From Sensors to Socio-Technical Systems: A PRISMA-Based Systematic Review of IoT-Driven Landslide Early Warning Systems

Landslides are among the most recurrent and destructive natural hazards worldwide, generating severe impacts on human life, infrastructure, and ecosystems. Recent advances in Internet of Things (IoT) technologies, smart sensing, and data-driven architectures have significantly enhanced the capabilities of Early Warning Systems (EWS) for landslide risk mitigation. However, current implementations remain fragmented across technological layers and lack standardized validation and socio-technical integration. This study presents a systematic literature review of IoT-based landslide EWS published between 2019 and 2025, following the PRISMA protocol. A total of 33 primary studies were selected from Scopus, Web of Science, and IEEE Xplore using predefined inclusion and exclusion criteria. The review analyzes key system dimensions, including sensing technologies, communication infrastructures, data processing architectures, validation approaches, and human-centered evaluation. The results reveal the predominance of Micro-Electro-Mechanical Systems (MEMS) and optical sensing technologies such as Fiber Bragg Grating (FBG) and Distributed Acoustic/Temperature Sensing (DAS/DTS), along with communication protocols such as Long Range (LoRa) and 5G. In addition, hybrid Edge–Cloud architectures are widely adopted to enable real-time monitoring and predictive analytics. Despite these advances, critical gaps persist in data validation standardization, system interoperability, and the integration of human-centered and participatory evaluation approaches. This study contributes by proposing an integrated socio-technical framework that unifies sensing, communication, data processing, validation, and human-centered dimensions in IoT-based EWS. The findings highlight the need for standardized validation protocols, AI-driven predictive integration, and user-centered design strategies to enhance the scalability, reliability, and social effectiveness of next-generation early warning systems.