Abstract

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Abstract

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 “” 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 , 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  and provide satisfactory results against EigenTrust, ServiceTrust, and ServiceTrust in terms of detecting malicious nodes and isolating them from providing recommendations.

Introduction

Industry 4.0 is the most significant industrial revolution, which is focused on creating smart factories by using smart machines [1]. Generally, Industry 4.0 is used for the automation and exchange of data between smart machines for manufacturing purposes, which comprise the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Cyber–Physical Systems (CPS). The IoT connects physical objects through the Internet using sensors, RFID tags, and various smart devices. Sensing devices are used to get the stimulus from the environment and respond to the system. IoT has become a fundamental part of the smart environments and provides multiple services in safety, transport, healthcare, surveillance systems, education, and more importantly, in the industrial domain. The communication in IIoT is based on IoT-enabled devices that can run numerous applications for collaborative communication in smart manufacturing to generate large amounts of data. This data needs to be trustworthy and secure by isolating false data generated by the malicious nodes. This can be achieved through edge intelligence, which refers to the process of data collection, analysis, and related calculations at the node that captures or generates the data. IIoT is providing solutions to smart manufacturing in combination with security mechanisms to ensure the reliability of data and to improve communication between smart machines. Although this makes IoT helpful in everyday life, it also opens the doors of threats and vulnerabilities [2].