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

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Abstract

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.

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No datasets were generated or analyzed during the current study.

References

  1. Nassereddine M, Khang A (2024) Applications of Internet of Things (IoT) in smart cities. In: Advanced IoT Technologies and Applications in the Industry 4.0 Digital Economy. CRC Press, Boca Raton, pp 109–136

  2. Khan N, Solvang WD, Yu H (2024) Industrial Internet of Things (IIoT) and other industry 4.0 technologies in spare parts warehousing in the oil and gas industry: a systematic literature review. Logistics 8(1):16

    Article  Google Scholar 

  3. Kumar R, Agrawal N (2023) Analysis of multi-dimensional Industrial IoT (IIoT) data in Edge-Fog-Cloud based architectural frameworks: a survey on current state and research challenges. J Ind Inf Integr 35:100504

    Google Scholar 

  4. Zhang T, Xue C, Wang J, Yun Z, Lin N, Han S (2024) A survey on Industrial Internet of Things (IIoT) testbeds for connectivity research. arXiv preprint arXiv:2404.17485

  5. Rath KC, Khang A, Roy D (2024) The role of Internet of Things (IoT) technology in Industry 4.0 economy. In: Advanced IoT Technologies and Applications in the Industry 4.0 Digital Economy. CRC Press, Boca Raton, pp 1–28

  6. Sharma M, Tomar A, Hazra A (2024) Edge computing for industry 5.0: fundamental, applications and research challenges. IEEE Intern Things J 11:19070–19093

    Article  MATH  Google Scholar 

  7. Akbari M (2024) Revolutionizing supply chain and circular economy with edge computing: Systematic review, research themes and future directions. Manag Decis 62(9):2875–2899

    Article  MATH  Google Scholar 

  8. Liang W, Liu Y, Yang C, Xie S, Li K, Susilo W (2024) On identity, transaction, and smart contract privacy on permissioned and permissionless blockchain: a comprehensive survey. ACM Comput Surv 56(12):1–35

    Article  Google Scholar 

  9. Teeluck R, Durjan S, Bassoo V (2021) Blockchain technology and emerging communications applications. In: Chandrashekhar Tamane S, Dey N, Hassanien A-E (eds) Security and privacy applications for Smart City development. Springer, Cham, pp 207–256

    Google Scholar 

  10. Chowdhury RH (2024) Blockchain and AI: driving the future of data security and business intelligence. World J Adv Res Rev 23(1):2559–2570

    Article  MATH  Google Scholar 

  11. Al-Shareeda MA, Alsadhan AA, Qasim HH, Manickam S (2024) Software defined networking for internet of things: review, techniques, challenges, and future directions. Bull Electr Eng Inform 13(1):638–647

    Article  Google Scholar 

  12. Bandani AK, Riyazuddien S, Bidare Divakarachari P, Patil SN, Arvind Kumar G (2023) Multiplicative long short-term memory-based software-defined networking for handover management in 5G network. SIViP 17(6):2933–2941

    Article  Google Scholar 

  13. Deng L, Liu S (2024) Deficiencies of the whale optimization algorithm and its validation method. Expert Syst Appl 237:121544

    Article  MATH  Google Scholar 

  14. Asaithambi S, Ravi L, Kotb H, Milyani AH, Azhari AA, Nallusamy S, Vairavasundaram S (2022) An energy-efficient and blockchain-integrated software defined network for the industrial internet of things. Sensors 22(20):7917

    Article  Google Scholar 

  15. Latif SA, Wen FBX, Iwendi C, Li-Li FW, Mohsin SM, Han Z, Band SS (2022) AI-empowered, blockchain and SDN integrated security framework for IoT network of cyber physical systems. Comput Commun 181:274–283

    Article  Google Scholar 

  16. Muthanna A, Ateya AA, Khakimov A, Gudkova I, Abuarqoub A, Samouylov K, Koucheryavy A (2019) Secure IoT network structure based on distributed fog computing, with sdn/blockchain

  17. Tekin N, Acar A, Aris A, Uluagac AS, Gungor VC (2023) Energy consumption of on-device machine learning models for IoT intrusion detection. Intern Things 21:100670

    Article  MATH  Google Scholar 

  18. Rahman A, Islam MJ, Rahman Z, Reza MM, Anwar A, Mahmud MP, Noor RM (2020) DistB-Condo: distributed blockchain-based IoT-Sdn model for smart condominium. IEEE Access 8:209594–209609

    Article  Google Scholar 

  19. Abou El Houda Z, Hafid A, Khoukhi L (2019) Co-IoT: a collaborative DDoS mitigation scheme in IoT environment based on blockchain using SDN. In: 2019 IEEE Global Communications Conference (GLOBECOM), Dec 2019. IEEE, pp 1–6

  20. Mohammadzadeh A, Chhabra A, Mirjalili S, Faraji A (2024) Use of whale optimization algorithm and its variants for cloud task scheduling: a review. In: Handbook of Whale Optimization Algorithm, Elsevier, Amsterdam, pp 47–68

  21. Braik M (2024) Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images. Clust Comput 27(1):231–267

    Article  MATH  Google Scholar 

  22. Akshima, Besselman T, Guo S, Xie Z, Ye Y (2024) Tight time–space Tradeoffs for the decisional Diffie–Hellman problem. In: Proceedings of the 56th Annual ACM Symposium on Theory of Computing, June 2024, pp 1739–1749

  23. Indakholasy B, Pardede AMH, Khair H (2024) Security of employee salary data using the Elgamal algorithm by utilizing the Diffie–Hellman algorithm key generator. Int J Inform Econ Manag Sci IJIEMS 3(1):38–47

    Google Scholar 

  24. Alamgir N, Nejati S, Bright C (2024) SHA-256 collision attack with programmatic SAT. arXiv preprint arXiv:2406.20072

  25. Shanmugasundaram S, Baby C (2024) Steady state analysis of (M/M/1):(Fcfs/K/∞) queueing network with feedback retrial and blocking. J Comput Anal Appl (JoCAAA) 33(05):485–491

    MATH  Google Scholar 

  26. Islam MA, Islam ME, Rashid A (2023) Stochastic optimization of level-dependent perishable inventory system by Jackson network. Ain Shams Eng J 14(4):101935

    Article  MATH  Google Scholar 

  27. Singh J, Gupta D (2017) Towards energy saving with smarter multi queue job scheduling algorithm in cloud computing. J Eng Appl Sci 12(10):8944–8948

    MATH  Google Scholar 

  28. Albinali H, Azzedin F (2024) Towards RPL attacks and mitigation taxonomy: systematic literature review approach. IEEE Trans Netw Serv Manag 21:5215–5238

    Article  MATH  Google Scholar 

  29. Hanoosh Z (2024) A survey on routing protocols in IoT based healthcare systems. Al-Furat J Innov Electron Comput Eng 3:80–87

    Article  Google Scholar 

  30. Awadallah MA, Makhadmeh SN, Al-Betar MA, Dalbah LM, Al-Redhaei A, Kouka S, Enshassi OS (2024) Multi-objective ant colony optimization. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-024-10178-4

    Article  Google Scholar 

  31. Alhijawi B, Awajan A (2024) Genetic algorithms: theory, genetic operators, solutions, and applications. Evol Intel 17(3):1245–1256

    Article  MATH  Google Scholar 

  32. Zhao L, Hu G, Xu Y (2024) Educational resource private cloud platform based on OpenStack. Computers 13(9):241

    Article  Google Scholar 

  33. Zy AT, Rifa'i, A. M., Kamalia AZ, Sulaeman AA (2024) Detecting DDoS attacks through decision tree analysis: an EDA approach with the CIC DDoS 2019 dataset. In: 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Aug 2024. IEEE, pp 202–207

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  1. Department of Computer Engineering, Payame Noor University (PNU), P.O. Box 19395-4697, Tehran, Iran

    Afsaneh Banitalebi Dehkordi

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Banitalebi Dehkordi, A. EDBLSD-IIoT: a comprehensive hybrid architecture for enhanced data security, reduced latency, and optimized energy in industrial IoT networks. J Supercomput 81, 359 (2025). https://doi.org/10.1007/s11227-024-06872-6

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