Device-to-Device (D2D) communication underlaying cellular networks is a promising add-on component for future radio communication systems. It provides more access opportunities for local device pairs and enhances system throughput (ST), especially when mobile relays (MR) are further enabled to facilitate D2D links when the channel condition of their desired links is unfavorable. However, mutual interference is inevitable due to spectral reuse, and moreover, selecting a suitable transmission mode to benefit the correlated resource allocation (RA) is another difficult problem. We aim to optimize ST of the hybrid system via joint consideration of mode selection (MS) and RA, which includes admission control (AC), power control (PC), channel assignment (CA) and relay selection (RS). However, the original problem is generally NP-hard; therefore, we decompose it into two parts where a hierarchical structure exists: (i) PC is mode-dependent, but its optimality can be perfectly addressed for any given mode with additional AC design to achieve individual quality-of-service requirements. (ii) Based on that optimality, the joint design of MS, CA and RS can be viewed from the graph perspective and transferred into the maximum weighted independent set problem, which is then approximated by our greedy algorithm in polynomial-time. Thanks to the numerical results, we elucidate the efficacy of our mechanism and observe a resulting gain in MR-aided D2D communication.

Workflow scheduling is one of the challenging problems in cloud computing, especially when service reliability is considered. To improve cloud service reliability, fault tolerance techniques such as fault recovery can be employed. Practically, fault recovery has impact on the performance of workflow scheduling. Such impact deserves detailed research. Only few research works on workflow scheduling consider fault recovery and its impact. In this paper, we investigate the problem of workflow scheduling in clouds, considering the probability that cloud resources may fail during execution. We formulate this problem as a multi-objective optimization model. The first optimization objective is to minimize the overall completion time and the second one is to minimize the overall execution cost. Based on the proposed optimization model, we develop a heuristic-based algorithm called Min-min based time and cost tradeoff (MTCT). We perform extensive simulations with four different real world scientific workflows to verify the validity of the proposed model and evaluate the performance of our algorithm. The results show that, as expected, fault recovery has significant impact on the two performance criteria, and the proposed MTCT algorithm is useful for real life workflow scheduling when both of the two optimization objectives are considered.

A common concern regarding cooperative spectrum sensing (CSS) schemes is the occupied bandwidth and the energy consumption during the transmissions of sensing information to the fusion center over the reporting control channels. This concern is intensified if the number of cooperating secondary users in the network is large. This article presents a new fusion strategy for a CSS scheme, aiming at increasing the energy efficiency of a recently proposed bandwidth-efficient fusion scheme. Analytical results and computational simulations unveil a high increase in energy efficiency when compared with the original approach, yet achieving better performances in some situations, and lower implementation complexity.

In this paper, we present a joint relay-and-antenna selection and power allocation strategy for multiple-input multi-output (MIMO) amplify-and-forward (AF) two-way relay networks (TWRNs). In our approach, we select the best transmit and receive antennas at the two sources, a best relay and a best transmit and receive antenna at the selected relay based on maximizing the minimum of the end-to-end received signal-to-noise-ratios (SNRs) under a total transmit power constraints. We obtained the closed-form solution for the optimal power allocation firstly. Then with the optimal allocation solution we found, we can reduce the joint relay-and-antenna selection to a simpler problem. Besides, the overall outage probability is investigated and a tight closed-form approximation is derived, which provides a method to evaluate the outage performance easily and fast. Simulation results are presented to verify the analysis.

Mobility models are invaluable for determining the performance of routing protocols in opportunistic networks. The movement of nodes has a significant influence on the topological structure and data transmission in networks. In this paper, we propose a new mobility model called the campus-based community mobility model (CBCNM) that closely reflects the daily life pattern of students on a real campus. Consequent on a discovery that the pause time of nodes in their community follows a power law distribution, instead of a classical exponential distribution, we abstract the semi-Markov model from the movement of the campus nodes and analyze its rationality. Then, using the semi-Markov algorithm to switch the movement of the nodes between communities, we infer the steady-state probability of node distribution at random time points. We verified the proposed CBCNM via numerical simulations and compared all the parameters with real data in several aspects, including the nodes' contact and inter-contact times. The results obtained indicate that the CBCNM is highly adaptive to an actual campus scenario. Further, the model is shown to have better data transmission network performance than conventional models under various routing strategies.

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.

Most of scheduling methods in the Grid only consider one special attribute of the resource or one aspect of QoS (Quality of Service) of the job. In this paper, we focus on the problem that how to consider two aspects simultaneously. Based on the requirements of the jobs and the attributes of the resources, jobs are categorized into three kinds: CPU-overload, memory-overload, and bandwidth-overload jobs. One job may belong to different kinds according to different attributes. We schedule the jobs in different categories in different orders, and then propose a scheduling method-MTS (multiple attributes scheduling method) to schedule Grid resources. Based on the comparisons between our method, Min-min, ASJS (Adaptive Scoring Job Scheduling), and MRS (Multi-dimensional Scheduling) show: (1) MTS reduces the execution time more than 15% to other methods, (2) MTS improves the number of the finished jobs before the deadlines of the jobs, and (3) MTS enhances the file size of transmitted files (input files and output files) and improves the number of the instructions of the finished jobs.

Hybrid deployment under current cloud data centers is a combination of online and offline services, which improves the utilization of the cluster resources. However, the performance of the cluster is often affected by the online services in the hybrid deployment environment. To improve the response time of online service (e.g. search engine), an effective scheduling algorithm based on Storm is proposed. At the component level, the algorithm dispatches the component with more influence to the optimal performance node. Inside the component, a reasonable resource allocation strategy is used. By searching the compressed index first and then filtering the complete index, the execution speed of the component is improved with similar accuracy. Experiments show that our algorithm can guarantee search accuracy of 95.94%, while increasing the response speed by 68.03%.

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

Differential detection schemes do not require any channel estimation, which can be employed under user mobility with low computational complexity. In this work, a soft-input soft-output (SISO) differential detection algorithm is proposed for amplify-and-forward (AF) over time-varying relaying channels based cooperative communications system. Furthermore, maximum-likelihood (ML) detector for M-ary differential Phase-shift keying (DPSK) is derived to calculate a posteriori probabilities (APP) of information bits. In addition, when the SISO is exploited in conjunction with channel decoding, iterative detection and decoding approach by exchanging extrinsic information with outer code is obtained. Finally, simulation results show that the proposed non-coherent approach improves detection performance significantly. In particular, the system can obtain greater performance gain under fast-fading channels.

In this paper, delay-constrained data transmission is considered over error-prone networks. Network coding is deployed for efficient information exchange, and an approximate decoding approach is deployed to overcome potential all-or-nothing problems. Our focus is on determining the cluster size and its impact on approximate decoding performance. Decoding performance is quantified, and we show that performance is determined only by the number of packets. Moreover, the fundamental tradeoff between approximate decoding performance and data transfer rate improvement is analyzed; as the cluster size increases, the data transfer rate improves and decoding performance is degraded. This tradeoff can lead to an optimal cluster size of network coding-based networks that achieves the target decoding performance of applications. A set of experiment results confirms the analysis.

This study introduces an image-based 2D-to-3D conversion system that provides significant stereoscopic visual effects for humans. The linear and atmospheric perspective cues that compensate each other are employed to estimate depth information. Rather than retrieving a precise depth value for pixels from the depth cues, a direction angle of the image is estimated and then the depth gradient, in accordance with the direction angle, is integrated with superpixels to obtain the depth map. However, stereoscopic effects of synthesized views obtained from this depth map are limited and dissatisfy viewers. To obtain impressive visual effects, the viewer's main focus is considered, and thus salient object detection is performed to explore the significance region for visual attention. Then, the depth map is refined by locally modifying the depth values within the significance region. The refinement process not only maintains global depth consistency by correcting non-uniform depth values but also enhances the visual stereoscopic effect. Experimental results show that in subjective evaluation, the subjectively evaluated degree of satisfaction with the proposed method is approximately 7% greater than both existing commercial conversion software and state-of-the-art approach.

The single image dehazing algorithm in existence can satisfy the demand only for improving either the effectiveness or efficiency. In order to solve the problem, a novel variational framework for single image dehazing based on restoration is proposed. Firstly, the initial atmospheric scattering model is transformed to meet the kimmel's Retinex variational model. Then, the green light component of image is considered as an input of the variational framework, which is generated by the sensitivity of green wavelength. Finally, the atmospheric transmission map is achieved by multi-resolution pyramid reduction to improve the visual effect of the results. Experimental results demonstrate that the proposed method can remove haze effectively with less memory consumption.

The emerging video coding standard High Efficiency Video Coding (HEVC) has shown almost 40% bit-rate reduction over the state-of-the-art Advanced Video Coding (AVC) standard but at about 40% computational complexity overhead. The main reason for HEVC computational complexity is the inter prediction that accounts for 60%-70% of the whole encoding time. In this paper, we propose several fast coding unit (CU) encoding schemes based on the Merge mode and motion estimation information to reduce the computational complexity caused by the HEVC inter prediction. Firstly, an early Merge mode decision method based on motion estimation (EMD) is proposed for each CU size. Then, a Merge mode based early termination method (MET) is developed to determine the CU size at an early stage. To provide a better balance between computational complexity and coding efficiency, several fast CU encoding schemes are surveyed according to the rate-distortion-complexity characteristics of EMD and MET methods as a function of CU sizes. These fast CU encoding schemes can be seamlessly incorporated in the existing control structures of the HEVC encoder without limiting its potential parallelization and hardware acceleration. Experimental results demonstrate that the proposed schemes achieve 19%-46% computational complexity reduction over the HEVC test model reference software, HM 16.4, at a cost of 0.2%-2.4% bit-rate increases under the random access coding configuration. The respective values under the low-delay B coding configuration are 17%-43% and 0.1%-1.2%.

Secure communication is an important aspect of today's interconnected environments and it can be achieved by the use of cryptographic algorithms and protocols. However, many existing cryptographic mechanisms are tightly integrated into communication protocols. Issues emerge when security vulnerabilities are discovered in cryptographic mechanisms because their replacement would eventually require replacing deployed protocols. The concept of cryptographic agility is the solution to these issues because it allows dynamic switching of cryptographic algorithms and keys prior to and during the communication. Most of today's secure protocols implement cryptographic agility (IPsec, SSL/TLS, SSH), but cryptographic agility mechanisms cannot be used in a standalone manner. In order to deal with the aforementioned limitations, we propose a lightweight cryptographically agile agreement model, which is formally verified. We also present a solution in the Agile Cryptographic Agreement Protocol (ACAP) that can be adapted on various network layers, architectures and devices. The proposed solution is able to provide existing and new communication protocols with secure communication prerequisites in a straightforward way without adding substantial communication overhead. Furthermore, it can be used between previously unknown parties in an opportunistic environment. The proposed model is formally verified, followed by a comprehensive discussion about security considerations. A prototype implementation of the proposed model is demonstrated and evaluated.

With the feature of convenience and low cost, remote healthcare monitoring (RHM) has been extensively used in modern disease management to improve the quality of life. Due to the privacy of health data, it is of great importance to implement RHM based on a secure and dependable network. However, the network connectivity of existing RHM systems is unreliable in disaster area because of the unforeseeable damage to the communication infrastructure. To design a secure RHM system in disaster area, this paper presents a Secure VANET-Assisted Remote Healthcare Monitoring System (SVC) by utilizing the unique "store-carry-forward" transmission mode of vehicular ad hoc network (VANET). To improve the network performance, the VANET in SVC is designed to be a two-level network consisting of two kinds of vehicles. Specially, an innovative two-level key management model by mixing certificate-based cryptography and ID-based cryptography is customized to manage the trust of vehicles. In addition, the strong privacy of the health information including context privacy is taken into account in our scheme by combining searchable public-key encryption and broadcast techniques. Finally, comprehensive security and performance analysis demonstrate the scheme is secure and efficient.

In this paper, we propose a symmetric key establishment scheme for wireless sensor networks which tries to minimize the resource usage while satisfying the security requirements. This is accomplished by taking advantage of the communication pattern of wireless sensor networks and adopting heterogeneous wireless sensor networks. By considering the unique communication pattern of wireless sensor networks due to the nature of information gathering from the physical world, the number of keys to be established is minimized and, consequently, the overhead spent for establishing keys decreases. With heterogeneous wireless sensor networks, we can build a hybrid scheme where a small number of powerful nodes do more works than a large number of resource-constrained nodes to provide enhanced security service such as broadcast authentication and reduce the burden of resource-limited nodes. In addition, an on-demand key establishment scheme is introduced to support extra communications and optimize the resource usage. Our performance analysis shows that the proposed scheme is very efficient and highly scalable in terms of storage, communication and computation overhead. Furthermore, our proposed scheme not only satisfies the security requirements but also provides resilience to several attacks.

With the swift growth of wireless technologies, an increasing number of users rely on the mobile services which can exchange information in mobile networks. Security is of key issue when a user tries to access those services in this network environment. Many authentication schemes have been presented with the purpose of authenticating entities and wishing to communicate securely. Recently, Chou et al. and Farash-Attari presented two ID authentication schemes. They both claimed that their scheme could withstand various attacks. However, we find that the two authentication schemes are vulnerable to trace attack while having a problem of clock synchronization. Additionally, we show that Farash-Attari's scheme is still susceptible to key-compromise impersonation attack. Therefore, we present an enhanced scheme to remedy the security weaknesses which are troubled in these schemes. We also demonstrate the completeness of the enhanced scheme through the Burrow-Abadi-Needham (BAN) logic. Security analysis shows that our scheme prevents the drawbacks found in the two authentication schemes while supporting better secure attributes. In addition, our scheme owns low computation overheads compared with other related schemes. As a result, our enhanced scheme seems to be more practical and suitable for resource-constrained mobile devices in mobile networks.

Dynamic ID-based authentication solves the ID-theft problem by changing the ID in each session instead of using a fixed ID while performing authenticated key exchanges between communicating parties. User anonymity is expected to be maintained and the exchanged key kept secret even if one of the long-term keys is compromised in the future. However, in the conventional dynamic ID-based authentication scheme, if the server's long-term key is compromised, user anonymity can be broken or the identities of the users can be traced. In addition, these schemes are vulnerable to replay attacks, in which any adversary who captures the authentication message can retransmit it, and eventually cause the legitimate user to be denied service. This paper proposes a novel dynamic ID-based authentication scheme that preserves forward anonymity as well as forward secrecy and obviates replay attacks.

The fourth industrial revolution is currently proceeding and is expected to significantly affect the way individuals live and in result change the society in various aspects. The ICT-based convergence industries of the fourth industrial revolution contain various fields such as driverless cars, lighter and tougher materials, robotics, 3D printing and even biotechnology. This paper examines the researches done in fourth industrial revolution field based on the articles submitted to APIC-IST 2015. The topics from articles related to the fourth industrial revolution were categorized based on the keyword frequency of main issues. Results suggested that IoT and Wireless sensor network related researches were among the fields that the most researches were done in it and topics like nanotechnology, biotechnology, driverless cars, sharing economy and 3D printing were not mentioned at all. This paper also suggests necessary contents in the fourth industrial revolution to be focused on for further research in this field.

Wireless sensor networks (WSN) are self-organized networks that typically consist of thousands of low-cost, low-powered sensor nodes. The reliability and availability of WSNs can be affected by faults, including those from radio interference, battery exhaustion, hardware and software failures, communication link errors, malicious attacks, and so on. Thus, we propose a novel multi-agent fault tolerant system for wireless sensor networks. Since a major requirement of WSNs is to reduce energy consumption, we use multi-agent and mobile agent configurations to manage WSNs that provide energy-efficient services. Mobile agent architecture have inherent advantages in that they provide energy awareness, scalability, reliability, and extensibility. Our multi-agent system consists of a resource manager, a fault tolerance manager and a load balancing manager, and we also propose fault-tolerant protocols that use multi-agent and mobile agent setups.

Spectrum sharing in centrally controlled cognitive radio (CR) networks has been widely studied, however, research on channel access for distributively controlled individual cognitive users has not been fully characterized. This paper conducts an analysis of random channel access of cognitive users controlled in a distributed manner in a CR network. Based on the proposed estimation method, each cognitive user can estimate the current channel condition by using its own Markov-chain model and can compute its own blocking probability, collision probability, and forced termination probability. Using the proposed scheme, CR with distributed control (CR-DC), CR devices can make self-controlled decisions based on the status estimations to adaptively control its system parameters to communicate better.

ICT contents market is globally considered as an industry of high added value, where sustainability is accomplished through stories that cover wide range of consumers. However, the need for long-term internationalization strategy is growing as countless number of applications and games has pushed the local market to its limit. Thus, this research explored on the industrial characteristics and the internationalization of ICT contents firms through acquisition of competency. Based on Resource Dependence theory, the authors studied how pursuit of legitimacy and autonomy functioned in the expansion process of ICT contents firms. A survey analysis of 212 key decision makers of Korean ICT content firms were performed, to reveal that resource environment and interdependence must undergo a thorough consideration.

A cloud computing system can be characterized by the provision of resources in the form of services to third parties on a leased, usage-based basis, as well as the private infrastructures maintained and utilized by individual organizations. To attain the desired reliability and energy efficiency in a cloud data center, trade-offs need to be carried out between system performance and power consumption. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. The work presented in this paper is directed towards the development of an Energy-efficient and Performance-aware Cloud System equipped with strategies for dynamic switching of optimization approach. Moreover, a platform is also provided for the deployment of a Wind Farm CMS (Condition Monitoring System) which allows ubiquitous access. Due to the geographically-dispersed nature of wind farms, the CMS can take advantage of the cloud's highly scalable architecture in order to keep a reliable and efficient operation capable of handling multiple simultaneous users and huge amount of monitoring data. Using the proposed cloud architecture, a Wind Farm CMS is deployed in a virtual platform to monitor and evaluate the aging conditions of the turbine's major components in concurrent, yet isolated working environments.

This paper presents a target model update scheme for the mean-shift tracking with background weighted histogram. In the scheme, the target candidate histogram is corrected by considering the back-projection weight of each pixel in the kernel after the best target candidate in the current frame image is chosen. In each frame, the target model is updated by the weighted average of the current target model and the corrected target candidate. We compared our target model update scheme with the previous ones by applying several test sequences. The experimental results showed that the object tracking accuracy was greatly improved by using the proposed scheme.

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.

Because of the properties of software such as invisibility, complexity, and changeability, software configuration management (SCM) for software artifacts generated during software life-cycle has been used for guarantee of the quality of the software. However, the existing SCM system has only focused on code artifacts and software development document artifacts such as Software Requirements Specification (SRS), Software Design Description (SDD), and Software Test Description (STD). Moreover, software research-oriented project comes out late the code artifacts and the software development document artifacts. Therefore, there is a need for trace and management of software research document artifacts composed of highly abstracted non-functional requirements like 'the purpose of the project', 'the objectives', and 'the progress' before generation of the code artifacts and the software development document artifacts for a long time. Nevertheless, the existing SCM system cannot trace and manage them. In this paper, we propose content-based configuration management system comprised of the relevance link generation phase and content-based testing phase to trace and manage them. The preliminary application results show applicability and feasibility of the proposed system.

In this paper, we address the channel zapping time problem of video streaming services based on HTTP Live Streaming (HLS) on smart Over-The-Top Set-Top Boxes (OTT STBs). Experimental analysis of the channel zapping time, show that smart OTT STBs inevitably suffer from the accumulated zapping time through channel change request, Internet Group Management Protocol (IGMP) leave/join, synchronization delay, video buffer delay, and STB processing delay when providing HLS services. As a practical solution for the zapping time reduction, an adaptive buffering method is proposed. The proposed method exploits two adaptive buffers added to the basic HLS player. These two adaptive buffers are responsible for constantly buffering previous and next channels relative to the current channel. Implementation and test results show that a stable zapping time less than one second can be achieved even under diverse video bitrate changes and varying network conditions by the proposed adaptive buffering method.

While Facebook and Twitter get worldwide attention, these popular SNSs are not available in China. As the leading local SNS, Sina Weibo has garnered much of the attention in China. The purpose of the study was to explore why Chinese college students use Sina Weibo and if gender differences exist in the motivations for using it. The results from a survey of 360 respondents show that Chinese students used Sina Weibo mainly for information-gathering, followed by accessibility to celebrity, social connection, self-presentation and entertainment. Among them the most dominant reason for using Sina Weibo was found to be information-gathering. This finding suggests that Sina Weibo functions as a platform to search for information on social issues and interests. The study also found that these motivations were significantly different between male and female users. Interestingly, female respondents used Sina Weibo much more broadly than male counterparts, accessing it to satisfy all needs such as information gathering, accessibility to celebrity, social connection, self-presentation and entertainment. Based on these findings limitations and direction for future studies are discussed.

Now, there are prodigiously expanding worldwide economic networks in the information society, which require their social structural changes through technology innovations. This paper so tries to formally define a process-centered knowledge model to be used to analyze policy-making procedures on technology innovations. The eventual goal of the proposed knowledge model is to apply itself to analyze a topic network based upon composite keywords from a document written in a natural language format during the technology innovation procedures. Knowledge model is created to topic network that compositing driven keyword through text mining from natural language in document. And we show that the way of analyzing knowledge model and automatically generating feature keyword and relation properties into topic networks.

This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.

This paper proposes "A Black Hole Detection Protocol Design based on a Mutual Authentication Scheme on VANET." It consists of the Mutual Authentication Scheme (MAS) that processes a Mutual Authentication by transferring messages among a Gateway Node, a Sensor Node, and a User Node and the Black Hole Detection Protocol (BHDP) which detects a Non-Authentication Node by using the Session Key computed in the MAS and a Black Hole by using the Broadcasting Table. Therefore, the MAS can reduce the operation count of hash functions more than the existing scheme and protect a privacy from an eavesdropping attack and an information exposure by hashing a nonce and user's ID and password. In addition, the MAS prevents a replay attack by using the randomly generated nonce and the time stamp. The BHDP improves Packet Delivery ratio and Throughput more than the AODV with Black hole by 4.79% and 38.28Kbps. Also, it improves Packet Delivery ratio and Throughput more than the IDSAODV by 1.53% and 10.45Kbps. Hence it makes VANET more safe and reliable.