Enabling multi-scale sensing with wireless-informed machine learning: Applications in earth and space

Shenoy, Jayanth

Permalink

https://hdl.handle.net/2142/127224 Copy

Description

Title

Enabling multi-scale sensing with wireless-informed machine learning: Applications in earth and space

Author(s)

Shenoy, Jayanth

Issue Date

2024-12-01

Doctoral Committee Chair(s)

Vasisht, Deepak

Committee Member(s)

• Caesar, Matthew
• Godfrey, Philip B
• Ranganathan, Vaishnavi

Department of Study

Siebel School Comp & Data Sci

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• machine learning
• wireless
• satellites
• sensing
• self-supervision

Abstract

This dissertation investigates how next-generation (next-gen) networks, including 5G, and beyond, can enable a dynamic, multi-scale sensing ecosystem by integrating global satellite systems with localized wireless technologies. These networks promise transformative capabilities, from near real-time environmental monitoring via satellites to precise indoor sensing for healthcare, security, and smart infrastructure. However, their growing scale introduces significant challenges. Satellite systems face data transfer bottlenecks, high mobility, and limited downlink capacities, while wireless sensing applications require machine learning models that depend heavily on large, annotated datasets. Additionally, privacy concerns surrounding the pervasive use of wireless sensing present further barriers to scalability. This dissertation focuses on answering the following key research question: How can wireless signal propagation models be integrated with machine learning to help enable multi-scale sensing? This work answers this question by developing novel machine learning frameworks that incorporate wireless domain knowledge to enhance predictability and reduce system overhead. By leveraging self-supervised and generative learning techniques, these frameworks address data inefficiencies, optimize resource allocation, and introduce privacy preserving mechanisms for wireless sensing. The proposed approaches streamline the coordination of complex satellite and wireless sensing systems, reducing reliance on expensive hardware and labor-intensive configurations. This research demonstrates how integrating domain knowledge into machine learning models enables next-gen networks to achieve scalability, efficiency, and robustness across both global and localized sensing applications. Ultimately, this work highlights the potential for intelligent, adaptable sensing architectures that bridge large-scale monitoring with fine-grained precision.

Graduation Semester

2024-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/127224

Copyright and License Information

Copyright 2024 Jayanth Shenoy

Owning Collections