Вопросы радиоэлектроники. 2021; : 23-27
Применение устройств интернета вещей для быстрой обработки данных
https://doi.org/10.21778/2218-5453-2021-4-23-27Аннотация
Системы интернета вещей (internet of things, IoT) вносят весомый вклад в рост общемирового трафика. Существует тенденция к снижению объема передаваемых и хранимых данных, для чего используются различные подходы. Большинство таких подходов предполагает традиционную обработку данных в облаке и на уровне гейтов, а конечные устройства остаются незадействованными в этом процессе. В статье рассматривается корреляционный метод обработки данных на конечном устройстве. Представлены результаты исследования максимальной производительности при реализации на ПЛИС при различных порядках согласованного фильтра N и различных разрядностях входного потока данных. Рассмотренный метод позволяет работать с высокоскоростными потоками данных и типовыми для IoT-систем каналами связи. Приведены граничные условия работоспособности для корреляционного метода. На основе представленной модели IoT-системы проанализированы задержки обработки данных для корреляционного метода и метода, использующего обработку данных на гейте.
Список литературы
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6. Harb H., Makhoul A. Energy-efficient sensor data collection approach for industrial process monitoring // IEEE Trans. Ind. Informat. 2018. Vol. 14. No. 2. P. 661–672.
7. Tayeh G. B., Makhoul A., Laiymani D., Demerjian J. A distributed real-time data prediction and adaptive sensing approach for wireless sensor networks // Pervasive Mobile Comput. 2018. Vol. 49. P. 62–75.
8. Bahi J., Makhoul A., Medlej M. A two tiers data aggregation scheme for periodic sensor networks // Ad Hoc & Sensor Wireless Networks. 2012.
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10. Jon Y. Adaptive sampling in wireless sensor networks for air monitoring system [dissertation]. Uppsala University, 2016.
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12. Braten A. E., Kraemer F. A., Palma D. Adaptive, correlation-based training data selection for IoT device management // 6th International Conference on Internet of Things: Systems, Management and Security (IOTSMS). Granada: 2019. P. 169–176.
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Issues of radio electronics. 2021; : 23-27
Using IoT devices for fast data processing
https://doi.org/10.21778/2218-5453-2021-4-23-27Abstract
IoT systems make a significant contribution to the growth of global traffic. There is a trend to reduce the volume of transmitted and stored data, for which different approaches are used. Most approaches use traditional data processing in the Cloud and at the gate level. The end devices are not involved to the data processing. The article describes the correlation method of data processing on the end device. The results of a research of the maximum performance when implemented on an FPGA, with different number of taps of the matched filter and different bit width of the input data stream are presented. This method allows to work with high-speed data streams and communication channels typical for IoT systems. The boundary conditions of operability for the correlation method are given. On the basis of the presented model of the IoT system, data processing delays for the correlation method and the method using data processing on the gate application of IoT devices for fast data processing are analyzed.
References
1. Cisco visual networking index predicts global annual IP traffic to exceed three zettabytes by 2021 [Elektronnyi resurs]. URL: https://newsroom.cisco.com/press-release-content?type=webcontent&articleId=1853168 (data obrashcheniya: 21.05.2021).
2. Jiao L., Friedman R., Fu X. M., et al. Cloud based computation offloading for mobile devices: state of the art, challenges and opportunities // Future Netw. Mobile summit. Lisbon: 2013. P. 1–11.
3. Zong J. I., Sustain C. J. Computational offloading for performance improvement and energy saving in mobile devices // Wireless Syst. 2019. Vol. 1. No. 4. P. 225–234.
4. Shi W., Pallis G., Xu Z. Edge computing // Proceedings of the IEEE. 2019. Vol. 107. No. 8. P. 1474–1481.
5. Bhuiyan M. Z. A., Wu J., Wang G., et al. E-sampling: event-sensitive autonomous adaptive sensing and low-cost monitoring in networked sensing systems // ACM Trans. Auton. Adapt. Syst. 2017. Vol. 12.
6. Harb H., Makhoul A. Energy-efficient sensor data collection approach for industrial process monitoring // IEEE Trans. Ind. Informat. 2018. Vol. 14. No. 2. P. 661–672.
7. Tayeh G. B., Makhoul A., Laiymani D., Demerjian J. A distributed real-time data prediction and adaptive sensing approach for wireless sensor networks // Pervasive Mobile Comput. 2018. Vol. 49. P. 62–75.
8. Bahi J., Makhoul A., Medlej M. A two tiers data aggregation scheme for periodic sensor networks // Ad Hoc & Sensor Wireless Networks. 2012.
9. Wu H., Wang J., Suo M., Mohapatra P. A holistic approach to reconstruct data in ocean sensor network using compression sensing // IEEE Access. 2017. P. 99.
10. Jon Y. Adaptive sampling in wireless sensor networks for air monitoring system [dissertation]. Uppsala University, 2016.
11. Tayeh G. B., Makhoul A., Demerjian J., Laiymani D. A new autonomous data transmission reduction method for wireless sensors networks // Proc. IEEE Middle East North Afr. Commun. Conf. (MENACOMM). 2018. P. 1–6.
12. Braten A. E., Kraemer F. A., Palma D. Adaptive, correlation-based training data selection for IoT device management // 6th International Conference on Internet of Things: Systems, Management and Security (IOTSMS). Granada: 2019. P. 169–176.
13. Kim S., Lee H., Ko H., et al. Pattern matching trading system based on the dynamic time warping algorithm // Sustainability. 2018. Vol. 10. 4641.
14. Su S., Sun Y., Gao X., et al. A correlation-change based feature selection method for IoT equipment anomaly detection // Applied sciences. 2019. Vol. 9 (3). 437.
15. Anufrienko A. Data processing by end devices in IoT systems // Computing, Telecommunications and Control. 2020. Vol. 13. No. 2. P. 7–13.
