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

TL;DR

This paper introduces the Prioritized Information Bottleneck framework with distributed online learning to optimize data sharing in edge video analytics, significantly reducing communication costs while maintaining high detection accuracy.

Contribution

It proposes a novel PIB framework that prioritizes data based on SNR and coverage, and a DOL-based gate mechanism for efficient edge server selection, with proven asymptotic optimality.

Findings

Improves mean object detection accuracy (MODA)

Reduces communication costs by 82.65%

Enhances performance under poor channel conditions

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.