Naive-LSTM enabled service identification of edge computing in power Internet of things

doi:10.19682/j.cnki.1005-8885.2024.0016

中国邮电高校学报(英文) ›› 2024, Vol. 31 ›› Issue (5): 34-41.doi: 10.19682/j.cnki.1005-8885.2024.0016

Naive-LSTM enabled service identification of edge computing in power Internet of things

白晖峰,霍超,张港红,尹志斌

北京智芯微电子科技有限公司

收稿日期:2023-04-28 修回日期:2023-12-31 出版日期:2024-10-31 发布日期:2024-10-31
通讯作者: 白晖峰 E-mail:baihuifeng1984@163.com
基金资助:
国家重点研发计划

Naive-LSTM enabled service identification of edge computing in power Internet of things

Received:2023-04-28 Revised:2023-12-31 Online:2024-10-31 Published:2024-10-31
Contact: Hui-Feng BAI E-mail:baihuifeng1984@163.com
Supported by:
National Key Research and Development Program of China

摘要/Abstract

摘要：

Great challenges and demands are presented by increasing edge computing services for current power Internet of things ( Power IoT) to deal with the serious diversity and complexity of these services. To improve the matching degree between edge computing and complex services, the service identification function is necessary for Power IoT. In this paper, a naive long short-term memory ( Naive-LSTM ) based service identification scheme of edge computing devices in the Power IoT was proposed, where the Naive-LSTM model makes full use of the most simplified structure and conducts discretization of the long short-term memory ( LSTM) model. Moreover, the Naive-LSTM based service identification scheme can generate the probability output result to determine the task schedule policy of Power IoT. After well learning operation, these Naive-LSTM classification engine modules in edge computing devices of Power IoT can perform service identification, by obtaining key characteristics from various service traffics. Testing results show that the Naive-LSTM based services identification scheme is feasible and efficient in improving the edge computing ability of the Power IoT.

关键词:

power Internet of things ( Power IoT), naive long short-term memory ( Naive-LSTM), services identification, edge computing

Abstract:

Key words:

power Internet of things ( Power IoT), naive long short-term memory ( Naive-LSTM), services identification, edge computing

参考文献

[1] RAFIQUE W, QI L Y, YAQOOB I, et al. Complementing IoT services through software defined networking and edge computing: a comprehensive survey. IEEE Communications Surveys & Tutorials, 2020, 22(3): 1761 - 1804.

[2] LIU Z, QIU X S, ZHANG S, et al. Service scheduling based on edge computing for power distribution IoT. Computers, Materials & Continua, 2020, 62(3): 1351 - 1364.

[3] LIU X L, YU J D, FENG Z Y, et al. Multi-agent reinforcement learning for resource allocation in IoT networks with edge computing. China Communications, 2020, 17(9): 220 - 236.

[4] GHOSH A M, GROLINGER K. Edge-cloud computing for IoT data analytics: embedding intelligence in the edge with deep learning. IEEE Transactions on Industrial Informatics, 2021, 17(3): 2191 - 2200.

[5] LOSADA M, CORTÉS A, IRIZAR A, et al. A flexible fog computing design for low-power consumption and low latency applications. Electronics, 2020, 10(1): 57 - 79.

[6] GAO W F, ZHAO Z W, YU Z X, et al. Edge-computing-based channel allocation for deadline-driven IoT networks. IEEE Transactions on Industrial Informatics, 2020, 16 ( 10 ): 6693 -6702.

[7] XU J L, WANG S G, ZHOU A, et al. Edgence: a blockchain- enabled edg-computing platform for intelligent IoT-based dApps. China Communications, 2020, 17(4): 78 - 87.

[8] ZHANG W Y, LI S G, LIU L Y, et al. Hetero-edge: orchestration of real-time vision applications on heterogeneous edge clouds. Proceedings of the 2019 IEEE Conference on Computer Communications ( INFOCOM’19), 2019, Apr 29 - May 2, Paris, France. Piscataway, NJ, USA: IEEE, 2019: 1270 - 1278.

[9] HABAK K, AMMAR M, HARRAS K A, et al. FemtoClouds: leveraging mobile devices to provide cloud service at the edge. Proceedings of the IEEE 8th International Conference on Cloud Computing, 2015, Jun 27 - Jul 2, New York, NY, USA. Piscataway, NJ, USA: IEEE, 2015: 9 - 16.

[10] MAHENGE M P J, LI C L, SANGA C A. Mobile edge computing: cost-efficient content delivery in resource-constrained mobile computing environment. International Journal of Mobile Computing and Multimedia Communications, 2019, 10(3): 23 - 46.

[11] BAHREINI T, GROSU D. Efficient algorithms for multi- component application placement in mobile edge computing. IEEE Transactions on Cloud Computing, 2020, 10(4): 2550 - 2563.

[12] JIAO T Z, FENG X Y, GUO C P, et al. Multi-agent deep reinforcement learning for efficient computation offloading in mobile edge computing. Computers, Materials & Continua, 2023, 76(3): 3585 - 3603.

[13] ZHANG D, RASHID T, LI X K, et al. HeteroEdge: taming the heterogeneity of edge computing system in social sensing. Proceedings of the 2019 International Conference on Internationalernet of Things Design and Implementation ( IoTDI’19), 2019, Apr 15 - 18, Montreal, Canada. Piscataway, NJ, USA: IEEE, 2019: 37 - 48.

[14] TANG L, HE S B. Multi-user computation offloading in mobile edge computing: a behavioral perspective. IEEE Network, 2018, 32(1): 48 - 53.

[15] ZHANG D, MA Y, ZHANG Y, et al. A real-time and non- cooperative task allocation framework for social sensing applications in edge computing systems. Proceedings of the 2018 IEEE Real- Time and Embedded Technology and Applications Symposium ( RTAS’18 ), 2018, Apr 11 - 13, Porto, Portugal. Piscataway, NJ, USA: IEEE, 2018: 316 - 326.

[16] BAGULA A, ABIDOYE A P, LUSILAO-ZODI G A. Service-aware clustering: an energy-efficient model for the Internet-of- things. Sensors, 2016, 16(1): Article 9.

[17] LU C H. IoT-enabled adaptive context-aware and playful cyber- Physical system for everyday energy savings. IEEE Transactions on Human-Machine Systems, 2018, 48(4): 380 - 391.

[18] SAMADI P, WEN K, XU J J, et al. Software-defined optical network for metro-scale geographically distributed data centers. Optics Express, 2016, 24(11): 12310 - 12320.

[19] BIAN W W, WANG H X, JI Y F. A balancing scheme for QoS- aware service provisioning in OPS networks. Photonic Network Communications, 2012, 23(2): 198 - 203.

[20] VAN HOUDT G, MOSQUERA C, NÁPOLESG G. A review on the long short-term memory model. Artificial Intelligence Review, 2020, 53(1): 5929 - 5955.

[21] GUPTA S, SHUKLA R S, SHUKLA R K, et al. Deep learning bidirectional LSTM based detection of prolongation and repetition in stuttered speech using weighted MFCC. International Journal of Advanced Computer Science and Applications, 2020, 11 ( 9 ): 345 - 357.

Naive-LSTM enabled service identification of edge computing in power Internet of things

Naive-LSTM enabled service identification of edge computing in power Internet of things

PDF

PDF (Mobile)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价