SENNSE IoT Platform for Real-Time Monitoring: Network Design and Hardware Implementation

The rise of the Internet of Things (IoT) has revolutionised environmental monitoring, enhancing comfort, energy efficiency, and sustainability across indoor and outdoor spaces. However, preventive conservation in cultural heritage still lacks real-time monitoring and predictive capabilities. This paper presents SENNSE, an IoT-driven platform designed to support the monitoring, management, and preservation of cultural heritage sites and artefacts. SENNSE integrates wireless sensor networks with cloud-based analytics to enable real-time data collection, processing, and automated environmental control. The platform's architecture spans multiple network layers, from sensing hardware to cloud services and dashboard-based data visualisation. We deployed SENNSE in a case study involving three indoor scenarios: semi-confined, closed, and open spaces. Key environmental parameters such as temperature, humidity, and light intensity were monitored to assess microenvironmental conditions. Using machine learning, SENNSE analysed sensor data to extract insights on workspace activity, including office hours, thermal comfort, meeting frequency, and energy use. For office activity detection, the model identified working and non-working periods with 95% training accuracy, 89% test accuracy, 0.99 precision, and 0.86 recall, demonstrating the predictive power of temporal and environmental data. To minimise manual intervention, SENNSE includes features such as over-the-air updates, configurable thresholds, system notifications, and automated email alerts. By integrating IoT technology with preventive conservation strategies, SENNSE offers a robust framework for real-time monitoring and long-term protection of cultural heritage.