SSL: Synchronous Self-paced Learning for Internet-of-Things Devices
Authors: 
Yuanyuan Bao, Liqiu Ma, Wai ChenAuthors Info & Claims
Yuanyuan Bao, Liqiu Ma, Wai ChenAuthors Info & ClaimsPages 13 - 18
Abstract
With the emergence of Internet-of-Things (IoT), we are witnessing rapid increases in the amount of IoT devices. However, due to IoT devices are often deployed in dynamic and unmanned environment, it is imperative that the IoT devices have automatic model construction capabilities. In this paper, we propose a novel two-step approach called synchronous self-paced learning (SSL) for the IoT devices to construct the model automatically. In the first step, we design a synchronous mechanism that makes correlated devices synchronized and obtain the pseudo labels to form the original training set. In the second step, we propose a sample selection method based on reliability and diversity to filter the original training set, based on which we automatically accomplish the model construction. We conduct empirical evaluation of our SSL method compared with three state-of-the-art methods; and the evaluation shows that by applying our SSL method, we can achieve the classifier with higher accuracy.
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Index Terms
- SSL: Synchronous Self-paced Learning for Internet-of-Things Devices
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Information & Contributors
Information
Published In
November 2018
47 pages
ISBN:9781450360517
DOI:10.1145/3277893
- Editors:
- Gowri Sankar Ramachandran,
- Bhaskar Krishnamachari,
- Program Chairs:
- Sid Chi-Kin Chau,
- Yang Yang,
- Tung Fung
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Published: 04 November 2018
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SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems
November 4, 2018
Shenzhen, China
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