In order to construct an efficient wireless sensor system, it is necessary to increase the lifetime of its battery-operated sensor nodes. To this end, a wireless body sensor system adopts a transmission power control (TPC) mechanism. However, existing TPC mechanisms adjust the transmission power level (TPL) according to the received signal strength indication (RSSI) value of the most recently received data packet. Therefore, they do not effectively cope with dynamically changing wireless body channel environments. In particular, when a wireless channel is unstable, changes in the TPL should be avoided to prevent energy consumption due to unnecessary transmission and reception of control packets. Accordingly, this paper proposes a new TPC mechanism that adaptively changes the control packet transmission interval on the basis of the current channel condition. Further, actual sensors are used to experimentally verify that the proposed mechanism (1) performs well in all channel environments and (2) facilitates the construction of an efficient wireless body sensor system.

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users’ tailored materials according to their learning style. That is, we analyzed the user’s data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

Automated protein function prediction defines the designation of functions of unknown protein functions by using computational methods. This technique is useful to automatically assign gene functional annotations for undefined sequences in next generation genome analysis (NGS). NGS is a popular research method since high-throughput technologies such as DNA sequencing and microarrays have created large sets of genes. These huge sequences have greatly increased the need for analysis. Previous research has been based on the similarities of sequences as this is strongly related to the functional homology. However, this study aimed to designate protein functions by automatically predicting the function of the genome by utilizing InterPro (IPR), which can represent the properties of the protein family and groups of the protein function. Moreover, we used gene ontology (GO), which is the controlled vocabulary used to comprehensively describe the protein function. To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one.

Neighbor discovery is one of the emerging research areas in a wireless sensor network. After sensors are distributed, neighbor discovery is the first process to set up a communication channel with neighboring sensors. This paper proposes a new block design–based asymmetric neighbor discovery protocol for sensor networks. We borrow the concept of combinatorial block designs for our block combination scheme for neighbor discovery. First, we introduce an asymmetric neighbor discovery problem and define a target research question. Second, we propose a new asymmetric block design–based neighbor discovery protocol and explain how it works. Third, we analyze the worst-case neighbor discovery latency numerically between our protocol and some well-known protocols in the literature, and compare and evaluate the performance between the proposed protocol and others. Our protocol reveals that the worst-case latency is much lower than that of Disco and U-Connect. Finally, we conclude that the minimum number of slots per a neighbor schedule shows the lowest discovery time in terms of discovery latency and energy consumption.

In the past, we used to call the name of members for the purpose of attendance checking by managers (or instructors). They verify the identity of member's participation by human recognition using facial and voice matching. This approach is time-consuming because the number of members is getting increased. Moreover, they may have to recheck any of the students’ presence at the end of the period manually. In this research, we offer a convenient novel attendance checking method to take advantage of Wi-Fi 802.11x technology. Our application initiates AP mode Wi-Fi service for checking attendance of users in which a token is generated only to a person who is close to a manager. If a member has the token, the smart application of the member will connect and report to attendance server. Otherwise, the smart application of the member will report to the server that the users/students are not near the manager. By this way managers/instructors can easily check the member's attendance. In addition, this research proposes a novel concept that unlimited number of devices can be supported. We make use of Wi-Fi scan (rather than connect) to the manager's AP enabled smart devices, resulting in an enhanced scalability.