Trust-Based Distributed Resource Allocation in Edge-Enabled IIoT Networks

ABSTRACT

The rapid evolution of Industrial Internet of Things (IIoT) has enabled large-scale connectivity among smart devices, sensors, and industrial systems, particularly in edge-enabled environments. However, distributed resource allocation in such networks faces significant challenges due to dynamic network conditions, limited edge resources, and the presence of untrusted or malicious nodes. Ensuring reliable and efficient resource sharing while maintaining system security and performance has become a critical concern.

This work proposes a trust-based distributed resource allocation framework for edge-enabled IIoT networks. The approach integrates trust evaluation mechanisms with decentralized decision-making to prioritize resource allocation among devices based on their reliability and behavior. By leveraging local interactions and historical performance metrics, each node dynamically computes trust scores, which guide the allocation of computational and communication resources at the network edge.

The proposed method employs a combination of trust modeling, distributed optimization, and adaptive scheduling to enhance system robustness and efficiency. Edge nodes collaboratively allocate resources without relying on a centralized controller, thereby reducing latency and improving scalability. Additionally, the framework incorporates mechanisms to detect and isolate malicious or low-trust nodes, ensuring secure and stable network operations.

Simulation results demonstrate that the proposed trust-based scheme significantly improves resource utilization, reduces task latency, and enhances overall network reliability compared to conventional allocation methods. The framework is particularly suitable for latency-sensitive and mission-critical industrial applications, where trust, efficiency, and decentralized control are essential.

Keywords: Resource allocation, edge computing, Internet of Things, Industrial IoT.