Model of elemental data flow distribution in the internet of things supporting fog platform
DOI:
https://doi.org/10.30837/ITSSI.2023.25.088Keywords:
fog computing, IoT, DBSCAN, C-Means, clustering, mathematical modelingAbstract
The subject of the research is models and methods for optimizing resource and task management in the fog computing environment of the Internet of Things (IoT). The increasing number of connected devices and the volumes of data collected in IoT networks make it essential to improve management systems that ensure the optimal distribution of tasks and resources. Fog computing addresses this challenge by distributing computational tasks closer to data sources and end-users. The goal of this work is to enhance the efficiency of fog computing technologies to achieve optimal task and resource allocation in IoT networks. The main tasks of this work are as follows. Firstly, considering the diverse requirements of computational resources and tasks in IoT, reviewing existing methods and developments in the field is necessary. Secondly, it is essential to investigate and compare clustering methods, particularly DBSCAN and C-Means, for effective resource management. The DBSCAN clustering method enables efficient task distribution based on their location, while the C-Means method allows grouping resources based on their characteristics. The final task involves developing a mathematical model that considers input parameters such as system response, cluster resource requirements, data proximity to processing, etc. This model will enable the analysis of potential scenarios and decision-making regarding the optimal distribution of tasks and resources in the IoT environment. Conclusion. This research aims to solve the urgent problem of managing resources and tasks in the fog IoT environment. A review of existing methods and developments in resource and task management in IoT is conducted. DBSCAN and C-Means clustering methods are compared to determine their effectiveness in resource management. A set-theoretic model is developed that considers various parameters for making optimal decisions on the distribution of tasks and resources. It is established that the use of clustering methods and the developed model help to improve system performance and ensure more efficient use of fog computing resources in the IoT environment.
References
Список літератури
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References
Alam, T. (2021), "Cloud-based IoT applications and their roles in smart cities". Smart Cities. Vol. 4(3). Р. 1196–1219. DOI:10.3390/smartcities4030064
Al-Haija, Q.A. (2022),"Top-Down Machine Learning-Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks". Frontiers in Big Data, Vol. 4. Р. 1–18. DOI: https://doi.org/10.3389/fdata.2021.782902
Uthayakumar, J., Vengattaraman, T. and Dhavachelvan, P. (2019),"A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks". Ad Hoc Networks, Vol. 83. P. 149–157. DOI: https://doi.org/10.1016/j.adhoc.2018.09.009
Al-Hawawreh, M., Elgendi I. and Munasinghe K. (2022), "An Online Model to Minimize Energy Consumption of IoT sensors in Smart Cities". IEEE Sensors Journal, Vol. 22(20). P. 19524–19532. DOI: https://doi.org/10.1109/JSEN.2022.3199590
Jing, W., Chen, G., and Cheng, Y. "DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark". International Journal of High Performance Computing and Networking, Vol. 13, No.4, 417 р., 2019. available at: https://www.inderscience.com/offers.php?id=99265
Cordova, I., Moh, T. (2015), "DBSCAN on Resilient Distributed Datasets". International Conference on High Performance Computing Simulation (HPCS). IEEE. P. 531–540. DOI: https://doi.org/10.1109/HPCSim.2015.7237086
Augustine, S., Ananth, J.P. (2020), "Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks". Wireless Networks, Vol. 26(7). P. 5113–5132. DOI: https://doi.org/10.1007/s11276-020-02352-w
He, Y., Tan, H., Luo, W., Mao, H., Ma, D., Feng, S., and Fan, J. (2011), "MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce", IEEE 17th International Conference on Parallel and Distributed Systems. IEEE. P. 473–480. DOI: 10.1109/ICPADS.2011.83
Han, D., Agrawal, A., Liao, W., and Choudhary, A. "Parallel DBSCAN Algorithm Using a Data Partitioning Strategy with Spark Implementation", in 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. P. 305–312. available at: https://www.scholars.northwestern.edu/en/publications/parallel-dbscan-algorithm-using-a-data-partitioning-strategy-with
Gong, Y., Sinnott, R. O., and Rimba, P. (2018), "RT-DBSCAN: RealTime Parallel Clustering of Spatio-Temporal Data Using SparkStreaming", Computational Science. P. 524–539. DOI:10.1007/978-3-319-93698-7_40
Ali N., Hamida S, Cherradi B et al. (2022), "A computational performance study of unsupervised data clustering algorithms on GPU". 2nd international conference on innovative research in applied science, engineering and technology (IRASET). IEEE, Meknes, 2022. Р 1–6. DOI:10.1109/IRASET52964.2022.9737871
Cook, S. "CUDA programming: a developer’s guide to parallel computing with GPUs". Elsevier,
MK, Amsterdam; Boston. 2013. 591 р. available at: https://usermanual.wiki/Pdf/Shane20CookCUDA20programming20A20developers20guide20to20parallel20computing20with20GPUsMorgan20Kaufmann202012.1739933505/help
Fritz F, Schmid M, Mottok J. "Accelerating real-time applications with predictable work-stealing. Architecture of computing systems". ARCS. Springer International Publishing, Cham, 2020. Р. 241–255. available at: https://europepmc.org/article/pmc/pmc7343420
Li Y, Zhao K, Chu X, Liu J. (2013), "Speeding up k-Means algorithm by GPUs". Journal of Computer and System Sciences Vol. 79, Issue 2. Р. 216–229. DOI: https://doi.org/10.1016/j.jcss.2012.05.004
Sanders, J, Kandrot, E. "CUDA by example: an introduction to general-purpose GPU programming". Addison-Wesley, Upper Saddle River, NJ. 2013. 311 р. available at: https://edoras.sdsu.edu/~mthomas/docs/cuda/cuda_by_example.book.pdf
Wasif, M. K., Narayanan, P. J. (2011), "Scalable clustering using multiple GPUs". 18th international conference on high performance computing. IEEE. Bengaluru. Р. 1–10. DOI: https://doi.org/10.1016/j.engappai.2017.10.023
Rodriguez, D., Gomez, D., Alvarez, D., Rivera, S. (2021), "A review of parallel heterogeneous computing algorithms in power systems". Algorithms. 2021. Vol.14(10). 275 р. DOI: https://doi.org/10.3390/a14100275
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