Nowadays, rapid developments of Internet of Things (IoT) technologies have increased possibilities of realizing smart cities where collaborations and integrations of various IoT application systems are essential. However, IoT application systems have often been designed and deployed independently without considering the standards of devices, logics, and data communications. In this paper, we present the design and implementation of the IoT server platform called Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) for integrating IoT application systems using standards. SEMAR offers Big Data environments with built-in functions for data aggregations, synchronizations, and classifications with machine learning. Moreover, plug-in functions can be easily implemented. Data from devices for different sensors can be accepted directly and through network connections, which will be used in real-time for user interfaces, text files, and access to other systems through Representational State Transfer Application Programming Interface (REST API) services. For evaluations of SEMAR, we implemented the platform and integrated five IoT application systems, namely, the air-conditioning guidance system, the fingerprint-based indoor localization system, the water quality monitoring system, the environment monitoring system, and the air quality monitoring system. When compared with existing research on IoT platforms, the proposed SEMAR IoT application server platform offers higher flexibility and interoperability with the functions for IoT device managements, data communications, decision making, synchronizations, and filters that can be easily integrated with external programs or IoT applications without changing the codes. The results confirm the effectiveness and efficiency of the proposal.

Nowadays, with the common use of smartphones in daily lives, mobile applications have become popular around the world, which will lead to a rise in Flutter framework. Developed by Google, Flutter with Dart programming provides a cross-platform development environment to create visually appealing and responsive user interfaces across mobile, web, and desktop platforms using a single codebase. However, due to time and staff limitations, the Flutter/Dart programming course is not included in curricula, even in IT departments in universities. Therefore, independent learning environments for students are essential to meet this growing popularity. Previously, we have developed programming learning assistant system (PLAS) as a web-browser-based self-learning platform for novice students. PLAS offers various types of exercise problems designed to cultivate programming skills step-by-step through a lot of code reading and code writing practices. Among them, one particular type is the code modification problem (CMP), which asks to modify the given source code to satisfy the new specifications. CMP is expected to be solved by novices with little effort if they have knowledge of other programming languages. Thus, PLAS with CMP will be an excellent platform for independent learning. In this paper, we present PLAS with CMP for the independent learning of Flutter/Dart programming. To improve the readability of the source code by students, we provided rich comments on grammar or behaviors. Besides, the code can be downloaded so that students can check and run it on an IDE. For evaluations, we generated 38 CMP instances for basic and multimedia/storage topics in Flutter/Dart programming and assigned them to 21 master students at Okayama University, Japan, who have never studied it. The results confirm the validity of the proposal.

Nowadays, human indoor localization services inside buildings or on underground streets are in strong demand for various location-based services. Since conventional GPS cannot be used, indoor localization systems using wireless technologies have been extensively studied. Previously, we studied a fingerprint-based indoor localization system using IEEE802.15.4 devices, called FILS15.4, to allow use of inexpensive, tiny, and long-life transmitters. However, due to the narrow channel band and the low transmission power, the link quality indicator (LQI) used for fingerprints easily fluctuates by human movements and other uncontrollable factors. To improve the localization accuracy, FILS15.4 restricts the detection granularity to one room in the field, and adopts multiple fingerprints for one room, considering fluctuated signals, where their values must be properly adjusted. In this paper, we present a fingerprint optimization method for finding the proper fingerprint parameters in FILS15.4 by extending the existing one. As the training phase using the measurement LQI, it iteratively changes fingerprint values to maximize the newly defined score function for the room detecting accuracy. Moreover, it automatically increases the number of fingerprints for a room if the accuracy is not sufficient. For evaluations, we applied the proposed method to the measured LQI data using the FILS15.4 testbed system in the no. 2 Engineering Building at Okayama University. The validation results show that it improves the average detection accuracy (at higher than 97%) by automatically increasing the number of fingerprints and optimizing the values.

Nowadays, indoor localization systems using IEEE 802.11 have been actively explored for location-based services, since GPS cannot identify floors or rooms in buildings. However, the user-side device is usually large and consumes high energy. In this paper, the authors propose a fingerprint-based indoor localization system using IEEE 802.15.4 that allows the use of a small device with a long-life battery, named FILS15.4. A user carries a small transmitter whose signal is received by multiple receivers simultaneously. The received signal strengths are compared with the fingerprints to find the current location. To address signal fluctuations caused by the low-power narrow-band signal, FILS15.4 limits one room as the localization unit, prepares plural fingerprints for each room, and allocates a sufficient number of receivers in the field. For evaluations, extensive experiments were conducted at #2 Engineering Building in Okayama University and confirmed high detection accuracy with sufficient numbers of receivers and fingerprints.

Our society now has driven us into an era where almost everything can be digitally monitored and controlled including the human body. The growth of wireless body area network (WBAN), as a specific scope of sensor networks which mounted or attached to human body also developing rapidly. It allows people to monitor their health and several daily activities. This study is intended to review the trend of WBAN especially on Android, one of the most popular smartphone platforms. A systematic literature review is concerned to the following parameters: the purpose of the device and/or application, the type of sensors, the type of Android device, and its connectivity. Most of the studies were more concern to healthcare or medical monitoring systems: blood pressure, electro cardiograph, tremor detection, etc. On the other hand, the rest of them aimed for activity tracker, environment sensing, and epidemic control. After all, those studies shown that not only Android can be a powerful platform to process data from various sensors but also smartphones can be a good alternative to develop WBANs for medical and other daily applications.

Mastery of digital electronics principles is essential for future engineers in the digital era. This article describes the use of simulations in an undergraduate electrical engineering course to promote the adoption of a learning-goal orientation. This study used experimental method. This was done by providing students with a simulation environment which students freely use to experiment with various circuit models. Students were then invited to reflect on how the simulation results compare with results from lab experiments. The module got 82% of positive rating from 28 students and all of them passed in the examination with 81.8 as the average score. Those majority students were motivated by the combination of two learning goals written in the module. Moreover, they also gain the ability to design more complex systems because of their combined experience. Additionally, the module also has been validated and got 83% of reliability. The final product of this research hereafter can be recommended to be used as teaching material.