CH-MAC:在LPWAN中通过编码和跳变实现低延迟可靠通信

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2023-08-25 DOI:10.1145/3617505
Junzhou Luo, Zhuqing Xu, Jingkai Lin, Ciyuan Chen, Runqun Xiong
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引用次数: 0

摘要

无线传感作为一种强大的环境传感技术,极易受到各种环境噪声的影响。LoRa是一种新型的低功耗广域网抗干扰技术,受到了科学界和工业界的广泛关注。然而,在要求传输可靠性的复杂无线传感环境中,LoRa传输存在严重的时延问题。在本文中,我们提出了CH-MAC,这是第一个基于分组本地损坏性质和信道时变性质的mac层协议,以减少具有可靠通信要求的LPWAN中的端到端传输延迟。具体来说,CH-MAC使用Luby Transform代码将有效载荷划分并编码为几个块,以便接收器可以在损坏的数据包中保留部分编码信息。此外,CH-MAC利用跳频技术将数据包的不同块以不同的信道传输,避免了突然的噪声碰撞。此外,CH-MAC采用动态数据包长度调整机制来缓解网络拥塞。在实际硬件测试平台和仿真平台上进行的大量评估表明,与现有方法相比,CH-MAC可以将端到端传输延迟降低2.63 x,通信成功率要求> \(95\% \)。
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CH-MAC: Achieving Low-latency Reliable Communication via Coding and Hopping in LPWAN
Wireless sensing has emerged as a powerful environmental sensing technology that is vulnerable to the impact of all kinds of ambient noises. LoRa is a novel interference-resilient technology of low-power wide-area networks (LPWAN), which has attracted wide attention from scientific and industrial communities. However, LoRa transmission suffers from serious latency in those complex wireless sensing environments requiring transmission reliability. In this paper, we present CH-MAC, the first MAC-layer protocol based on the local corruption nature of packets and the time-varying nature of channels to reduce end-to-end transmission latency in LPWAN with reliable communication requirements. Specifically, CH-MAC employs Luby Transform code to divide and encode the payload into several blocks such that the receiver can retain part of the coded information in the corrupted packets. In addition, CH-MAC utilizes hopping to transmit different blocks of a packet with various channels to avoid sudden noise collision. Moreover, CH-MAC adopts a dynamic packet length adjustment mechanism to mitigate network congestion. Extensive evaluations on a real-world hardware testbed and a simulation platform show that CH-MAC can reduce end-to-end transmission latency by 2.63 × with a communication success rate requirement of > \(95\% \) compared with state-of-the-art methods.
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3.70%
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