GreenSynapse: Integrating Cognitive AI and Digital Infrastructure for Sustainable Smart Cities

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This paper offers a novel integration of neuro-symbolic AI, reinforcement learning, and blockchain for urban sustainability, with strong empirical validation.

🏢実務担当者:City planners and utility managers can leverage the reported energy and carbon reduction figures (27% and 25%) to justify investments in similar AI-driven smart city platforms.

🏛政策担当者:The framework demonstrates how AI can enforce emission caps and resource limits in real time, supporting regulatory compliance and adaptive urban policy.

As urbanization intensifies, the pursuit of sustainable, intelligent, and adaptive cities becomes paramount. Traditional smart city solutions tend to remain in silo, non-interoperable, inflexible and lack transparency, thereby restricting their applicability in real-time decision making and implementation of sustainability. This paper presents a framework called GreenSynapse, which is a cognitive AI-driven platform that is well integrated with real-time data, neuro-symbolic reasoning, and digital twin infrastructure to coordinate smart city services within a sustainability limit. The system uses reinforcement learning agents to have an adaptive control of energy, mobility, waste, and environmental domains and symbolic reasoning to impose regulatory policies like an emission cap and water usage limits. Transparency and traceability of decisions are ensured by a hybrid neuro-symbolic AI model leading to the public trust in it. The framework also uses blockchain to govern data sustainably and uses the principles of Green AI to reduce computational emissions. Massive simulations and smart-zone implementations proved the effectiveness of the system: energy usage was minimized by up to 27%, carbon footprint was decreased by more than 25%, the accuracy of AI decisions was 94.6%, and the reaction to the context became less laggy by 42.3%. Also, there was a general improvement in the citizen satisfaction by 17%. These findings confirm that GreenSynapse has the potential to close the sustainability goals and the real-time urban responsiveness gap. The research article adds to a scalable and interpretable AI system that fits the worldwide city sustainability objectives. GreenSynapse provides a new path to the further development of smart cities or offers to integrate cognition, adaptability, and ethical AI into an urban digital environment.

• semanticscholar https://doi.org/10.1109/icc-cns70518.2026.11606136first seen 2026-07-25 05:37:35