The IPv4 addressing scheme, which was proposed by IETF in 1981, provides 4.3 billion unique 32-bit IP addresses but has been fully exhausted in Feb, 2011. This exhaustion of unique IP addresses poses significant challenges to the addition of new devices to the Internet as well as offering new services. Internet of Things, which provides interconnected uniquely identifiable devices in the existing Internet infrastructure, will be greatly affected by the lack of unique IP addresses. In order to connect to the existing Internet infrastructure, every new device needs a uniquely identified IP address for communication. It has been estimated that by the year 2020 more than 30 billion devices would be connected to the Internet. In order to meet the challenge of such vast requirement of unique IP addresses, the devices in IoT will have to adopt IPv6, which is the latest version of Internet Protocol. IPv6 uses 128-bit IP addresses and offers 2¹²⁸ unique IP addresses. Therefore, it expands IPv4 and provides new features of end to end connections as well as new services. In this paper, the various challenges with respect to providing connectivity, security, mobility, etc., have been discussed and how IPv6 helps in meeting those challenges.

The reduction of power consumption in large-scale datacenters is highly-dependent on the use of virtualization to consolidate multiple workloads. However, these consolidation strategies must also take into account additional important parameters such as performance, reliability, and profitability. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. In this paper, we put forward a data center monitoring strategy which dynamically alters its approach depending on the cloud system's current state. Results show that our proposed scheme outperformed strategies which only focus on a single metric such as SLA-Awareness and Energy Efficiency.

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

In this paper, we investigate the problem of achieving global optimization for distributed carrier aggregation (CA) in small cell networks, using a game theoretic solution. To cope with the local interference and the distinct cost of intra-band and inter-band CA, we propose a non-cooperation game which is proved as an exact potential game. Furthermore, we propose a spatial adaptive play learning algorithm with heterogeneous learning parameters to converge towards NE of the game. In this algorithm, heterogeneous learning parameters are introduced to accelerate the convergence speed. It is shown that with the proposed game-theoretic approach, global optimization is achieved with local information exchange. Simulation results validate the effectivity of the proposed game-theoretic CA approach.

The spectral efficiency of cellular networks can be improved when proximate users engage in device-to-device (D2D) communications to communicate directly without going through a base station. However, D2D communications that are not properly designed may generate interference with existing cellular networks. In this paper, we study resource allocation and power control to minimize the probability of an outage and maximize the overall network throughput. We investigate three power control-based schemes: the Partial Co-channel based Overlap Resource Power Control (PC.OVER), Fractional Frequency Reuse based Overlap Resource Power Control (FFR.OVER) and Fractional Frequency Reuse based Adaptive Power Control (FFR.APC) and also compare their performance. In PC.OVER, a certain portion of the total bandwidth is dedicated to the D2D. The FFR.OVER and FFR.APC schemes combine the FFR techniques and the power control mechanism. In FFR, the entire frequency band is partitioned into two parts, including a central and edge sub-bands. Macrocell users (mUEs) transmit using uniform power in the inner and outer regions of the cell, and in all three schemes, the D2D receivers (D2DRs) transmit with low power when more than one D2DRs share a resource block (RB) with the macrocells. For PC.OVER and FFR.OVER, the power of the D2DRs is reduced to its minimum, and for the FFR.APC scheme, the transmission power of the D2DRs is iteratively adjusted to satisfy the signal to interference ratio (SIR) threshold. The three schemes exhibit a significant improvement in the overall system capacity as well as in the probability of a user outage when compared to a conventional scheme.

TV white space (TVWS) is showing promise to become the first widespread practical application of cognitive technology. In fact, regulators worldwide are beginning to allow access to the TV band for secondary users, on the provision that they access the geolocation database. Device-to-device (D2D) can improve the spectrum efficiency, but large-scale D2D communications that underlie TVWS may generate undesirable interference to TV receivers and cause severe mutual interference. In this paper, we use an established geolocation database to investigate the power allocation problem, in order to maximize the total sum throughput of D2D links in TVWS while guaranteeing the quality-of-service (QoS) requirement for both D2D links and TV receivers. Firstly, we formulate an optimization problem based on the system model, which is nonconvex and intractable. Secondly, we use an effective approach to convert the original problem into a series of convex problems and we solve these problems using interior point methods that have polynomial computational complexity. Additionally, we propose an iterative algorithm based on the barrier method to locate the optimal solution. Simulation results show that the proposed algorithm has strong performance with high approximation accuracy for both small and large dimensional problems, and it is superior to both the active set algorithm and genetic algorithm.

Backpressure based scheduling has been considered as a promising technique for improving the throughput of a wide range of communication networks. However, this scheduling technique has not been well studied for heterogeneous wireless networks. In this paper, we propose a virtual-queue based backpressure scheduling (VQB) algorithm for heterogeneous multi-hop wireless networks. The VQB algorithm introduces a simple virtual queue for each flow at a node for backpressure scheduling, whose length depends on the cache size of the node. When calculating flow weights and making scheduling decisions, the length of a virtual queue is used instead of the length of a real queue. We theoretically prove that VQB is throughput-optimal. Simulation results show that the VQB algorithm significantly outperforms a classical backpressure scheduling algorithm in heterogeneous multi-hop wireless networks in terms of the packet delivery ratio, packet delivery time, and average sum of the queue lengths of all nodes per timeslot.

Orthogonal frequency division multiplexing (OFDM) signals suffer from the problem of high peak-to-average power ratio (PAPR), which complicates the design of analog front-end of the system. Companding is a well-known PAPR reduction technique that involves transforming signal amplitudes using a deterministic function. OFDM signal amplitude, on average, is Rayleigh distributed but the distribution can vary significantly from symbol to symbol, especially when constellation size increases. In this paper, a new adaptive companding scheme is proposed along with its design methodology aiming at optimizing the compander performance by accommodating this variation in its design. This is achieved by designing compander parameters separately for statistically dissimilar symbols in OFDM waveform and making the compander select from these parameters, during run-time, according to the features of input symbols.

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.

Information Centric Networking (ICN) has recently attracted great attention. It names the content decoupling from the location and introduces network caching, making the content to be cached anywhere within the network. The benefits of such design are obvious, however, many challenges still need to be solved. Among them, the local caching policy is widely discussed and it can be further divided into two parts, namely the cache permission policy and the cache replacement policy. The former is used to decide whether an incoming content should be cached while the latter is used to evict a cached content if required. The Internet is a user-oriented network and popular contents always have much more requests than unpopular ones. Caching such popular contents closer to the user's location can improve the network performance, and consequently, the local caching policy is required to identify popular contents. However, considering the line speed requirement of ICN routers, the local caching policy whose complexity is larger than O(1) cannot be applied. In terms of the replacement policy, Least Recently Used (LRU) is selected as the default one for ICN because of its low complexity, although its ability to identify the popular content is poor. Hence, the identification of popular contents should be completed by the cache permission policy. In this paper, a cache permission policy called Cache-Filter, whose complexity is O(1), is proposed, aiming to store popular contents closer to users. Cache-Filter takes the content popularity into account and achieves the goal through the collaboration of on-path nodes. Extensive simulations are conducted to evaluate the performance of Cache-Filter. Leave Copy Down (LCD), Move Copy Down (MCD), Betw, ProbCache, ProbCache⁺, Prob(p) and Probabilistic Caching with Secondary List (PCSL) are also implemented for comparison. The results show that Cache-Filter performs well. For example, in terms of the distance to access to contents, compared with Leave Copy Everywhere (LCE) used by Named Data Networking (NDN) as the permission policy, Cache-Filter saves over 17% number of hops.

The unstable characteristic values of communication signals along with the varying SNR (Signal Noise Ratio) environment make it difficult to identify the modulations of signals. Most of relevant literature revolves around signal recognition under stable SNR, and not applicable for signal recognition at varying SNR. To solve the problem, this research developed a novel communication signal recognition algorithm based on Holder coefficient and cloud theory. In this algorithm, the two-dimensional (2D) Holder coefficient characteristics of communication signals were firstly calculated, and then according to the distribution characteristics of Holder coefficient under varying SNR environment, the digital characteristics of cloud model such as expectation, entropy, and hyper entropy are calculated to constitute the three-dimensional (3D) digital cloud characteristics of Holder coefficient value, which aims to improve the recognition rate of the communication signals. Compared with traditional algorithms, the developed algorithm can describe the signals' features more accurately under varying SNR environment. The results from the numerical simulation show that the developed 3D feature extraction algorithm based on Holder coefficient cloud features performs better anti-noise ability, and the classifier based on interval gray relation theory can achieve a recognition rate up to 84.0%, even when the SNR varies from -17dB to -12dB.

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

Multi-hop relaying communications have great potentials in improving transmission performance by deploying relay nodes. The benefit is critically dependent on the accuracy of the channel state information (CSI) of all the transmitting links. However, the CSI has to be estimated. In this paper, we investigate the channel estimation problem in one-way multi-hop MIMO amplify-and-forward (AF) relay system, where both the two-hop and three-hop communication link exist. Traditional point-to-point MIMO channel estimation methods will result in error propagation in estimating relay links, and separately tackling the channel estimation issue of each link will lose the gain as part of channel matrices involved in multiple communication links. In order to exploit all the available gains, we develop a novel channel estimation model by structuring different communication links using the PARAFAC and PARATUCK2 tensor analysis. Furthermore, a two-stage fitting algorithm is derived to estimate all the channel matrices involved in the communication process. In particular, essential uniqueness is further discussed. Simulation results demonstrate the advantage and effectiveness of the proposed channel estimator.

The bilinear pairing, also known as Weil pairing or Tate pairing, is widely used in cryptography and its properties help to construct cryptographic schemes for different applications in which the security of the transmitted data is a major concern. In remote login authentication schemes, there are two major requirements: i) proving the identity of a user and the server for legitimacy without exposing their private keys and ii) freedom for a user to choose and change his password (private key) efficiently. Most of the existing methods based on the bilinear property have some security breaches due to the lack of features and the design issues. In this paper, we develop a new scheme using the bilinear property of an elliptic point and the biometric characteristics. Our method provides many features along with three major goals. a) Checking the correctness of the password before sending the authentication message, which prevents the wastage of communication cost; b) Efficient password change phase in which the user is asked to give a new password after checking the correctness of the current password without involving the server; c) User anonymity - enforcing the suitability of our scheme for applications in which a user does not want to disclose his identity. We use BAN logic to ensure the mutual authentication and session key agreement properties. The paper provides informal security analysis to illustrate that our scheme resists all the security attacks. Furthermore, we use the AVISPA tool for formal security verification of our scheme.

Transceiver optimization in multiple input multiple output (MIMO) cognitive systems is studied in this paper. The joint transceiver beamformer design is introduced to minimize the transmit power at secondary base station (SBS) while simultaneously controlling the interference to primary users (PUs) and satisfying the secondary users (SUs) signal-to-interference-plus-noise ratio (SINR) based on the convex optimization method. Due to the limited cooperation between SBS and PUs, the channel state information (CSI) usually cannot be obtained perfectly at the SBS in cognitive system. In this study, both perfect and imperfect CSI scenarios are considered in the beamformer design, and the proposed method is robust to CSI error. Numerical results validate the effectiveness of the proposed algorithm.

In this paper, a new mathematical scheme of Macro Handover Management (MHM) in F-HMIPv6 networks based on Location Based Services (LBS) is proposed. Previous schemes based on F-HMIPv6 protocol usually suffer from three major drawbacks: First, They don't exploit the information about the user mobility behavior in order to reduce handover effects. Second, they only focus on the micro mobility level. Third, they don't consider the quality of service (QoS) of the traffic. The proposed MHM scheme avoids these drawbacks using the available information about Mobile Node (MN) such as user mobility patterns and MN's velocity to predict handover and improve network's QoS. It also takes the traffic type in consideration since it presents a major factor in locating QoS for the user. MHM is analyzed and compared with the F-HMIPv6. The results show that MHM improves the performance in terms of packet delivery cost, location update cost, and handover latency. The design of MHM comprises software package in the MN in addition to a hardware part in the network side. It has implications for communication, design, and pricing of mobile services.

Quaternions have been commonly employed in color image processing, but when the existing pure quaternion representation for color images is used in perceptual hashing, it would degrade the robustness performance since it is sensitive to image manipulations. To improve the robustness in color image perceptual hashing, in this paper a full quaternion representation for color images is proposed by introducing the local image luminance variances. Based on this new representation, a novel Full Quaternion Discrete Cosine Transform (FQDCT)-based hashing is proposed, in which the Quaternion Discrete Cosine Transform (QDCT) is applied to the pseudo-randomly selected regions of the novel full quaternion image to construct two feature matrices. A new hash value in binary is generated from these two matrices. Our experimental results have validated the robustness improvement brought by the proposed full quaternion representation and demonstrated that better performance can be achieved in the proposed FQDCT-based hashing than that in other notable quaternion-based hashing schemes in terms of robustness and discriminability.

The rock fracture detection by image analysis is significant for fracture measurement and assessment engineering. The paper proposes a novel image segmentation algorithm for the centerline tracing of a rock fracture based on Hessian Matrix at Multi-scales and Steger algorithm. A traditional fracture detection method, which does edge detection first, then makes image binarization, and finally performs noise removal and fracture gap linking, is difficult for images of rough rock surfaces. To overcome the problem, the new algorithm extracts the centerlines directly from a gray level image. It includes three steps: (1) Hessian Matrix and Frangi filter are adopted to enhance the curvilinear structures, then after image binarization, the spurious-fractures and noise are removed by synthesizing the area, circularity and rectangularity; (2) On the binary image, Steger algorithm is used to detect fracture centerline points, then the centerline points or segments are linked according to the gap distance and the angle differences; and (3) Based on the above centerline detection roughly, the centerline points are searched in the original image in a local window along the direction perpendicular to the normal of the centerline, then these points are linked. A number of rock fracture images have been tested, and the testing results show that compared to other traditional algorithms, the proposed algorithm can extract rock fracture centerlines accurately.

Rapid popularization of smart cell phone equipped with camera has led to a number of new legal and criminal problems related to multimedia such as digital image, which makes cell phone source identification an important branch of digital image forensics. This paper proposes a classifier combination based source identification strategy for cell phone images. To identify the outlier cell phone models of the training sets in multi-class classifier, a one-class classifier is orderly used in the framework. Feature vectors including color filter array (CFA) interpolation coefficients estimation and multi-feature fusion is employed to verify the effectiveness of the classifier combination strategy. Experimental results demonstrate that for different feature sets, our method presents high accuracy of source identification both for the cell phone in the training sets and the outliers.

In radar imaging, a target is usually consisted of a few strong scatterers which are sparsely distributed. In this paper, an improved sparse signal recovery algorithm based on smoothed l₀ (SL0) norm method is proposed to achieve high resolution ISAR imaging with limited pulse numbers. Firstly, one new smoothed function is proposed to approximate the l₀ norm to measure the sparsity. Then a single loop step is used instead of two loop layers in SL0 method which increases the searching density of variable parameter to ensure the recovery accuracy without increasing computation amount, the cost function is undated in every loop for the next loop until the termination is satisfied. Finally, the new set of solution is projected into the feasible set. Simulation results show that the proposed algorithm is superior to the several popular methods both in terms of the reconstruction performance and computation time. Real data ISAR imaging obtained by the proposed algorithm is competitive to several other methods.

In this paper, due to the network security defense is mainly static defense, a dynamic classification network security defense strategy model is proposed by analyzing the security situation of complex computer network. According to the network security impact parameters, eight security elements and classification standard are obtained. At the same time, the dynamic classification algorithm based on fuzzy theory is also presented. The experimental analysis results show that the proposed model and algorithm are feasible and effective. The model is a good way to solve a safety problem that the static defense cannot cope with tactics and lack of dynamic change.

Communications among multi-party must be fast, cost effective and secure. Today's computing environments such as internet conference, multi-user games and many more applications involve multi-party. All participants together establish a common session key to enable multi-party and secure exchange of messages. Multi-party password-based authenticated key exchange scheme allows users to communicate securely over an insecure network by using easy-to-remember password. Kwon et al. proposed a practical three-party password-based authenticated key exchange (3-PAKE) scheme to allow two users to establish a session key through a server without pre-sharing a password between users. However, Kwon et al.'s scheme cannot meet the security requirements of key authentication, key confirmation and anonymity. In this paper, we present a novel, simple and efficient multi-party password-based authenticated key exchange (M-PAKE) scheme based on the elliptic curve cryptography for mobile environment. Our proposed scheme only requires two round-messages. Furthermore, the proposed scheme not only satisfies security requirements for PAKE scheme but also achieves efficient computation and communication.

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

This paper proposes a novel progressive secret image-hiding scheme based on the inpainting technique, the vector quantization technique (VQ) and the exploiting modification direction (EMD) technique. The proposed scheme first divides the secret image into non-overlapping blocks and categorizes the blocks into two groups: complex and smooth. The blocks in the complex group are compressed by VQ with PCA sorted codebook to obtain the VQ index table. Instead of embedding the original secret image, the proposed method progressively embeds the VQ index table into the cover images by using the EMD technique. After the receiver recovers the complex parts of the secret image by decoding the VQ index table from the shadow images, the smooth parts can be reconstructed by using the inpainting technique based on the content of the complex parts. The experimental results demonstrate that the proposed scheme not only has the advantage of progressive data hiding, which involves more shadow images joining to recover the secret image so as to produce a higher quality steganography image, but also can achieve high hiding capacity with acceptable recovered image quality.

To address the contradiction between data aggregation and data security in wireless sensor networks, a Recoverable Privacy-preserving Integrity-assured Data Aggregation (RPIDA) scheme is proposed based on privacy homomorphism and aggregate message authentication code. The proposed scheme provides both end-to-end privacy and data integrity for data aggregation in WSNs. In our scheme, the base station can recover each sensing data collected by all sensors even if these data have been aggregated by aggregators, thus can verify the integrity of all sensing data. Besides, with these individual sensing data, base station is able to perform any further operations on them, which means RPIDA is not limited in types of aggregation functions. The security analysis indicates that our proposal is resilient against typical security attacks; besides, it can detect and locate the malicious nodes in a certain range. The performance analysis shows that the proposed scheme has remarkable advantage over other asymmetric schemes in terms of computation and communication overhead. In order to evaluate the performance and the feasibility of our proposal, the prototype implementation is presented based on the TinyOS platform. The experiment results demonstrate that RPIDA is feasible and efficient for resource-constrained sensor nodes.

According to the characteristics of adaptive JPEG steganography which determines the changed DCT coefficients based on embedding distortion, a new steganalysis method by selecting the DCT coefficients with small distortion values is proposed. Firstly, the principle of adaptive JPEG steganography through minimizing distortion is introduced. Secondly, the practicability of selecting the changed DCT coefficients according to distortion values is studied. Thirdly, the proposed steganalysis method is given and the embedding sensitivity of the steganalysis feature extracted from the selected DCT coefficients is analyzed. Lastly, the implement processes of the proposed method are presented and analyzed in details. In the experiments, PQt, PQe and J-UNIWARD steganography are used as examples to verify the effect of the proposed steganalysis method for adaptive JPEG steganography. A serial experimental results show the detection accuracy can be improved obviously, especially when the payload is relatively low.