AI Strategy and Concepts Bibliography

WIKINDX Resources

Carvalho, G., Cabral, B., Pereira, V., & Bernardino, J. (2020). Computation offloading in edge computing environments using artificial intelligence techniques. Engineering Applications of Artificial Intelligence, 95, 103840. 
Added by: SijanLibrarian (2020-08-12 09:53:26)   Last edited by: SijanLibrarian (2020-08-12 09:55:05)
Resource type: Journal Article
BibTeX citation key: Carvalho2020
Email resource to friend
View all bibliographic details
Categories: Artificial Intelligence, Computer Science, Data Sciences, Decision Theory, Engineering, General, Military Science
Subcategories: 5G, Autonomous systems, Big data, Command and control, Edge AI, Informatics, Internet of things, JADC2, Machine learning, Q-learning, Robotics
Creators: Bernardino, Cabral, Carvalho, Pereira
Collection: Engineering Applications of Artificial Intelligence
Views: 46/61
Views index: 15%
Popularity index: 3.75%
Abstract
Edge Computing (EC) is a recent architectural paradigm that brings computation close to end-users with the aim of reducing latency and bandwidth bottlenecks, which 5G technologies are committed to further reduce, while also achieving higher reliability. EC enables computation offloading from end devices to edge nodes. Deciding whether a task should be offloaded, or not, is not trivial. Moreover, deciding when and where to offload a task makes things even harder and making inadequate or off-time decisions can undermine the EC approach. Recently, Artificial Intelligence (AI) techniques, such as Machine Learning (ML), have been used to help EC systems cope with this problem. AI promises accurate decisions, higher adaptability and portability, thus diminishing the cost of decision-making and the probability of error. In this work, we perform a literature review on computation offloading in EC systems with and without AI techniques. We analyze several AI techniques, especially ML-based, that display promising results, overcoming the shortcomings of current approaches for computing offloading coordination We sorted the ML algorithms into classes for better analysis and provide an in-depth analysis on the use of AI for offloading, in particular, in the use case of offloading in Vehicular Edge Computing Networks, actually one technology that gained more relevance in the last years, enabling a vast amount of solutions for computation and data offloading. We also discuss the main advantages and limitations of offloading, with and without the use of AI techniques.
  
wikindx 6.2.2 ©2003-2020 | Total resources: 1447 | Username: -- | Bibliography: WIKINDX Master Bibliography | Style: American Psychological Association (APA) | Database queries: 65 | DB execution: 0.32354 secs | Script execution: 0.33845 secs