In the past few years, the Smart City concept became one of the main driving forces for the transition towards sustainable economy and improved mobility. Tourism, as one of the fastest growing economies worldwide, is an integrated part of the Smart City paradigm. Taking into consideration recent studies performed by the United Nations, stating that almost one third of the population is directly affected by disability, the concept of Accessible Tourism needs also to be integrated in the future vision for tourism, especially in the context of Smart Cities, environments fully benefiting from the recent technological advances. Within the combined framework of Smart Cities and Accessible Tourism, the Internet-of-Things (IoT) concept is the key technological point for the development of smart urban environments. IoT and big data are both technology-driven developments, leading to scenarios such as the Smart Cities one that has the potential to make citizen live smarter, more sustainable and more accessible. This chapter analyses the key requirements for IoT applications in a Smart City context, the state-of-the-art for the use of IoT for Accessible Tourism applications and proposes an architecture together with its practical implementation, tailored for the use-case of accessible tourism for physically impaired persons.

Technological advancements in low-cost devices, the Internet of Things (IoT), numerical models, big data infrastructures, and high-performance computing are revolutionizing urban management, particularly air quality governance. This study examines the application of smart technologies to address urban air quality challenges using integrated sensor networks and predictive models. The decision support system (DSS), AERQ, incorporates the AERMOD modeling tool, achieving a 10 m spatial and 1 h temporal resolution for air quality predictions. It processes hourly climate and traffic data via a high-performance computing (HPC) platform, significantly enhancing prediction accuracy and decision-making efficiency. AERMOD has been calibrated and validated for NO₂, showing a good performance against observations. Tested in Cagliari, Sardinia, Italy, AERQ demonstrated a 99% reduction in computation time compared to modern desktop systems, delivering detailed 5-year scenarios in under 15 h. AERQ equips stakeholders with air quality indices, scenario analyses, and mitigation strategies, combining advanced visualization tools with actionable insights. By enabling data-driven decisions, the system empowers policymakers, urban planners, and citizens to improve air quality and public health. This study underscores the transformative potential of integrating advanced technologies into urban management, providing a scalable model for efficient, informed, and responsive air quality governance.

Emerging technologies such as the Internet of Things (IoT), big data, mobile devices, high-performance computing, and advanced modeling are reshaping urban management. When integrated with conventional tools, these innovations enable smarter governance—particularly in air quality control—improving public health and quality of life. Yet, urban expansion driven by economic growth continues to worsen pollution and its health impacts. This study presents AERQ, a decision support system (DSS) designed to address urban air quality challenges through real-time sensor data and the AERMOD dispersion model. Applied to Cagliari (Italy), AERQ is used to evaluate key traffic-related pollutants (CO, PM, NO₂) and simulate mitigation scenarios. Results are delivered via a user-friendly web-based platform for policymakers, technicians, and citizens. AERQ supports data-driven planning and near real-time responses, demonstrating the potential of integrated digital tools for sustainable urban governance. In the case study, it achieved 10 m spatial and 1 h temporal resolution, while reducing simulation time by 99%—delivering detailed five-year scenarios in just 15 h.

The prediction and monitoring of plant diseases and pests are key activities in agriculture. These activities enable growers to take preventive measures to reduce the spread of diseases and harmful insects. Consequently, they reduce crop loss, make pesticide and resource use more efficient, and preserve plant health, contributing to environmental sustainability. We illustrate the SMARTerra decision support system, which processes daily measured and predicted weather data, spatially interpolating them at high resolution across the entire Sardinia region. From these data, SMARTerra generates risk predictions for plant pests and diseases. Currently, models for predicting the risk of rice blast disease and the hatching of locust eggs are implemented in the infrastructure. The web interface of the SMARTerra platform allows users to visualize detailed risk maps and promptly take preventive measures. A simple notification system is also implemented to directly alert emergency responders. Model outputs by the SMARTerra infrastructure are comparable with results from in-field observations produced by the LAORE Regional Agency. The infrastructure provides a database for storing the time series and risk maps generated, which can be used by agencies and researchers to conduct further analysis.