IoT4Fun Rapid Prototyping Toolkit for Smart Toys

Simple item page
dc.contributor.authorDe Albuquerque, Anna Priscilla
dc.contributor.authorKelner, Judith
dc.contributor.authorDias Nogueira, Thiago
dc.contributor.authorSilva Rocha Junior, Railton
dc.date.accessioned2020-01-04T07:27:15Z
dc.date.available2020-01-04T07:27:15Z
dc.date.issued2020-01-07
dc.description.abstractRapid prototyping tools turn the design of smart toys faster and easier for creative teams. Appropriate tools for smart toys should meet a list of requirements, which include distributed data collection and adaptability for assorted toy shapes and size. The IoT4Fun toolkit innovates by mixing the embedded, modular, and plug-and-play approaches. It supports motion tracking data, wireless communication, and contactless identification. IoT4Fun demonstrates its effectiveness to design a variety of smart toy solutions by fitting into a hula-hoop toy until spherical, cubic, and wearable shapes. Solutions connect with either mobile applications or other toys and play rules range from open-ended to closed behaviors. End-users exhaustively tested developed solutions, and technical assessment evaluates their integrity after playtesting sessions. Results show comparative data on battery consumption and vulnerabilities threats for data security and privacy of each design. Future versions of IoT4Fun can benefit from miniaturization, robustness, and reliability improvements.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.183
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63922
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning, Robotic, and Toy Computing
dc.subjectbattery consumption
dc.subjecthardware requirements
dc.subjectinternet of things
dc.subjectrapid prototyping
dc.subjectsmart toys
dc.titleIoT4Fun Rapid Prototyping Toolkit for Smart Toys
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0148.pdf
Size:
4.56 MB
Format:
Adobe Portable Document Format
Download

Collections

Machine Learning, Robotic, and Toy Computing