IoT-generated data are characterized by its continuous generation, large amount, and unstructured format. Existing relational database technologies are inadequate to handle such IoT-generated data due to the limited processing speed and the significant storage-expansion cost. Thus, big data processing technologies, which are normally based on distributed file systems, distributed database management, and parallel processing technologies, have arisen as a core technology to implement IoTgenerated data repositories. In this study, we propose a sensorintegrated RFID data repository-implementation model using MongoDB, the most popular big data-savvy document-oriented database system now. Firstly, we devise a data repository schema that can effectively integrate and store the heterogeneous IoT data sources such as RFID, sensor, and GPS, by extending the event data types in Electronic Product Code Information Services (EPCIS) standard, a de facto standard for the information exchange services for RFID-based traceability. Secondly, we propose an effective shard key to maximize query speed and uniform data distribution over data servers. Lastly, through a series of experiments measuring query speed and the level of data distribution, we show that the proposed design strategy, which is based on horizontal data partitioning and a compound shard key, is effective and efficient for the IoT-generated RFID/sensor big data.
The prediction of internal defects of metal casting immediately after the casting process saves unnecessary time and money by reducing the amount of inputs into the next stage, such as the machining process, and enables flexible scheduling. Cyber-physical production systems (CPPS) perfectly fulfill the aforementioned requirements. This study deals with the implementation of CPPS in a real factory to predict the quality of metal casting and operation control. First, a CPPS architecture framework for quality prediction and operation control in metal-casting production was designed. The framework describes collaboration among internet of things (IoT), artificial intelligence, simulations, manufacturing execution systems, and advanced planning and scheduling systems. Subsequently, the implementation of the CPPS in actual plants is described. Temperature is a major factor that affects casting quality, and thus, temperature sensors and IoT communication devices were attached to casting machines. The well-known NoSQL database, HBase and the high-speed processing/analysis tool, Spark, are used for IoT repository and data pre-processing, respectively. Many machine learning algorithms such as decision tree, random forest, artificial neural network, and support vector machine were used for quality prediction and compared with R software. Finally, the operation of the entire system is demonstrated through a CPPS dashboard. In an era in which most CPPS-related studies are conducted on high-level abstract models, this study describes more specific architectural frameworks, use cases, usable software, and analytical methodologies. In addition, this study verifies the usefulness of CPPS by estimating quantitative effects. This is expected to contribute to the proliferation of CPPS in the industry.
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. To ensure that all processes within the manufacturing industry are functioning smoothly, the big data processing is needed. Thus, in this study an open source-based real-time data processing (OSRDP) architecture framework was proposed. OSRDP architecture framework consists of several open sources technologies, including Apache Kafka, Apache Storm and NoSQL MongoDB that are effective and cost efficient for real-time data processing. Several experiments and impact analysis for manufacturing sustainability are provided. The results showed that the proposed system is capable of processing a massive sensor data efficiently when the number of sensors data and devices increases. In addition, the data mining based on Random Forest is presented to predict the quality of products given the sensor data as the input. The Random Forest successfully classifies the defect and non-defect products, and generates high accuracy compared to other data mining algorithms. This study is expected to support the management in their decision-making for product quality inspection and support manufacturing sustainability.
Sustainability relies on the environmental, social and economical systems: the three pillars of sustainability. The social sustainability mostly advocates the people's welfare, health, safety, and quality of life. In the agricultural food industry, the aspects of social sustainability, such as consumer health and safety have gained substantial attention due to the frequent cases of food-borne diseases. The food-borne diseases due to the food degradation, chemical contamination and adulteration of food products pose a serious threat to the consumer's health, safety, and quality of life. To ensure the consumer's health and safety, it is essential to develop an efficient system which can address these critical social issues in the food distribution networks. This research proposes an ePedigree (electronic pedigree) traceability system based on the integration of RFID and sensor technology for real-time monitoring of the agricultural food to prevent the distribution of hazardous and adulterated food products. The different aspects regarding implementation of the proposed system in food chains are analyzed and a feasible integrated solution is proposed. The performance of the proposed system is evaluated and finally, a comprehensive analysis of the proposed ePedigree system's impact on the social sustainability in terms of consumer health and safety is presented.
Abstract:A car sharing service has been highlighted as a new urban transport alternative for an environmentally friendly economy. As the demand for the service from customers increases, car sharing operators need to introduce a new service such as a one-way option that will allow customers to return the car to different stations. Due to the complexity of the one-way system, it needs to be managed and optimized for real cases. This paper focuses on developing a simulation model in order to help operators evaluate the performance of the one-way service. In addition, this research demonstrates a strategy for an open one-way service that can increase revenue and customer satisfaction. A real case dataset is used for investigation to find the best result from the simulation. The result showed that the total number of cars, number of one-way reservations and station size have an impact on one-way performance. Thus, company profit and customer satisfaction can be maximized by optimizing these factors.
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