An Enhanced Generative AI-Based Assistant for IoT Application System Deployment on SEMAR Platform

Abstract

Nowadays, IoT application systems are increasing in importance for improving efficiency, safety, and quality of operations, services, and products in various sectors of factories, shops, offices, and farms. However, the hurdle in deploying a new system is high for many possible users due to lack of expertise in their organizations. To solve this drawback, we have studied and developed a SEMAR IoT application platform that implements necessary functions to allow a new deployment by only setting the configuration file. To assist its setup, we have also offered a generative AI-based assistant to produce the file and sensor connection programs depending on user requirements automatically. Unfortunately, this assistant is limited to mainly sensor-level configurations. In this paper, we enhance this generative AI-based assistant to support the end-to-end deployment of an IoT application system on the SEMAR platform, involving sensors, edge devices, and the server. The proposal accepts inputs on hardware specifications and application-level requirements. Then, it analyses them using an LLM-based reasoning mechanism to determine suitable deployment strategies. After that, it automatically generates deployment artifacts, including the configuration file, the Docker configuration, the environment variables, and the firmware configurations tailored to the target environment. For evaluation, we conducted a within-subject user study involving 13 participants across three IoT application scenarios and two edge device platforms under controlled laboratory conditions and evaluated the usability through the System Usability Scale (SUS). In addition, we measured the average time of the setup process as the deployment performance. The results showed that the proposed system achieved a high usability score (SUS = 85.00) and reduced the mean deployment time by 24.8% compared with the conventional manual process (21.80 min vs. 16.40 min).