An approach to bridge the gap between ubiquitous embedded devices and JFML: A new module for Internet of Things

Rodríguez-Lozano, Francisco Javier; Gámez-Granados, Juan Carlos; Baños, Oresti; Alcalá‐Fdez, Jesús; Soto-Hidalgo, J. M.

doi:10.1109/fuzz45933.2021.9494483

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…Soto-Hidalgo et al [3] developed a new JFML module that facilitates the designer to develop and implement different components of Fuzzy Rule-Based Systems (FRBSs), including inputs, outputs, rule base, membership functions, operators, and learning elements, all on an embedded platform using IEEE Std 1855-2016. The new module supported Wi-Fi, Bluetooth, and USB connection types, thus enabling the implementation of FRBSs on a remote computer or embedded system [22].…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Stand-alone and Cloud-Based Fuzzy Logic Systems for Human Fall Detection

Pandya

Pourabdollah

Lotfi

2022

Int. J. Fuzzy Syst.

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Traditionally, fuzzy logic systems are linked to specific hardware or software systems. Observations reveal that dispersed and distributed designs of intelligent systems are gaining attraction. Due to the possible complexities of fuzzy logic computations, distributed architectures have the potential to add value to the development of fuzzy systems. However, the absence of best practices and standard methodologies may prevent widespread adoption. By broadening the IEEE-1855 (2016) standard in terms of system definition and data exchange, this research offers a standard solution for building a Service-Oriented Architecture (SOA) as a novel method of implementing fuzzy logic systems by means of a cloud-based collecting, processing, and examining data over the web. A comparison between the performances of a stand-alone hardware-dependent solution and a cloud-based solution (known as fuzzy-as-a-service) is performed. The analysis is also carried out on two different cloud service providers and software libraries (Amazon Web Services using JFML as a java-based library and Azure Web Services using Simpful as a python-based library). The analysis and evaluation are performed on a human fall detection scenario involving wearable sensors. The proposed algorithm can identify between fall and non-fall events. However, the results show that the processing time taken per 10,000 samples using smartwatch and mobile was 2220 s and 101 s for a cloud-based non-fuzzy machine learning system, 1111 s and 45 s for a cloud-based fuzzy system with AWS and JFML, and 1250 s and 97 s for a cloud-based fuzzy system with Microsoft Azure and Simpful libraries. It has been observed that a smartwatch with a fuzzy stand-alone crashed after processing 5000 samples and a mobile phone requires 179.42 s to process 10,000 samples.

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Section: Related Workmentioning

confidence: 99%