Journal Article10.48550/arXiv.2211.14049
Task-Oriented Communication for Edge Video Analytics
TL;DR: In this article , a task-oriented communication framework for edge video analytics is proposed, where multiple devices collect the visual sensory data and transmit the informative features to an edge server for processing.
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Abstract: With the development of artificial intelligence (AI) techniques and the increasing popularity of camera-equipped devices, many edge video analytics applications are emerging, calling for the deployment of computation-intensive AI models at the network edge. Edge inference is a promising solution to move the computation-intensive workloads from low-end devices to a powerful edge server for video analytics, but the device-server communications will remain a bottleneck due to the limited bandwidth. This paper proposes a task-oriented communication framework for edge video analytics, where multiple devices collect the visual sensory data and transmit the informative features to an edge server for processing. To enable low-latency inference, this framework removes video redundancy in spatial and temporal domains and transmits minimal information that is essential for the downstream task, rather than reconstructing the videos at the edge server. Specifically, it extracts compact task-relevant features based on the deterministic information bottleneck (IB) principle, which characterizes a tradeoff between the informativeness of the features and the communication cost. As the features of consecutive frames are temporally correlated, we propose a temporal entropy model (TEM) to reduce the bitrate by taking the previous features as side information in feature encoding. To further improve the inference performance, we build a spatial-temporal fusion module at the server to integrate features of the current and previous frames for joint inference. Extensive experiments on video analytics tasks evidence that the proposed framework effectively encodes task-relevant information of video data and achieves a better rate-performance tradeoff than existing methods.
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Citations
Opportunities, Applications, and Challenges of Edge-AI Enabled Video Analytics in Smart Cities: A Systematic Review
Elarbi Badidi,Karima Moumane,Firdaous El Ghazi +2 more
TL;DR: The various artificial intelligence models and privacy-preserving techniques used in Edge AI-assisted video analytics in smart cities are examined, including security and surveillance, transportation and traffic management, healthcare, education, sports and entertainment, and many more.
28
Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication
TL;DR: In this article , a survey of unified/universal performance assessment metrics of SemCom and goal-oriented SemCom is presented, as the existing metrics are purely statistical and hardly applicable to reasoning type tasks that constitute the heart of 6G and beyond.
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Green Edge AI: A Contemporary Survey
TL;DR: A contemporary survey on green edge AI is presented by analyzing the principal energy consumption components of edge AI systems to identify the fundamental design principles of green edgeAI, and exploring energy-efficient design methodologies for the three critical tasks in edgeAI systems, including training data acquisition, edge training, and edge inference.
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Prioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics
Zhengru Fang,Senkang Hu,Jingjing Wang,Yiqin Deng,Xianhao Chen,Yuguang Fang +5 more
- 30 Aug 2024
TL;DR: This paper introduces the Prioritized Information Bottleneck (PIB) framework for edge video analytics, prioritizing shared data based on SNR and camera coverage to reduce redundancy, latency, and communication costs, improving object detection accuracy and reducing costs by 82.80%.
Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework
Hongru Li,Wentao Yu,Hengtao He,Jiawei Shao,S. Song,Jun Zhang,Khaled Ben Letaief +6 more
- 21 May 2023
TL;DR: Wang et al. as mentioned in this paper proposed a class conditional information bottleneck (CCIB) approach to detect out-of-distribution (OoD) data in task-oriented communication networks.
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