A fog computing approach for localization in WSN

Bhargava, Kriti and Ivanov, Stepan (2018) A fog computing approach for localization in WSN. In: 2017 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications : Engaged Citizens and their New Smart Worlds, PIMRC 2017 - Conference Proceedings. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC . Institute of Electrical and Electronics Engineers Inc., CAN, pp. 1-7. ISBN 9781538635315

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The Fog Computing paradigm proposes an extension of the cloud-based computing to the network edges in the Internet of Things. It facilitates localized analysis closer to the data sources for improved responsiveness of the system as well as cloud-based learning for historical analysis. In this paper, we present our fog-enabled Wireless Sensor Network (WSN) system for activity monitoring and localization in the context of Ambient Assisted Living. Our WSN architecture consists of two types of devices - a wearable sensor device and a cloud gateway node. We discuss our Edge Mining approach for real-time activity classification on the sensor device as well as the Genetic Algorithm used for cloud-based analysis. The design of our analytical framework together with the communication model addresses the challenge of sensor-cloud integration. We evaluate the performance of our system for outdoor localization of the elderly. The analysis is based on acceleration data collected using our wearable device across different activity sequences obtained from the Kasteren dataset.

Item Type: Book Section
Additional Information: Funding Information: ACKNOWLEDGMENT This work has received support from the Science Foundation Ireland (SFI) and the Agriculture and Food Development Authority, Ireland (TEAGASC) as part of the SFI TEAGASC Future Agri-Food Partnership, in a project (13/IA/1977) titled “Using precision technologies, technology platforms and computational biology to increase the economic and environmental sustainability of pasture based production systems”. Publisher Copyright: © 2017 IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2200/2208
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Depositing User: Admin SSL
Date Deposited: 19 Oct 2022 23:14
Last Modified: 07 Jun 2023 18:37
URI: http://repository-testing.wit.ie/id/eprint/4794

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