Despite Wireless Sensor Networks (WSNs) significantly developing over the past decade, these networks, like most wireless networks, remain susceptible to malicious interference and spectrum coexistence. Other vulnerabilities arise as WSN applications adopt open standards and typically resource and energy-constrained commercial-off-the-shelf equipment. Deployments include safety-critical applications such as the internet of things, medical, aerospace and space and deep-sea exploration. To manage safety and privacy requirements across such a diverse wireless landscape, security on wireless edge devices needs improvement while maintaining low complexity. This paper improves wireless edge device security by developing a novel intelligent interference diagnostic framework. Received in-phase (I) and quadrature-phase (Q) samples are exclusively utilized to detect modern, subtle and traditional crude jamming attacks. This I/Q sample utilization inherently enables decentralized decision-making, where the low-order features were extracted in a previous study focused on classifying typical 2.4-2.5 GHz wireless signals. The associated optimal intelligent models are leveraged as the foundation for this paper’s work. Initially, Matlab Monte Carlo simulations investigate the ideal case, which incorporates no hardware limitations, identifies the required data type of signal interactions and motivates a hardware investigation. Software-defined radios (SDRs) collect the required live over-the-air I/Q data and transmit matched signal (ZigBee) and continuous-wave interference in developed ZigBee wireless testbeds. Low complexity supervised machine learning models are developed based exclusively on the low-order features and achieve an average accuracy among the developed models above 98%. The designed methodology involves examining ZigBee over-the-air data for artificial jamming and SDR jamming of ZigBee signals transmitted from SDR and commercial (XBee) sources. This approach expands to a legitimate node classification technique and an overall algorithm for wireless edge device interference diagnostic tools. The investigation includes developing Support Vector Machine, XGBoost and Deep Neural Network (DNN) models, where XGBoost is optimal. Adapting the optimized models to global positioning system signals establishes the transferability of the designed methodology. Implementing the designed approaches on a Raspberry Pi embedded device examines a relatively resource-constrained deployment. The primary contribution is the real experimentally validated interference diagnostic framework that enables independent device operation, as no channel assumptions, network-level information or spectral images are required. Developed models exclusively use I/Q data low-order features and achieve high accuracy and generalization to unseen data.

尽管无线传感器网络 (WSN) 在过去十年中取得了长足的发展，但与大多数无线网络一样，这些网络仍然容易受到恶意干扰和频谱共存的影响。随着 WSN 应用程序采用开放标准以及通常资源和能源受限的商用现成设备，其他漏洞也随之出现。部署包括安全关键应用，例如物联网、医疗、航空航天和太空以及深海探索。为了在如此多样化的无线环境中管理安全和隐私要求，无线边缘设备的安全性需要改进，同时保持低复杂性。本文通过开发一种新颖的智能干扰诊断框架来提高无线边缘设备的安全性。接收到的同相 (I) 和正交相位 (Q) 样本专门用于检测现代、微妙和传统的原始干扰攻击。这种 I/Q 样本利用本质上支持分散决策，其中低阶特征在之前的研究中被提取出来，重点是对典型的 2.4-2.5 GHz 无线信号进行分类。相关的最优智能模型被用作本文工作的基础。最初，Matlab Monte Carlo 模拟研究理想情况，它不包含硬件限制，识别信号交互所需的数据类型并激发硬件研究。软件定义无线电 (SDR) 收集所需的实时无线 I/Q 数据并在已开发的 ZigBee 无线测试平台中传输匹配信号 (ZigBee) 和连续波干扰。低复杂度监督机器学习模型完全基于低阶特征开发，并且在开发的模型中实现了 98% 以上的平均准确率。设计的方法包括检查 ZigBee 空中数据，以对从 SDR 和商业 (XBee) 源传输的 ZigBee 信号进行人工干扰和 SDR 干扰。这种方法扩展到合法节点分类技术和无线边缘设备干扰诊断工具的整体算法。调查包括开发支持向量机、XGBoost 和深度神经网络 (DNN) 模型，其中 XGBoost 是最佳的。使优化模型适应全球定位系统信号建立了设计方法的可转移性。在 Raspberry Pi 嵌入式设备上实施设计的方法检查相对资源受限的部署。主要贡献是真正经过实验验证的干扰诊断框架，该框架支持独立设备操作，因为不需要信道假设、网络级信息或频谱图像。开发的模型专门使用 I/Q 数据低阶特征，并实现对未知数据的高精度和泛化。