For rapid deployments of various IoT application systems, we have developed Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) as an integrated server platform. It is equipped with rich functions for collecting, analyzing, and visualizing various data. Unfortunately, the proper configuration of SEMAR with a variety of IoT devices can be complex and challenging for novice users, since it often requires technical expertise. The assistance of Generative AI can be helpful to solve this drawback. In this paper, we present an implementation of a sensor input setup assistance service for SEMAR using prompt engineering techniques and Generative AI. A user needs to define the requirement specifications and environments of the IoT application system for sensor inputs, and give them to the service. Then, the service provides step-by-step guidance on sensor connections, communicating board configurations, network connections, and communication protocols to the user, which can help the user easily set up the configuration to connect the relevant devices to SEMAR. For evaluations, we applied the proposal to the input sensor setup processes of three practical IoT application systems with SEMAR, namely, a smart light, water heater, and room temperature monitoring system. In addition, we applied it to the setup process of an IoT application system for a course for undergraduate students at the Insitut Bisnis dan Teknologi (INSTIKI), Indonesia. The results demonstrate the effectiveness of the proposed service for SEMAR.

The Internet of Things (IoT) technology has grown rapidly over the past decade, resulting in deployments of thousands of IoT devices around the world. Then, managing firmware updates for these numerous devices poses significant challenges. Firmware updates face issues such as version rollback, modified firmware files, and potential man-in-the-middle (MITM) attacks, highlighting the need for a secure over-the-air (OTA) firmware update mechanism. In this paper, we propose an automated OTA firmware update mechanism, integrated with continuous integration (CI) and continuous delivery (CD) to ensure trusted sources for firmware origins. It offers security, error handling during firmware updates, and monitoring of the update process. For evaluations, we implemented the proposal with the SEMAR IoT application server that has been implemented in our previous studies. Then, we verified the integrity and authentication, measured the performance and resource utilization, and performed benchmarking tests to assess the efficiency. The results demonstrate that the proposal is sufficiently reliable and efficient.

In recent years, Location-Based Augmented Reality (LAR) systems have been increasingly implemented in various applications for tourism, navigation, education, and entertainment. Unfortunately, the LAR content creation using conventional desktop-based authoring tools has become a bottleneck, as it requires time-consuming and skilled work. Previously, we proposed an in-situ mobile authoring tool as an efficient solution to this problem by offering direct authoring interactions in real-world environments using a smartphone. Currently, the evaluation through the comparison between the proposal and conventional ones is not sufficient to show superiority, particularly in terms of interaction, authoring performance, and cognitive workload, where our tool uses 6DoF device movement for spatial input, while desktop ones rely on mouse-pointing. In this paper, we present a comparative study of authoring performances between the tools across three authoring phases: (1) Point of Interest (POI) location acquisition, (2) AR object creation, and (3) AR object registration. For the conventional tool, we adopt Unity and ARCore SDK. As a real-world application, we target the LAR content creation for pedestrian landmark annotation across campus environments at Okayama University, Japan, and Brawijaya University, Indonesia, and identify task-level bottlenecks in both tools. In our experiments, we asked 20 participants aged 22 to 35 with different LAR development experiences to complete equivalent authoring tasks in an outdoor campus environment, creating various LAR contents. We measured task completion time, phase-wise contribution, and cognitive workload using NASA-TLX. The results show that our tool made faster creations with 60% lower cognitive loads, where the desktop tool required higher mental efforts with manual data input and object verifications.

Recently, artificial intelligence (AI) has been adopted in a number of Internet of Things (IoT) application systems to enhance intelligence. We have developed a ready-made server with rich built-in functions to collect, process, display, analyze, and store data from various IoT devices, the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform, in which various AI techniques have been implemented to enhance its capabilities. In this paper, we present an application of SEMAR to a drone-based wall inspection system using an object detection AI model called You Only Look Once (YOLO). This system aims to detect wall cracks at high places using images taken via a camera on a flying drone. An edge computing device is installed to control the drone, sending the taken images through the Kafka system, storing them with the drone flight data, and sending the data to SEMAR. The images are analyzed via YOLO through SEMAR. For evaluations, we implemented the system using Ryze Tello for the drone and Raspberry Pi for the edge, and we evaluated the detection accuracy. The preliminary experiment results confirmed the effectiveness of the proposal.

With rapid developments of wireless communication and Internet of Things (IoT) technologies, an increasing number of devices and sensors are interconnected, generating massive amounts of data in real time. Among the underlying protocols, Message Queuing Telemetry Transport (MQTT) has become a widely adopted lightweight publish–subscribe standard due to its simplicity, minimal overhead, and scalability. Then, understanding such protocols is essential for students and engineers engaging in IoT application system designs. However, teaching and learning MQTT remains challenging for them. Its asynchronous architecture, hierarchical topic structure, and constituting concepts such as retained messages, Quality of Service (QoS) levels, and wildcard subscriptions are often difficult for beginners. Moreover, traditional learning resources emphasize theory and provide limited hands-on guidance, leading to a steep learning curve. To address these challenges, we propose an AI-assisted, exercise-based learning platform for MQTT. This platform provides interactive exercises with intelligent feedback to bridge the gap between theory and practice. To lower the barrier for learners, all code examples for executing MQTT communication are implemented in Python for readability, and Docker is used to ensure portable deployments of the MQTT broker and AI assistant. For evaluations, we conducted a usability study using two groups. The first group, who has no prior experience, focused on fundamental concepts with AI-guided exercises. The second group, who has relevant background, engaged in advanced projects to apply and reinforce their knowledge. The results show that the proposed platform supports learners at different levels, reduces frustrations, and improves both engagement and efficiency.