In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works.

With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the smart home scenerio, a host often meets interferences from other devices and unequal Received Signal Strength (RSS) from Access Points (APs). This results in throughput unfairness/insufficiency problems between hosts communicating concurrently in WLAN. Previously, we have studied the throughput request satisfaction method to address this problem. It calculates the target throughput from measured single and concurrent throughputs of hosts and controls the actual throughput at this target one by applying traffic shaping at the AP. However, the insufficiency problem of maximizing the throughput is not solved due to interferences from other hosts. In this paper, we present an extension of the throughput request satisfaction method to maximize the throughput of a high-priority host under concurrent communications. It recalculates the target throughput to increase the actual throughput as much as possible while the other hosts satisfy the least throughput. For evaluations, we conduct experiments using the test-bed system with Raspberry Pi as the AP devices in several topologies in indoor environments. The results confirm the effectiveness of our proposal.

For decades, analog dial gauges have been essential for measuring and monitoring data at various industrial instruments including production machines and laboratory equipment. Among them, we focus on the instrument for creep test in a mechanical engineering laboratory, which evaluates material strength under sustained stress. Manual reading of gauges imposes significant labor demands, especially in long-duration tests. This burden further increases under low-lighting environments, where poor visibility can lead to misreading data points, potentially compromising the accuracy of test results. In this paper, to address the challenges, we implement a creep test assisting system that possesses the following features: (1) to save the installation cost, a web camera and Raspberry Pi are employed to capture images of the dial gauge and automate the needle reading by image processing in real time, (2) to ensure reliability under low-lighting environments, a smart lighting mechanism is integrated to turn on a supplementary light when the dial gauge is not clearly visible, and (3) to allow a user to stay in a distant place from the instrument during a creep test, material break is detected and the corresponding message is notified to a laboratory staff using LINE automatically. For evaluations, we install the implemented system into a material strength measuring instrument at Okayama University, Japan, and confirm the effectiveness and accuracy through conducting experiments under various lighting conditions.