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A Simulation-driven Methodology for IoT Data Mining Based on Edge Computing

Published:08 March 2021Publication History
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Abstract

With the ever-increasing diffusion of smart devices and Internet of Things (IoT) applications, a completely new set of challenges have been added to the Data Mining domain. Edge Mining and Cloud Mining refer to Data Mining tasks aimed at IoT scenarios and performed according to, respectively, Cloud or Edge computing principles. Given the orthogonality and interdependence among the Data Mining task goals (e.g., accuracy, support, precision), the requirements of IoT applications (mainly bandwidth, energy saving, responsiveness, privacy preserving, and security) and the features of Edge/Cloud deployments (de-centralization, reliability, and ease of management), we propose EdgeMiningSim, a simulation-driven methodology inspired by software engineering principles for enabling IoT Data Mining. Such a methodology drives the domain experts in disclosing actionable knowledge, namely descriptive or predictive models for taking effective actions in the constrained and dynamic IoT scenario. A Smart Monitoring application is instantiated as a case study, aiming to exemplify the EdgeMiningSim approach and to show its benefits in effectively facing all those multifaceted aspects that simultaneously impact on IoT Data Mining.

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        • Published in

          cover image ACM Transactions on Internet Technology
          ACM Transactions on Internet Technology  Volume 21, Issue 2
          June 2021
          599 pages
          ISSN:1533-5399
          EISSN:1557-6051
          DOI:10.1145/3453144
          • Editor:
          • Ling Liu
          Issue’s Table of Contents

          Copyright © 2021 Association for Computing Machinery.

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 8 March 2021
          • Accepted: 1 May 2020
          • Revised: 1 April 2020
          • Received: 1 March 2020
          Published in toit Volume 21, Issue 2

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