Department of Electrical Engineering and Information Technology, University of Applied Sciences, 04107 Leipzig, Germany.
Laboratory of Science and Technologies of Information and Communication, National School of Electronic and Telecommunication of Sfax, Sfax 3000, Tunisia.
Sensors (Basel). 2023 Feb 21;23(5):2394. doi: 10.3390/s23052394.
Wireless sensor network (WSN) with energy-saving capabilities have drawn considerable attention in recent years, as they are the key for long-term monitoring and embedded applications. To improve the power efficiency of wireless sensor nodes, a wake-up technology was introduced in the research community. Such a device reduces the system's energy consumption without affecting the latency. Thereby, the introduction of wake-up receiver (WuRx)-based technology has grown in several sectors. The use of WuRx in a real environment without consideration of physical environmental conditions, such as the reflection, refraction, and diffraction caused by different materials, that affect the reliability of the whole network. Indeed, the simulation of different protocols and scenarios under such circumstances is a success key for a reliable WSN. Simulating different scenarios is required to evaluate the proposed architecture before its deployment in a real-world environment. The contribution of this study emerges in the modeling of different link quality metrics, both hardware and software metrics that will be integrated into an objective modular network testbed in C++ (OMNeT++) discrete event simulator afterward are discussed, with the received signal strength indicator (RSSI) for the hardware metric case and the packet error rate (PER) for the software metric study case using WuRx based on a wake-up matcher and SPIRIT1 transceiver. The different behaviors of the two chips are modeled using machine learning (ML) regression to define parameters such as sensitivity and transition interval for the PER for both radio modules. The generated module was able to detect the variation in the PER distribution as a response in the real experiment output by implementing different analytical functions in the simulator.
近年来，具有节能功能的无线传感器网络（WSN）引起了相当大的关注，因为它们是长期监测和嵌入式应用的关键。为了提高无线传感器节点的功率效率，研究界引入了唤醒技术。这种设备在不影响延迟的情况下降低系统的能耗。因此，基于唤醒接收器（WuRx）的技术在多个领域得到了发展。在实际环境中使用 WuRx 时，如果不考虑物理环境条件，例如不同材料引起的反射、折射和衍射，会影响整个网络的可靠性。实际上，在这种情况下模拟不同的协议和场景是可靠 WSN 的成功关键。在将其部署到真实环境之前，需要模拟不同的场景来评估所提出的架构。本研究的贡献在于对不同链路质量指标进行建模，包括硬件和软件指标，这些指标将被集成到一个基于 C++（OMNeT++）离散事件模拟器的目标模块化网络测试平台中，随后讨论了硬件指标的接收信号强度指示器（RSSI）和软件指标的数据包错误率（PER）研究案例，使用基于唤醒匹配器和 SPIRIT1 收发器的 WuRx。使用机器学习（ML）回归对这两种芯片的不同行为进行建模，以定义 PER 的参数，例如灵敏度和过渡间隔，用于这两个无线电模块。通过在模拟器中实现不同的分析函数，生成的模块能够检测到 PER 分布的变化，作为实际实验输出的响应。
