Architecture and iot security systems based on fog computing
DOI:
https://doi.org/10.30837/ITSSI.2024.27.054Keywords:
cloud; fog computing; architecture; Internet of Things; IoT security.Abstract
The subject of the study is is the security architecture of the Internet of Things (IoT) based on fog computing, which allows providing efficient and secure services for many IoT users. The goal is to investigate the security architecture for IoT systems based on fog computing. To achieve the goal, the following tasks were solved: the concept of fog computing is proposed, its architecture is considered and a comparative analysis of fog and cloud computing architectures is made; the principles of designing and implementing the architecture of a fog computing system are outlined; multi-level security measures based on fog computing are investigated; and the areas of use of fog computing-based Internet of Things networks are described. When performing the tasks, such research methods were used as: theoretical analysis of literature sources; analysis of the principles of designing and implementing the security architecture of the Internet of Things; analysis of security measures at different levels of the architecture. The following results were obtained: the architecture of fog computing is considered and compared with the cloud architecture; the principles of designing and implementing the architecture of fog computing systems are formulated; multi-level IoT security measures based on fog computing are proposed. Conclusions: research on IoT security systems based on fog computing has important theoretical implications. The fog computing architecture, in contrast to the cloud architecture, better meets the demand for high traffic and low latency of mobile applications, providing more advantages for systems that require real-time information processing. When designing and implementing the architecture of fog computing systems, the factors of memory capacity, latency, and utility should be taken into account to effectively integrate fog technologies with IoT. To ensure a high level of system security, multi-level security measures should be implemented using both software and hardware solutions.
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Kartheek, D., Bhushan, Bharath. (2020), "Security Issues in Fog Computing for Internet of Things", Security Issues in Fog Computing for Internet of Things. 11 р. DOI: https://doi.org/10.4018/978-1-7998-0194-8.ch003
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Alrawais, A., Alhothaily, A., Hu, C. (2017), "Fog computing for the Internet of Things: security and privacy issues". IEEE Internet Comput 21(2), P. 34–42. DOI: https://ieeexplore.ieee.org/document/7867732
Thota, C., Sundarasekar, R., Manogaran, G. (2018), "Centralized fog computing security platform for IoT and cloud in healthcare system". Fog computing: breakthroughs in research and practice, P. 365–378. DOI: https://doi.org/10.4018/978-1-5225-5649-7.ch018
Zhang, P.Y., Zhou, M.C., Fortino, G. (2018), "Security and trust issues in fog computing: a survey". Future Generation Computer Systems, 88, P. 16–27. DOI: https://doi.org/10.1016/j.future.2018.05.008
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S. Shen, L. Huang, H. Zhou, S. Yu, E. Fan and Q. Cao. (2018), "Multistage Signaling Game-Based Optimal Detection Strategies for Suppressing Malware Diffusion in Fog-Cloud-Based IoT Networks," IEEE Internet of Things Journal, 2, P. 1043–1054. DOI: https://ieeexplore.ieee.org/document/8264678
Mulfari, D., Celesti, A., & Villari, M. (2015), "A computer system architecture providing a user-friendly man machine interface for accessing assistive technology in cloud computing". Journal of Systems and Software, P. 129–138. DOI: https://doi.org/10.1016/j.jss.2014.10.035
Wang, D., Fan, J., Fu, H., & Zhang, B. (2018), "Research on optimization of big data construction engineering quality management based on RNN-LSTM", Complexity Problems Handled by Big Data Technology. Р. 1–17. DOI: https://doi.org/10.1155/2018/9691868
Sharma, P. K., Chen, M. Y., & Park, J. H. (2017), "A software defined fog node based distributed blockchain cloud architecture for IoT", IEEE Access, 6, P. 115–124. DOI: https://ieeexplore.ieee.org/document/8053750
Massonet, P., Deru, L., Achour, A., Dupont, S., Croisez, L. M., Levin, A., & Villari, M. (2017), "Security in lightweight network function virtualisation for federated cloud and IoT", 2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud). P. 148–154. DOI: https://ieeexplore.ieee.org/abstract/document/8114476
Li, G., Wu, J., Li, J., Wang, K., & Ye, T. (2018), "Service popularity-based smart resources partitioning for fog computing-enabled industrial Internet of Things", IEEE Transactions on Industrial Informatics, 14, P. 4702–4711. DOI: https://ieeexplore.ieee.org/abstract/document/8377998
Aimin, Y., Shanshan, L., Honglei, L., & Donghao, J. (2018), "Edge extraction of mineralogical phase based on fractal theory", Chaos, Solitons & Fractals, 117. P. 215–221. DOI: https://doi.org/10.1016/j.chaos.2018.09.028
R. Yaroshevych, V. Tkachov, A. Kovalenko and D. Rosinskyi (2022), "Modelling the Domain Architecture of the Tactile Internet Using a Foggy Infrastructure," 2022 IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T). P. 512–516. DOI: 10.1109/PICST57299.2022.10238653
Dutta, J., & Roy, S. (2017), "IoT-fog-cloud based architecture for smart city: Prototype of a smart building". 2017 7th international conference on cloud computing, data science & engineering-confluenc. P. 237–242. DOI: https://ieeexplore.ieee.org/abstract/document/7943156
Peralta, G., Iglesias-Urkia, M., Barcelo, M., Gomez, R., Moran, A., & Bilbao, J. (2017), "Fog computing based efficient IoT scheme for the Industry 4.0", 2017 IEEE international workshop of electronics, control, measurement, signals and their application to mechatronics (ECMSM). P. 1–6. DOI: https://ieeexplore.ieee.org/abstract/document/7945879
Fu, H., Li, Z., Liu, Z., & Wang, Z. (2018), "Research on big data digging of hot topics about recycled water use on micro-blog based on particle swarm optimization", Sustainability, 10. 2488 р. DOI: https://doi.org/10.3390/su10072488
Ong, S. P., Cholia, S., Jain, A., Brafman, M., Gunter, D., Ceder, G., & Persson, K. A. (2015), "The Materials Application Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles", Computational Materials Science, 97, P. 209–215. DOI: https://doi.org/10.1016/j.commatsci.2014.10.037
Goldstein, S. W. (2018), Information processing using a population of data acquisition devices U.S. Patent No. 10,045,321. available at: https://patents.google.com/patent/US20160309312A1/en
Son, J., & Buyya, R. (2018), "A taxonomy of software-defined networking (SDN)-enabled cloud computing". ACM computing surveys (CSUR), 51. P. 1–36. DOI: https://doi.org/10.1145/3190617
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