使用离散事件模拟器对6TiSCH网络的性能分析
Performance Analysis of 6TiSCH Networks Using Discrete Events Simulator
AI总结：
本文通过离散事件模拟器评估6TiSCH网络的可扩展性，重点分析队列大小、单跳重试次数和时隙帧长度对误包率与时延的影响，发现增大队列并调整重试次数可显著降低误包率。
摘要：
物联网（IoT）使小型设备能够感知、响应和通信，其应用范围从普通智能家居物品到复杂工业过程。为了给越来越多的物联网设备提供接入，尤其是在长距离通信场景中，一个稳健的低功耗广域网（LPWAN）协议变得至关重要。为此广泛采用的协议是6TiSCH，它建立在IEEE 802.15.4标准之上。它引入了时隙信道跳频（TSCH）模式作为一种新的介质访问控制（MAC）层运行模式，并结合IEEE 802.15.4g（该标准同样定义了MAC层和物理层（PHY）层），为LPWAN提供IPv6连接。值得注意的是，6TiSCH已被无线智能泛在网络（Wi-SUN）等重要标准所采用。本研究评估了6TiSCH的可扩展性，重点关注队列大小、最大单跳重试次数和时隙帧长度等关键参数。使用开源模拟器进行了计算仿真，并获得了以下结果：增大传输队列大小，同时调整重试次数和时隙帧长度，可以降低误包率（PER）。值得注意的是，重试次数的影响尤为显著。此外，随着网络规模扩大，对时延的影响取决于这些参数的具体组合。
英文摘要：
The Internet of Things (IoT) empowers small devices to sense, react, and communicate, with applications ranging from smart ordinary household objects to complex industrial processes. To provide access to an increasing number of IoT devices, particularly in long-distance communication scenarios, a robust low-power wide area network (LPWAN) protocol becomes essential. A widely adopted protocol for this purpose is 6TiSCH, which builds upon the IEEE 802.15.4 standard. It introduces time-slotted channel hopping (TSCH) mode as a new medium access control (MAC) layer operating mode, in conjunction with IEEE 802.15.4g, which also defines both MAC and physical layer (PHY) layers and provides IPv6 connectivity for LPWAN. Notably, 6TiSCH has gained adoption in significant standards such as Wireless Intelligent Ubiquitous Networks (Wi-SUN). This study evaluates the scalability of 6TiSCH, with a focus on key parameters such as queue size, the maximum number of single-hop retries, and the slotframe length. Computational simulations were performed using an open-source simulator and obtained the following results: increasing the transmission queue size, along with adjusting the number of retries and slotframe length, leads to a reduction in the packet error rate (PER). Notably, the impact of the number of retries is particularly pronounced. Furthermore, the effect on latency varies based on the specific combination of these parameters as the network scales.
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