SELLMA : semantic location through on-device LLMs and WiFi sensing

Korelič, Martin (author), Machidon, Octavian-Mihai (author), Pejović, Veljko (author)

Understanding a user's semantic location is of critical importance in numerous areas of mobile computing, such as mobile healthcare, mobile advertising, and mobile personal assistance. Nevertheless, inferring semantic location remains challenging and often relies on translating raw geographical coordinates via third-party online services. In this paper we introduce SELLMA, an approach for semantic location inference that harnesses Wi-Fi SSID sensing and on-device querying of a specially crafted LLM. We implement SELLMA in Android and show that it can uncover a number of environmental and geographical descriptors of a users location in a privacy-preserving manner, without the need for GPS querying, and without reliance on Web-based services.

• Language: English
• Keywords: mobile sensing, large language models, semantic location, location sensing, LLM fine-tuning, WiFi sensing, ubiquitous computing, human-centered computing, ubiquitous and mobile computing, computer systems organization, embedded and cyber-physical systems, computing methodologies, machine learning
• Typology: 1.08 - Published Scientific Conference Contribution
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: Str. 7-12
• PID: 20.500.12556/RUL-168462
• UDC: 004
• DOI: 10.1145/3721888.3722091
• COBISS.SI-ID: 232037379
• Publication date in RUL: 14.04.2025

Record is a part of a monograph

• Title: EdgeSys ’25 : proceedings of the 8th International Workshop on Edge Systems, Analytics and Networking
• Place of publishing: New York (NY)
• Publisher: Association for Computing Machinery
• Year: 2025
• ISBN: 979-8-4007-1559-4
• COBISS.SI-ID: 231904003

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: mobilno zaznavanje, veliki jezikovni modeli, opisna lokacija