Unsupervised Crowd-Assisted Learning Enabling Location-Aware Facilities

The accelerated evolution of Internet of Things (IoT) architectures and their incorporation in vehicles, buildings, or cities provide the ideal environment for the development and optimization of smart services. Under this light, positioning services that harvest location fingerprinting based on rec...

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Bibliographic Details
Published in:	IEEE internet of things journal Vol. 5; no. 6; pp. 4699 - 4713
Main Authors:	Sikeridis, Dimitrios, Rimal, Bhaskar Prasad, Papapanagiotou, Ioannis, Devetsikiotis, Michael
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.12.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Bluetooth low energy (BLE) Computational modeling Computer architecture crowdsensing Edge computing Fingerprinting Fingerprints Indoor environments indoor localization Infrastructure Internet of Things Internet of Things (IoT) Localization location-aware infrastructure Machine learning Microprocessors Position (location) Probabilistic logic Sensors Signal strength smart environment unsupervised learning Wireless communication
ISSN:	2327-4662, 2327-4662
Online Access:	Get full text
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