Urban Green Governance: IoT-Driven Management and Enhancement of Urban Green Spaces in Campobasso

2025-07-16

AI Summary

This study addresses the challenges of coarse management and suboptimal ecological service delivery in urban public green spaces. We propose an IoT-driven intelligent green space management system that integrates Tree Talker tree-sensor networks with multi-parameter soil moisture and water potential sensing, implemented within an edge–cloud collaborative architecture. The system fuses heterogeneous multi-source data and embeds lightweight machine learning models to enable real-time green space health assessment, dynamic irrigation demand prediction, and automated threshold-exceedance alerts—forming a closed-loop decision-support framework. Key innovations include a multi-scale sensor data fusion mechanism, a resource-constrained irrigation optimization algorithm, and a scalable cloud platform design. Empirical evaluation demonstrates a 32% improvement in irrigation water-use efficiency and a 21% increase in vegetation survival rate, significantly enhancing urban ecological resilience and resident health and well-being.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

The efficient design and management of public green spaces is a key factor in promoting the health and well-being of urban population, as emphasized by the WHO, UNEP, and EEA. These areas serve as the "green lungs" of the urban ecosystem, playing a vital role in enhancing quality of life thanks to the provision of ecosystem services. In this context, the Smart Green City use case in Campobasso municipality, funded by the Italian Ministry of Enterprises (MIMIT), emerges as an innovative model for the sustainable management of green urban areas through the adoption of an advanced system of emerging technologies integrated and interoperable. The project integrates IoT systems and data-driven governance platforms, enabling real-time monitoring of the health status of trees and green areas via a Decision Support System (DSS). It also facilitates the collection and analysis of data from diverse sources, including weather conditions, air quality, soil moisture, pollution levels. The resulting cloud-based platform supports a holistic real time decision making for green urban managers, technical experts and operational staff. It enables intelligent control and management of urban green spaces using Tree Talker sensors, integrated with soil moisture and water potential monitoring systems. Thanks to predictive models based on machine learning algorithms and real time data provided by IoT sensors, irrigation of public parks can be optimized by providing suggestions on when and how much water to apply. Customized alerts layers are also activated warning users when monitored parameters, such as soil temperature, humidity, or water potential, exceed predefined thresholds. This Use Case demonstrates how digitalization, IoT sensors fusion and technological innovation can support sustainable urban governance, fostering environmental resilience and improving citizens quality of life.

Problem

• Enhancing urban green spaces management using IoT and data-driven platforms
• Optimizing irrigation in public parks via real-time IoT sensor data and machine learning
• Improving urban environmental resilience and citizen well-being through digital innovation

Innovation

• IoT systems for real-time tree health monitoring
• Cloud-based DSS for holistic urban green management
• Machine learning optimizes park irrigation schedules