컴퓨터네트워크연구실

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Changwon National University 컴퓨터네트워크연구실

컴퓨터네트워크연구실

창원대학교 컴퓨터네트워크연구실에 오신것을 환영합니다.

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Project
Deadline-Aware Redundant Transmission on Multi-Paths for Real-Time Service

This study addresses the challenge of meeting deadlines in real-time applications such as video streaming and online gaming, in which data block delivery is time sensitive. Traditional loss-recovery solutions such as retransmission are inadequate, particularly in multi-path networks with long round-trip times, resulting in delays and missed deadlines. The proposed deadline-aware redundant transmission (DART) approach proactively transmits redundant packets across multiple paths, considering the next transmission time and one-way delay on each path to meet deadlines with minimal overhead. It also addresses deadlocks caused by insufficient network capacity by prioritizing the transmission of the smallest data block in the queue. DART, which operates independently of existing schedulers, simplifies implementation and reduces disturbances to existing schedulers. Simulation results demonstrate that DART increases the block transmission success rates by 5% to 10%, depending on the network conditions, while requiring minimal overheads. DOI: https://ieeexplore.ieee.org/document/10937038
Project
Improving Sensor Adaptability and Functionality in Cartographer Simultaneous Localization and Mapping

Abstract: This paper aims to address sensor-related challenges in simultaneous localization and mapping (SLAM) systems, specifically within the open-source Google Cartographer project, which implements graph-based SLAM. The primary problem tackled is the adaptability and functionality of SLAM systems in diverse robotic applications. To solve this, we developed a novel SLAM framework that integrates five additional functionalities into the existing Google Cartographer and Robot Operating System (ROS). These innovations include an inertial data generation system and a sensor data preprocessing system to mitigate issues arising from various sensor configurations. Additionally, the framework enhances system utility through real-time 3D topographic mapping, multi-node SLAM capabilities, and elliptical sensor data filtering. The average execution times for sensor data preprocessing and virtual inertial data generation are 0.55 s and 0.15 milliseconds, indicating a low computational overhead. Elliptical filtering has nearly the same execution speed as the existing filtering scheme. DOI: https://doi.org/10.3390/s25061808
Project
Improving the Quality of Experience of Video Streaming Through a Buffer-Based Adaptive Bitrate Algorithm and Gated Recurrent Unit-Based Network Bandwidth Prediction

https://doi.org/10.3390/app142210490

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