Autonomous Crowdsensing: Operating and Organizing Crowdsensing for Sensing Automation
Wansen Wu, Weiyi Yang, Juanjuan Li, Yong Zhao, Zhengqiu Zhu, Bin Chen, Sihang Qiu, Yong Peng, Fei-Yue Wang
TL;DR
The paper presents Autonomous Crowdsensing (ACS) as a new Crowdsensing Intelligence (CSI) paradigm to address the heavy human labor, slow response, and rigidity of traditional crowdsensing in Cyber-Physical-Social Systems (CPSS). It integrates Decentralized Autonomous Organizations (DAO), blockchain-enabled smart contracts, Large Language Models (LLMs) for digital agents, and Human-Oriented Operating Systems (HOOS) with scenarios engineering to automate sensing campaigns and data management. A 6A-goal framework defining autonomous generation, growth, organization, control, assistance, and verification guides ACS, with TAO concepts proposed to overcome DAO limitations. The work outlines challenges such as AI bias and privacy concerns and suggests research directions to realize scalable, autonomous, and inclusive sensing applications in urban and CPSS contexts.
Abstract
The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This letter reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by a range of technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the ``6A-goal" of ACS and propose potential avenues for future research.