Data Stream Mining in Fog Computing Environment with Feature Selection Using Ensemble of Swarm Search Algorithms

Fong, Simon; Li, Tengyue; Mohammed, Sabah

doi:10.1007/978-981-15-6695-0_3

Cited by 1 publication

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“…Feature selection powered by swarm search is used as a pre-processing method for improving the accuracy and speed of local Fog data analytics. Fong and Mohammed [ 63 ] conduct an experiment testing several feature selection search methods on the Gas Sensor Array Drift dataset, confronting a conventional decision tree algorithm (C4.5) with a data stream mining decision tree algorithm, called Hoeffding Tree (HT), and conclude that fog computing using HT coupled with Harmony feature selection could reach good accuracy, low latency and scalability for this dataset.…”

Section: Answering the Rqsmentioning

confidence: 99%

A Systematic Literature Review on Distributed Machine Learning in Edge Computing

Filho

Marques

Chang

et al. 2022

Sensors

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Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies.

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Section: Answering the Rqsmentioning

confidence: 99%