Data-driven and distributed governance of building facilities management using decentralized autonomous organization, digital twin, and large language models
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910224608133120 |
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| author | Ly, Reachsak Shojaei, Alireza Gao, Xinghua Agee, Philip Akanmu, Abiola |
| author_facet | Ly, Reachsak Shojaei, Alireza Gao, Xinghua Agee, Philip Akanmu, Abiola |
| contents | While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures that are vulnerable to cyber attacks, limited contextual understanding, and decision-making processes that exclude key stakeholders from governance. This paper introduces a novel AI- and data-driven distributed governance framework for smart building management that integrates decentralized autonomous organizations (DAOs), digital twins, large language models (LLMs), and blockchain technology. The framework enables transparent collective decision-making through a DAO governance platform, implements data-driven management using IoT and digital twins, incorporates LLM-based virtual assistants for enhanced decision support, and utilizes blockchain for secure building automation. A full-stack decentralized application was developed to facilitate user interaction with these integrated components. The system was evaluated for cost efficiency, scalability, data security, and usability using the System Usability Scale (SUS). Expert interviews were also conducted to assess its practical benefits and implementation challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16298 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-driven and distributed governance of building facilities management using decentralized autonomous organization, digital twin, and large language models Ly, Reachsak Shojaei, Alireza Gao, Xinghua Agee, Philip Akanmu, Abiola Computers and Society Artificial Intelligence Multiagent Systems While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures that are vulnerable to cyber attacks, limited contextual understanding, and decision-making processes that exclude key stakeholders from governance. This paper introduces a novel AI- and data-driven distributed governance framework for smart building management that integrates decentralized autonomous organizations (DAOs), digital twins, large language models (LLMs), and blockchain technology. The framework enables transparent collective decision-making through a DAO governance platform, implements data-driven management using IoT and digital twins, incorporates LLM-based virtual assistants for enhanced decision support, and utilizes blockchain for secure building automation. A full-stack decentralized application was developed to facilitate user interaction with these integrated components. The system was evaluated for cost efficiency, scalability, data security, and usability using the System Usability Scale (SUS). Expert interviews were also conducted to assess its practical benefits and implementation challenges. |
| title | Data-driven and distributed governance of building facilities management using decentralized autonomous organization, digital twin, and large language models |
| topic | Computers and Society Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2605.16298 |