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Fusion: Practice and Applications
Volume 13 , Issue 2, PP: 91-105 , 2023 | Cite this article as | XML | Html |PDF

Title

Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies

  Andres Leon Yacelga 1 * ,   Nelson B. Arevalo 2 ,   Luis Albarracin Zambrano 3

1  Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
    (ui.andresleon@uniandes.edu.ec)

2  Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
    (up.nelsonbecerra@uniandes.edu.ec)

3  Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
    (uq.luisalbarracin@uniandes.edu.ec)

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Doi   :   https://doi.org/10.54216/FPA.130208

Received: April 25, 2023 Revised: July 11, 2023 Accepted: September 22, 2023

Abstract :

The Industrial Internet of Things (IIoT) has ushered in a new era of connectivity and intelligence in industrial settings. At the heart of this transformative landscape lies Fog Computing, a distributed computing paradigm that brings processing power and intelligence closer to the edge of industrial networks. This paper provides a comprehensive survey of Fog Computing's pivotal role in IIoT, elucidating its significance, challenges, emerging trends, and strategies for successful implementation. We delve into the challenges that industrial environments present for Fog Computing, encompassing issues such as scalability, cybersecurity, data management, and interoperability. Strategies for mitigating these challenges are explored, ranging from efficient resource management to robust cybersecurity measures. Furthermore, we investigate recent developments and innovations in Fog Computing, including the integration of Edge AI, 5G networks, and hybrid cloud-fog architectures, shaping the landscape of IIoT. Promising research areas and opportunities are identified, with a focus on optimizing edge AI, secure data sharing, and sustainable Fog Computing practices.

Keywords :

Fog Computing; Industrial Internet of Things (IIoT); Edge Computing , Edge AI; 5G Integration; Hybrid Cloud-Fog Architectures; Cybersecurity in IIoT; Interoperability Standards.

References :

[1]         Qayyum, T., Trabelsi, Z., Waqar Malik, A., & Hayawi, K. (2022). Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT). Journal of Cloud Computing, 11(1), 72.

[2]         Abdel-Basset, M., Chang, V., Hawash, H., Chakrabortty, R. K., & Ryan, M. (2020). Deep-IFS: Intrusion detection approach for industrial internet of things traffic in fog environment. IEEE Transactions on Industrial Informatics, 17(11), 7704-7715.

[3]         Chalapathi, G. S. S., Chamola, V., Vaish, A., & Buyya, R. (2021). Industrial internet of things (iiot) applications of edge and fog computing: A review and future directions. Fog/edge computing for security, privacy, and applications, 293-325.

[4]         Tange, K., De Donno, M., Fafoutis, X., & Dragoni, N. (2020). A systematic survey of industrial Internet of Things security: Requirements and fog computing opportunities. IEEE Communications Surveys & Tutorials, 22(4), 2489-2520.

[5]         Mao, W., Zhao, Z., Chang, Z., Min, G., & Gao, W. (2021). Energy-efficient industrial internet of things: Overview and open issues. IEEE transactions on industrial informatics, 17(11), 7225-7237.

[6]         Cao, B., Fu, Y., Sun, Z., Liu, X., He, H., & Lv, Z. (2021, October). A resource allocation strategy in fog-cloud computing towards the Internet of Things in the 5g era. In 2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (pp. 1-6). IEEE.

[7]         M. Muthuswamy, A. M. Ali, and A. Abdelhafeez, “Breaking the Silence: Convolutional Neural Networks for Sign Language Recognition in the Deaf Community”, SMIJ, vol. 1, Oct. 2022.

[8]         Malik, U. M., Javed, M. A., Zeadally, S., & ul Islam, S. (2021). Energy-efficient fog computing for 6G-enabled massive IoT: Recent trends and future opportunities. IEEE Internet of Things Journal, 9(16), 14572-14594.

[9]         Yin, Z., Xu, F., Li, Y., Fan, C., Zhang, F., Han, G., & Bi, Y. (2022). A multi-objective task scheduling strategy for intelligent production line based on cloud-fog computing. Sensors, 22(4), 1555.

[10]      Feng, W., Zhang, N., Lin, S., Li, S., Wang, Z., Ai, B., & Zhong, Z. (2022). Energy-efficient collaborative offloading in NOMA-enabled fog computing for Internet of Things. IEEE Internet of Things Journal, 9(15), 13794-13807.

[11]      Abdel-Basset, M., Moustafa, N., Hawash, H., Ding, W., Abdel-Basset, M., Moustafa, N., ... & Ding, W. (2022). Internet of things, preliminaries and foundations. Deep learning techniques for IoT security and privacy, 37-65.

[12]      Zhang, C. (2020). Design and application of fog computing and Internet of Things service platform for smart city. Future Generation Computer Systems, 112, 630-640.

[13]      Saeed, W., Ahmad, Z., Jehangiri, A. I., Mohamed, N., Umar, A. I., & Ahmad, J. (2021). A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing. KSII Transactions on Internet & Information Systems, 15(1).

[14]      Moura, J., & Hutchison, D. (2020). Fog computing systems: State of the art, research issues and future trends, with a focus on resilience. Journal of Network and Computer Applications, 169, 102784.

[15]      Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., & Wu, D. O. (2020). Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials, 22(4), 2462-2488.

[16]      Fersi, G. (2021). Fog computing and Internet of Things in one building block: A survey and an overview of interacting technologies. Cluster Computing, 24(4), 2757-2787.

[17]      Zhou, H., Pal, S., Jadidi, Z., & Jolfaei, A. (2022). A Fog-Based Security Framework for Large-Scale Industrial Internet of Things Environments. IEEE Internet of Things Magazine, 6(1), 64-68.

[18]      Apat, H. K., Nayak, R., & Sahoo, B. (2023). A comprehensive review on Internet of Things application placement in Fog computing environment. Internet of Things, 100866.

[19]      Abdel-Basset, M., Hawash, H., Chakrabortty, R. K., & Ryan, M. (2021). Energy-net: a deep learning approach for smart energy management in iot-based smart cities. IEEE Internet of Things Journal, 8(15), 12422-12435.

[20]      Teoh, Y. K., Gill, S. S., & Parlikad, A. K. (2021). IoT and fog computing based predictive maintenance model for effective asset management in industry 4.0 using machine learning. IEEE Internet of Things Journal.

[21]      Stojanović, M. D., & Rakas, S. V. B. (2020). Challenges in securing industrial control systems using Future Internet technologies. In Cyber Security of Industrial Control Systems in the Future Internet Environment (pp. 1-26). IGI Global.

[22]      A. M.Ali and A. Abdelhafeez, “DeepHAR-Net: A Novel Machine Intelligence Approach for Human Activity Recognition from Inertial Sensors”, SMIJ, vol. 1, Nov. 2022. https://doi.org/10.61185/SMIJ.2022.8463

[23]      Younan, M., Houssein, E. H., Elhoseny, M., & Ali, A. A. (2020). Challenges and recommended technologies for the industrial internet of things: A comprehensive review. Measurement, 151, 107198.

[24]      Sabireen, H., & Neelanarayanan, V. J. I. E. (2021). A review on fog computing: Architecture, fog with IoT, algorithms and research challenges. Ict Express, 7(2), 162-176.

[25]      Gowda, D., Sharma, A., Rao, B. K., Shankar, R., Sarma, P., Chaturvedi, A., & Hussain, N. (2022). Industrial quality healthcare services using Internet of Things and fog computing approach. Measurement: Sensors, 24, 100517.

[26]      Sadri, A. A., Rahmani, A. M., Saberikamarposhti, M., & Hosseinzadeh, M. (2022). Data reduction in Fog computing and internet of things: A systematic literature survey. Internet of Things, 100629.

[27]      Puliafito, C., Mingozzi, E., Longo, F., Puliafito, A., & Rana, O. (2019). Fog computing for the internet of things: A survey. ACM Transactions on Internet Technology (TOIT), 19(2), 1-41.

[28]      Singh, J., Singh, P., & Gill, S. S. (2021). Fog computing: A taxonomy, systematic review, current trends and research challenges. Journal of Parallel and Distributed Computing, 157, 56-85.

[29]      Qiu, T., Zhao, Z., Zhang, T., Chen, C., & Chen, C. P. (2019). Underwater Internet of Things in smart ocean: System architecture and open issues. IEEE transactions on industrial informatics, 16(7), 4297-4307.

[30]      Vazquez, M. Y. L., Arteaga, B. S. M., López, J. A. M., & Martinez, M. A. Q. (2020). Design of an IOT architecture in medical environments for the treatment of hypertensive patients. Revista Ibérica de Sistemas e Tecnologias de Informação, (E33), 188-200.

[31]      Martinez, M. A. Q., González, G. A. L., Rios, M. D. G., & Vazquez, M. Y. L. (2021). Selection of LPWAN technology for the adoption and efficient use of the IoT in the rural areas of the province of Guayas USING AHP method. In Advances in Artificial Intelligence, Software and Systems Engineering: Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, July 16-20, 2020, USA (pp. 497-503). Springer International Publishing.

[32]      Martínez Martínez, R., Acurio Padilla, P. E., & Jami Carrera, J. E. (2022). Distance of Similarity Measure under Neutrosophic Sets to Assess the Challenges of IoT in Supply Chain and COVID-19. International Journal of Neutrosophic Science (IJNS), 18(4).

Cite this Article as :
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MLA Andres Leon Yacelga, Nelson B. Arevalo, Luis Albarracin Zambrano. "Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies." Fusion: Practice and Applications, Vol. 13, No. 2, 2023 ,PP. 91-105 (Doi   :  https://doi.org/10.54216/FPA.130208)
APA Andres Leon Yacelga, Nelson B. Arevalo, Luis Albarracin Zambrano. (2023). Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies. Journal of Fusion: Practice and Applications, 13 ( 2 ), 91-105 (Doi   :  https://doi.org/10.54216/FPA.130208)
Chicago Andres Leon Yacelga, Nelson B. Arevalo, Luis Albarracin Zambrano. "Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies." Journal of Fusion: Practice and Applications, 13 no. 2 (2023): 91-105 (Doi   :  https://doi.org/10.54216/FPA.130208)
Harvard Andres Leon Yacelga, Nelson B. Arevalo, Luis Albarracin Zambrano. (2023). Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies. Journal of Fusion: Practice and Applications, 13 ( 2 ), 91-105 (Doi   :  https://doi.org/10.54216/FPA.130208)
Vancouver Andres Leon Yacelga, Nelson B. Arevalo, Luis Albarracin Zambrano. Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies. Journal of Fusion: Practice and Applications, (2023); 13 ( 2 ): 91-105 (Doi   :  https://doi.org/10.54216/FPA.130208)
IEEE Andres Leon Yacelga, Nelson B. Arevalo, Luis Albarracin Zambrano, Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies, Journal of Fusion: Practice and Applications, Vol. 13 , No. 2 , (2023) : 91-105 (Doi   :  https://doi.org/10.54216/FPA.130208)
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