Artificial Intelligence of Things (AIoT) for Disaster Monitoring using Wireless Mesh Network
Mau‐Luen Tham, Yi Jie Wong, Ban-Hoe Kwan, Xin Hao Ng, Yasunori Owada
Abstract
The inherent characteristics of Internet of things (IoT) such as low computation power of IoT nodes and transmission reliability of IoT links demand a new paradigm for efficient data processing and dissemination. This is especially true for disaster situations with high possibility of communication breakdowns. On one hand, the concept of artificial intelligence of things (AIoT) has been introduced as a technology to push data storage and computing closer to the network edge. On the other hand, wireless mesh network offers a strong self-healing capability and network robustness against disaster damages. To enable smart disaster monitoring applications, we first implement a lightweight multi-task model that performs joint disaster classification and victim detection. These AI outputs are then wirelessly synchronized via a mesh network solution called NerveNet. All the experiments are conducted in a real urban environment, including static and mobile nodes. Experimental results validate the effectiveness of the proposed solution, where text and images can be synchronized within two minutes across a multi-hop Wi-Fi network. Furthermore, the optimized AI model has ultra-low power consumption around 1.23 W with frames per second (FPS) of 2.01.