Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning

Gamazo-Real, Jose-Carlos; Torres Fernández, Raúl; Murillo Armas, Adrián

doi:10.1016/j.engappai.2023.107149

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning

Cited by 3 publications

References 66 publications

A Comprehensive Review of Machine Learning and Edge Computing Integration in the IoT Environments

A Comprehensive Review of Machine Learning and Edge Computing Integration in the IoT Environments

Experience with the Implementation of Machine Learning on ESP32-Based Edge Devices

Performance Evaluation of Split Computing with TinyML on IoT Devices

Contact Info

Product

Resources

About