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      다중지역 배전반 모니터링을 위한 저전력 장거리 무선통신의 end-to-end 네트워크 성능 분석 = End-to-End Network Performance Analysis of Low-Power Long-Range Wireless Communication for Multi-Region Power Distribution Panel Monitoring

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      https://www.riss.kr/link?id=A109493262

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      다국어 초록 (Multilingual Abstract)

      This study systematically analyzes the end-to-end network performance of Low Power Wide Area Network (LPWAN) technology. The research system was constructed using IoT devices with embedded LoRa communication modules, and a comprehensive network model was implemented based on the OMNeT++ simulator to evaluate performance under various scenarios. The study conducted and analyzed detailed experiments including RSSI value histogram analysis, variations in spreading factor and transmission power, and energy consumption patterns of end devices. Results demonstrated that automatic adjustment of spreading factor and transmission power optimizes network performance and energy efficiency. Furthermore, the study identified a significant correlation between energy consumption patterns and the dynamic changes in spreading factor and transmission power. These findings demonstrate that the adaptive data rate(ADR) mechanism in LoRa networks can effectively optimize network performance and energy efficiency. The analysis results are expected to provide important guidelines for future design and optimization of LoRa networks.
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      This study systematically analyzes the end-to-end network performance of Low Power Wide Area Network (LPWAN) technology. The research system was constructed using IoT devices with embedded LoRa communication modules, and a comprehensive network model ...

      This study systematically analyzes the end-to-end network performance of Low Power Wide Area Network (LPWAN) technology. The research system was constructed using IoT devices with embedded LoRa communication modules, and a comprehensive network model was implemented based on the OMNeT++ simulator to evaluate performance under various scenarios. The study conducted and analyzed detailed experiments including RSSI value histogram analysis, variations in spreading factor and transmission power, and energy consumption patterns of end devices. Results demonstrated that automatic adjustment of spreading factor and transmission power optimizes network performance and energy efficiency. Furthermore, the study identified a significant correlation between energy consumption patterns and the dynamic changes in spreading factor and transmission power. These findings demonstrate that the adaptive data rate(ADR) mechanism in LoRa networks can effectively optimize network performance and energy efficiency. The analysis results are expected to provide important guidelines for future design and optimization of LoRa networks.

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