maize yield losses in the region. Controlling this pest requires knowledge on the time, location and extent of infestation. In addition, the insect pest's abundance and environmental conditions should be predicted as early as possible for integrated pest management to be effective. Consequently, a fall armyworm pheromone trap was deployed as a monitoring tool in the present study. The trap inspection is currently carried out manually every week. The purpose of this paper is to bring automation to the trap. We modify the trap and integrate Internet of Things technologies which include a Raspberry Pi 3 Model B+ microcomputer , Atmel 8-bit AVR microcontroller, 3G cellular modem and various sensors powered with an off-grid solar photovoltaic system to capture real-time fall armyworm moth images, environmental conditions and provide real-time indications of the pest occurrences. The environmental conditions include Geographical Positioning System coordinates, temperature, humidity, wind speed and direction. The captured images together with environmental conditions are uploaded to the cloud server where the image is classified instantly using Google's pre-trained InceptionV3 Machine Learning model. Intended users view captured data including prediction accuracy via a web application. Once this smart technology is adopted, the labour-intensive task of monitoring will reduce while stakeholders shall be provided with a near real-time insight into the FAW situation in the field therefore enabling pro-activeness in their management of such a devastating pest.

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“…The welding between relay and Arduino Mega 2560 R3 board by arranging jumper wires as shown in Table 9. To develop a smart hydro-organic farming systems in greenhouse and applications, we have developed an Arduino IDE program to enable connected sensor devices to process various measurements and then send the values of different environmental factors to be saved on cloud database [30]. Figure 16 illustrates how to monitor and manage the smart hydro-organic farming system in a greenhouse for Khok Nong Na.…”

mentioning

confidence: 99%

IoT-Based Automatic Hydro-Organic Smart Farming System in Greenhouse with Solar Panels for Khok Nong Na

Soodtoetong,

Channgam,

Gedkhaw

2023

Int. J. Onl. Eng.

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In this research, we propose IoT-Based Automatic Hydro-Organic Smart Farming System (AHOSFS) in Greenhouse with Solar Panels for Khok Nong Na, design and development with Internet of Things and solar energy. Data is collected from sensors for the following factors that affect plant growth: humidity, temperature, light, pH, EC, air quality, water temperature, and water level. The Firebase cloud-based application was used to collect all the data, and it enables farmers to monitor and control the system using their smartphones. The results showed AHOSFS can mix water and nutrient solution, which measure pH of 6.61 and EC of 1.23, control water level and mix nutrient solution in appropriate quantities. Green oak can thrive at pH levels between 6.0 and 7.0, EC levels between 1.1 and 1.7, and humidity level between 75 and 85. The sensor measured temperature value between 18 and 25 °C, which was adequate for the growth of green oak. Sensor also measured water temperature between 25 and 28 °C. The comparative value of cultivation in a hydro-organic system deployed between AB solution, organic fertilizer Formula 1 and Formula 2 found that organic fertilizer formula 1 has an effective and approximate AB solution at 81.64%. The fertilizer formula 2 has AB solution at 89.72%, which was more secure at a comparable level.

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Research and Application of the IOT Gateway Based on the Real-Time Specification for Java

Cited by 5 publications

References 4 publications

Improving Performance During Camera Surveillance by Integration of Edge Detection in IoT System

Improving Performance During Camera Surveillance by Integration of Edge Detection in IoT System

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

IoT-Based Automatic Hydro-Organic Smart Farming System in Greenhouse with Solar Panels for Khok Nong Na

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