راهکارهای مدیریت جریان داده توزیع شده در ساختار شبکه اینترنت اشیا به منظور بهبود پارامترهای کیفیت سرویس: یک مرور نظاممند
محورهای موضوعی : فناوری های نوین در سیستم های توزیع شده و محاسبات الگوریتمیعباس جلیلوند 1 , محمد فرخ زاده اجیرلو 2
1 - استادیار–دانشگاه آزاد اسلامی واحد کرج- گروه کامپیوتر-کرج- ایران
2 - دانشگاه آزاد اسلامی واحد کرج- گروه کامپیوتر-کرج- ایران
کلید واژه: اینترنت اشیا, جریان داده, ساختار توزیع شده, کیفیت سرویس, محاسبات توزیعی, مرور نظاممند,
چکیده مقاله :
اینترنت اشیا به عنوان یکی از تحولآفرینترین مفاهیم در فناوری اطلاعات، با هدف اتصال اشیای فیزیکی به اینترنت و ایجاد امکان جمعآوری، انتقال و اشتراکگذاری دادهها میان آنها شکل گرفته است. این ساختار گسترده موجب پدیدآمدن شبکههایی از دستگاههای هوشمند شده که قادرند بهطور خودکار با یکدیگر و با محیط پیرامون تعامل داشته باشند. در سالهای اخیر، ظهور رویکردهای غیرمتمرکز در اینترنت اشیا موجب گسترش قابلیتهای آن در زمینه مدیریت داده و همکاری توزیعشده میان سیستمها شده است. این رویکردها با بهرهگیری از محاسبات توزیعشده، رایانش ابری و معماری سرویسمحور، امکان پردازش همزمان و مقیاسپذیر دادهها را فراهم میسازند. با افزایش چشمگیر تعداد و تنوع دستگاههای متصل، حجم عظیم دادههای تولیدی چالشهای قابل توجهی را در زمینه نگهداری، پردازش و مدیریت جریان داده ایجاد کرده است. مقاله حاضر با رویکردی مروری به بررسی و تحلیل عمیق رویکردهای موجود در مدیریت جریان داده توزیعشده در ساختار شبکه اینترنت اشیا میپردازد. در این بررسی، روشهای مختلف از نظر ساختار، کارایی، کیفیت خدمات و چالشهای پیادهسازی بهصورت مفصل مورد ارزیابی قرار گرفته و نقاط قوت و محدودیتهای هر دسته از رویکردها تبیین میگردد. هدف این مرور ارائه تصویری جامع از وضعیت فعلی پژوهشهای حوزه مدیریت داده توزیعشده در اینترنت اشیا و ترسیم مسیرهای تحقیقاتی آینده برای بهبود عملکرد و بهرهوری این سیستمها است.
The Internet of Things (IoT) has emerged as one of the most transformative concepts in information technology, aiming to connect physical objects to the internet and enable them to collect, transmit, and share data. This extensive framework has led to the development of networks of smart devices capable of autonomous interaction with each other and their surrounding environments. In recent years, the advent of decentralized approaches within IoT has significantly expanded its capabilities in data management and distributed collaboration among systems. These approaches, leveraging distributed computing, cloud computing, and service-oriented architectures, facilitate real-time and scalable data processing. However, with the dramatic increase in the number and diversity of connected devices, the massive volume of generated data poses substantial challenges in terms of data storage, processing, and stream management. This paper adopts a comprehensive review approach to examine and analyze existing methods for distributed data stream management within IoT network infrastructures. The various methodologies are evaluated based on their architecture, efficiency, quality of service (QoS) considerations, and implementation challenges. Furthermore, the strengths and limitations of each category of approaches are elucidated. The objective of this review is to provide a holistic overview of the current state of research in distributed data management for IoT, thereby identifying future research directions aimed at enhancing the performance and efficiency of these complex systems.
[1] H. Cai, B. Xu, L. Jiang, and A. V. Vasilakos, "IoT-based big data storage systems in cloud computing: perspectives and challenges," IEEE Internet of Things Journal, vol. 4, no. 1, pp. 75-87, 2016.
[2] G. Bedi, G. K. Venayagamoorthy, R. Singh, R. R. Brooks, and K.-C. Wang, "Review of Internet of Things (IoT) in electric power and energy systems," IEEE Internet of things Journal, vol. 5, no. 2, pp. 847-870, 2018.
[3] A. Mukherjee, H. S. Paul, S. Dey, and A. Banerjee, "ANGELS for distributed analytics in IoT," in 2014 IEEE World Forum on Internet of Things (WF-IoT), 2014: IEEE, pp. 565-570.
[4] Y.-S. Jeong and S.-H. Sim, "Hierarchical multipath blockchain based IoT information management techniques for efficient distributed processing of intelligent IoT information," Sensors, vol. 21, no. 6, p. 2049, 2021.
[5] K. Yasumoto, H. Yamaguchi, and H. Shigeno, "Survey of real-time processing technologies of iot data streams," Journal of Information Processing, vol. 24, no. 2, pp. 195-202, 2016. [6] L. Wang and R. Ranjan, "Processing distributed internet of things data in clouds," IEEE Cloud Computing, vol. 2, no. 1, pp. 76-80, 2015.
[7] Y. Jin, Y. Liu, and W. Si, "Editorial for the special issue on “intelligent agent distributed signal processing for IoT”," Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 2, pp. 447-449, 2020.
[8] H. Yang and F. Wang, "Wireless network intrusion detection based on improved convolutional neural network," Ieee Access, vol. 7, pp. 64366-64374, 2019.
[9] B. A. Yosuf, M. Musa, T. Elgorashi, and J. Elmirghani, "Energy efficient distributed processing for IoT," IEEE Access, vol. 8, pp. 161080-161108, 2020.
[10] X. Zeng, S. K. Garg, P. Strazdins, P. P. Jayaraman, D. Georgakopoulos, and R. Ranjan, "IOTSim: A simulator for analysing IoT applications," Journal of Systems Architecture, vol. 72, pp. 93-107, 2017.
[11] Y. Nakamura, H. Suwa, Y. Arakawa, H. Yamaguchi, and K. Yasumoto, "Middleware for proximity distributed real-time processing of IoT data flows," in 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), 2016: IEEE, pp. 771-772.
[12] S. Choochotkaew, H. Yamaguchi, T. Higashino, M. Shibuya, and T. Hasegawa, "EdgeCEP: Fully-distributed complex event processing on IoT edges," in 2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2017: IEEE, pp. 121-129.
[13] C. Kim, H. Kim, and H. Jung, "IoT distributed processing system based on resource allocation algorithm," International Journal of Applied Engineering Research, vol. 12, no. 24, pp. 15050-15054, 2017.
[14] O. Carvalho, E. Roloff, and P. O. Navaux, "A distributed stream processing based architecture for IoT smart grids monitoring," in Companion proceedings of the10th international conference on utility and cloud computing, 2017, pp. 9-14.
[15] H. Nasiri, S. Nasehi, and M. Goudarzi, "Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities," Journal of Big Data, vol. 6, no. 1, p. 52, 2019.
[16] Y. Song, S. S. Yau, R. Yu, X. Zhang, and G. Xue, "An approach to QoS-based task distribution in edge computing networks for IoT applications," in 2017 IEEE international conference on edge computing (EDGE), 2017: IEEE, pp. 32-39.
[17] S. K. Singh, J. Cha, T. W. Kim, and J. H. Park, "Machine learning based distributed big data analysis framework for next generation web in IoT," Computer Science and Information Systems, vol. 18, no. 2, pp. 597-618, 2021.
[18] S. Deng, Z. Xiang, J. Yin, J. Taheri, and A. Y. Zomaya, "Composition-driven IoT service provisioning in distributed edges," IEEE Access, vol. 6, pp. 54258-54269, 2018.
[19] H. Xie and Z. Qin, "A lite distributed semantic communication system for Internet of Things," IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 142-153, 2020.
[20] M. H. ur Rehman, I. Yaqoob, K. Salah, M. Imran, P. P. Jayaraman, and C. Perera, "The role of big data analytics in industrial Internet of Things," Future Generation Computer Systems, vol. 99, pp. 247-259, 2019.
[21] X. Lv and M. Li, "Application and research of the intelligent management system based on internet of things technology in the era of big data," Mobile Information Systems, vol. 2021, no. 1, p. 6515792, 2021.
[22] B. Pourghebleh, N. Hekmati, Z. Davoudnia, and M. Sadeghi, "A roadmap towards energy‐efficient data fusion methods in the Internet of Things," Concurrency and Computation: Practice and Experience, vol. 34, no. 15, p. e6959, 2022.
[23] Z. Ye, Y. Wei, J. Li, G. Yan, and L. Wang, "A distributed pavement monitoring system based on Internet of Things," Journal of traffic and transportation engineering (English edition), vol. 9, no. 2, pp. 305-317, 2022.
[24] J. Jiang, F. Liu, W. W. Ng, Q. Tang, W. Wang, and Q.-V. Pham, "Dynamic incremental ensemble fuzzy classifier for data streams in green internet of things," IEEE transactions on green communications and networking, vol. 6, no. 3, pp. 1316-1329, 2022.
