Designing an IoT-Supported Tsunami Preparedness Model Using Sensor Data, Local Wisdom, and Community Communication Channels
Muhammad Fikri Akbar, Sandy Allifiansyah, Nada Arina Romli, Eko Aziz Apriadi, E. Nugrahaeni P., Mega Ayu Permatasari, Abdul Razaque Chhachhar

Universitas Negeri Jakarta, Indonesia
Universitas Indonesia Mandiri, Indonesia
University of Sindh, Pakistan

Abstract

Coastal areas in Indonesia remain highly vulnerable to tsunami risks, particularly in regions where technical early-warning systems are not yet fully connected to community-level understanding and response. This study proposes an IoT-supported tsunami preparedness model that integrates sensor data, local wisdom, and community communication channels to strengthen last-mile disaster communication in Bandar Lampung. The study is grounded in the argument that disaster preparedness cannot rely solely on technological detection systems, but requires the translation of technical signals into clear, trusted, culturally relevant, and actionable public messages.

Using a qualitative exploratory case study approach, this research draws on expert-based focus group discussion data and a structured readiness assessment involving key stakeholders in disaster management, community leadership, social inclusion, risk communication, local governance, and IoT engineering. The assessment examines four domains: development communication strategies, IoT utilization, community and institutional preparedness, and integration between communication and technology. The findings indicate that Bandar Lampung^s tsunami preparedness system is moderately ready. Existing strengths include recognizable communication pathways through local government, RT/RW networks, mosque loudspeakers, community leaders, and emerging use of IoT-based environmental monitoring. However, gaps remain in sensor reliability, routine maintenance, public understanding of IoT signals, two-way feedback mechanisms, inclusion of vulnerable groups, evacuation drills, and standardized message translation.

The proposed model adopts a hub-and-spoke workflow in which IoT sensors transmit environmental data to a local decision-support hub. The data are then converted into standardized warning messages and disseminated through multiple trusted channels, including official media, mosque public-address systems, neighborhood coordinators, traditional leaders, and locally recognized warning practices such as kentongan. This model positions local wisdom not as a symbolic cultural element, but as a functional component of early warning communication. The study contributes to disaster preparedness research by offering a people-centered IoT communication model for sustainable coastal resilience and supporting SDG 11 and SDG 13.

Keywords: IoT, tsunami preparedness, sensor data, local wisdom, disaster communication, coastal resilience

Topic: Energy and Environmental Physics