Secure Computing for Fog-Enabled Industrial IoT.

Area of Science:

• Computer Science
• Network Security
• Industrial Internet of Things (IIoT)

Background:

• Smart cities leverage the Industrial Internet of Things (IIoT) for industrial applications, connecting sensor nodes with limited capacity.
• IIoT nodes offload complex tasks to fog nodes for faster execution, but face vulnerabilities from malicious attacks and resource limitations.
• Malicious attacks can overload fog nodes, increasing task computation delay for legitimate IIoT nodes.

Purpose of the Study:

• To propose a novel mechanism, Trust-based Efficient Execution of Offloaded IIoT Trusted tasks (EEOIT), to address challenges in IIoT fog computing.
• To enhance the task execution capability of fog nodes by detecting malicious nodes and managing computing resources efficiently.
• To ensure IIoT tasks are completed within the specified time frame, even with complex tasks and potential attacks.

Main Methods:

• Developed EEOIT, a trust-based mechanism for fog nodes in IIoT environments.
• Implemented a system to detect malicious nodes and manage resource allocation for offloaded tasks.
• Conducted simulations to evaluate EEOIT's performance against existing techniques under various task densities.

Main Results:

• EEOIT significantly outperforms existing techniques in IIoT settings with varying task densities.
• The proposed EEOIT technique enhances the computation of trustable tasks, improving network efficiency.
• EEOIT demonstrates reduced task execution time, increased mean trust values, and accommodates more legitimate nodes.

Conclusions:

• EEOIT effectively addresses the challenges of malicious attacks and resource limitations in IIoT fog computing.
• The system provides a robust solution for efficient and timely execution of IIoT tasks.
• EEOIT enhances overall network performance by prioritizing and processing trustable tasks more effectively.