## Literature Review
### Big Data Processing
A previous study compared the performance of MongoDB and Oracle with insert, update, and delete tests [32]. MongoDB outperforms oracle in all tests. In addition, MongoDB has been proven to be effective for storing data from the supply chain, geographic information systems and manufacturing. [PERSON] 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. In addition, MongoDB outperformed MySQL in read and write tests. [PERSON] et al. conducted a comparative study among six popular databases (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling a variety of geospatial data [34]. The results showed that MongoDB was adequate in terms of parallel query and resource consumption (i.e., CPU, memory, network). [PERSON] et al. proposed MongoOS, 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. [PERSON] et al. proposed a big data processing system based on Apache Spark and MongoDB to identify profitable areas from large amounts of taxi trip data [36]. The experimental results showed that the proposed system was scalable and efficient enough in processing profitable-area queries from huge amounts of big taxi trip data. Finally, [PERSON] et al. proposed a flexible data schema based on NoSQL MongoDB for the virtualization of manufacturing machines [37]. The proposed system was evaluated against several query statements. The results showed that MongoDB can accommodate any type of machine data and could easily be implemented across a variety of machines on the factory floor. Previous studies have shown a significant impact from the integration of several big data technologies. [PERSON] et al. proposed a scalable framework for home automation in smart cities [38]. The proposed framework utilized Apache Kafka as a message broker to handle incoming IoT data and MongoDB to store the sensor data. The proposed system was able to reduce the processing time when the amount of data and nodes increased. [PERSON] et al. proposed a smart city system using Apache Kafka and Apache Storm to handle and process IoT-generated data in real-time [39]. Experimental results showed that the proposed system was capable of effectively and efficiently processing the IoT-generated data in real-time. [PERSON] et al. proposed a management system for smart environments using big data technologies [40]. The proposed system utilized Apache Storm to process the data in real-time and MongoDB to store huge amounts of sensor data. A case study on smart homes was performed, and the results showed that the proposed system was able to manage large amounts of smart environmental data in real-time. [PERSON] et al. proposed an integration of Apache Kafka, Apache Storm, and MongoDB for processing streaming spatiotemporal data [41]. The proposed system was tested using the Taiyuan BeiDou bus location data. The proposed system was capable of processing large amounts of sensor data per second and was around three times faster than the traditional model. Finally, [PERSON] et al. proposed an open source-based real-time data processing system consisting of Apache Kafka, Apache Storm, and MongoDB [42]. The proposed system was implemented to monitor the injection molding process in real-time. The proposed system was capable of processing a massive amount of sensor data efficiently when the amount of data and the number of devices increased. Integration of Apache Kafka, Apache Storm, and MongoDB can be used for big data processing to handle manufacturing sensor data.