Fog-centric localization for ambient assisted living

Bhargava, Kriti and McManus, Gary and Ivanov, Stepan (2018) Fog-centric localization for ambient assisted living. In: 2017 International Conference on Engineering, Technology and Innovation : Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings. 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings . Institute of Electrical and Electronics Engineers Inc., PRT, pp. 1424-1430. ISBN 9781538607749

Full text not available from this repository. (Request a copy)

Abstract

Ambient Assisted Living (AAL) is a novel discipline that aims at improving the quality of life for all generations, especially the elderly, with the help of information and communication technologies. Behavioral tracking AAL systems necessitate the monitoring and understanding of daily activities and preferences of the user for design of customized, context-aware services and detection of behavior anomalies. Localization of the user is, therefore, key to facilitate real-time activity monitoring in AAL applications. Although several localization techniques have been proposed to date, majority of them incur a high operational cost owing to dependency on dense sensor deployments for ambient intelligence or use of expensive hardware such as GPS receivers. In this paper, we propose a low-cost Wireless Sensor Networks (WSN) system, comprising of a single wearable device and a cloud gateway, for outdoor localization in the context of AAL. With the inception of the Fog Computing paradigm, we consider the implementation of a light-weight data mining technique, Iterative Edge Mining (IEM), on the wearable device for on-board activity recognition. IEM is based on the classification of signal distributions to enable real-time mobility tracking as the user moves around an environment. Given the topology information and the activity sequence generated by the algorithm, we estimate the user location by associating the distance covered over time with the orientation values. Alerts are signaled locally upon detection of behavior anomalies and transmitted to the gateway node using a delay-tolerant communication framework. As such, IEM runs autonomously on the sensor node without interaction with external objects, thereby, improving the responsiveness as well as the operational cost of our system. We evaluate the performance of IEM in terms of localization accuracy in an outdoor environment.

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 Funding Information: 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”. The work has also been promoted under the project CARELINK, AAL-CALL-2016-049 funded by AAL JP, and co-funded by the European Commission and National Funding Authorities of Ireland, Belgium, Portugal and Switzerland. Publisher Copyright: © 2017 IEEE.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1700/1706
Departments or Groups:
Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:18
Last Modified:	14 Jul 2023 06:10
URI:	http://repository-testing.wit.ie/id/eprint/5169

Actions (login required)

View Item