Martian Dust Storm Detection with THz Opportunistic Integrated Sensing and Communication in the Internet of Space (IoS)

TL;DR

This paper introduces the Mars Dust Storm Detector (MDSD), which uses THz signals from communication devices on Mars to detect and analyze dust storms in real-time, enhancing planetary atmospheric monitoring.

Contribution

The work demonstrates repurposing THz ISAC signals for environmental sensing on Mars, extending ISAC technology to planetary exploration and atmospheric monitoring.

Findings

Linear interpolation achieves correlation >0.90 at high Node Density Factors

Nearest-neighbor and IDW algorithms provide full coverage in sparse networks

Dust particle size uncertainty is the main source of estimation error

Abstract

This paper presents the Mars Dust Storm Detector (MDSD), a system that leverages the THz Opportunistic Integrated Sensing and Communications (OISAC) signals between Mars surface assets (rovers and landers) to extract environmental information, particularly dust storm properties. The MDSD system utilizes the multi-parameter sensitivity of THz signal attenuation between Martian communication devices to provide rich, real-time data on storm intensity, particle characteristics, and potentially even electrification state. This approach, incorporating HITRAN spectroscopic data and Martian-specific atmospheric parameters, allows for accurate modeling and analysis. The system's ability to repurpose THz ISAC signals for environmental sensing demonstrates an efficient use of resources in the challenging Martian environment, utilizing communication infrastructure to enhance our understanding of Mars' atmospheric dynamics. The system's performance is evaluated through extensive simulations under various Node Density Factors (NDFs), comparing different interpolation algorithms for dust storm intensity mapping. Results demonstrate that linear interpolation achieves superior accuracy (correlation >0.90) at high NDFs, while nearest-neighbor and IDW algorithms maintain complete spatial coverage in sparse networks. Error analysis identifies dust particle size uncertainty as the primary contributor to estimation errors, though the system shows resilience to Martian atmospheric variations. This work extends the opportunistic use of ISAC technology to planetary exploration, contributing to both Mars atmospheric monitoring capabilities and ISAC applications in the Internet of Space (IoS).