The Industrial Internet of Things (IIoT) has revolutionized the industrial sector by providing advanced and intelligent applications. The objects and nodes communicate with one another to collect, exchange, and analyze a large amount of sensing data using techno-social systems, thereby challenging the security and trustworthiness of the data. To achieve effective communication in IIoT, trustworthy relationships must be established among these objects. This makes trust an important security parameter in an IoT-based environment to achieve secure and reliable service communication at the edge nodes. In this paper, we propose an adaptive Context-Based Trust Evaluation System (CTES), which calculates distributed trust at the node level to achieve edge intelligence. Each edge node takes recommendations from its context-similar nodes to calculate the trust of serving nodes. This collaborative trust calculation mechanism helps in filtering out malicious nodes in the network. The weighing factor “$\mathrm{\mu }$” is dynamically assigned based on the previously calculated trust score experienced by the edge node. This research also focuses on formal verification of the proposed CTES model. We analyze the efficiency of CTES in terms of accuracy, dynamic assignment of $\mathrm{\mu }$, and resiliency against Ballot Stuffing and Bad Mouthing attacks to avoid malicious nodes. The results ensure the significance of the proposed CTES model for dynamic assignment of $\mathrm{\mu }$ and provide satisfactory results against EigenTrust, ServiceTrust, and ServiceTrust$++$ in terms of detecting malicious nodes and isolating them from providing recommendations.

工业物联网（IIoT）通过提供先进的智能应用程序，彻底改变了工业领域。对象和节点之间相互通信以使用技术社会系统收集，交换和分析大量传感数据，从而挑战了数据的安全性和可信赖性。为了在IIoT中实现有效的通信，必须在这些对象之间建立可信赖的关系。这使信任成为基于IoT的环境中重要的安全参数，以在边缘节点实现安全可靠的服务通信。在本文中，我们提出了一种自适应的基于上下文的信任评估系统（CTES），该系统在节点级别计算分布式信任以实现边缘智能。每个边缘节点从其上下文相似节点中获取建议，以计算服务节点的信任度。这种协作信任计算机制有助于过滤掉网络中的恶意节点。权重系数“$\mathrm{\mu }$系统会根据边缘节点之前计算出的信任度分数动态分配“”。这项研究还侧重于对提出的CTES模型的形式验证。我们从准确性，动态分配方面分析CTES的效率$\mathrm{\mu }$，并具有针对选票填充和不良嘴攻击的弹性，可避免恶意节点。结果确保提出的CTES模型对动态分配的意义。$\mathrm{\mu }$ 并针对EigenTrust，ServiceTrust和ServiceTrust提供令人满意的结果$++$ 在检测恶意节点并使它们脱离提供建议方面。