On Computing In the Network: Covid-19 Coughs Detection Case Study

TL;DR

This paper demonstrates that in-network computing significantly improves latency and traffic filtering for Covid-19 cough detection in airports compared to traditional edge computing, using simulation-based performance analysis.

Contribution

It provides a comparative analysis of in-network computing versus edge computing for health monitoring applications in airports, highlighting performance benefits.

Findings

In-network computing reduces RTT compared to edge computing.

In-network computing enhances traffic filtering efficiency.

Simulation results confirm performance improvements.

Abstract

Computing in the network (COIN) is a promising technology that allows processing to be carried out within network devices such as switches and network interface cards. Time sensitive application can achieve their quality of service (QoS) target by flexibly distributing the caching and computing tasks in the cloud-edge-mist continuum. This paper highlights the advantages of in-network computing, comparing to edge computing, in terms of latency and traffic filtering. We consider a critical use case related to Covid-19 alert application in an airport setting. Arriving travelers are monitored through cough analysis so that potentially infected cases can be detected and isolated for medical tests. A performance comparison has been done between an architecture using in-network computing and another one using edge computing. We show using simulations that in-network computing outperforms edge computing in terms of Round Trip Time (RTT) and traffic filtering.