Web of Things (WoT) technology facilitates the standardized integration of IoT devices ubiquitously deployed in daily environments, promoting diverse WoT applications to automatically sense and regulate the environment. In WoT environment, heterogeneous applications, user activities, and environment changes collectively influence device behaviors, posing risks of unexpected violations of safety and security properties. Existing work on violation identification primarily focuses on the analysis of automated applications, lacking consideration of the intricate interactions in the environment. Moreover, users' intention for violation resolving strategy is much less investigated. To address these limitations, we introduce EnvGuard, an environment-centric approach for property customizing, violation identification and resolution execution in WoT environment. We evaluated EnvGuard in two typical WoT environments. By conducting user studies and analyzing collected real-world environment data, we assess the performance of EnvGuard, and construct a dataset from the collected data to support environment-level violation identification. The results demonstrate the superiority of EnvGuard compared to previous state-of-the-art work, and confirm its usability, feasibility and runtime efficiency.

翻译：Web of Things（WoT）技术促进了日常环境中广泛部署的物联网设备的标准化集成，推动多样化的WoT应用自动感知和调节环境。在WoT环境中，异构应用、用户活动及环境变化共同影响设备行为，导致可能违反安全与安防属性的意外风险。现有关于违规识别的工作主要集中于自动化应用的分析，缺乏对环境中复杂交互的考虑。此外，用户对违规解决策略的意图研究较少。为应对这些局限，我们提出EnvGuard，一种以环境为中心的方法，用于WoT环境中的属性定制、违规识别与解决执行。我们在两个典型WoT环境中评估了EnvGuard。通过开展用户研究并分析收集的真实环境数据，我们评估了EnvGuard的性能，并从收集的数据中构建数据集以支持环境级违规识别。结果表明，与先前最先进的工作相比，EnvGuard具有优越性，并证实了其可用性、可行性和运行时效率。