TY - JOUR
T1 - Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing
AU - Seng, Jasmine Kah Phooi
AU - Ang, Kenneth Li minn
AU - Peter, Eno
AU - Mmonyi, Anthony
N1 - Funding Information:
Deep Q-networks find additional applications in managing streaming multimedia data for autonomous vehicles as captured in research presented by (Park et al., 2020) []. The research addressed the challenge of establishing reliable video streaming in fast moving autonomous vehicles and proposes a combined Mobile Edge Computing and DQN driven solution. Their design was constituted of two DQN-based decision support applications with one dedicated to the offloading decision algorithm and the other charged with the data compression decision algorithm. Autonomous vehicles benefit from the operation of a large number of digital cameras fitted at differing locations responsible for image capturing and processing. This function has high requirements for speed to support the decision-making processes that influence the safety factor of the vehicles. Caching along the MEC supported by 5G technologies provides reasonable support. However, the method proposed seeks to achieve a greater bandwidth efficiency which promotes video offloading and compression operations for fast streaming as represented in .
Publisher Copyright:
© 2022 by the authors.
PY - 2022/7
Y1 - 2022/7
N2 - The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.
AB - The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.
KW - edge AI
KW - edge computing
KW - edge multimedia
KW - edge multimedia analytics
KW - intelligence edge
KW - multimedia processing
UR - http://www.scopus.com/inward/record.url?scp=85136344461&partnerID=8YFLogxK
U2 - 10.3390/electronics11142239
DO - 10.3390/electronics11142239
M3 - Review article
AN - SCOPUS:85136344461
SN - 2079-9292
VL - 11
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 14
M1 - 2239
ER -
