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Fine-Grained Data Processing Framework for Heterogeneous IoT Devices in Sub-aquatic Edge Computing Environment

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Abstract

Sub-aquatic data processing is a procedure that exchanges datasets through underwater sensory devices in the distributed computing environment. This paradigm has evolved techniques of data exchange and signal processing over time and uses big data frameworks to store processed datasets at edge nodes. Also, it uses modern IoT devices that capture sensory data tuples of water temperature, turbidity, speed, and pressure levels. Recently, we observe that the edge nodes that acquire the dataset of heterogeneous IoT devices are becoming overwhelmed with the issue of tuple non-classification at the level of data encapsulation. This issue raises a few concerns such as (a) ineffective tuple wrapup, (b) bundle compression failovers, (c) bundle block placement latency, and (d) end-of-file replica build latency. This paper proposes a fine-grained processing framework that normalizes tuple non-classification through enhanced false-positive function and assembles IoT sensory tuples with the in-memory capacity to rectify compression failovers. This solution leads to a tremendous decrease in bundle block placement and end-of-file replica latencies. The simulation results depict the effectiveness of fine-grained processing framework through easing the edge nodes in the sub-aquatic distributed environment.

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Acknowledgements

This paper was produced with support from the Ministry of Science and ICT’s Broadcasting and Communication Development Fund and may differ from the official opinion of the Ministry of Science and ICT.

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Correspondence to Nawab Muhammad Faseeh Qureshi.

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Koo, J., Qureshi, N.M.F. Fine-Grained Data Processing Framework for Heterogeneous IoT Devices in Sub-aquatic Edge Computing Environment. Wireless Pers Commun 116, 1407–1422 (2021). https://doi.org/10.1007/s11277-020-07803-3

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