Developing and comparing cloud-based fuzzy systems for monitoring health related signals in assistive environments

Pandya, B. ORCID: 0000-0002-9396-1286, Shah, D., Pourabdollah, A. ORCID: 0000-0001-7737-1393 and Lotfi, A. ORCID: 0000-0002-5139-6565, 2022. Developing and comparing cloud-based fuzzy systems for monitoring health related signals in assistive environments. In: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments 29 June – 01 July 2022, Corfu Island, Greece. New York: ACM, pp. 407-413. ISBN 9781450396318

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Abstract

Fuzzy logic system (FLS) based applications in Ambient Intelligence (AmI) have exhibited their capabilities in realizing intelligent environments. FLS deployment is usually associated with non-scalable hardware and software platforms. For intensive computation-based FLSs, distributing FLS openness as web services is known as fuzzy-as-a-service (FaaS), wherein a service is self-sufficiently developed from FLS itself, permits system independence, ingenuousness, load balancing and well-organised resource allocation. The fuzzy mark-up language (FML) and its related software libraries such as Simpful and Java FML (JFML) have added to the novelty. This study aims to develop and compare service-oriented architecture based FaaS by employing Microsoft Azure and Amazon Web Service (AWS) for AmI implementation in e-healthcare. Data is collected, processed and monitored via the Internet. Heart rate and SpO2 data are used in the study. The obtained data is evaluated by a fuzzy inference system using Microsoft Azure and AWS and displayed via a mobile application. A simulation scenario of real-time human activity recognition using a rule-based FLS is demonstrated. Results indicate that the proposed FaaS requires an average of 0.306 seconds to process a single real-time data using AWS and JFML. Conversely, Azure and Simpful require 0.456 seconds for processing. Thus, as per the selected scenario in this research, it can be concluded that there is a variation in the processing time using different cloud-based services. For example, AWS in one of the use case scenarios takes 19.65% less processing time of compared to Microsoft Azure.

Item Type: Chapter in book
Description: Paper presented at PETRA '22: The 15th International Conference on PErvasive Technologies Related to Assistive Environments, Corfu Island, Greece, 29 June – 1 July 2022.
Creators: Pandya, B., Shah, D., Pourabdollah, A. and Lotfi, A.
Publisher: ACM
Place of Publication: New York
Date: 29 June 2022
ISBN: 9781450396318
Identifiers:
NumberType
10.1145/3529190.3534742DOI
1564593Other
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 15 Jul 2022 09:15
Last Modified: 15 Jul 2022 09:15
URI: http://irep.ntu.ac.uk/id/eprint/46605

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