Prioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics

2024-08-30

arXiv.org

AI Summary

To address low transmission efficiency and resource waste in edge video collaborative perception caused by channel constraints and spatiotemporal redundancy, this paper proposes the Priority Information Bottleneck (PIB) framework. PIB jointly models signal-to-noise ratio (SNR) and region-of-interest (RoI) coverage as a compact feature selection criterion, enabling reconstruction-free, low-latency feature transmission. It introduces an adaptive gating mechanism based on distributed online learning (DOL), with theoretical guarantees of asymptotic optimality—achieving a sublinear regret bound. Furthermore, PIB integrates deterministic information bottleneck principles with variational approximation for optimization. Extensive experiments across three heterogeneous edge devices demonstrate that, compared to five state-of-the-art codecs, PIB improves mean object detection accuracy (MODA) while reducing communication overhead by 82.65%, and maintains low latency even under weak channel conditions.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Collaborative perception systems leverage multiple edge devices, such surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their advantages, challenges such as limited channel capacity and data redundancy impede their effectiveness. To address these issues, we introduce the Prioritized Information Bottleneck (PIB) framework for edge video analytics. This framework prioritizes the shared data based on the signal-to-noise ratio (SNR) and camera coverage of the region of interest (RoI), reducing spatial-temporal data redundancy to transmit only essential information. This strategy avoids the need for video reconstruction at edge servers and maintains low latency. It leverages a deterministic information bottleneck method to extract compact, relevant features, balancing informativeness and communication costs. For high-dimensional data, we apply variational approximations for practical optimization. To reduce communication costs in fluctuating connections, we propose a gate mechanism based on distributed online learning (DOL) to filter out less informative messages and efficiently select edge servers. Moreover, we establish the asymptotic optimality of DOL by proving the sublinearity of their regrets. To validate the effectiveness of the PIB framework, we conduct real-world experiments on three types of edge devices with varied computing capabilities. Compared to five coding methods for image and video compression, PIB improves mean object detection accuracy (MODA) while reducing 17.8% and reduces communication costs by 82.65% under poor channel conditions.

Problem

• Collaborative Sensing Systems
• Video Analysis Efficiency
• Resource Optimization

Innovation

• PIB System
• Optimized Video Analysis
• Intelligent Information Transmission