A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applications
원문 초록
Edge technology aims to bring Cloud resources (specifically, the compute, storage, and network) to the closed proximity of the Edge devices, i.e., smart devices where the data are produced and consumed. Embedding computing and application in Edge devices lead to emerging of two new concepts in Edge technology, namely, Edge computing and Edge analytics. Edge analytics uses some techniques or algorithms to analyze the data generated by the Edge devices. With the emerging of Edge analytics, the Edge devices have become a complete set. Currently, Edge analytics is unable to provide full support for the execution of the analytic techniques. The Edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply, small memory size, limited resources, etc. This article aims to provide a detailed discussion on Edge analytics. A clear explanation to distinguish between the three concepts of Edge technology, namely, Edge devices, Edge computing, and Edge analytics, along with their issues. Furthermore, the article discusses the implementation of Edge analytics to solve many problems in various areas such as retail, agriculture, industry, and healthcare. In addition, the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues, emerging challenges, research opportunities and their directions, and applications.
함께 살펴볼 논문
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Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning
Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks
PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification
토론
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