10 posts tagged with "real-time monitoring"

Abstract

Current technology such as Bluetooth Low Energy (BLE) provides an efficient way for Real-Time Location System (RTLS). This study proposes a BLE-based Real-Time Location System that utilizes Smartphone and NoSQL database as gateway and data storage respectively. Firstly, we develop a smartphone-based tracking app to gather the location of employees. Secondly, the generated sensor data from gateway is then stored into NoSQL MongoDB. The proposed system was tested for monitoring the movement of employees in the workplace. The results showed that commercial versions of the BLE-based device and the proposed system are sufficiently efficient for RTLS. Furthermore, proposed system is capable of processing a massive input/output of sensor data efficiently when the number of BLE-based devices and users increases.

Published in: BDIOT 2018 Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things, ACM New York, NY, USA ©2018
DOI: 10.1145/3289430.3289470

Abstract

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

Published in: Sensors
DOI: 10.3390/s18092946

Abstract

Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.

Published in: Sensors
DOI: 10.3390/s18072183

Abstract

Now days, customer’s health awareness is of extreme significance. Food can become contaminated at any point during production, distribution and preparation. Therefore, it is of key importance for the perishable food supply chain to monitor the food quality and safety. Traceability system offers complete food information and therefore, it guarantees food quality and safety. The current study postulates a low cost IoT-based traceability system that utilized RFID and smartphone-based sensors. The RFID handheld reader based on smartphone is utilized to track and trace product information. In addition the smartphone-based sensor is used to measure temperature, humidity, and location (based on GPS sensor) during storage and transportation. The proposed system was verified for kimchi supply chain in Korea, and revealed significant benefits to managers as well as customers by providing the real-time location as well as complete temperature and humidity history. The results displayed that compared to the traditional methods, the proposed system is capable of tracking products as well as processing an immense input of sensor data efficiently and effectively.

Abstract

To make manufacturers more competitive, there is a need to integrate advanced computing and cyber-physical systems to take advantage of the current technologies. With the advent of smart sensors such as IoT technologies (1), collecting data has become a simple task, but the question remains if these devices or data provide the right information for the right purpose at the right time. Data is not useful unless it is processed in a way that provides context and meaning that can be understood by the right personnel. Just connecting sensors to a machine or connecting a machine to another machine will not give users the insights needed to make better decisions. Thus, in this paper we proposed the real time monitoring system that utilized machine learning algorithm to predict the quality of product based on sensor data that was gathered by IoT device and showed the result in real time.

Published in: KSMTE Annual Autumn Conference 2017
Link: http://www.dbpia.co.kr/Journal/ArticleDetail/NODE07285510

Abstract

Smart factory is quickly becoming the new reality in the market, and every innovative manufacturer must embrace it to stay competitive.

Abstract

Currently, the manufacturing industry is experiencing a data-driven revolution. There are multiple processes in the manufacturing industry and will eventually generate a large amount of data. Collecting, analyzing and storing a large amount of data are one of key elements of the smart manufacturing industry.