# Service-Chain-Driven Communication and Computing Integration Networking: A Case Study of Levee Piping Hazard Inspection via Remote Sensing

## TL;DR

This paper introduces a new network architecture for integrating sensing, communication, and computation to enable real-time levee hazard detection using remote sensing.

## Contribution

The novel (Com)2INet architecture integrates sensing, transmission, and computation for real-time, scalable hazard inspection.

## Key findings

- Thermal infrared imagery effectively retrieves land surface temperature fields for hazard segmentation.
- The multi-path transmission mechanism improves data flow efficiency across heterogeneous networks.
- The SACM algorithm enables dynamic processing for precise hazard identification in emergency scenarios.

## Abstract

Computing power network (CPN) is designed to utilize multi-dimensional resources to complete computing tasks. However, in practical applications, the CPN architecture has difficulty in coordinating cross-domain heterogeneous resources, making it impossible to achieve the real-time and high scalability requirements of computationally intensive and time-sensitive tasks such as levee piping hazard inspection via remote sensing in emergency scenarios. Based on this, we propose a communication and computation integrated network architecture, referred to as (Com)2INet, that integrates “sensing”, “transmission”, and “computation” phases. In the sensing phase, thermal infrared imagery is utilized to retrieve land surface temperature fields through radiative transfer mechanisms, providing a reliable foundation for visual segmentation of piping hazards. In the transmission phase, we adopt the designed multi-path transmission mechanism to promote the efficient data flow across heterogeneous networks. In the computation phase, the proposed SACM algorithm, which is functionally decomposed and implemented as service chains within the proposed network architecture, dynamically processes the retrieved temperature fields to achieve precise hazard identification. This integrated framework ensures seamless interaction between sensing, communication, and computation, addressing the challenges of real-time hazard detection in emergency scenarios.