• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.139-145
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• 2010

In this paper, we propose robust cell ID detection algorithm and SNR estimation algorithm applicable to mobile base station, which can be operated independently. The proposed cell ID estimation uses signal subspace to estimate cell IDs used in cell. The proposed SNR estimation algorithm uses number of noise subspace vectors and the corresponding eigen-vectors. Through the computer simulations, we showed that performance of the proposed cell ID detection and SNR estimation algorithms are superior to existing correlation based algorithms. Also we showed that the proposed algorithm is suitable to fast moving channel in high background noise and strong interference signal.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.120-126
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• 2020

Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4835-4855
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• 2018

In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using Majorization-Minimization-based Non-convex Optimization (MM-NcO). Furthermore, it is shown that, typically, the sparse signals that are recovered using the proposed iterative algorithm are not globally optimal and the performance of the iterative algorithm depends on the initial point. Therefore, a modified MM-NcO-based iterative algorithm is developed that uses prior information of both support and amplitude of the sparse signal to enhance recovery performance. Finally, the modified MM-NcO-based iterative algorithm is used to estimate the time-varying sparse wireless channels with temporal correlation. The numerical results show that the new algorithm performs better than related algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5150-5169
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• 2015

Mobile Ad hoc NETworks (MANETs) are the best choice when mobility, scalability, and decentralized network infrastructure are needed. Because of critical mission applications of MANETs, network security is the vital requirement. Most routing protocols in MANETs assume that every node in the network is trustworthy. However, due to the open medium, the wide distribution, and the lack of nodes' physical protection, attackers can easily compromise MANETs by inserting misbehaving nodes into the network that make blackhole attacks. Previous research to detect the misbehaving nodes in MANETs used the overhearing methods, or additional ACKnowledgement (ACK) packets to confirm the reception of data packets. In this paper a special lightweight acknowledgement-based method is developed that, contrary to existing methods, it uses ACK packets of MAC layer instead of adding new ACK packets to the network layer for confirmations. In fact, this novel method, named PIGACK, uses ACK packets of MAC 802.11 to piggyback confirmations from a receiver to a sender in the same transmission duration that the sender sends a data packet to the receiver. Analytical and simulation results show that the proposed method considerably decreases the network overhead and increases the packet delivery ratio compared to the well-known method (2ACK).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1052-1070
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• 2016

Wireless Body Area Networks (WBANs) have received a lot of attention as a promising technology for medical and healthcare applications. A WBAN should guarantee energy efficiency, data reliability, and low data latency because it uses tiny sensors that have limited energy and deals with medical data that needs to be timely and correctly transferred. To satisfy this requirement, many multi-radio multi-channel MAC protocols have been proposed, but these cannot be implemented on current off-the-shelf sensor nodes because they do not support multi-radio transceivers. Thus, recently single-radio multi-channel MAC protocols have been proposed; however, these methods are energy inefficient due to data duplication. This paper proposes a TDMA-based single-radio, multi-channel MAC protocol that uses the Unbalanced Star+Mesh topology to satisfy the requirements of WBANs. Our analytical analysis together experiments using real sensor nodes show that the proposed protocol outperforms existing methods in terms of energy efficiency, reliability, and low data latency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2261-2280
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• 2014

Recently, network traffic has become more complex and diverse due to the emergence of new applications and services. Therefore, the importance of application-level traffic classification is increasing rapidly, and it has become a very popular research area. Although a lot of methods for traffic classification have been introduced in literature, they have some limitations to achieve an acceptable level of performance in real-time application-level traffic classification. In this paper, we propose a novel application-level traffic classification method using payload size sequence (PSS) signature. The proposed method generates unique PSS signatures for each application using packet order, direction and payload size of the first N packets in a flow, and uses them to classify application traffic. The evaluation shows that this method can classify application traffic easily and quickly with high accuracy rates, over 99.97%. Furthermore, the method can also classify application traffic that uses the same application protocol or is encrypted.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.97-104
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• 2017

This study explored the potential associations among older adults' online community uses, socialization, and social isolation. Results from the hierarchical regression analysis indicated that the quality and size of personal networks composed of online community members positively influences older adults' socialization and reduces social isolation. However, the frequency of meeting with online community members in offline settings was not significantly associated with socialization. Moreover, the amount of time using online communities indirectly and significantly affected social isolation, mediated by socialization. Results from this study address the positive roles of online community uses in overcoming psychological difficulties among elderly people.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.896-909
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• 2010

IEEE 802.11 medium access control (MAC) employs the distributed coordination function (DCF) as the fundamental medium access function. DCF operates with binary exponential backoff (BEB) in order to avoid frame collisions. However it may waste wireless resources because collisions occur when multiple stations are contending for frame transmissions. In order to solve this problem, a binary negative-exponential backoff (BNEB) algorithm has been proposed that uses the maximum contention window size whenever a collision occurs. However, when the number of contending stations is small, the performance of BNEB is degraded due to the unnecessarily long backoff time. In this paper, we propose the adaptive BNEB (A-BNEB) algorithm to maximize the throughput regardless of the number of contending stations. A-BNEB estimates the number of contending stations and uses this value to adjust the maximum contention window size. Simulation results show that A-BNEB significantly improves the performance of IEEE 802.11 DCF and can maintain a high throughput irrespective of the number of contending stations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.753-775
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• 2010

Greedy forwarding is a key mechanism of geographic routing using distance as a metric. As greedy forwarding only uses 1-hop neighbor node information, it minimizes routing overhead and is highly scalable. In existing greedy forwarding schemes, a node selects a next forwarding node based only on the distance. However, the signal strength in a realistic environment reduces exponentially depending on the distance, so that by considering only the distance, it may cause a large number of data packet retransmissions. To solve this problem, many greedy forwarding schemes have been proposed. However, they do not consider the unreliable and asymmetric characteristics of wireless links and thus cause the waste of limited battery resources due to the data packet retransmissions. In this paper, we propose a reliable and energy-efficient opportunistic greedy forwarding scheme for unreliable and asymmetric links (GF-UAL). In order to further improve the energy efficiency, GF-UAL opportunistically uses the path that is expected to have the minimum energy consumption among the 1-hop and 2-hop forwarding paths within the radio range. Comprehensive simulation results show that the packet delivery rate and energy efficiency increase up to about 17% and 18%, respectively, compared with the ones in PRR${\times}$Distance greedy forwarding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4971-4987
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• 2019

Node localization is the basic task of underwater wireless sensor networks (UWSNs). Most of the existing underwater localization methods rely on ranging accuracy. Due to the special environment conditions in the ocean, beacon nodes are difficult to deploy accurately. The narrow bandwidth and high delay of the underwater acoustic communication channel lead to large errors. In order to reduce the ranging error and improve the positioning accuracy, we propose a localization algorithm based on ranging correction and inertial coordination. The algorithm can be divided into two parts, Range Correction based Localization algorithm (RCL) and Inertial Coordination based Localization algorithm (ICL). RCL uses the geometric relationship between the node positions to correct the ranging error and obtain the exact node position. However, when the unknown node deviates from the deployment area with the movement of the water flow, it cannot communicate with enough beacon nodes in a certain period of time. In this case, the node uses ICL algorithm to combine position data with motion information of neighbor nodes to update its position. The simulation results show that the proposed algorithm greatly improves the positioning accuracy of unknown nodes compared with the existing localization methods.