A Prototype Design of LLM-Based Autonomous Web Crowdsensing

Zhengqiu Zhu, Yatai Ji, Sihang Qiu, Yong Zhao, Kai Xu, Rusheng Ju, Bin Chen

Abstract: The expanded demands of complex sensing campaigns involving Cyber-Physical-Social spaces have brought forth a multitude of challenges for web crowdsensing applications, such as substantial human efforts, potential user privacy breaches, and interest diminishes of users. Significant advancements have occurred in the application of Large Language Models (LLMs) for various tasks, such as conversational engagement, social simulation, and decision-making. Despite this, their potential to empower web crowdsensing activities is under-explored. To bridge this gap, we explore the design of a LLM-based autonomous web crowdsensing framework for flood-related data collection to mitigate the workload and professional demands on individuals in this poster.

External IDs:dblp:conf/icwe/ZhuJQZXJC24