# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 7 Conclusions  
The integration of urban monitoring technologies is essential for enhancing urban liveability and accurately identifying the UHI effect. This study has conducted a comprehensive evaluation of several low-cost, real-time monitoring networks operating in a medium-sized city, revealing significant disparities in data quality and system limitations. These findings underscore the need for rigorous data analysis that accounts for the specific characteristics of each network in climate-related research. Equally important is the implementation of regular maintenance, calibration, and quality control procedures to ensure data reliability. However, these measures alone are insufficient. A precise characterization of each station, including its location, surrounding environment, and regularly updated metadata, is imperative for meaningful analysis. A key objective of this study was to document the deployment of a purpose-built monitoring network, offering a replicable model for similar initiatives. Although the pilot was implemented in Caceres, it forms part of a broader coordinated project aimed at replicating the system in other urban areas, such as Bilbao. The use of open-source hardware and software enhances adaptability, allowing the system to evolve with emerging needs. Nonetheless, rapid technological advancements may render systems obsolete, necessitating timely upgrades. Purpose-built monitoring systems are particularly well-suited for multidisciplinary projects of limited duration in small to medium-sized cities. Their ability to meet specific project requirements enables the development of cost-effective, tailored solutions. However, their deployment and maintenance require specialized personnel, making it essential to delegate responsibility to capable institutions or organizations. In contrast, large cities, where UHI effects are more pronounced, can benefit from existing CWONs, which offer immediate access to historical data without the need for prior deployment, provided that the particular characteristics of each station are takeninto consideration. However, reliance on CWON does not guarantee long-term system continuity or data consistency. This research also identifies future directions and challenges. While the primary focus has been on UHI, the monitoring infrastructure developed here is adaptable to other smart city domains, including air quality, noise, mobility, and waste management. To remain relevant, network design must be modular and adaptable, accommodating rapid advancements in sensing, telecommunications, data processing, and storage technologies. To reduce long-term maintenance costs and minimize manual interventions, the integration of internal self-diagnosis and predictive maintenance modules is proposed. These would monitor component health, detect anomalies, and issue alerts. The potential for self-healing mechanisms, such as automated reboots or remote reconfiguration, has also been considered. In order to ensure the reliability of citizen devices in research contexts, CWON providers should require operators to properly install the devices, following the guidelines provided by them and the WMO, and provide precise metadata, including installation height, accurate georeferencing (e.g., by incorporating GPS), sensor type, AWS type, and the presence of solar protection and ventilation. Furthermore, the provision of precise information regarding identification, the type of AWS, and the presence of solar protection and ventilation is imperative. Furthermore, the CWON should provide detailed information on the calibration process carried out, if applicable. Conceptualization, M.L.B. and B.M.P. ; methodology, M.L.B. and B.M.P. ; software, I.T.A.P., P.B.G.d.C. and A.M.-G.; validation, M.L.B. and B.M.P. ; formal analysis, M.L.B. and B.M.P. ; investigation, M.L.B. and B.M.P. ; resources, I.T.A.P. ; data curation, M.L.B. and B.M.P. ; writing--original draft preparation, M.L.B. and B.M.P. ; writing--review and editing, M.L.B. and B.M.P. ; visualization, M.L.B. ; supervision, M.L.B.