The separated management and operation of commercial IP/optical multilayer networks makes network operators look for a unified control plane (UCP) to reduce their capital and operational expenditure. Software-defined networking (SDN) provides a central control plane with a programmable mechanism, regarded as a promising UCP for future optical networks. The general control and scheduling mechanism in SDN-based optical burst switching (OBS) networks is insufficient so the controller has to process a large number of messages per second, resulting in low network resource utilization. In view of this, this paper presents the burst-flow scheduling mechanism (BFSM) with a proposed scheduling algorithm considering channel usage. The simulation results show that, compared with the general control and scheduling mechanism, BFSM provides higher resource utilization and controller performance for the SDN-based OBS network in terms of burst loss rate, the number of messages to which the controller responds, and the average latency of the controller to process a message.

Cloud computing is an upcoming technology and emerging concept in the field of information technologies. Companies have begun to invest in cloud computing with the expectation that it will improve their business performances, operations, and processes. However, many companies are not so much aware of the cloud computing, so they can hesitate to adopt this new technology. The aims of the study are analyzing factors affecting the adoption of cloud computing and applying structural equation modeling technique to analyze the important dimensions of the adoption. Concordantly, previous studies are examined and expert interviews are arranged. Based on both our literature review and expert interviews, a model is proposed to measure the adoption of cloud computing. It is obvious that there are scarce researches about cloud computing adoption in the literature. Thus, the originality of the paper lies on that it proposes a research model for cloud computing adoption and it investigates various dimensions of cloud computing adoption in detail.

Service discovery is one of the major challenges in cloud computing environment with a large number of service providers and heterogeneous services. Non-uniform naming conventions, varied types and features of services make cloud service discovery a grueling problem. With the proliferation of cloud services, it has been laborious to find services, especially from Internet-based service repositories. To address this issue, services are crawled and clustered according to their similarity. The clustered services are maintained as a catalogue in which the data published on the cloud provider's website are stored in a standard format. As there is no standard specification and a description language for cloud services, new efficient and intelligent mechanisms to discover cloud services are strongly required and desired. This paper also proposes a key-value representation to describe cloud services in a formal way and to facilitate matching between offered services and demand. Since naïve users prefer to have a query in natural language, semantic approaches are used to close the gap between the ambiguous user requirements and the service specifications. Experimental evaluation measured in terms of precision and recall of retrieved services shows that the proposed approach outperforms existing methods.

Compressed sensing (CS) possesses the potential benefits for spectrum sensing of wideband signal in cognitive radio. The sparsity of signal in frequency domain denotes the number of occupied channels for spectrum sensing. This paper presents a scheme of adaptively adjusting the number of compressed measurements to reduce the unnecessary computational complexity when priori information about the sparsity of signal cannot be acquired. Firstly, a method of sparsity estimation is introduced because the sparsity of signal is not available in some cognitive radio environments, and the relationship between the amount of used data and estimation accuracy is discussed. Then the SNR of the compressed signal is derived in the closed form. Based on the SNR of the compressed signal and estimated sparsity, an adaptive algorithm of adjusting the number of compressed measurements is proposed. Finally, some simulations are performed, and the results illustrate that the simulations agree with theoretical analysis, which prove the effectiveness of the proposed adaptive adjusting of compressed measurements.

In order to improve the Quality-of-Service (QoS) of latency sensitive applications in next-generation cellular networks, multi-path is adopted to transmit packet stream in real-time to achieve high-quality video transmission in heterogeneous wireless networks. However, multi-path also introduces two important challenges: out-of-order issue and reordering delay. In this paper, we propose a new architecture based on Software Defined Network (SDN) for flow aggregation and flow splitting, and then design a Multiple Access Radio Scheduling (MARS) scheme based on relative Round-Trip Time (RTT) measurement. The QoS metrics including end-to-end delay, throughput and the packet out-of-order problem at the receiver have been investigated using the extensive simulation experiments. The performance results show that this SDN architecture coupled with the proposed MARS scheme can reduce the end-to-end delay and the reordering delay time caused by packet out-of-order as well as achieve a better throughput than the existing SMOS and Round-Robin algorithms.

Sensing coverage is a fundamental issue for Wireless Sensor Networks (WSNs). Several coverage configuration protocols have been developed; most of them presume the availability of precise knowledge about each node location via GPS receivers. However, equipping each sensor node with a GPS is very expensive in terms of both energy and cost. On the other hand, several GPS-less localization algorithms that aim at obtaining nodes locations with a low cost have been proposed. Although their deep correlation, sensing coverage and localization have long been treated separately. In this paper, we analyze, design and evaluate a novel integrated framework providing both localization and coverage guarantees for WSNs. We integrate the well-known Coverage Configuration Protocol CCP with an improved version of the localization algorithm AT-Dist. We enhanced the original specification of AT-Dist in order to guarantee the necessary localization accuracy required by CCP. In our proposed framework, a few number of nodes are assumed to know their exact positions and dynamically vary their transmission ranges. The remaining sensors positions are derived, as accurately as possible, using this little initial location information. All nodes positions (exact and derived) are then used as an input for the coverage module. Extensive simulation results show that, even with a very low anchor density, our proposal reaches the same performance and efficiency as the ideal CCP based on complete and precise knowledge of sensors coordinates.

Performance enhancement is one of the most important issues in high performance distributed computing systems. In such computing systems, online users submit their jobs anytime and anywhere to a set of dynamic resources. Jobs arrival and processes execution times are stochastic. The performance of a distributed computing system can be improved by using an effective load balancing strategy to redistribute the user tasks among computing resources for efficient utilization. This paper presents a multi-class load balancing strategy that balances different classes of user tasks on multiple heterogeneous computing nodes to minimize the per-class mean response time. For a wide range of system parameters, the performance of the proposed multi-class load balancing strategy is compared with that of the random distribution load balancing, and uniform distribution load balancing strategies using simulation. The results show that, the proposed strategy outperforms the other two studied strategies in terms of average task response time, and average computing nodes utilization.

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

This paper investigates the simultaneous wireless information and power transfer (SWIPT) for two-hop orthogonal frequency division multiplexing (OFDM) decode-and-forward (DF) relay network, where a relay harvests energy from radio frequency signals transmitted by a source and then uses the harvested energy to assist information transmission from the source to its destination. The power splitting receiver is considered at the relay. To explore the performance limit of such a SWIPT-enabled system, a resource allocation (RA) optimization problem is formulated to maximize the achievable information rate of the system, where the power allocation, the subcarrier pairing and the power splitting factor are jointly optimized. As the problem is non-convex and there is no known solution method, we first decompose it into two separate subproblems and then design an efficient RA algorithm. Simulation results demonstrate that our proposed algorithm can achieve the maximum achievable rate of the system and also show that to achieve a better system performance, the relay node should be deployed near the source in the SWIPT-enabled two-hop OFDM DF relay system, which is very different from that in conventional non-SWIPT system where the relay should be deployed at the midpoint of the line between the source and the destination.

In this paper, we investigate the degrees of freedom (DoF) of a two-cluster multiple-input multiple-output (MIMO) multiway relay interference channel (mRIC), where there are two relays and two users per cluster. In this channel, users within the same cluster exchange messages among themselves with the help of two relays.We first obtain the DoF upper bound of the considered MIMO mRIC based on cut-set bound. Then, we propose a novel transmission strategy, blind interference neutralization (BIN), to approach the DoF upper bound. This new method utilizes the overheard information at two relays and focuses on the beamforming matrix designs at two relays so that the channel state information (CSI) at users is not required. Through theoretical analysis and numerical simulations, we show that the DoF upper bound can be obtained by using the BIN scheme. From simulation results, we show that the proposed BIN scheme can provide significant performance gain over the conventional time division multiple access (TDMA) scheme in terms of DoF. In addition, we show that the BIN scheme is a superior approach to the existing signal space alignment (SSA) schemes for the considered mRIC.

In this paper, we consider secure communications in multi-hop relaying systems, where multiple decode-and-forward (DF) relays are located at each individual hop and perform cooperative beamforming to improve physical layer security. In order to determine the cooperative relay beamformer at each hop, we propose an iterative beamformer update scheme using semidefinite relaxation and bisection techniques. Numerical results are presented to verify the secrecy rate performance of the proposed scheme.

Orthogonal frequency division multiplexing (OFDM) signals suffer from the problem of large peak-to-average power ratio (PAPR) which complicates the design of the analog front-end of the system. Companding is a well-known PAPR reduction technique that reduces the PAPR by transforming the signal amplitude using a deterministic function. In this paper, a novel piecewise linear companding transform is proposed. The design criteria for the proposed transform is developed by investigating the relationships between the compander and decompander's profile and parameters with the system's performance metrics. Using analysis and simulations, we relate the companding parameters with the bit error rate (BER), out-of-band interference (OBI), amount of companding noise, computational complexity and average power. Based on a set of criteria developed thereof, we formulate the design of the proposed transform. The main aim is to preserve the signal's attributes as much as possible for a predetermined amount of PAPR reduction. Simulations are carried out to evaluate and compare the proposed scheme with the existing companding transforms to demonstrate the enhancement in PAPR, BER and OBI performances.

The wireless body area networks (WBANs) consist of wearable computing devices and can support various healthcare-related applications. There exist two crucial issues when WBANs are utilized for healthcare applications. One is the protection of the sensitive biometric data transmitted over the insecure wireless channels. The other is the design of effective medical management mechanisms. In this paper, a secure medical information management system is proposed and implemented on a TinyOS-based WBAN test bed to simultaneously address these two issues. In this system, the electronic medical record (EMR) is bound to the biometric data with a novel fragile zero-watermarking scheme based on the modified visual secret sharing (MVSS). In this manner, the EMR can be utilized not only for medical management but also for data integrity checking. Additionally, both the biometric data and the EMR are encrypted, and the EMR is further protected by the MVSS. Our analysis and experimental results demonstrate that the proposed system not only protects the confidentialities of both the biometric data and the EMR but also offers reliable patient information authentication, explicit healthcare operation verification and undeniable doctor liability identification for WBANs.

Modern web services have been broadly deployed on the Internet. Most of these services use multi-tier architecture for flexible scaling and software reusability. However, managing the performance of multi-tier web services under dynamic and unpredictable workload, and different resource demands in each tier is a critical problem for a service provider. When offering quality of service assurance with least resource usage costs, web service providers should adopt self-adaptive resource provisioning in each tier. Recently, a number of rule- and model-based approaches have been designed for dynamic resource management in virtualized data centers. This survey investigates the challenges of resource provisioning and provides a competing assessment on the existing approaches. After the evaluation of their benefits and drawbacks, the new research direction to improve the efficiency of resource management and recommendations are introduced.

The Structural SIMilarity (SSIM) index is one of the most widely-used methods for perceptual image quality assessment (IQA). It is based on the principle that the human visual system (HVS) is sensitive to the overall structure of an image. However, it has been reported that indices predicted by SSIM tend to be biased depending on the type of distortion, which increases the deviation from the main regression curve. Consequently, SSIM can result in serious performance degradation. In this study, we investigate the aforementioned phenomenon from a new perspective and review a constant that plays a big role within the SSIM metric but has been overlooked thus far. Through an experimental study on the influence of this constant in evaluating images with SSIM, we are able to propose a new solution that resolves this issue. In the proposed IQA method, we first design a system to classify different types of distortion, and then match an optimal constant to each type. In addition, we supplement the proposed method by adding color perception-based structural information. For a comprehensive assessment, we compare the proposed method with 15 existing IQA methods. The experimental results show that the proposed method is more consistent with the HVS than the other methods.

Recently, various and effective contrast based salient object detection models to focus on a single target have been proposed. However, there is a lack of research on detection of multiple objects, and also it is a more challenging task than single target process. In the multiple target problem, we are confronted by new difficulties caused by distinct difference between properties of objects. The characteristic of existing models depending on the global maximum distribution of data point would become a drawback for detection of multiple objects. In this paper, by analyzing limitations of the existing methods, we have devised three main processes to detect multiple salient objects. In the first stage, regional features are extracted from over-segmented regions. In the second stage, the regional features are categorized into homogeneous cluster using the mean-shift algorithm with the kernel function having various sizes. In the final stage, we compute saliency scores of the categorized regions using only spatial features without the contrast features, and then all scores are integrated for the final salient regions. In the experimental results, the scheme achieved superior detection accuracy for the SED2 and MSRA-ASD benchmarks with both a higher precision and better recall than state-of-the-art approaches. Especially, given multiple objects having different properties, our model significantly outperforms all existing models.

Image distortions are typically characterized by degradations of structures. Dictionaries learned from natural images can capture the underlying structures in images, which are important for image quality assessment (IQA). This paper presents a general-purpose no-reference image quality metric using a GRadient-Induced Dictionary (GRID). A dictionary is first constructed based on gradients of natural images using K-means clustering. Then image features are extracted using the dictionary based on Euclidean-norm coding and max-pooling. A distortion classification model and several distortion-specific quality regression models are trained using the support vector machine (SVM) by combining image features with distortion types and subjective scores, respectively. To evaluate the quality of a test image, the distortion classification model is used to determine the probabilities that the image belongs to different kinds of distortions, while the regression models are used to predict the corresponding distortion-specific quality scores. Finally, an overall quality score is computed as the probability-weighted distortion-specific quality scores. The proposed metric can evaluate image quality accurately and efficiently using a small dictionary. The performance of the proposed method is verified on public image quality databases. Experimental results demonstrate that the proposed metric can generate quality scores highly consistent with human perception, and it outperforms the state-of-the-arts.

Although the face almost always has an axisymmetric structure, it is generally not symmetrical image for the face image. However, the mirror image of the face image can reflect possible variation of the poses and illumination opposite to that of the original face image. A robust minimum squared error classification (RMSEC) algorithm is proposed in this paper. Concretely speaking, the original training samples and the mirror images of the original samples are taken to form a new training set, and the generated training set is used to perform the modified minimum sqreared error classification(MMSEC) algorithm. The extensive experiments show that the accuracy rate of the proposed RMSEC is greatly increased, and the the proposed RMSEC is not sensitive to the variations of the parameters.

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

Haze or fog is a common natural phenomenon. In foggy weather, the captured pictures are difficult to be applied to computer vision system, such as road traffic detection, target tracking, etc. Therefore, the image dehazing technique has become a hotspot in the field of image processing. This paper presents an overview of the existing achievements on the image dehazing technique. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on two main works, that is, image dehazing scheme based on atmospheric veil and image dehazing scheme based on dark channel prior. After the overview and a comparative study, we propose an improved image dehazing method, which is based on two image dehazing schemes mentioned above. Our image dehazing method can obtain the fog-free images by proposing a more desirable atmospheric veil and estimating atmospheric light more accurately. In addition, we adjust the transmission of the sky regions and conduct tone mapping for the obtained images. Compared with other state of the art algorithms, experiment results show that images recovered by our algorithm are clearer and more natural, especially at distant scene and places where scene depth jumps abruptly.

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

The several noise level estimation algorithms that have been developed for use in image processing and computer graphics generally exhibit good performance. However, there are certain special types of noisy images that such algorithms are not suitable for. It is particularly still a challenge to use the algorithms to estimate the noise levels of complex textured photographic images because of the inhomogeneity of the original scenes. Similarly, it is difficult to apply most conventional noise level estimation algorithms to images rendered by the Monte Carlo (MC) method owing to the spatial variation of the noise in such images. This paper proposes a novel noise level estimation method based on histogram modification, and which can be used for more accurate estimation of the noise levels in both complex textured images and MC-rendered images. The proposed method has good performance, is simple to implement, and can be efficiently used in various image-based and graphic applications ranging from smartphone camera noise removal to game background rendition.

Image authentication is a technique aiming at protecting the integrity of digital images. Reversible image authentication has attracted much attention of researcher because it allows to authenticate tampered regions in the image and to reconstruct the stego image to its original version losslessly. In this paper, we propose a new, reversible image authentication scheme based on adaptive prediction error expansion (PEE) technique. In the proposed scheme, each image block is classified into smooth or complex regions. Then, according to the characteristic of each block, the authentication code is embedded adaptively to achieve high performance of tamper detection. The experimental results demonstrated that the proposed scheme achieves good quality of stego images. In addition, the proposed scheme has ability to reconstruct the stego image to its original version, if no modification is performed on it. Also demonstrated in the experimental results, the proposed scheme provides higher accuracy of tamper detection than state-of-the-art schemes.

Verifier-local revocation (VLR) seems to be the most flexible revocation approaches for any group signature scheme, because it just only requires the verifiers to possess some up-to-date revocation information, but not the signers. Langlois et al. (PKC 2014) proposed the first VLR group signature based on lattice assumptions in the random oracle model. Their scheme has at least Õ(n²) ⋅ log N bit group public key and Õ(n) ⋅ log N bit signature, respectively. Here, n is the security parameter and N is the maximum number of group members. In this paper, we present a simpler lattice-based VLR group signature, which is more efficient by a O(log N) factor in both the group public key and the signature size. The security of our VLR group signature can be reduced to the hardness of learning with errors (LWE) and small integer solution (SIS) in the random oracle model.

Secure multi-party computation (MPC) has been a research focus of cryptography in resent studies. However, hiding the topology of the network in secure computation is a rather novel goal. Inspired by a seminal paper [1], we proposed a topology-hiding broadcast protocol based on NTRUEncrypt and secret sharing. The topology is concealed as long as any part of the network is corrupted. And we also illustrated the merits of our protocol by performance and security analysis.