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An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid | Wireless Personal Communications Skip to main content
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An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid

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Abstract

Big data analytics has simplified processing complexity of large dataset in a distributed environment. Many state-of-the-art platforms i.e. smart grid has adopted the processing structure of big data and manages a large volume of data through MapReduce paradigm at distribution ends. Thus, whenever a wireless IoT edge node bundles a sensor dataset into storage media, MapReduce agent performs analytics and generates output into the grid repository. This practice has efficiently reduced the consumption of resources in such a giant network and strengthens other components of the smart grid to perform data analytics through aggregate programming. However, it consumes an operational latency of accessing large dataset from a central repository. As we know that, smart grid processes I/O operations of multi-homing networks, therefore, it accesses large datasets for processing MapReduce jobs at wireless IoT edge nodes. As a result, aggregate MapReduce at wireless IoT edge node produces a network congestion and operational latency problem. To overcome this issue, we propose Wireless IoT Edge-enabled Block Replica Strategy (WIEBRS), that stores in-place, partition-based and multi-homing block replica to respective edge nodes. This reduces the delay latency of accessing datasets for aggregate MapReduce and increases the performance of the job in the smart grid. The simulation results show that WIEBRS effective decreases operational latency with an increment of aggregate MapReduce job performance in the smart grid.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1A6A3A11932892).

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Authors and Affiliations

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

    Nawab Muhammad Faseeh Qureshi & Jaehyoun Kim

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

    Isma Farah Siddiqui

  3. Department of Computer Systems Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

    Mukhtiar Ali Unar

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

    Muhammad Aslam Uqaili

  5. Department of Software, Sungkyunkwan University, Suwon, South Korea

    Choon Sung Nam

  6. Department of Software Engineering, Sungkyunkwan University, Suwon, South Korea

    Dong Ryeol Shin

  7. Faculty of Science and Technology, University of the Faroe Islands, Tórshavn, Faroe Islands

    Ali Kashif Bashir

  8. Department of Computer Science and Engineering, Hanyang University ERICA, Ansan, South Korea

    Asad Abbas

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  1. Nawab Muhammad Faseeh Qureshi
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  2. Isma Farah Siddiqui
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  6. Dong Ryeol Shin
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  9. Asad Abbas
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Correspondence to Nawab Muhammad Faseeh Qureshi.

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Qureshi, N.M.F., Siddiqui, I.F., Unar, M.A. et al. An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid. Wireless Pers Commun 106, 2225–2236 (2019). https://doi.org/10.1007/s11277-018-5936-6

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