A latency sensitive and agile IIoT architecture with optimized edge node selection and task scheduling

Abstract

The Industrial Internet of Things (IIoT) has revolutionized conventional manufacturing industries by improving control, monitoring, and management, resulting in increased agility and long-term sustainability. The demand for processing and storage resources increases as the number of IoT devices and the data they generate increases. Cloud computing often serves these needs, but in certain scenarios, real-time data processing near the source is necessary to guarantee low latency, minimize bandwidth usage, and enhance data security. This work proposes a novel edge computing-based IIoT architecture for delay-sensitive monitoring and control in manufacturing industries. In contrast to previous work, this work is designed to enhance system performance by ensuring the availability of edge nodes and distributing task execution loads evenly across multiple edge computing nodes. The proposed architecture is further optimized for efficient allocation of resources and scheduling of tasks by the addition of a task scheduling algorithm by using Round Trip Time (RTT) and Resource Logger (RL). In an effort to optimize the response time of both the complete system and individual Device Nodes (DN), the work also considers the deployment of heterogeneous Edge Nodes (EN). The evaluation results show that the proposed architecture reduces the communication delay and overhead by 88.28% compared to the traditional cloud-based approach and 12.32% compared to the previously proposed methods ATS-FOA and RBEC/PENB, respectively, for executing IIoT applications. The proposed Resource Logger, Round Trip Time-Based Edge Node Selection (RLRTT-ESA), and task scheduling algorithms further improved the total application response time by 47.25% in comparison to the previously proposed methods ATS-FOA and RBEC/PENB, respectively.