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

Since customer attention is increasing due to growing customer health awareness, it is important for the perishable food supply chain to monitor food quality and safety. This study proposes a real-time monitoring system that utilizes smartphone-based sensors and a big data platform. Firstly, we deve...

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Main Authors: Ganjar Alfian, Muhammad Syafrudin, Jongtae Rhee
Format: Article
Language:English
Published: MDPI AG 2017-11-01
Series:Sustainability
Subjects:
IoT
Online Access:https://www.mdpi.com/2071-1050/9/11/2073
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spelling doaj-1452cf2c91894b399f5f06e16f12ff832020-11-24T21:43:36ZengMDPI AGSustainability2071-10502017-11-01911207310.3390/su9112073su9112073Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply ChainGanjar Alfian0Muhammad Syafrudin1Jongtae Rhee2u-SCM Research Center, Nano Information Technology Academy, Dongguk University, Seoul 100715, KoreaDepartment of Industrial and Systems Engineering, Dongguk University, Seoul 100715, KoreaDepartment of Industrial and Systems Engineering, Dongguk University, Seoul 100715, KoreaSince customer attention is increasing due to growing customer health awareness, it is important for the perishable food supply chain to monitor food quality and safety. This study proposes a real-time monitoring system that utilizes smartphone-based sensors and a big data platform. Firstly, we develop a smartphone-based sensor to gather temperature, humidity, GPS, and image data. The IoT-generated sensor on the smartphone has characteristics such as a large amount of storage, an unstructured format, and continuous data generation. Thus, in this study, we propose an effective big data platform design to handle IoT-generated sensor data. Furthermore, the abnormal sensor data generated by failed sensors is called outliers and may arise in real cases. The proposed system utilizes outlier detection based on statistical and clustering approaches to filter out the outlier data. The proposed system was evaluated for system and gateway performance and tested on the kimchi supply chain in Korea. The results showed that the proposed system is capable of processing a massive input/output of sensor data efficiently when the number of sensors and clients increases. The current commercial smartphones are sufficiently capable of combining their normal operations with simultaneous performance as gateways for transmitting sensor data to the server. In addition, the outlier detection based on the 3-sigma and DBSCAN were used to successfully detect/classify outlier data as separate from normal sensor data. This study is expected to help those who are responsible for developing the real-time monitoring system and implementing critical strategies related to the perishable supply chain.https://www.mdpi.com/2071-1050/9/11/2073IoTsensorbig dataoutlier detectionperishable supply chain
collection DOAJ
language English
format Article
sources DOAJ
author Ganjar Alfian
Muhammad Syafrudin
Jongtae Rhee
spellingShingle Ganjar Alfian
Muhammad Syafrudin
Jongtae Rhee
Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain
Sustainability
IoT
sensor
big data
outlier detection
perishable supply chain
author_facet Ganjar Alfian
Muhammad Syafrudin
Jongtae Rhee
author_sort Ganjar Alfian
title Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain
title_short Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain
title_full Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain
title_fullStr Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain
title_full_unstemmed Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain
title_sort real-time monitoring system using smartphone-based sensors and nosql database for perishable supply chain
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2017-11-01
description Since customer attention is increasing due to growing customer health awareness, it is important for the perishable food supply chain to monitor food quality and safety. This study proposes a real-time monitoring system that utilizes smartphone-based sensors and a big data platform. Firstly, we develop a smartphone-based sensor to gather temperature, humidity, GPS, and image data. The IoT-generated sensor on the smartphone has characteristics such as a large amount of storage, an unstructured format, and continuous data generation. Thus, in this study, we propose an effective big data platform design to handle IoT-generated sensor data. Furthermore, the abnormal sensor data generated by failed sensors is called outliers and may arise in real cases. The proposed system utilizes outlier detection based on statistical and clustering approaches to filter out the outlier data. The proposed system was evaluated for system and gateway performance and tested on the kimchi supply chain in Korea. The results showed that the proposed system is capable of processing a massive input/output of sensor data efficiently when the number of sensors and clients increases. The current commercial smartphones are sufficiently capable of combining their normal operations with simultaneous performance as gateways for transmitting sensor data to the server. In addition, the outlier detection based on the 3-sigma and DBSCAN were used to successfully detect/classify outlier data as separate from normal sensor data. This study is expected to help those who are responsible for developing the real-time monitoring system and implementing critical strategies related to the perishable supply chain.
topic IoT
sensor
big data
outlier detection
perishable supply chain
url https://www.mdpi.com/2071-1050/9/11/2073
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AT jongtaerhee realtimemonitoringsystemusingsmartphonebasedsensorsandnosqldatabaseforperishablesupplychain
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