Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach
<|reference_start|>Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach: The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs). Therefore, the advancement of parallel intelligence-powered Crowdsensing Intelligence (CSI) is witnessed, which is currently advancing towards linguistic intelligence. In this paper, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0. It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., inter-human, human-AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multi-agent systems, scenarios engineering and conversational human-AI cooperation. Finally, we present potential industrial applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.<|reference_end|>
@article{zhu2024conversational,
    title={Conversational Crowdsensing: A Parallel Intelligence Powered Novel
  Sensing Approach},
    author={Zhengqiu Zhu, Yong Zhao, Bin Chen, Sihang Qiu, Kai Xu, Quanjun Yin,
  Jincai Huang, Zhong Liu, Fei-Yue Wang},
    journal={arXiv preprint arXiv:2402.06654},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06654},
    primaryClass={cs.AI cs.HC}
}