An AI-Driven Framework for Energy-Efficient Environmental Monitoring in Smart Cities Using Edge Intelligence
arXiv:2605.22824v1 Announce Type: cross Abstract: Environmental monitoring is a crucial component of the smart city infrastructure. It enables informed decision making which enhances sustainability, public health and urban planning. However, the large-scale deployments of the smart sensors have raised concerns on excessive energy consumption and redundant data collection as well as limited sensor lifespan. To resolve these issues, we present an AI-driven framework for energy-efficient environmental monitoring in smart cities utilizing edge intelligence. Our proposed framework leverages TinyML-
The proliferation of IoT sensors in smart cities is creating an unsustainable energy footprint and data overload, necessitating new AI-driven approaches to optimize resource use and extend device longevity.
This development indicates a growing maturity in applying AI, specifically TinyML and edge intelligence, to real-world infrastructure challenges, making smart city deployments more viable and sustainable.
The focus shifts from simply deploying more sensors to intelligently managing their operation and data processing at the edge, reducing energy consumption and improving efficiency for environmental monitoring.
- · Smart city solution providers
- · Edge AI hardware manufacturers
- · Urban planning departments
- · Environmental monitoring companies
- · Legacy sensor infrastructure providers
- · Cloud-centric data processing services (without edge adaptation)
Increased adoption of energy-efficient AI solutions in smart city infrastructure will follow.
This could lead to more comprehensive and real-time environmental insights becoming available to city managers.
Improved environmental data might influence policy-making faster, fostering healthier urban environments and driving demand for more integrated urban AI systems.
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Read at arXiv cs.AI