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<p>The Industrial Internet of Things (IIoT) has brought about a significant transformation across various industries, including transportation networks, smart factories, industrial power grids, and intelligent supply chains. By enabling intelligent communication among industrial machinery, this technology allows devices to autonomously connect and exchange operational data. However, despite its numerous advantages, IIoT faces major challenges, including data security vulnerabilities, high-energy consumption of sensors, significant latency in data transmission, limited scalability, high costs associated with network maintenance and development, inefficient resource allocation, susceptibility to cyberattacks, and fluctuations in Quality of Service (QoS) in dynamic environments. Addressing these challenges necessitates the development of comprehensive and adaptive solutions. This paper introduces an optimized hybrid architecture called EDBLSD-IIoT to tackle these challenges effectively. By integrating emerging technologies such as edge computing, blockchain, software-defined networking (SDN), and cloud computing, this architecture leverages their combined advantages to improve IIoT network performance. Edge computing reduces latency by processing data at locations closest to the source, while the SDN controller optimizes data traffic and minimizes energy consumption by employing the whale optimization algorithm (WOA) to select the best cluster head. Blockchain technology enhances data transmission security through a distributed ledger, addressing trust and tampering issues in IIoT networks. To evaluate the proposed framework, a case study was conducted in a smart car manufacturing factory. Various simulation scenarios were designed to assess parameters such as data transmission latency, bandwidth, throughput, CPU workload, energy consumption, and average end-to-end delay. The evaluation results indicate that the EDBLSD-IIoT architecture outperforms frameworks based on ant colony optimization (ACO) and genetic algorithm (GA). Specifically, this architecture achieves a latency of 0.45 ns in the largest network size, CPU usage of 3.1%, throughput of 18.4 Mbps in scenarios with the highest node count, a 72% reduction in energy consumption under the highest transaction request load, and a bandwidth of 0.3 KB per request per second at the highest packet input rate. Furthermore, the proposed method demonstrates superior average end-to-end delay performance compared to the routing protocol (RPL) and channel-aware routing protocol (CARP), underscoring its efficiency and robustness in addressing the multifaceted challenges of IIoT.</p>

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EDBLSD-IIoT: a comprehensive hybrid architecture for enhanced data security, reduced latency, and optimized energy in industrial IoT networks

  • 期刊论文

  • 发表日期：

    2025年1月3日

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  • Afsaneh Banitalebi Dehkordi

摘要

The Industrial Internet of Things (IIoT) has brought about a significant transformation across various industries, including transportation networks, smart factories, industrial power grids, and intelligent supply chains. By enabling intelligent communication among industrial machinery, this technology allows devices to autonomously connect and exchange operational data. However, despite its numerous advantages, IIoT faces major challenges, including data security vulnerabilities, high-energy consumption of sensors, significant latency in data transmission, limited scalability, high costs associated with network maintenance and development, inefficient resource allocation, susceptibility to cyberattacks, and fluctuations in Quality of Service (QoS) in dynamic environments. Addressing these challenges necessitates the development of comprehensive and adaptive solutions. This paper introduces an optimized hybrid architecture called EDBLSD-IIoT to tackle these challenges effectively. By integrating emerging technologies such as edge computing, blockchain, software-defined networking (SDN), and cloud computing, this architecture leverages their combined advantages to improve IIoT network performance. Edge computing reduces latency by processing data at locations closest to the source, while the SDN controller optimizes data traffic and minimizes energy consumption by employing the whale optimization algorithm (WOA) to select the best cluster head. Blockchain technology enhances data transmission security through a distributed ledger, addressing trust and tampering issues in IIoT networks. To evaluate the proposed framework, a case study was conducted in a smart car manufacturing factory. Various simulation scenarios were designed to assess parameters such as data transmission latency, bandwidth, throughput, CPU workload, energy consumption, and average end-to-end delay. The evaluation results indicate that the EDBLSD-IIoT architecture outperforms frameworks based on ant colony optimization (ACO) and genetic algorithm (GA). Specifically, this architecture achieves a latency of 0.45 ns in the largest network size, CPU usage of 3.1%, throughput of 18.4 Mbps in scenarios with the highest node count, a 72% reduction in energy consumption under the highest transaction request load, and a bandwidth of 0.3 KB per request per second at the highest packet input rate. Furthermore, the proposed method demonstrates superior average end-to-end delay performance compared to the routing protocol (RPL) and channel-aware routing protocol (CARP), underscoring its efficiency and robustness in addressing the multifaceted challenges of IIoT.