In general, Hadoop improves the task scheduling performance by determining data locality based on the location in which the input splits and MapTask are executed. However, if an input split consists of multiple data blocks that are distributed and stored in different nodes, this data location method fails to cope with the degradation in processing performance due to the increased frequency of data block copying. We propose a task scheduling algorithm that solves this issue by defining a method to classify data locality taking into account the location of all data blocks that comprise an input split, categorizing tasks based on the defined method, and sequentially assigning tasks according to a given priority. This study measures the performance of the proposed algorithm through a comparison of the total processing time, MapTask performance time, and data block copying frequency between the proposed algorithm and Hadoop's default task scheduling algorithm. The test results show that the proposed algorithm improved the total processing time by up to 25% and the data block copying frequency by up to 28%, when compared to the default algorithm.

In the modern digital age, users are exposed to a vast amount of content and information, and the importance of recommendation systems is increasing accordingly. Traditional recommendation systems mainly use matrix factorization and collaborative filtering methods, but problems with scalability due to an increase in the amount of data and slow learning and inference speeds occur due to an increase in the amount of computation. To overcome these problems, this study focused on optimizing LightGCN, the basic structure of the graph-convolution-network-based recommendation system. To improve this, techniques and structures were proposed. We propose an embedding enhancement method to strengthen the robustness of embedding and a non-combination structure to overcome LightGCN’s weight sum structure through this method. To verify the proposed method, we have demonstrated its effectiveness through experiments using the SELFRec library on various datasets, such as Yelp2018, MovieLens-1M, FilmTrust, and Douban-book. Mainly, significant performance improvements were observed in key indicators, such as Precision, Recall, NDCG, and Hit Ratio in Yelp2018 and Douban-book datasets. These results suggest that the proposed methods effectively improved the recommendation performance and learning efficiency of the LightGCN model, and the improvement of LightGCN, which is most widely used as a backbone network, makes an important contribution to the entire field of GCN-based recommendation systems. Therefore, in this study, we improved the learning method of the existing LightGCN and changed the weight sum structure to surpass the existing accuracy.

In order to build an efficient security architecture, previous studies have attempted to understand complex system architectures and message flows to detect various attack packets. However, the existing hardware-based single security architecture cannot efficiently handle a complex system structure. To solve this problem, we propose a software-defined networking (SDN) policy-based scheme for an efficient security architecture. The proposed scheme considers four policy functions: separating, chaining, merging, and reordering. If SDN network functions virtualization (NFV) system managers use these policy functions to deploy a security architecture, they only submit some of the requirement documents to the SDN policy-based architecture. After that, the entire security network can be easily built. This paper presents information about the design of a new policy functions model, and it discusses the performance of this model using theoretical analysis.

In wireless body sensor network systems (WB-SNSs), the sensor nodes have very limited battery power because they are tiny, lightweight, and wearable or implantable. As a result, WB-SNSs require a very efficient transmission power control (TPC) algorithm for effectively reducing energy consumption and extending the lifetime of sensor nodes. To achieve this goal, we propose a novel TPC algorithm referred to as hybrid TPC. The hybrid TPC algorithm adaptively selects a conservative or an aggressive control mechanism depending on current channel conditions. The conservative control mechanism, which slowly changes transmission power level (TPL), is suitable in a dynamic environment. On the other hand, the aggressive control mechanism, which rapidly changes TPL, is ideal in a static environment. In order to evaluate the effectiveness of the hybrid TPC algorithm, we implemented various TPC algorithms and compared their performances against the hybrid TPC algorithm in different channel environments. The experimental results showed that the hybrid TPC algorithm outperformed other TPC algorithms in all channel environments.

Due do the various requirements of sensor applications, it is desired to design a neighbor discovery protocol that supports both the symmetric and asymmetric duty cycles. This letter proposes a new block-based neighbor discovery protocol for asymmetric sensor networks by adapting the theory of balanced incomplete block designs and the Chinese remainder theorem. Through the simulation study, it is demonstrated that the proposed block-based neighbor discovery protocol outperforms other neighbor discovery methods, such as Disco, U-Connect, SearchLight, Hedis, and Todis.

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.