An ANFIS-based compatibility scorecard for IoT integration in websites

Kumar, A ORCID: https://orcid.org/0000-0003-4263-7168 and Arora, A (2020) An ANFIS-based compatibility scorecard for IoT integration in websites. Journal of Supercomputing, 76 (4). pp. 2568-2596. ISSN 0920-8542

Preview

Accepted Version
Available under License In Copyright.
Download (1MB) | Preview

Official URL: https://link.springer.com/article/10.1007/s11227-0...

Abstract

Cyber-physical systems and Internet of Things (IoT) form two different levels of the vertical digital integration. Integration of websites with IoT-connected devices has compelled creation of new web design and development strategies where websites are designed keeping in mind the permutations of smart devices. The design should be seamless across different devices and the website design company or web designer should be well informed and aware of the different considerations for design with IoT interactions. In this work, we expound the effectiveness of IoT integration in website design. To realize an IoT-powered IT ecosystem as an essential technology for improving customer experience, a strength–weakness–opportunity–threat analysis is done. Further, with an intent to apprehend the integration support that an existing GUI front end may provide to a smart device, an ANFIS model is proposed to determine the compatibility of an e-commerce website for integration with IoT devices. A dataset of 600 e-commerce websites from.com domain is used to train and test the learning model. Seven features (page loading speed, broken links, browser compatibility, resolution, total size, privacy and security, and interface and typography) which impact the compatibility of IoT integration in websites have been used. Evaluation criteria for assigning score to each feature has been identified. Finally, the compatibility score, the IoTScoresite which evaluates the websites’ integration capabilities and support to IoT devices is generated by adding all the feature scores. The preliminary results generated using the prediction model clearly determine the worthiness of website for IoT integration.

Item Type:	Article
Peer-reviewed:	Yes
Date Deposited:	20 Apr 2022 14:51
Publisher:	Springer Verlag
Additional Information:	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11227-019-03026-x
Divisions:	Faculties > Science and Engineering
Subject terms:	Science & Technology, Technology, Computer Science, Hardware & Architecture, Computer Science, Theory & Methods, Engineering, Electrical & Electronic, Computer Science, Engineering, IoT integration, Website design, Cyber-physical systems, ANFIS, Machine learning, THINGS IOT, INTERNET, INFORMATION, ELEMENTS, SYSTEM, MODEL, 0803 Computer Software, 0805 Distributed Computing, Distributed Computing
URI:	https://e-space.mmu.ac.uk/id/eprint/629565
DOI:	https://doi.org/10.1007/s11227-019-03026-x
ISSN	0920-8542
e-ISSN	1573-0484

Impact and Reach

Statistics

DownloadsShow export options

Activity Overview

6 month trend

395Downloads

6 month trend

71Hits

Additional statistics for this dataset are available via IRStats2.

Altmetric

Repository staff only

Edit record