ESRRA-IoT: Edge-based spatial redundancy reduction approach for Internet of Things

Ismael, Waleed M.; Gao, Mingsheng; Yemeni, Zaid

doi:10.1016/j.iot.2021.100388

Cited by 10 publications

(2 citation statements)

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“…The works [ 42 ] and [ 24 ] have used a component-based C++ and modular OMNeT++ [ 48 ] network simulator library to perform their experiments. The works [ 32 , 41 , 49 ] and [ 33 ] also used the same approach to evaluated their solution. They have written their simulator using the Python programming language and used the dataset presented by the work [ 43 ] to perform their experiments.…”

Section: Systematic Literature Mapping Reportmentioning

confidence: 99%

“…In [ 42 ] and [ 24 ] works, the authors used the OMNeT++ [ 48 ] network simulator library, while the works [ 23 ] and [ 45 ] used MATLAB. The authors [ 32 , 41 , 49 ] and [ 33 ] wrote their simulations using the Python programming language, while the works [ 40 ] and [ 50 ] simulated using the Java programming language. Finally, the selection of the edge hardware when implementing the data reduction techniques in the edge-based architecture can be explored considering different data workloads classification.…”

Section: Open Research Issuesmentioning

confidence: 99%

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An overview of data reduction solutions at the edge of IoT systems: a systematic mapping of the literature

et al. 2022

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Internet of Things (IoT) is a technology that connects devices of different types and characteristics through a network. The massive quantity of the heterogeneous generated data by the sensors imposes many challenges in making these data available to IoT applications. Data reduction and preprocessing are promising concepts that help to handle these data efficiently before storing them. Applying data reduction methods at the edge has emerged as an efficient solution. In such context, this systematic mapping is intended to investigate the data reduction solutions performed exclusively at the edge through a set of research questions. To reach this objective, we performed a Systematic Literature Mapping (SLM) in which 35 papers were strictly analyzed among a total of 853 articles. Finally, we present the results of these analyses answering questions that relate to the researcher’s used techniques, hardware technologies, used data type, and contributed objects to perform the data reduction techniques on the edge of the IoT systems.

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Section: Systematic Literature Mapping Reportmentioning

confidence: 99%