• Title/Summary/Keyword: Throughput Evaluation

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Apriori Based Big Data Processing System for Improve Sensor Data Throughput in IoT Environments (IoT 환경에서 센서 데이터 처리율 향상을 위한 Apriori 기반 빅데이터 처리 시스템)

  • Song, Jin Su;Kim, Soo Jin;Shin, Young Tae
    • KIPS Transactions on Computer and Communication Systems
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    • v.10 no.10
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    • pp.277-284
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    • 2021
  • 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.

A New Dual Connective Network Resource Allocation Scheme Using Two Bargaining Solution (이중 협상 해법을 이용한 새로운 다중 접속 네트워크에서 자원 할당 기법)

  • Chon, Woo Sun;Kim, Sung Wook
    • KIPS Transactions on Computer and Communication Systems
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    • v.10 no.8
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    • pp.215-222
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    • 2021
  • In order to alleviate the limited resource problem and interference problem in cellular networks, the dual connectivity technology has been introduced with the cooperation of small cell base stations. In this paper, we design a new efficient and fair resource allocation scheme for the dual connectivity technology. Based on two different bargaining solutions - Generalizing Tempered Aspiration bargaining solution and Gupta and Livne bargaining solution, we develop a two-stage radio resource allocation method. At the first stage, radio resource is divided into two groups, such as real-time and non-real-time data services, by using the Generalizing Tempered Aspiration bargaining solution. At the second stage, the minimum request processing speeds for users in both groups are guaranteed by using the Gupta and Livne bargaining solution. These two-step approach can allocate the 5G radio resource sequentially while maximizing the network system performance. Finally, the performance evaluation confirms that the proposed scheme can get a better performance than other existing protocols in terms of overall system throughput, fairness, and communication failure rate according to an increase in service requests.

Analysis of Remote Driving Simulation Performance for Low-speed Mobile Robot under V2N Network Delay Environment (V2N 네트워크 지연 환경에서 저속 이동 로봇 원격주행 모의실험을 통한 성능 분석)

  • Song, Yooseung;Min, Kyoung-wook;Choi, Jeong Dan
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.21 no.3
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    • pp.18-29
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    • 2022
  • Recently, cooperative intelligent transport systems (C-ITS) testbeds have been deployed in great numbers, and advanced autonomous driving research using V2X communication technology has been conducted actively worldwide. In particular, the broadcasting services in their beginning days, giving warning messages, basic safety messages, traffic information, etc., gradually developed into advanced network services, such as platooning, remote driving, and sensor sharing, that need to perform real-time. In addition, technologies improving these advanced network services' throughput and latency are being developed on many fronts to support these services. Notably, this research analyzed the network latency requirements of the advanced network services to develop a remote driving service for the droid type low-speed robot based on the 3GPP C-V2X communication technology. Subsequently, this remote driving service's performance was evaluated using system modeling (that included the operator behavior) and simulation. This evaluation showed that a respective core and access network latency of less than 30 ms was required to meet more than 90 % of the remote driving service's performance requirements under the given test conditions.

Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model (기계 학습 모델을 통해 XGBoost 기법을 활용한 부산 컨테이너 물동량 예측)

  • Nguyen Thi Phuong Thanh;Gyu Sung Cho
    • Journal of Internet of Things and Convergence
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    • v.10 no.1
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    • pp.39-45
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    • 2024
  • Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.