Fine-Grained Data Processing Framework for Heterogeneous IoT Devices in Sub-aquatic Edge Computing Environment
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Fine‑Grained Data Processing Framework for Heterogeneous IoT Devices in Sub‑aquatic Edge Computing Environment Jahwan Koo1 · Nawab Muhammad Faseeh Qureshi2 Accepted: 3 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Edge computing · Internet of things (IoT) · Big data processing · Bloom filter
1 Introduction Sub-aquatic is a feature that is a contrast to the traditional open air environment and allows devices to be used with a limitation of waterproof functionality [1]. There are several technological advancements occuring in the medium of the land such as, underwater communication and signaling [2], data processing [3], network infrastructures [4], centralized and edge node communication [5] and internet of underwater things (IoUT) [6].
* Nawab Muhammad Faseeh Qureshi [email protected] Jahwan Koo [email protected] 1
College of Software, Sungkyunkwan University, Suwon, South Korea
2
Department of Computer Education, Sungkyunkwan University, Seoul, South Korea
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J. Koo, N. M. F. Qureshi
These technologies have two different working impacts such as when they are used in the water but with the wireless communication then we only find a single option that is related to underwater wireless sensor networks (UWSN) [7]. The wired connectivity medium still works in the same way like open air [8]. The big data analytics provides a platform to process and exchange the large-scale datasets in the distributed underwater and air medium computing environment [9]. This analytics depends on the interaction of various nodes having persistent storage devices at the far distant places [10]. Apache Hadoop provides a functional way to process large-scale datasets in t
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