Distributed Remote IoT Sensor Data Monitoring and Control through the ARTIK Cloud

2018 ◽  
Vol 43 (12) ◽  
pp. 2100-2106
Author(s):  
Jum-Han Bae ◽  
Jong Tae Kim
Keyword(s):  
Sensor Data ◽  
Data Monitoring ◽  
Monitoring And Control ◽  
And Control

A Blockchain-Enabled Approach for Online Stream Sensor Data Protection in Cyber-Physical Manufacturing Systems

2021 ◽  
Author(s):  
Zhangyue Shi ◽  
Chenang Liu ◽  
Chen Kan ◽  
Wenmeng Tian ◽  
Yang Chen
Keyword(s):  
Information Technologies ◽  
Manufacturing Systems ◽  
Rapid Development ◽  
Data Access ◽  
Sensor Data ◽  
False Alarms ◽  
Monitoring And Control ◽  
Stream Data ◽  
Physical Security ◽  
And Control

Abstract With the rapid development of the Internet of Things and information technologies, more and more manufacturing systems become cyber-enabled, which significantly improves the flexibility and productivity of manufacturing. Furthermore, a large variety of online sensors are also commonly incorporated in the manufacturing systems for online quality monitoring and control. However, the cyber-enabled environment may pose the collected online stream sensor data under high risks of cyber-physical attacks as well. Specifically, cyber-physical attacks could occur during the manufacturing process to maliciously tamper the sensor data, which could result in false alarms or failures of anomaly detection. In addition, the cyber-physical attacks may also illegally access the collected data without authorization and cause leakage of key information. Therefore, it becomes critical to develop an effective approach to protect online stream data from these attacks so that the cyber-physical security of the manufacturing systems could be assured. To achieve this goal, an integrative blockchain-enabled method, is proposed by leveraging both asymmetry encryption and camouflage techniques. A real-world case study that protects cyber-physical security of collected stream data in additive manufacturing is provided to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time and the risk of unauthorized data access is significantly reduced as well.

scholarly journals Automated data monitoring of MEMS cleanroom parametric requirements

2021 ◽  
Vol 23 (2) ◽  
pp. 701
Author(s):  
Jean M. Capanang ◽  
Jobelle P. Panganiban ◽  
Glenn N. Ortiz ◽  
Mark Joseph B. Enojas
Keyword(s):  
User Interface ◽  
Relative Humidity ◽  
Mechanical Systems ◽  
Data Monitoring ◽  
Monitoring And Control ◽  
Data Logger ◽  
Micro Electro Mechanical Systems ◽  
Particle Count ◽  
Lcd Display ◽  
And Control

<p>Cleanroom parameters such as temperature, relative humidity and particle count are vital in maintaining cleanliness. People and machines working inside the cleanroom are main contributors for the sudden changes of the separameters. Measurements and monitoring of these parameters are therefore necessary to reduce rejects and downtime in the production of micro-electro-mechanical systems (MEMS). This paper presents a method of developmentof an automated data monitoring of MEMS cleanroom parametric requirements. The prototype developed uses DHT11 sensor and Sharp dust sensor for measuring the temperature, humidity and particle count respectively which are displayed in an LCD display. These parameters are recorded through a data logger for analysis and control. Additionally, agraphical user interface was also developed using visual studio for the working personnel and for supervisory monitoring and control. As a result, the possible quality compromise in the production of MEMS is detected when the monitored parameters are beyond the range.</p>

scholarly journals Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 11910
Author(s):  
Dalia Mahmoud ◽  
Marcin Magolon ◽  
Jan Boer ◽  
M.A Elbestawi ◽  
Mohammad Ghayoomi Mohammadi
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
In Situ Monitoring ◽  
Sensor Data ◽  
Learning Approaches ◽  
Monitoring And Control ◽  
In Situ Sensors ◽  
Applications Of Machine Learning ◽  
And Control

One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

scholarly journals Sensor Data Analysis On Monitoring And Control System Of Temperature And Humidity Based On Android In Soybean Seed Storage Room Using Nodemcu

JAICT ◽  
2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Sindung HW Sasono
Keyword(s):  
Control System ◽  
Data Analysis ◽  
Soybean Seed ◽  
Seed Storage ◽  
Sensor Data ◽  
Monitoring And Control ◽  
Storage Room ◽  
Temperature State ◽  
Temperature And Humidity ◽  
And Control

soybean seeds may be damaged during storage time. Temperature and humidity of soybean seed storage room, is one of the external factors of damage to the seed. The system consists of monitoring and control temperature and humidity in some rooms used as soybean seed storage room samples. This study discusses and perform sensor data analysis using several types of temperature and humidity sensors based on Internet of Things. Sensor nodes generate data and processed by a microcontroller NodeMCU ESP8266, and the results data is then transmitted by the Internet network using MQTT broker and stored in the database. Results data is then analyzed to monitor the condition of soybean seed storage room. SHT30 sensor has the most excellent temperature accuracy of 98, 21%. DHT22 sensor has the most excellent moisture accuracy of 95.74%. Data sending to the database has a good level of dataloss category for node 1 is 3.39%, 4.33% for node 2, and 3.22% for node 3. Air conditioner control system using Android can keep the room temperature state in the range of 18-23oC and humidity of 40-60% with an air conditioning remote control setting at 20oC on an area of 36 m2.

scholarly journals WSNs based Oil Well Health Monitoring and Control Using ARM9 Processor

2013 ◽  
Vol 10 (1) ◽  
pp. 1178-1185
Author(s):  
B.Siri Dhatri ◽  
Y.Chalapathi Rao ◽  
Dr.Ch.Santhi Rani
Keyword(s):  
Gas Sensor ◽  
Health Monitoring ◽  
Oil And Gas ◽  
Ph Sensor ◽  
Oil Field ◽  
Sensor Data ◽  
Oil Well ◽  
Monitoring And Control ◽  
Alert Message ◽  
And Control

The existing oil pumping system is a high power consuming process and has incapabilitys of CPUs structural health monitoring. Due to the environmental conditions and remote locations of oil and gas sites, it is expensive to physically visit assets for maintenance and repair. As the demand for oil and gas increases, reducing operating and maintenance costs and increasing reliability, this paper develops a sensor network based monitoring and control system, and improves the level of oil field security, enhance the security checking, and strengthen the management of digitalization and information. The system mainly consists of various sensors like temperature sensor, voltage sensor, current sensor, level sensor, PH sensor and gas sensor. Here we use gas sensor to detect the flammable gas which generally evolves from the oil wells. If any such detection occurs, automatically exhaust fan will switch on to pass away the particles. As like, in case if temperature level is high, cooling fan will trigger to reduce or maintain the particular temperature in the wells. With the help of current and potential transformer we can find out the fluctuations in the pumping section. If the level of oil varies from the indicated level it gives an alert message via voice recorder. To measure the level of humidity, we use PH sensor. All the sensors data is transmitted and monitored in PC using ARM processor. Also we can communicate this sensor data to other PCs using Zigbee technology.

scholarly journals Load Sharing of Transformers for Intelligent Electric Power Management

2019 ◽  
pp. 153-157
Keyword(s):  
Electric Power ◽  
Real Time ◽  
Power Management ◽  
Power Grid ◽  
Load Sharing ◽  
Data Monitoring ◽  
Monitoring And Control ◽  
The Past ◽  
And Control ◽  
Expensive Process

Power management has been one of the focused areas of research from the past few decades. Power blackout is the main problem nowadays and it occurs mainly due to outdated infrastructure used for electricity in industries. In a traditional grid, the consumer load information is obtained manually which is a time-consuming and expensive process. In this paper, a prototype is developed for the real-time off-site data monitoring and control of the consumer loads in the distribution network of the power grid. The designed prototype avoids the tripping of loads by the use of load sharing.

scholarly journals Autonomous surface vessel for search and rescue operation

2021 ◽  
Vol 10 (3) ◽  
pp. 1701-1708
Author(s):  
Hasmah Mansor ◽  
Muhamad Haziq Norhisam ◽  
Zulkifli Zainal Abidin ◽  
Teddy Surya Gunawan
Keyword(s):  
Human Life ◽  
Search And Rescue ◽  
Sensor Data ◽  
Monitoring And Control ◽  
Rescue Operation ◽  
Ground Station ◽  
Global Positioning ◽  
Control Centre ◽  
And Control ◽  
Surface Vessel

Search and rescue operation is performed to save human life, for example during natural disasters, unfortunate incidents on the land, in the deepwater, or lakes. There were incidents happened to the search and rescue crew during the operation although they were well trained. A new method using robotic technology is important to reduce the crew's risk during operations. This research proposed a development of an autonomous surface vessel for search and rescue operations for deepwater applications. The proposed autonomous surface vessel is equipped with a global positioning system (GPS) and underwater sensor to search for the victims, black box, debris, or other evidence on the surface and underwater. The vessel was designed with monitoring and control via radio frequency wireless communication. The autonomous surface vessel prototype was developed and tested successfully with the telemetry at the ground station. The ground station acts as the control centre of the overall system. Results showed the vessel successfully operated autonomously. The operator at the ground station was able to monitor the sensor data and control the vessel's manoeuvre according to the created path. The telemetry coverage to monitor the water surroundings and control the vessel's manoeuvre was around 100 meters.

Research on Warehouse Environment Monitoring and Control System Based on Multi-Sensor Data Fusion

2015 ◽  
Vol 742 ◽  
pp. 640-647 ◽  
Author(s):  
Yan Wei Wang ◽  
Ting Hui Li ◽  
Jin Jie Bi ◽  
Hai Yan Li ◽  
Gui Yan Li
Keyword(s):  
Data Fusion ◽  
Environmental Parameters ◽  
Sensor Data ◽  
Monitoring And Control ◽  
Large Space ◽  
Monitoring And Control System ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Weighted Algorithm ◽  
And Control

Due to the characters of large space and uneven distributing of the temperature and humidity which always exist in the warehouse, the reliability and accuracy of data are influenced when using the wireless sensor network to collect the environmental parameters which are large redundancy and errors. According to the above-mentioned characters, a self-adaptive weighted algorithm based on multi-sensor data fusion was presented. The simulation results show that, the compute of the method is simple, it needs without any prior knowledge of sensor to give the fusion value with least variance, and therefore the proposed method improves the accuracy of measured data.

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