In this paper, a new green clustering algorithm is proposed to be as a first approach in the framework of an energy efficient strategy for centralized enterprise high-density WLANs. Traditionally, in order to maintain the network coverage, all the APs within the WLAN have to be powered-on. Nevertheless, the new algorithm can power-off a large proportion of APs while the coverage is maintained as its always-on counterpart. The two main components of the new approach are the faster procedure based on K-means and the more accurate procedure based on Evolutionary Algorithm (EA), respectively. The two procedures are processes in parallel for different designed requirements and there is information interaction in between. In order to implement the new algorithm, EA is applied to handle the optimization of multiple objectives. Moreover, we adapt the method for selection and recombination, and then introduce a new operator for mutation. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% to 90% of energy consumption can be saved while it is able to maintain the original network coverage during periods when few users are online or the traffic load is low.

Cooperative spectrum sensing has been considered as a promising approach to improve the sensing performance in distributed cognitive radio networks. However, there may exist some selfish secondary users (SUs) who are unwilling to cooperate. The presence of selfish SUs could cause catastrophic damage to the performance of cooperative spectrum sensing. Following the social perspective, we propose a Social Tie-based Incentive Scheme (STIS) to deal with the selfish problem for cooperative spectrum sensing in distributed cognitive radio networks. This scheme inspires SUs to contribute sensing information for the SUs who have social tie but not others, and such willingness varies with the strength of social tie value. The evaluation of each SU's social tie derives from its contribution for others. Finally, simulation results validate the effectiveness of the proposed scheme.

Cognitive Radio (CR) has been recently proposed as a promising technology to remedy the problems of spectrum scarcity and spectrum underutilization by enabling unlicensed users to opportunistically utilize temporally unused licensed spectrums in a cautious manner. In Cognitive Radio Ad Hoc Networks (CRAHNs), data routing is one of the most challenging tasks since the channel availability and node mobility are unpredictable. Moreover, the network performance is severely degraded due to large numbers of path failures. In this paper, we propose the Fault-Tolerant Cognitive Ad-hoc Routing Protocol (FTCARP) to provide fast and efficient route recovery in presence of path failures during data delivery in CRAHNs. The protocol exploits the joint path and spectrum diversity to offer reliable communication and efficient spectrum usage over the networks. In the proposed protocol, a backup path is utilized in case a failure occurs over a primary transmission route. Different cause of a path failure will be handled by different route recovery mechanism. The protocol performance is compared with that of the Dual Diversity Cognitive Ad-hoc Routing Protocol (D2CARP). The simulation results obviously prove that FTCARP outperforms D2CARP in terms of throughput, packet loss, end-to-end delay and jitter in the high path-failure rate CRAHNs.

Outdated Channel State Information (CSI) was proved to have negative effect on performance in two-way relay networks. The diversity order of widely used opportunistic relay selection (ORS) was degraded to unity in networks with outdated CSI. This paper proposed a multiple relay selection scheme for amplify-and-forward (AF) based two-way relay networks (TWRN) with outdated CSI. In this scheme, two sources exchange information through more than one relays. We firstly select N best relays out of all candidate relays with respect to signal-noise ratio (SNR). Then, the ratios of the SNRs on the rest of the candidate relays to that of the Nth highest SNR are tested against a normalized threshold ${\mu}{\in}[0,1]$, and only those relays passing this test are selected in addition to the N best relays. Expressions of outage probability, average bit error rate (BER) and ergodic channel capacity were obtained in closed-form for the proposed scheme. Numerical results and Simulations verified our theoretical analyses, and showed that the proposed scheme had significant gains comparing with conventional ORS.

The recent advances in wireless communication systems and semiconductor technologies are paving the way for new applications over wireless sensor networks. Health-monitoring application (HMA) is one such emerging technology that is focused on sensing and reporting human vital signs through the communication network comprising sensor devices in the vicinity of the human body. The sensed vital signs can be divided into two categories based on the importance and the frequency of occurrence: occasional emergency signs and regular normal signs. The occasional emergency signs are critical, so they have to be delivered by the specified deadlines, whereas the regular normal signs are non-critical and are only required to be delivered with best effort. Handling the occasional emergency sign is one of the most important attributes in HMA because a human life may depend on correct handling of the situation. That is why the underlying network protocol suite for HMA should ensure that the emergency signs will be reported in a timely manner. However, HMA based on IEEE 802.15.4 might not be able to do so owing to the lack of an appropriate emergency-handling mechanism. Hence, in this paper, we propose a new emergency-handling mechanism to reduce the emergency reporting delay in IEEE 802.15.4 through the modified superframe structure. A fraction of an inactive period is modified into three new periods called the emergency reporting period, emergency beacon period, and emergency transmission period, which are used opportunistically only for immediate emergency reporting and reliable data transmission. Extensive simulation is performed to evaluate the performance of the proposed scheme. The results reveal that the proposed scheme achieves improved latency and higher emergency packets delivery ratio compared with the conventional IEEE 802.15.4 MAC.

Most recent research on iterative solutions for interference alignment (IA) presents solutions assuming channel reciprocity based on the suppression of interference from undesired sources by using an appropriate decoding matrix also known as a receiver combining matrix for multiple input multiple output (MIMO) interference channel networks and reciprocal networks. In this paper, we present an alternative solution for IA by designing precoding and decoding matrices based on the concept of signal leakage (the measure of signal power that leaks to unintended users) on each transmit side. We propose an iterative algorithm for an IA solution based on maximization of the signal-to-leakage-and-noise ratio (SLNR) of the transmitted signal from each transmitter. In order to make an algorithm removing the requirement of channel reciprocity, we deploy maximization of the signal-to-interference-and-noise ratio (SINR) in the design of the decoding matrices. We show through simulation that minimizing the leakage in each transmission can help achieve enhanced performance in terms of aggregate sum capacity in the system.

Overlay routing has emerged as a promising approach to improve reliability and efficiency of the Internet. For one-hop overlay source routing, when a given primary path suffers from the link failure or performance degradation, the source can reroute the traffic to the destination via a strategically placed relay node. However, the over-heavy traffic passing through the same relay node may cause frequent package loss and delay jitter, which can degrade the throughput and utilization of the network. To overcome this problem, we propose a Load-Balanced One-hop Overlay Multipath Routing algorithm (LB-OOMR), in which the traffic is first split at the source edge nodes and then transmitted along multiple one-hop overlay paths. In order to determine an optimal split ratio for the traffic, we formulate the problem as a linear programming (LP) formulation, whose goal is to minimize the worse-case network congestion ratio. Since it is difficult to solve this LP problem in practical time, a heuristic algorithm is introduced to select the relay nodes for constructing the disjoint one-hop overlay paths, which greatly reduces the computational complexity of the LP algorithm. Simulations based on a real ISP network and a synthetic Internet topology show that our proposed algorithm can reduce the network congestion ratio dramatically, and achieve high-quality overlay routing service.

This paper proposes a new resampling detection method for images that detects whether an image has been resampled and recovers the corresponding resampling rate. The proposed method uses a given set of zeroing masks for various resampling factors to evaluate the convolution values of the input image with the zeroing masks. Improving upon our previous work, the proposed method detects more resampling factors by checking for some periodicity with an approximate detection mechanism. The experimental results demonstrate that the proposed method is effective and efficient.

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.

This paper presents a multiple-watermarking scheme for copyright protection and the prevention of illegal copying of mobile 3D contents with low resolution. The proposed scheme embeds a unique watermark and a watermark certification authority (WCA) watermark into the spatial and encryption domains of a mobile 3D content based on the buyer-seller watermarking protocol. The seller generates a unique watermark and embeds it into the local maximum curvedness of a 3D object. After receiving the encrypted watermark from the WCA, the seller embeds it into the encrypted vertex data using an operator that satisfies the privacy homomorphic property. The proposed method was implemented using a mobile content tool, and the experimental results verify its capability in terms of copyright protection and the prevention of illegal copying.

To investigate schemes of secret key generation from Ultra-wideband (UWB) channel, we study a statistical characterization of UWB outdoor channel for a campus playground scenario based on extensive measurements. Moreover, an efficient secret key generation mechanism exploiting multipath relative delay is developed, and verification of this algorithm is conducted in UWB Line-of-sight (LOS) outdoor channels. For the first time, we compare key-mismatch probability of UWB indoor and outdoor environments. Simulation results demonstrate that the number of multipath proportionally affects key generation rate and key-mismatch probability. In comparison to the conventional method using received signal strength (RSS) as a common random source, our mechanism achieves better performance in terms of common secret bit generation. Simultaneously, security analysis indicates that the proposed scheme can still guarantee security even in the sparse outdoor physical environment free of many reflectors.

Recently a new government has announced an action plan known as the government 3.0, which aims to provide customized services for individual people, generate more jobs and support creative economy. Leading on from previous similar initiatives, the new scheme seeks to focus on open, share, communicate, and collaborate. In promoting Government 3.0, the crucial factor might be how to align the core services and policies of Government 3.0 with correspoding technologies. The paper describes the concepts and features of Government 3.0, identifies emerging Internet-based technologies and services toward the initiative, and finally provides improvement plans for Government 3.0. As a result, 10 issues to be brought together include: Smart Phone Applications and Service, Mobile Internet Computing and Application, Wireless and Sensor Network, Security & Privacy in Internet, Energy-efficient Computing & Smart Grid, Multimedia & Image Processing, Data Mining and Big Data, Software Engineering, Internet Business related Policy, and Management of Internet Application.

This study investigates the decisive factors affecting the survival or collapse of cable networks in the Korean paid broadcasting market from 1993 to 2010. The effects of performance variables discussed in previous studies were verified through survival analyses. According to the results, the most influential factors in the survival of cable networks were horizontal integration (MPP), channel genre, the scheduling of HDTV content, and the proportion of access program scheduling. These findings imply that the differentiation strategy of content suppliers is the most decisive factor among all business performance-related factors. Specifically, the content supply power and strategy of the program provider (PP) have the greatest influence on cable network viability. The present study is meaningful as the first attempt to establish the performance of PPs in Korean pay broadcasting markets through the viewpoint of organizational mortality. Accordingly, the present study can supplement the results of cross-sectional studies conducted with smaller samples.

The purpose of this study is to qualitatively identify the typologies and characteristics of the big data marketing strategy in major companies that are taking advantage of the big data business in Korea. Big data means piles accumulated from converging platforms such as computing infrastructures, smart devices, social networking and new media, and big data is also an analytic technique itself. Numerous enterprises have grown conscious that big data can be a most significant resource or capability since the issue of big data recently surfaced abruptly in Korea. Companies will be obliged to design their own implementing plans for big data marketing and to customize their own analytic skills in the new era of big data, which will fundamentally transform how businesses operate and how they engage with customers, suppliers, partners and employees. This research employed a Q-study, which is a methodology, model, and theory used in 'subjectivity' research to interpret professional panels' perceptions or opinions through in-depth interviews. This method includes a series of q-sorting analysis processes, proposing 40 stimuli statements (q-sample) compressed out of about 60 (q-population) and explaining the big data marketing model derived from in-depth interviews with 20 marketing managers who belong to major companies(q-sorters). As a result, this study makes fundamental contributions to proposing new findings and insights for small and medium-size enterprises (SMEs) and policy makers that need guidelines or direction for future big data business.

Social media is considered a valuable platform for gathering and analyzing the collective and subconscious opinions of people in Internet and mobile environments, where they express, explicitly and implicitly, their daily preferences for brands and products. Extracting and tracking the various attitudes and concerns that people express through social media could enable us to categorize brands and decipher individuals' cognitive decision-making structure in their choice of brands. We investigate the cognitive network structure of consumers by building an inter-category map through the mining of big data. In so doing, we create an improved online recommendation model. Building on economic sociology theory, we suggest a framework for revealing collective preference by analyzing the patterns of brand names that users frequently mention in the online public sphere. We expect that our study will be useful for those conducting theoretical research on digital marketing strategies and doing practical work on branding strategies.

In digital radio broadcasting systems, long delays are incurred in service start time when tuning to a particular frequency because several synchronization steps, such as symbol timing synchronization, frame synchronization, and carrier frequency offset and sampling frequency offset compensation are necessary. Therefore, the operation of the synchronization blocks causes delays ranging from several hundred milliseconds to a few seconds until the start of the radio service after frequency tuning. Furthermore, if spectrum inversed signals are transmitted in digital radio broadcasting systems, the receivers are unable to decode them, even though most receivers can demodulate the spectral inversed signals in analog radio broadcasting systems. Accordingly, fast synchronization techniques and a method for spectral inversion detection are required in digital radio broadcasting systems that are to replace the analog radio systems. This paper presents a joint detection method of frame, integer carrier frequency offset, and spectrum inversion for DRM Plus digital broadcasting systems. The proposed scheme can detect the frame and determine whether the signal is normal or spectral inversed without any carrier frequency offset and sampling frequency offset compensation, enabling fast frame synchronization. The proposed method shows outstanding performance in environments where symbol timing offsets and sampling frequency offsets exist.

Relay technology is becoming more important for mobile communications and wireless internet of things (IoT) networking because of the extended access network coverage range and reliable quality of service (QoS) it can provide at low power consumption levels. Existing mobile multihop relay (MMR) technology uses fixed-point stationary relay stations (RSs) and a divided time-frame (or frequency-band) to support the relay operation. This approach has limitations when a local fixed-point stationary RS does not exist. In addition, since the time-frame (or frequency-band) channel resources are pre-divided for the relay operation, there is no way to achieve high channel utilization using intelligent opportunistic techniques. In this paper, a different approach is considered, where the use of mobile/IoT devices as RSs is considered. In applications that use mobile/IoT devices as relay systems, due to the very limited battery energy of a mobile/IoT device and unequal channel conditions to and from the RS, both minimum energy consumption and QoS support must be considered simultaneously in the selection and configuration of RSs. Therefore, in this paper, a mobile RS is selected and configured with the objective of minimizing power consumption while satisfying end-to-end data rate and bit error rate (BER) requirements. For the RS, both downlink (DL) to the destination system (DS) (i.e., IoT device or user equipment (UE)) and uplink (UL) to the base station (BS) need to be adaptively configured (using adaptive modulation and power control) to minimize power consumption while satisfying the end-to-end QoS constraints. This paper proposes a minimum transmission power consuming RS selection and configuration (MPRSC) scheme, where the RS uses cognitive radio (CR) sub-channels when communicating with the DS, and therefore the scheme is named MPRSC-CR. The proposed MPRSC-CR scheme is activated when a DS moves out of the BS's QoS supportive coverage range. In this case, data transmissions between the RS and BS use the assigned primary channel that the DS had been using, and data transmissions between the RS and DS use CR sub-channels. The simulation results demonstrate that the proposed MPRSC-CR scheme extends the coverage range of the BS and minimizes the power consumption of the RS through optimal selection and configuration of a RS.

VoIP (Voice over Internet Protocol) is a technology of transmitting and receiving voice and data over the Internet network. As the telecommunication industry is moving toward All-IP environment with growth of broadband Internet, the technology is becoming more important. Although the early VoIP services failed to gain popularity because of problems such as low QoS (Quality of Service) and inability to receive calls as the phone number could not be assigned, they are currently established as the alternative service to the conventional wired telephone due to low costs and active marketing by carriers. However, VoIP is vulnerable to eavesdropping and DDoS (Distributed Denial of Service) attack due to its nature of using the Internet. To counter the VoIP security threats efficiently, it is necessary to develop the criterion or the model for estimating the information security level of VoIP service providers. In this study, we developed reasonable security indicators through questionnaire study and statistical approach. To achieve this, we made use of 50 items from VoIP security checklists and verified the suitability and validity of the assessed items through Multiple Regression Analysis (MRA) using SPSS 18.0. As a result, we drew 23 indicators and calculate the weight of each indicators using Analytic Hierarchy Process (AHP). The proposed indicators in this study will provide feasible and reliable data to the individual and enterprise VoIP users as well as the reference data for VoIP service providers to establish the information security policy.

Scrum is one of the most popular and efficient agile development methods. However, like other agile methods such as Extreme Programming (XP), Feature Driven Development (FDD), and the Dynamic Systems Development Method (DSDM), Scrum has been criticized because of lack of support to develop secure software. Thus, in 2011, we published research proposing the idea of a security backlog (SB). This paper represents the continuation of our previous research, with a focus on the evaluation in industry-based case study. Our findings highlight an improved agility in Scrum after the integration of SB. Furthermore, secure software can be developed quickly, even in situations involving requirement changes of software. Based on our experimental findings, we noticed that, when integrating SB, it is quite feasible to develop secure software using an agile Scrum model.

Wind energy has proven its viability by the emergence of countless wind turbines around the world which greatly contribute to the increased electrical generating capacity of wind farm operators. These infrastructures are usually deployed in not easily accessible areas; therefore, maintenance routines should be based on a well-guided decision so as to minimize cost. To aid operators prior to the maintenance process, a condition monitoring system should be able to accurately reflect the actual state of the wind turbine and its major components in order to execute specific preventive measures using as little resources as possible. In this paper, we propose a fault detection approach which combines cluster analysis and frequent pattern mining to accurately reflect the deteriorating condition of a wind turbine and to indicate the components that need attention. Using SCADA data, we extracted operational status patterns and developed a rule repository for monitoring wind turbine systems. Results show that the proposed scheme is able to detect the deteriorating condition of a wind turbine as well as to explicitly identify faulty components.

The current networks do not disclose information about a management domain due to scalability, manageability and commercial reasons. Therefore, it is very hard to calculate an optimal path to the destination. Also, due to poor information sharing, if an error occurs in the intermediate path, it is very difficult to re-search the path and find the best path. Hence, to manage each domain more efficiently, an architecture with top-level path computation node which can obtain information of separate nodes are highly needed This study aims to investigate a federation of a united network around NRN(National Research Network) that could allow resource sharing between countries and also independent resource management for each country. Considering first the aspects that can be accessed from the perspective of a national research network, ICE(Information Control Element) and GFO(Global Federation Organizer)-based architecture is designed as a top-level path computation element to support traffic engineering and applied to the multi-domain network. Then, the federation for the independent management of resources and resource information sharing among national research networks have been examined.

This paper formalizes a special type of social networking knowledge, which is called "workflow performer-role affiliation networking knowledge." A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. In Particular, these affiliated relationships exhibit a stream of organizational work-sharing knowledge and utilize business process intelligence to explore resources allotting and planning knowledge concealed in the corresponding workflow model. In this paper, we particularly focus on the performer-role affiliation relationships and their implications as organizational and business process intelligence in workflow-driven organizations. We elaborate a series of theoretical formalisms and practical implementation for modeling, discovering, and visualizing workflow performer-role affiliation networking knowledge, and practical details as workflow performer-role affiliation knowledge representation, discovery, and visualization techniques. These theoretical concepts and practical algorithms are based upon information control net methodology for formally describing workflow models, and the affiliated knowledge eventually represents the various degrees of involvements and participations between a group of performers and a group of roles in a corresponding workflow model. Finally, we summarily describe the implications of the proposed affiliation networking knowledge as business process intelligence, and how worthwhile it is in discovering and visualizing the knowledge in workflow-driven organizations and enterprises that produce massively parallel interactions and large-scaled operational data collections through deploying and enacting massively parallel and large-scale workflow models.

In the $21^{st}$ century, IT reform has led to the development of cyber-infrastructure owing to the outstanding enhancement of computer and network performance. The ripple effect has continued to increase. Accordingly, this study suggests a new computational research environment using mobile devices. In order to simplify the access of supercomputer, Science AppStore, task management and virtualization technologies are developed on mobile devices. User can be able to research by utilizing computational science SW such as compressible flow solver and nano device simulation tool that in installed on supercomputer in mobile environments. Also, this research environment makes it possible to monitor the simulation result and covers 14 university, 33 subjects, and 1,202 individuals.

This paper proposes a matching engine for a query-by-singing/humming (QbSH) system with polyphonic music files like MP3 files. The pitch sequences extracted from polyphonic recordings may be distorted. So we use chroma-scale representation, pre-processing, compensation, and asymmetric dynamic time warping to reduce the influence of the distortions. From the experiment with 28 hour music DB, the performance of our QbSH system based on polyphonic database is very promising in comparison with the published QbSH system based on monophonic database. It shows 0.725 in MRR(Mean Reciprocal Rank). Our matching engine can be used for the QbSH system based on MIDI DB also and that performance was verified by MIREX 2011.