Data Mining at the IoT Edge
| Autor: | Giancarlo Fortino, Giuseppe Di Fatta, Pietro Gerace, Claudio Savaglio |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Emergency management
Edge device Distributed database Computer science business.industry Internet of Things 020206 networking & telecommunications 02 engineering and technology Energy consumption computer.software_genre Clustering Data modeling 0202 electrical engineering electronic engineering information engineering Edge Computing 020201 artificial intelligence & image processing Enhanced Data Rates for GSM Evolution Data mining Distributed Data Mining business Cluster analysis computer Edge computing |
| Zdroj: | ICCCN 2019 28TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN), 30/07/2019 info:cnr-pdr/source/autori:Savaglio, Claudio; Gerace, Pietro; Di Fatta, Giuseppe; Fortino, Giancarlo/congresso_nome:2019 28TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN)/congresso_luogo:/congresso_data:30%2F07%2F2019/anno:2019/pagina_da:/pagina_a:/intervallo_pagine International Conference on Computer Communications and Networks, ICCCN, 30/07/2019 info:cnr-pdr/source/autori:Savaglio, Claudio; Gerace, Pietro; Di Fatta, Giuseppe; Fortino, Giancarlo/congresso_nome:International Conference on Computer Communications and Networks, ICCCN/congresso_luogo:/congresso_data:30%2F07%2F2019/anno:2019/pagina_da:/pagina_a:/intervallo_pagine |
| DOI: | 10.1109/icccn.2019.8846941 |
| Popis: | The Internet of Things (IoT) enables the interconnection of new cyber-physical devices which generate significant traffic of distributed, heterogeneous and dynamic data at the network edge. Since several IoT applications demand for short response times (e.g., industrial applications, emergency management, real-time systems) and, at the same time, rely on resource-constrained devices, the adoption of traditional Data Mining techniques is neither effective nor efficient. Therefore, conventional Data Mining techniques need to be adjusted for optimizing response times, energy consumption and data traffic while still providing adequate accuracy as required by the IoT application. In this paper, new Data Mining approaches particularly tailored for the IoT scenario have been investigated, in particular with respect to the promising, emerging novel distributed computing paradigm of Edge Computing. In detail, two approximated versions of K-Means clustering algorithm, centralized and distributed, have been implemented in the EdgeCloudSim simulation framework and validated on a real system. As highlighted by the algorithm performance analysis, choosing an approximated and distributed clustering solution can provide benefits in terms of computation, communication and energy consumption, while maintaining high levels of accuracy. The management of such trade-off, obviously, has to be done in the light of the specific IoT application requirements. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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