A Spatio-Temporal Enhanced Metadata Model for Interdisciplinary Instant Point Observations in Smart Cities

Chen, Nengcheng; Liu, Yingbing; Li, Jia; Chen, Zeqiang

doi:10.3390/ijgi6020050

Cited by 5 publications

(2 citation statements)

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“…The results showed that MongoDB was adequate in terms of parallel query and resource consumption (i.e., CPU, memory, network). Chen et al proposed MongoSOS, a sensor observation service based on MongoDB, for handling spatiotemporal data [ 35 ]. The proposed system was capable of handling read and write access for navigation and positioning data in a millisecond and the performance improved by around two percent compared with the traditional model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Big data analysis has led to significant improvements in the manufacturing industry, such as reducing energy consumption [ 17 ], improving production scheduling and logistics planning [ 18 ], mitigating social risks [ 19 ], and facilitating better decision making [ 20 ]. Previous studies have shown significant benefits from several big data technologies in processing and storing large volumes of data quickly, such as with the application of Apache Kafka [ 21 , 22 , 23 , 24 , 25 , 26 ], Apache Storm [ 27 , 28 , 29 , 30 , 31 ], and NoSQL MongoDB [ 32 , 33 , 34 , 35 , 36 , 37 ]. Previous studies showed significant advantages from the integration of big data technologies such as reducing the processing time for home automation systems [ 38 ], providing effective and efficient solutions for processing IoT-generated data for smart cities [ 39 ], and handling large amounts of smart environmental data in real-time [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

262

103

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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.

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Section: Literature Reviewmentioning

confidence: 99%