Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Perception System

Core Concepts

A novel edge-cloud collaboration framework, LAECIPS, that enables flexible utilization of both large and small models in an online manner to achieve high accuracy and low latency for IoT-based perception systems.

Abstract

The paper proposes a new edge-cloud collaboration framework called LAECIPS to address the challenges in leveraging large vision models for IoT-based perception systems. The key highlights are:

LAECIPS employs a loose-coupling design that allows the large vision model on the cloud and the lightweight model on the edge to be plug-and-play, greatly improving system flexibility.

LAECIPS uses a hard input mining-based edge-cloud co-inference strategy that is optimized for both low response latency and high inference accuracy.

LAECIPS enables online adjustment of the collaboration strategy and continual training of the edge model under the supervision of the large vision model, making the system adaptive to data distribution drifts in dynamic IoT environments.

Theoretical analysis proves the feasibility of incorporating large vision models, edge small models, and edge-cloud co-inference strategies into the LAECIPS framework in a plug-and-play manner.

Experiments on real-world robotic semantic segmentation datasets show that LAECIPS outperforms state-of-the-art competitors in accuracy, latency, and communication overhead while having better adaptability to dynamic environments.

Stats

The cloud-edge collaborative inference latency is (d1 - d0) * E_P(x,y)[h(f(x))] + d0, where d1 is the latency of the edge model inference and d0 is the latency of the cloud model inference.
The cloud upload rate is E_P(x,y)[1(h(f(x)) < δ)], where δ is the confidence threshold for hard input detection.

Quotes

"Recent large vision models (e.g., SAM) enjoy great potential to facilitate intelligent perception with high accuracy."
"Edge-cloud collaboration with large-small model co-inference offers a promising approach to achieving high inference accuracy and low latency."
"LAECIPS enables online adjustment of the collaboration strategy and continual training of the edge model under the supervision of the large vision model, making the system adaptive to data distribution drifts in dynamic IoT environments."

Key Insights Distilled From

LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Perception System

by Shijing Hu,R... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10498.pdf

Deeper Inquiries

How can the LAECIPS framework be extended to support other perception tasks beyond semantic segmentation, such as object detection and instance segmentation

To extend the LAECIPS framework to support other perception tasks beyond semantic segmentation, such as object detection and instance segmentation, several modifications and adaptations can be made:

Model Architecture: Integrate object detection and instance segmentation models into the framework alongside the semantic segmentation model. This would involve modifying the co-inference strategy to accommodate the different output formats and requirements of these tasks.

Data Processing: Adjust the hard input mining strategy to identify challenging inputs specific to object detection and instance segmentation tasks. This may involve considering factors like object occlusion, varying object sizes, and complex backgrounds.

Collaboration Strategy: Develop a tailored collaboration strategy that optimizes the interaction between the edge and cloud models for object detection and instance segmentation tasks. This strategy should balance accuracy, latency, and communication overhead based on the unique characteristics of these tasks.

Adaptive Updates: Implement adaptive update mechanisms for the object detection and instance segmentation models to ensure they can continuously learn and improve based on feedback from the large vision model and real-world data streams.

By incorporating these adjustments, the LAECIPS framework can be effectively extended to support a broader range of perception tasks beyond semantic segmentation, enhancing its versatility and applicability in diverse IoT scenarios.

What are the potential challenges and limitations of the LAECIPS framework when deployed in real-world IoT environments with limited network bandwidth and device heterogeneity

The LAECIPS framework, when deployed in real-world IoT environments with limited network bandwidth and device heterogeneity, may face several challenges and limitations:

Network Bandwidth: Limited network bandwidth can lead to delays in transmitting data between the edge and cloud nodes, impacting the real-time performance of the system. This can result in increased latency and reduced overall efficiency.

Device Heterogeneity: Variability in edge devices' processing capabilities and resources can pose challenges in ensuring consistent performance and compatibility across different devices. Optimizing the framework for diverse hardware configurations may be complex.

Data Security: Transmitting data between edge and cloud nodes raises concerns about data privacy and security. Ensuring secure communication and data protection mechanisms is crucial in IoT environments.

Scalability: Scaling the framework to accommodate a large number of edge devices and handle increasing data volumes can strain the system's resources and scalability. Efficient resource management and load balancing are essential.

To address these challenges, the LAECIPS framework may need to implement efficient data compression techniques, prioritize critical data transmission, optimize network protocols for low bandwidth environments, and adapt the collaboration strategy to account for device heterogeneity. Continuous monitoring and optimization of the system's performance in real-world IoT settings are essential to overcome these limitations.

How can the LAECIPS framework be further optimized to reduce the communication overhead between the edge and cloud while maintaining high inference accuracy

To further optimize the LAECIPS framework and reduce communication overhead between the edge and cloud while maintaining high inference accuracy, the following strategies can be considered:

Edge Data Processing: Implement edge data preprocessing and filtering techniques to reduce the amount of data transmitted to the cloud. This can involve local data aggregation, feature extraction, and early data filtering to minimize unnecessary data transfer.

Edge Model Optimization: Optimize the edge model to perform initial inference and filtering of data before sending only relevant or challenging inputs to the cloud for further processing. This can help reduce the overall communication overhead.

Dynamic Thresholding: Implement dynamic thresholding mechanisms in the hard input mining strategy to adaptively adjust the criteria for sending data to the cloud based on the current network conditions, device capabilities, and task requirements.

Edge Caching: Utilize edge caching mechanisms to store and reuse previously processed data or model outputs, reducing the need for repeated transmissions to the cloud. This can improve response times and reduce communication overhead.

By incorporating these optimization strategies, the LAECIPS framework can enhance its efficiency, reduce communication costs, and maintain high inference accuracy in real-world IoT environments with limited network bandwidth and device heterogeneity.