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Security Data Fusion Method for IoT Perception Layer for New Electric Power Systems

Published:06 May 2024Publication History

ABSTRACT

Due to the growth of Internet of Things (IOT) technology in recent years, IOT has effectively improved the efficiency of information transmission and production. To ensure the security and effectiveness of data fusion in the new electric power system (EPS)-oriented IOT, this article proposes a new secure data fusion method in the sensing layer of IOT. In this article, the collected data are mapped into pattern codes and then fused and transmitted to ensure the confidentiality of the data. At the same time, the security of fusion information is ensured by monitoring the fusion nodes by monitoring nodes. Finally, the security and rationality of the security data fusion method of IOT sensing layer in this article are proved by many simulation experiments. The simulation results show that: the accuracy of security data fusion in the sensing layer of IOT can be stabilized at about 93∼95% by using this method; Moreover, the simulation running time of the algorithm proposed in this article is less, and the data fusion efficiency is higher. Applying this method to secure data fusion of IOT sensing layer can effectively improve the efficiency of secure data fusion of IOT sensing layer, and reduce communication overhead and storage overhead on the premise of ensuring security attributes.

References

  1. Huican Chen, Jiecong Wang, Junlei Liu, Bilawal Rehman, Feng Qian, and Chongru Liu. 2020. Validation method for simulation model of Internet of Things-Aided power system. IEEE Access 8, (January 2020), 1185–1192. DOI:https://doi.org/10.1109/access.2019.2952561.Google ScholarGoogle ScholarCross RefCross Ref
  2. Xiangyu Kong, Yong Xu, Zaibin Jiao, Delong Dong, Xiaoxiao Yuan, and Shupeng Li. 2020. Fault location technology for power system based on information about the power internet of things. IEEE Transactions on Industrial Informatics 16, 10 (October 2020), 6682–6692. DOI:https://doi.org/10.1109/tii.2019.2960440.Google ScholarGoogle ScholarCross RefCross Ref
  3. Hui Lin, Sahil Garg, Jia Hu, Xiaoding Wang, Md. Jalil Piran, and M. Shamim Hossain. 2022. Data fusion and transfer learning empowered granular trust evaluation for Internet of Things. Information Fusion 78, (February 2022), 149–157. DOI:https://doi.org/10.1016/j.inffus.2021.09.001.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Difang Chen, Yuhua Xu, and Shijun Luo. 2021. Mining and construction of Information Opportunity cooperation mode based on big data fusion internet of things. IEEE Access 9, (January 2021), 29401–29415. DOI:https://doi.org/10.1109/access.2021.3058357.Google ScholarGoogle ScholarCross RefCross Ref
  5. Xia‐Ting Feng, Jing Zhang, Chenghao Ren, and Tingting Guan. 2018. An unequal clustering algorithm concerned with Time-Delay for internet of things. IEEE Access 6, (January 2018), 33895–33909. DOI:https://doi.org/10.1109/access.2018.2847036.Google ScholarGoogle ScholarCross RefCross Ref
  6. ZiXiang Nie, YuanZhenTai Long, Senlin Zhang, and Yueming Lu. 2022. A controllable privacy data transmission mechanism for Internet of things system based on blockchain. International Journal of Distributed Sensor Networks 18, 3 (March 2022), 155013292210884. DOI:https://doi.org/10.1177/15501329221088450.Google ScholarGoogle ScholarCross RefCross Ref
  7. Quan Fang, Mingming Zhang, Song Hu, and Yuhang Chen. 2021. Research on data fusion scheme of power internet of things based on cloud and NFV. Procedia Computer Science 183, (January 2021), 115–119. DOI:https://doi.org/10.1016/j.procs.2021.02.038.Google ScholarGoogle ScholarCross RefCross Ref
  8. Wenxiu Ding, Xuyang Jing, Zheng Yan, and Laurence T. Yang. 2019. A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion. Information Fusion 51, (November 2019), 129–144. DOI:https://doi.org/10.1016/j.inffus.2018.12.001.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Yongfeng Cui, Yuankun Ma, Zhongyuan Zhao, Ya Li, Wei Liu, and Wanneng Shu. 2018. Research on data fusion algorithm and anti-collision algorithm based on internet of things. Future Generation Computer Systems 85, (August 2018), 107–115. DOI:https://doi.org/10.1016/j.future.2018.03.016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Zhihan Lv and Houbing Song. 2020. Mobile internet of things under data physical fusion technology. IEEE Internet of Things Journal 7, 5 (May 2020), 4616–4624. DOI:https://doi.org/10.1109/jiot.2019.2954588.Google ScholarGoogle ScholarCross RefCross Ref
  11. Yi Lyu and Peng Yin. 2020. Internet of Things transmission and network reliability in complex environment. Computer Communications 150, (January 2020), 757–763. DOI:https://doi.org/10.1016/j.comcom.2019.11.054.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Nimisha Ghosh, Rourab Paul, Satyabrata Maity, Krishanu Maity, and Sayantan Saha. 2020. Fault Matters: Sensor data fusion for detection of faults using Dempster–Shafer theory of evidence in IoT-based applications. Expert Systems With Applications 162, (December 2020), 113887. DOI:https://doi.org/10.1016/j.eswa.2020.113887.Google ScholarGoogle ScholarCross RefCross Ref
  13. Jianwei Hou, Leilei Qu, and Wenchang Shi. 2019. A survey on internet of things security from data perspectives. Computer Networks 148, (January 2019), 295–306. DOI:https://doi.org/10.1016/j.comnet.2018.11.026.Google ScholarGoogle ScholarCross RefCross Ref
  14. Qing Liu and Ming Zhang. 2020. Network security situation detection of internet of things for smart city based on fuzzy neural network. International Journal of Reasoning-based Intelligent Systems 12, 3 (January 2020), 222. DOI:https://doi.org/10.1504/ijris.2020.109650.Google ScholarGoogle ScholarCross RefCross Ref
  15. Rodrigo Román, Javier López, and Stefanos Gritzalis. 2018. Evolution and trends in IoT Security. IEEE Computer 51, 7 (July 2018), 16–25. DOI:https://doi.org/10.1109/mc.2018.3011051.Google ScholarGoogle ScholarCross RefCross Ref

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      BDMIP '23: Proceedings of the 2023 International Conference on Big Data Mining and Information Processing
      November 2023
      223 pages
      ISBN:9798400709166
      DOI:10.1145/3645279

      Copyright © 2023 ACM

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      Publication History

      • Published: 6 May 2024

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