Wireless Link Quality Prediction in IoT Networks
| Autor: | Miguel Landry Foko Sindjoung, Pascale Minet |
|---|---|
| Přispěvatelé: | Wireless Networking for Evolving & Adaptive Applications (EVA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
020203 distributed computing
Data collection business.industry Computer science Wireless network Real-time computing 020206 networking & telecommunications 02 engineering and technology Random forest Support vector machine [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] Machine learning Link quality estimation 0202 electrical engineering electronic engineering information engineering Wireless Latency (engineering) Internet of Things business TSC |
| Zdroj: | PEMWN 2019-8th IFIP/IEEE International Conference on Performance Evaluation and Modeling inWired andWireless Networks PEMWN 2019-8th IFIP/IEEE International Conference on Performance Evaluation and Modeling inWired andWireless Networks, Nov 2019, Paris, France PEMWN |
| Popis: | International audience; The knowledge of link quality in IoT networks will allow a more accurate selection of wireless links to build the routes used by data gathering. Therefore, the number of retransmissions on these links is decreased, leading to a shorter end-to-end latency, a better end-to-end reliability and a larger network lifetime. In this paper, we propose to predict link quality by means of machine learning techniques applied on two metrics: RSSI and PDR. The accuracy obtained by Logistic Regression, Linear Support Vector Machine, Support Vector Machine and Random Forest classifier is obtained on the traces of a real IoT network deployed at Grenoble. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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