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http://dx.doi.org/10.3745/KTCCS.2021.10.10.277

Apriori Based Big Data Processing System for Improve Sensor Data Throughput in IoT Environments  

Song, Jin Su (숭실대학교 컴퓨터학과)
Kim, Soo Jin (숭실대학교 컴퓨터학과)
Shin, Young Tae (숭실대학교 컴퓨터학부)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.10, no.10, 2021 , pp. 277-284 More about this Journal
Abstract
Recently, the smart home environment is expected to be a platform that collects, integrates, and utilizes various data through convergence with wireless information and communication technology. In fact, the number of smart devices with various sensors is increasing inside smart homes. The amount of data that needs to be processed by the increased number of smart devices is also increasing, and big data processing systems are actively being introduced to handle it effectively. However, traditional big data processing systems have all requests directed to cluster drivers before they are allocated to distributed nodes, leading to reduced cluster-wide performance sharing as cluster drivers managing segmentation tasks become bottlenecks. In particular, there is a greater delay rate on smart home devices that constantly request small data processing. Thus, in this paper, we design a Apriori-based big data system for effective data processing in smart home environments where frequent requests occur at the same time. According to the performance evaluation results of the proposed system, the data processing time was reduced by up to 38.6% from at least 19.2% compared to the existing system. The reason for this result is related to the type of data being measured. Because the amount of data collected in a smart home environment is large, the use of cache servers plays a major role in data processing, and association analysis with Apriori algorithms stores highly relevant sensor data in the cache.
Keywords
IoT; Smart home; Apache Spark; Redis; Association Algorithm;
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