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Crowdsourcing Through TinyML as aWay to Engage End-Users in IoT Solutions
Universitat Politecnica de Valencia, Spain.
The Abdus Salam International Centre for Theoretical Physics, Italy.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0003-0372-7195
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (Software and Information Quality)ORCID iD: 0000-0003-1154-5308
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2023 (English)In: Mobile Crowdsourcing: From Theory to Practice / [ed] Jie Wu, En Wang, Switzerland: Springer, 2023, 1, p. 359-387Chapter in book (Other academic)
Abstract [en]

This book offers the latest research results in recent development on the principles, techniques and applications in mobile crowdsourcing. It presents state-of-the-art content and provides an in-depth overview of the basic background in this related field. Crowdsourcing involves a large crowd of participants working together to contribute or produce goods and services for the society. The early 21st century applications of crowdsourcing can be called crowdsourcing 1.0, which includes businesses using crowdsourcing to accomplish various tasks, such as the ability to offload peak demand, access cheap labor, generate better results in a timely matter, and reach a wider array of talent outside the organization.  Mobile crowdsensing can be described as an extension of crowdsourcing to the mobile network to combine the idea of crowdsourcing with the sensing capacity of mobile devices. As a promising paradigm for completing complex sensing and computation tasks, mobile crowdsensing serves the vital purpose of exploiting the ubiquitous smart devices carried by mobile users to make conscious or unconscious collaboration through mobile networks. Considering that we are in the era of mobile internet, mobile crowdsensing is developing rapidly and has great advantages in deployment and maintenance, sensing range and granularity, reusability, and other aspects. Due to the benefits of using mobile crowdsensing, many emergent applications are now available for individuals, business enterprises, and governments. In addition, many new techniques have been developed and are being adopted.

Place, publisher, year, edition, pages
Switzerland: Springer, 2023, 1. p. 359-387
Series
Wireless Networks, ISSN 2366-1186, E-ISSN 2366-1445
Keywords [en]
Internet of Things, IoT, Crowd sourcing, Machine Learning, ML, TinyML
National Category
Computer Engineering
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-123416DOI: 10.1007/978-3-031-32397-3_14Scopus ID: 2-s2.0-85165996934ISBN: 9783031323973 (electronic)ISBN: 9783031323966 (print)OAI: oai:DiVA.org:lnu-123416DiVA, id: diva2:1785400
Available from: 2023-08-02 Created: 2023-08-02 Last updated: 2023-08-25Bibliographically approved

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Ahlgren, FredrikOlsson, Tobias

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