Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain

Alfian, Ganjar; Syafrudin, Muhammad; Rhee, Jongtae

doi:10.3390/su9112073

Cited by 57 publications

(40 citation statements)

References 36 publications

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“…An embedding scheme-based sensor data repository is commonly utilized in NoSQL MongoDB databases to improve performance [ 67 ]. We found that the embedding scheme is appropriate for a large sensor data repository, which requires fast read and write performance [ 33 ]. Thus, in our study, we utilized an embedding scheme-based sensor data repository.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, MongoDB has been proven to be effective for storing data from the supply chain, geographic information systems and manufacturing. Alfian et al utilized MongoDB to store IoT-generated sensor data for monitoring a perishable food supply chain [ 33 ]. In the study, MongoDB was capable of processing a huge amount of input/output sensor data efficiently when the number of sensors and clients increased.…”

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%

See 2 more Smart Citations

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

Self Cite

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: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

262

103

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show abstract

“…The DBSCAN-based outlier detection also showed significant results on detecting the outlier sensor data. Alfian et al proposed a real-time monitoring system that is based on smartphone sensors for perishable food [26]. As outliers arise in sensor data due to inadequacies in sensing devices and network communication glitches, Alfian et al used outlier detection that is based on DBSCAN to refine the outlier data.…”

Section: Outlier Detection Methodsmentioning

confidence: 99%

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

et al. 2018

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As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.

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“…Figure 8a shows prototype real-time monitoring system architecture to filter false positives, ensuring only products actually moved through the gate are sent to the representational state transfer application programming interface (REST API), for presentation in web dashboard(s) and/or database storage. We used the MongoDB V3.4.9 database since it can efficiently store continuously-generated sensor/RFID data from manufacturing [56][57][58], healthcare [59], and supply chain [60]. The real-time monitoring system used java programming language V1.8.0 to receive tag information from the readers, filter false positives using trained RF, and send products moved through the gate information to the server via REST API.…”

Section: Management Implicationsmentioning

confidence: 99%

False Positive RFID Detection Using Classification Models

et al. 2019

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Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

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Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain

Cited by 57 publications

References 36 publications

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

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

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

False Positive RFID Detection Using Classification Models

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