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Edge-Node-Aware Adaptive Data Processing Framework for Smart Grid

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Abstract

Smart grid is an autonomous power generation and production system, that includes various energy management sub-systems such as energy efficient resources, smart appliances, renewable energy resources and smart meters. It is a sensory-based power network that adopts digital technology to supply electricity to consumers and industries. Recently, it is observed that the grid infrastructure is rapidly transforming network topology towards green and eco-friendly computing. For this, edge-computing environment is being adopted to inter-connect distributed IoT nodes in peer-to-peer sequential order. With this approach, the grid ideally cope to green computing issues than traditional infrastructure, however, it faces certain data processing problems such as differentiating IoT and non-IoT data segments, storing data chunk with end of file (EOF) assurance, management of replica data segments and systematic analytics of edge node datasets. This produces operational latency at edges as well as onto segment reservoir and results delay in exchanging data segments at smart grid. This paper presents an Edge-node-aware framework that empowers nodes to intelligently process IoT and counterpart data segments, a portable plugin to identify EOF data chunk, reliable replica management system and hassle-free dataset analytics. The simulation results show that the proposed framework effectively manage data segments and store into smart grid reservoir.

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References

  1. Farhangi, H. (2009). The path of the smart grid. IEEE Power and Energy Magazine, 8(1), 18–28.

    Article  Google Scholar 

  2. Li, H., Ota, K., & Dong, M. (2018). Learning IoT in edge: Deep learning for the internet of things with edge computing. IEEE Network, 32(1), 96–101.

    Article  Google Scholar 

  3. Siddiqui, I. F., et al. (2017). Optimizing lifespan and energy consumption by smart meters in green-cloud-based smart grids. IEEE Access, 5, 20934–20945.

    Article  Google Scholar 

  4. Hussain, Md., Alam, M. S., & Beg, M. M. (2018). Fog computing in IoT aided smart grid transition-requirements, prospects, status quos and challenges. arXiv preprint arXiv:1802.01818.

  5. Bonomi, F., et al. (2012). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on mobile cloud computing. ACM.

  6. Xu, S., Qian, Y., & Hu, R. H. (2018). Reliable and resilient access network design for advanced metering infrastructures in smart grid. IET Smart Grid, 1.1, 24–30.

    Article  Google Scholar 

  7. Ashok, K., Divan, D., & Lambert, F. (2018). Grid edge analytics platform with AMI data. In 2018 IEEE power & energy society innovative smart grid technologies conference (ISGT). IEEE.

  8. Simmhan, Y., et al. (2018). Towards a datadriven IoT software architecture for smart city utilities. Software: Practice and Experience, 48(7), 1390–1416.

    Google Scholar 

  9. Manogaran, G., et al. (2018). A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Computer Systems, 82, 375–387.

    Article  Google Scholar 

  10. Al-Turjman, F. (2018). Mobile couriers selection for the smart-grid in smart-cities pervasive sensing. Future Generation Computer Systems, 82, 327–341.

    Article  Google Scholar 

  11. Han, K., et al. (2018). Application-driven end-to-end slicing: When wireless network virtualization orchestrates with NFV-based mobile edge computing. IEEE Access, 6, 26567–26577.

    Article  Google Scholar 

  12. de Assuncao, M. D., da Silva Veith, A., & Buyya, R. (2018). Distributed data stream processing and edge computing: A survey on resource elasticity and future directions. Journal of Network and Computer Applications, 103, 1–17.

    Article  Google Scholar 

  13. Yang, J., Lu, Z., & Wu, J. (2018). Smart-toy-edge-computing-oriented data exchange based on blockchain. Journal of Systems Architecture, 87, 36–48.

    Article  Google Scholar 

  14. Xing, J., Dai, H., & Yu, Z. (2018). A distributed multi-level model with dynamic replacement for the storage of smart edge computing. Journal of Systems Architecture, 83, 1–11.

    Article  Google Scholar 

  15. Pappu, S. J., et al. (2018). Identifying topology of low voltage distribution networks based on smart meter data. IEEE Transactions on Smart Grid, 9.5, 5113–5122.

    Article  Google Scholar 

  16. Danielsson, P.-E. (1980). Euclidean distance mapping. Computer Graphics and Image Processing, 14(3), 227–248.

    Article  Google Scholar 

  17. Qureshi, N. M. F., et al. (2018). An aggregate mapreduce data block placement strategy for wireless IoT edge nodes in smart grid. Wireless Personal Communications, 1–12.

  18. Power Grid Datasets. https://github.com/ComplexNetTSP/ComplexNetWiki/wiki/PowerGrid-datasets. Accessed Sept 2018.

  19. Memos, V. A., et al. (2018). An efficient algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework. Future Generation Computer Systems, 83, 619–628.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

    Isma Farah Siddiqui

  2. Department of Computer Education, Sungkyunkwan University, Seoul, South Korea

    Nawab Muhammad Faseeh Qureshi

  3. Faculty of Electrical, Electronics, and Computer Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

    Bhawani Shankar Chowdhry

  4. Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

    Muhammad Aslam Uqaili

Authors
  1. Isma Farah Siddiqui
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  2. Nawab Muhammad Faseeh Qureshi
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  3. Bhawani Shankar Chowdhry
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  4. Muhammad Aslam Uqaili
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Correspondence to Bhawani Shankar Chowdhry.

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Siddiqui, I.F., Qureshi, N.M.F., Chowdhry, B.S. et al. Edge-Node-Aware Adaptive Data Processing Framework for Smart Grid. Wireless Pers Commun 106, 179–189 (2019). https://doi.org/10.1007/s11277-019-06264-7

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  • DOI: https://doi.org/10.1007/s11277-019-06264-7

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