# Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments
## 1 Introduction  
Cultural Heritage (CH) sites and monuments are highly impacted by Climate Change (CC) and related hazards, such as landslides. The deterioration of these monuments may cause significant adverse impacts on the economies, politics and societies of the historical areas around them [1, 2]. The deterioration of CH sites is one of the biggest challenges in conservation. Aspects such as building technologies and materials, structural responses, preventive measures and restoration strategies, resilience and adaptation methodologies should be included in analysis. Currently, there is no specific process towards understanding and quantifying CC effects on historic areas or assessing quantitatively and qualitatively the impact of various climatic parameters on the CH sites. In order to better study the phenomenon of the deterioration patterns of building materials there is a need to identify the various building materials at study areas and study their physical and mechanical characterisation [3]. Next, recession models should be enriched with critical factors, which have not been studied so far, related to the micro-climate of the area. HYPERION [4] aims to support the development of such models by utilizing IoT network devices, called smart tags, which will be installed within four historic study areas. The IoT sensor network is based on small, autonomous, unattended, reliable and long-life wireless devices that are deployed in certain selected parts of the CH site that will continuously monitor for environmental changes. The collected measurements will be used to improve a number of dynamical and statistical models that will provide climate and environmental change information to extract quantified risk information for the CH areas. In this paper the distributed smart sensor network that is deployed at the Cultural Heritagemonuments in the four pilot cities are described. The network includes multiple smart tags that record specific measurement, based on the needed of the models, at the proximity of the Cultural Heritage monuments with different orientations aiming to provide the data needed for the study of the material degradation due to the climate conditions. The monitoring of historic buildings and monuments is a complex challenge that has attracted a lot of research interest over the years. The majority of the conducted research has been focused on the assessment and the evaluation of the structural and material conditions of a monument, using basically optical methods, such as infrared photography [5] and laser-induced fluorescence [6]. Furthermore, accelerometers and vibration sensors have been developed and used to assess the vulnerability of a historical structure to geo-hazards, such as seismic events and landslides, and identify the damage inflicted by them [7,8]. In the same manner, climate change is a subject that interests and occupies the scientific and academic communities, especially nowadays that extreme phenomena related to it are occurring often. The rapid technological development and the introduction of the Internet of Things has encouraged scientists and developers to create smart and autonomous systems capable of recording specific environmental measurements. Plenty of the developed systems aim to assist the agriculture by monitoring the micro-climate and water parameters to control the irrigation [9]. There are a few studies that focused on the development of micro-climate monitoring systems for cultural heritage structures and monuments. Most of these systems were deployed and tested on a specific monument, which was the main subject of study [8,10,11]. In this paper, we introduce a network of multiple smart IoT sensors, customized for multiple cultural heritage monuments, aiming to monitor specific micro-climate parameters in the vicinity of these buildings' walls. Within the context of the HYPERION project, four cultural heritage sites across Europe have been selected as the main research areas.