Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
162 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Autonomous Crowdsensing: Operating and Organizing Crowdsensing for Sensing Automation (2401.03229v1)

Published 6 Jan 2024 in cs.CY

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, LLMs, 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. Z. Zhu, Y. Zhao, B. Chen, S. Qiu, Z. Liu, K. Xie, and L. Ma, “A crowd-aided vehicular hybrid sensing framework for intelligent transportation systems,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1484–1497, 2022.
  2. E. Wang, M. Zhang, B. Yang, Y. Yang, and J. Wu, “Large-Scale Spatiotemporal Fracture Data Completion in Sparse CrowdSensing,” IEEE Transactions on Mobile Computing, 2023.
  3. Z. Zhu, B. Chen, W. Liu, Y. Zhao, Z. Liu, and Z. Zhao, “A cost-quality beneficial cell selection approach for sparse mobile crowdsensing with diverse sensing costs,” IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3831–3850, 2020.
  4. D. Zhang, L. Wang, H. Xiong, and B. Guo, “4W1H in mobile crowd sensing,” IEEE Communications Magazine, vol. 52, no. 8, pp. 42–48, 2014.
  5. F.-Y. Wang, J. Yang, X. Wang, J. Li, and Q.-L. Han, “Chat with ChatGPT on industry 5.0: Learning and decision-making for intelligent industries,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 4, pp. 831–834, 2023.
  6. Y. Zhao, Z. Zhu, B. Chen, S. Qiu, J. Huang, X. Lu, W. Yang, C. Ai, K. Huang, C. He et al., “Toward parallel intelligence: An interdisciplinary solution for complex systems,” The Innovation, vol. 4, no. 6, p. 100521, 2023.
  7. J. Li, R. Qin, and F.-Y. Wang, “The future of management: Dao to smart organizations and intelligent operations,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022.
  8. J. Li, R. Qin, W. Ding, G. Wang, T. Wang, and F. Wang, “A New Framework for Web3-powered decentralized autonomous organizations and operations,” Acta Automatica Sinica, vol. 49, no. 5, pp. 985–998, 2023.
  9. F.-Y. Wang, J. Li, R. Qin, J. Zhu, H. Mo, and B. Hu, “Chatgpt for computational social systems: From conversational applications to human-oriented operating systems,” IEEE Transactions on Computational Social Systems, vol. 10, no. 2, pp. 414–425, 2023.
  10. Z. Zhu, X. Wang, Y. Zhao, S. Qiu, Z. Liu, B. Chen, and F.-Y. Wang, “Crowdsensing intelligence by decentralized autonomous vehicles organizations and operations,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 4, pp. 804–808, 2022.
  11. Y. Zhao, C. Hu, Z. Zhu, S. Qiu, B. Chen, P. Jiao, and F.-Y. Wang, “Crowd Sensing Intelligence for ITS: Participants, Methods, and Stages,” IEEE Transactions on Intelligent Vehicles, 2023.
  12. F.-Y. Wang and B. Chen, “Crowdsensing intelligence: Advancing trustworthy, intelligent, interactive, and cooperative sensing and computing for Transportation 5.0,” IEEE Transactions on Intelligent Vehicles, 2023.
  13. C. Zhao, X. Dai, Y. Lv, J. Niu, and Y. Lin, “Decentralized autonomous operations and organizations in transverse: Federated intelligence for smart mobility,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2062–2072, 2022.
  14. R. Qin, W. Ding, J. Li, S. Guan, G. Wang, Y. Ren, and Z. Qu, “Web3-based decentralized autonomous organizations and operations: Architectures, models, and mechanisms,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2073–2082, 2022.
  15. F.-Y. Wang, Q. Miao, X. Li, X. Wang, and Y. Lin, “What does ChatGPT say: The DAO from algorithmic intelligence to linguistic intelligence,” IEEE/CAA Journal of Automatica Sinica, 2023.
  16. H. Du, S. Teng, H. Chen, J. Ma, X. Wang, C. Gou, B. Li, S. Ma, Q. Miao, X. Na et al., “Chat with ChatGPT on intelligent vehicles: An ieee tiv perspective,” IEEE Transactions on Intelligent Vehicles, 2023.
  17. Y. Ren, H. Jiang, X. Feng, Y. Zhao, R. Liu, and H. Yu, “ACP-based modeling of the parallel vehicular crowd sensing system: Framework, components and an application example,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1536–1548, 2022.
  18. Y. Wang, X. Wang, X. Wang, J. Yang, O. Kwan, L. Li, and F.-Y. Wang, “The ChatGPT After: Building Knowledge Factories for Knowledge Workers with Knowledge Automation,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 11, pp. 2041–2044, 2023.
  19. F.-Y. Wang and C. Lv, “Foundation Vehicles: From Foundation Intelligence to Foundation Transportation for Future Mobility,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 10, pp. 4287–4291, 2023.
  20. Y. Tian, X. Li, H. Zhang, C. Zhao, B. Li, X. Wang, and F.-Y. Wang, “VistaGPT: Generative Parallel Transformers for Vehicles With Intelligent Systems for Transport Automation,” IEEE Transactions on Intelligent Vehicles, 2023.
  21. J. Li and F.-Y. Wang, “The TAO of Blockchain Intelligence for Intelligent Web 3.0,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 12, pp. 2183–2186, 2023.
  22. X. Li, P. Ye, J. Li, Z. Liu, L. Cao, and F.-Y. Wang, “From features engineering to scenarios engineering for trustworthy AI: I&I, C&C, and V&V,” IEEE Intelligent Systems, vol. 37, no. 4, pp. 18–26, 2022.
  23. F.-Y. Wang, Y. Tang, and P. J. Werbos, “Guest editorial: Cyber–Physical–Social intelligence: toward metaverse-based smart societies of 6I and 6S,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2018–2024, 2023.
  24. Y. Shen, Y. Liu, Y. Tian, and X. Na, “Parallel sensing in metaverses: Virtual-real interactive smart systems for “6S” sensing,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 12, pp. 2047–2054, 2022.
  25. L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y. Lin, W. X. Zhao, Z. Wei, and J.-R. Wen, “A Survey on Large Language Model based Autonomous Agents.” [Online]. Available: https://arxiv.org/pdf/2308.11432.pdf
  26. J. Li, X. Liang, R. Qin, and F. Wang, “From DAO to TAO: Finding the essence of decentralization,” in 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2023), 2023, pp. 1–4.
  27. C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “LLM-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 2998–3009.
  28. S. Imai, “Is github copilot a substitute for human pair-programming? an empirical study,” in Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings, 2022, pp. 319–321.
  29. L. Bärmann, R. Kartmann, F. Peller-Konrad, A. Waibel, and T. Asfour, “Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models,” 2023. [Online]. Available: https://arxiv.org/abs/2309.04316
  30. S. Yao, J. Yu, Y. Yu, J. Xu, X. Dai, H. Li, F.-Y. Wang, and Y. Lin, “Towards Integrated Traffic Control with Operating Decentralized Autonomous Organization,” 2023. [Online]. Available: https://arxiv.org/abs/2308.03769
  31. S. Gravitas, “Auto-GPT: An autonomous GPT-4 experiment,” preprint, 2023.
  32. T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom, “Toolformer: Language Models Can Teach Themselves to Use Tools,” 2023. [Online]. Available: https://arxiv.org/abs/2302.04761
  33. Y. Gao, T. Sheng, Y. Xiang, Y. Xiong, H. Wang, and J. Zhang, “Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System,” 2023. [Online]. Available: https://arxiv.org/abs/2303.14524
  34. Y. Wang, F. Wang, G. Wang, X. Wang, Y. Wang, and R. Li, “Parallel hospitals: From hospital information system (HIS) to hospital smart operating system (HSOS),” Acta Automatica Sinica, vol. 47, no. 11, pp. 2585–2599, 2021.
  35. F. Wang, “Parallel doctors and parallel hospitals: Impact and perspective of ChatGPT-like AIGC and AGI on medicine and medicare,” Medical Journal of Peking Union Medical College Hospital, vol. 14, no. 4, pp. 673–679, 2023.
  36. Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, L. Jiang, X. Zhang, S. Zhang, J. Liu, A. H. Awadallah, R. W. White, D. Burger, and C. Wang, “AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.” [Online]. Available: https://arxiv.org/pdf/2308.08155.pdf
  37. Y. Weng, M. Zhu, F. Xia, B. Li, S. He, S. Liu, B. Sun, K. Liu, and J. Zhao, “Large Language Models are Better Reasoners with Self-Verification,” 2022. [Online]. Available: https://arxiv.org/abs/2212.09561
  38. Y. Zhang, Y. Li, L. Cui, D. Cai, L. Liu, T. Fu, X. Huang, E. Zhao, Y. Zhang, Y. Chen, L. Wang, A. T. Luu, W. Bi, F. Shi, and S. Shi, “Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models,” 2023. [Online]. Available: https://arxiv.org/abs/2309.01219
  39. Y. Liu, Y. Yao, J.-F. Ton, X. Zhang, R. Guo, H. Cheng, Y. Klochkov, M. F. Taufiq, and H. Li, “Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models’ Alignment,” 2023. [Online]. Available: https://arxiv.org/abs/2308.05374
  40. K.-C. Yeh, J.-A. Chi, D.-C. Lian, and S.-K. Hsieh, “Evaluating Interfaced LLM Bias,” in Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023), 2023, pp. 292–299.
  41. H. Duan, A. Dziedzic, M. Yaghini, N. Papernot, and F. Boenisch, “On the privacy risk of in-context learning,” in The 61st Annual Meeting Of The Association For Computational Linguistics, 2023.
  42. M. Phute, A. Helbling, M. Hull, S. Peng, S. Szyller, C. Cornelius, and D. H. Chau, “LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked,” 2023. [Online]. Available: https://arxiv.org/abs/2308.07308
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now