Authors :
Koffka Khan
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/u98wfp47
DOI :
https://doi.org/10.5281/zenodo.8397990
Abstract :
IoT Video Streaming Classification
Framework" is a structured taxonomy designed to
comprehensively categorize and organize the
multifaceted landscape of video streaming within the
Internet of Things (IoT) ecosystem. This taxonomy
encompasses a wide range of dimensions that play a
pivotal role in defining and shaping video streaming
applications across diverse IoT domains. These
dimensions include the application domain, network
architecture, video quality, latency requirements, device
types, connectivity options, encoding and compression
techniques, security and privacy considerations,
scalability, analytics and AI integration, energy
efficiency strategies, and data storage approaches. This
classification framework serves as a valuable resource
for IoT developers, researchers, and industry
professionals seeking to understand, design, and
implement video streaming solutions in IoT scenarios. By
providing a systematic and organized structure, it
facilitates the exploration of the nuanced considerations
and choices involved in IoT video streaming applications,
thereby aiding in the development of efficient, secure,
and high-performance systems tailored to specific use
cases within the IoT landscape.
Keywords :
IoT, video streaming, taxonomy, domain, AI.
IoT Video Streaming Classification
Framework" is a structured taxonomy designed to
comprehensively categorize and organize the
multifaceted landscape of video streaming within the
Internet of Things (IoT) ecosystem. This taxonomy
encompasses a wide range of dimensions that play a
pivotal role in defining and shaping video streaming
applications across diverse IoT domains. These
dimensions include the application domain, network
architecture, video quality, latency requirements, device
types, connectivity options, encoding and compression
techniques, security and privacy considerations,
scalability, analytics and AI integration, energy
efficiency strategies, and data storage approaches. This
classification framework serves as a valuable resource
for IoT developers, researchers, and industry
professionals seeking to understand, design, and
implement video streaming solutions in IoT scenarios. By
providing a systematic and organized structure, it
facilitates the exploration of the nuanced considerations
and choices involved in IoT video streaming applications,
thereby aiding in the development of efficient, secure,
and high-performance systems tailored to specific use
cases within the IoT landscape.
Keywords :
IoT, video streaming, taxonomy, domain, AI.
