Owing to the increasing interest in the integration of the medical technology and the information and communications technology, research on wireless body area networks (WBANs), which apply a sensor network to the human body, is being actively conducted. Existing sensor network technology has the potential to be used in a WBAN; however, it has some limitations. In particular, a WBAN has a very different network environment compared to a sensor network that uses free space, because wireless sensors in a BAN transmit through parts of the human body. Therefore, research on WBANs involves a variety of research areas that differ slightly from those of conventional sensor networks and take into account the characteristics of the human body. This study investigates the environmental characteristics of a WBAN that differ from conventional sensor networks and examines the areas that have been studied in academia to realize more efficient WBANs. From studies published since 2001, when the concept of the WBAN was introduced, research trends in WBANs were investigated using the systematic literature review (SLR) technique. The investigation in this study includes the classification of research and research fields in line with research content. Further, survey results are presented and the outlook for further study is summarized.

The introduction of useful microblogging services like Twitter derives significant changes in various areas of society. The smart tools such as mobile devices and microblogging services have led to great changes in the traditional way of education. It is possible to access good quality educational contents and methods in a more convenient way. Although a lot of smart tools have been applied for educational application, there are only limited researches that demonstrate the educational effectiveness of smart tools through experiment considerations. In this study, research documents about smart learning from 2007 have been treated based on 5 major searching sites; we investigate the research trends of smart learning of education. The opinion of the researchers about the educational use of smart tools is analyzed. Then, through substantial experiment, we are acquainted that the smart tools have a positive effect on the educational environment. As a practical application, the Twitter microblogging service is used for educational communications in 2 classes of a university during a semester. As a result, we can find that smart tools contribute to increase the efficiency of education, but learners and teachers need to pay considerable efforts on the use of smart tools to achieve the purpose.

One of the core technologies of wireless body area networks (WBANs) is the routing technology. For effective routing in WBANs, various network operations such as lifetime extension and energy efficiency are required to be considered. However, the characteristics of the body serve as the most important management elements for stable operation and for guaranteeing effective WBAN management. Therefore, in this study, a routing protocol, even energy consumption and backside routing, is proposed and designed for stable operation of WBANs; it considers even energy consumption for the lifetime extension of the network and the path loss of the node located at the back of the body. For this study, the Mobile-ATTEMPT (M-ATTEMPT) as a recent routing technology is analysed, and an improved algorithm is proposed. Finally, the excellence of the proposed protocol is proved through a variety of experiments. The results show a significant improvement than previous routing technologies in terms of network lifetime, throughput and residual energy.

Finding a target quickly is one of the most important tasks in drone operations. In particular, rapid target detection is a critical issue for tasks such as finding rescue victims during the golden period, environmental monitoring, locating military facilities, and monitoring natural disasters. Therefore, in this study, an improved hierarchical probabilistic target search algorithm based on the collaboration of drones at different altitudes is proposed. This is a method for reducing the search time and search distance by improving the information transfer methods between high-altitude and low-altitude drones. Specifically, to improve the speed of target detection, a high-altitude drone first performs a search of a wide area. Then, when the probability of existence of the target is higher than a certain threshold, the search information is transmitted to a low-altitude drone which then performs a more detailed search in the identified area. This method takes full advantage of fast searching capabilities at high altitudes. In other words, it reduces the total time and travel distance required for searching by quickly searching a wide search area. Several drone collaboration scenarios that can be performed by two drones at different altitudes are described and compared to the proposed algorithm. Through simulations, the performances of the proposed algorithm and the cooperation scenarios are analyzed. It is demonstrated that methods utilizing hierarchical searches with drones are comparatively excellent and that the proposed algorithm is approximately 13% more effective than a previous method and much better compared to other scenarios.

In Wireless Sensor Networks (WSN), energy is one of the most important resources since each node collects, processes and transmits data to its base station. Most of the traditional works in WSNs are consisted of static nodes and one base station. Recently, some mobile data gathering methods are proposed to prolong the operation time of sensor networks. One or more mobile collectors are used to gather sensed data from sensor nodes at short transmission ranges. This paper presents to find optimal visiting points and data gathering path for a mobile sink within clusters. With defining an optimal clustering and data gathering path, this method improves the data collection performance as well as the network lifetime extension of sensor networks. The network lifetime is increased to 20% when sensor nodes are divided into from 4 clusters to 15 clusters.