Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air quality in the campus, endangering the respiratory and cognitive health of students and staffs. Besides, in universities in Indonesia, automobile emissions of harmful substances such as carbon monoxide (CO), nitrogen dioxide (NO₂), and hydrocarbon (HC) have been a serious problem for a long time. Almost everybody is using a motorbike or a car every day in daily life, while the number of students is continuously increasing. However, people in many campuses including managements do not be aware these problems, since air quality is not monitored. In this paper, we present a real-time air quality monitoring system utilizing Internet of Things (IoT) integrated sensors capable of detecting pollutants and measuring environmental conditions to visualize them. By transmitting data to the SEMAR IoT application server platform via an ESP32 microcontroller, this system provides instant alerts through a web application and Telegram notifications when pollutant levels exceed safe thresholds. For evaluations of the proposed system, we adopted three sensors to measure the levels of CO, NO₂, and HC and conducted experiments in three sites, namely, Mechatronics Laboratory, Power and Emission Laboratory, and Parking Lot, at the State Polytechnic of Malang, Indonesia. Then, the results reveal Good, Unhealthy, and Dangerous for them, respectively, among the five categories defined by the Indonesian government. The system highlighted its ability to monitor air quality fluctuations, trigger warnings of hazardous conditions, and inform the campus community. The correlation of the sensor levels can identify the relationship of each pollutant, which provides insight into the characteristics of pollutants in a particular scenario.

Musculoskeletal disorders (MSDs) can significantly impact individuals’ quality of life (QoL), often requiring effective rehabilitation strategies to promote recovery. However, traditional rehabilitation methods can be expensive and may lack engagement, leading to poor adherence to therapy exercise routines. An exergame system can be a solution to this problem. In this paper, we investigate appropriate hand gestures for controlling video games in a rehabilitation exergame system. The Mediapipe Python library is adopted for the real-time recognition of gestures. We choose 10 easy gestures among 32 possible simple gestures. Then, we specify and compare the best and the second-best groups used to control the game. Comprehensive experiments are conducted with 16 students at Andalas University, Indonesia, to find appropriate gestures and evaluate user experiences of the system using the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). The results show that the hand gestures in the best group are more accessible than in the second-best group. The results suggest appropriate hand gestures for game controls and confirm the proposal’s validity. In future work, we plan to enhance the exergame system by integrating a diverse set of video games, while expanding its application to a broader and more diverse sample. We will also study other practical applications of the hand gesture control function.

In location-based augmented reality (LAR) applications, a simple and effective authoring tool is essential to create immersive AR experiences in real-world contexts. Unfortunately, most of the current tools are primarily desktop-based, requiring manual location acquisitions, the use of software development kits (SDKs), and high programming skills, which poses significant challenges for novice developers and a lack of precise LAR content alignment. In this paper, we propose an intuitive in situ authoring tool with visual-inertial sensor fusions to simplify the LAR content creation and storing process directly using a smartphone at the point of interest (POI) location. The tool localizes the user’s position using smartphone sensors and maps it with the captured smartphone movement and the surrounding environment data in real-time. Thus, the AR developer can place a virtual object on-site intuitively without complex programming. By leveraging the combined capabilities of Visual Simultaneous Localization and Mapping(VSLAM) and Google Street View (GSV), it enhances localization and mapping accuracy during AR object creation. For evaluations, we conducted extensive user testing with 15 participants, assessing the task success rate and completion time of the tool in practical pedestrian navigation scenarios. The Handheld Augmented Reality Usability Scale (HARUS) was used to evaluate overall user satisfaction. The results showed that all the participants successfully completed the tasks, taking $16.76$ s on average to create one AR object in a 50 m radius area, while common desktop-based methods in the literature need 1–8 min on average, depending on the user’s expertise. Usability scores reached $89.44$ for manipulability and $85.14$ for comprehensibility, demonstrating the high effectiveness in simplifying the outdoor LAR content creation process.

Nowadays, Internet of Things (IoT) application systems play an essential role in smart cities, industry, healthcare, agriculture, and smart homes. For non-expert users, designing and implementing IoT application systems remains challenging, especially when configuring sensors, edge devices, and server platforms. To support configuration tasks of IoT application systems, we have developed an AI-based setup assistance tool. However, AI models still fail to reliably support newly released or previously unseen devices, sometimes producing incomplete or erroneous outputs that may lead to configuration failures. Incorporating their technical-document information into Retrieval-Augmented Generation (RAG) is an effective way to supplement AI knowledge and improve reliability. In this paper, we propose a generative AI-based technical data extraction tool to address the challenges. It extracts essential technical information using the schema-based extraction from given PDF or HTML datasheets and converts it into a structured format suitable for AI-supported configurations. A local vector database is used to enable semantic similarity retrieval and provide document-grounded evidence for RAG-based answering, ensuring consistent support for previously unseen IoT devices. For evaluations, we applied the proposal to several sensor and device datasheets and compared extracted specifications with ground-truth values to measure accuracy and completeness. Then, we compared end-to-end configuration QA reliability against a commercial baseline (ChatPDF) using the golden benchmark. The results show that the proposed tool reliably acquires key specifications and significantly improves end-to-end configuration QA reliability. Across 960 golden QA pairs, the proposed method improves Recall from 0.636 to 0.926 and Accuracy from 0.595 to 0.807 compared with ChatPDF.

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.