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 perspective reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by foundation intelligence and its associated 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.
Autonomous Crowdsensing: Operating and Organizing Crowdsensing for Sensing Automation
IEEE Transactions on Intelligent Vehicles ; 9 , 3 ; 4254-4258
01.03.2024
1164134 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Large Scale Active Vehicular Crowdsensing
IEEE | 2018
|Environmental Monitoring via Vehicular Crowdsensing
IEEE | 2018
|Sense4FL: Vehicular Crowdsensing Enhanced Federated Learning for Autonomous Driving
ArXiv | 2025
|