Device-to-Device (D2D) communication underlaying macro-small cell networks, as one of the promising technologies in the era of 5G, is able to improve spectral efficiency and increase system capacity. In this paper, we model the cross- and co-tier D2D communications in two-tier macro-small cell networks. To avoid the complicated interference for cross-tier D2D, we propose a mode selection scheme with a dedicated resource sharing strategy. For co-tier D2D, we formulate a joint optimization problem of power control and resource reuse with the aim of maximizing the overall outage capacity. To solve this non-convex optimization problem, we devise a heuristic algorithm to obtain a suboptimal solution and reduce the computational complexity. System-level simulations demonstrate the effectiveness of the proposed method, which can provide enhanced system performance and guarantee the quality-of-service (QoS) of all devices in two-tier macro-small cell networks. In addition, our study reveals the high potential of introducing cross- and co-tier D2D in small cell networks: i) cross-tier D2D obtains better performance at low and medium small cell densities than co-tier D2D, and ii) co-tier D2D achieves a steady performance improvement with the increase of small cell density.

This paper presents an effective management of VM (Virtual Machine) for heterogeneous cloud using Common Deployment Model (CDM) brokering mechanism. The effective utilization of VM is achieved by means of task scheduling with VM placement technique. The placements of VM for the physical machine are analyzed with respect to execution time of the task. The idle time of the VMis utilized productively in order to improve the performance. The VMs are also scheduled to maintain the state of the current VM after the task completion. CDM based algorithm maintains two directories namely Active Directory (AD) and Passive Directory (PD). These directories maintain VM with proper configuration mapping of the physical machines to perform two operations namely VM migration and VM roll back. VM migration operation is performed from AD to PD whereas VM roll back operation is performed from PD to AD. The main objectives of the proposed algorithm is to manage the VM's idle time effectively and to maximize the utilization of resources at the data center. The VM placement and VM scheduling algorithms are analyzed in various dimensions of the cloud and the results are compared with iCanCloud model.

In this paper, we study how to optimize the data shuffle phase by leveraging the flow relationship in datacenter networks (DCNs). In most of the clustering computer frameworks, the completion of a transfer (a group of flows that can enable a computation stage to start or complete) is determined by the flow completing last, so that limiting the rate of other flows (not the last one) appropriately can save bandwidth without impacting the performance of any transfer. Furthermore, for the flows enter network late, more bandwidth can be assigned to them to accelerate the completion of the entire transfer. Based on these characteristics, we propose the flow chasing algorithm (FCA) to optimize the completion of the entire transfer. We implement FCA on a real testbed. By evaluation, we find that FCA can not only reduce the completion time of data transfer by 6.24% on average, but also accelerate the completion of data shuffle phase and entire job.

This paper proposes a novel channel assignment scheme called multi-cluster based dynamic channel assignment (MC-DCA) to improve system performance for the downlink of dense femtocell networks (DFNs) based on orthogonal frequency division multiple access (OFDMA) and frequency division duplexing (FDD). In order to dynamically assign channels for femtocell access points (FAPs), the MC-DCA scheme uses a heuristic method that consists of two steps: one is a multiple cluster assignment step to group FAPs using graph coloring algorithm with some extensions, while the other is a dynamic subchannel assignment step to allocate subchannels for maximizing the system capacity. Through simulations, we first find optimum parameters of the multiple FAP clustering to maximize the system capacity and then evaluate system performance in terms of the mean FAP capacity, unsatisfied femtocell user equipment (FUE) probability, and mean FAP power consumption for data transmission based on a given FUE traffic load. As a result, the MC-DCA scheme outperforms other schemes in two different DFN environments for commercial and office buildings.

For monitoring forest fires, a real-time system to prevent fires in wider areas should be supported consistently. However, there has still been a lack of the support for real-time system related to forest fire monitoring. In addition, the 'real-time' processing in a forest fire detection system can lead to excessive consumption of energy. To solve these problems, the intelligent data acquisition of sensing nodes is required, and the maximum energy savings as well as rapid and accurate detection by flame sensors need to be done. In this regard, this paper proposes a node built-in filter algorithm for intelligent data collection of sensing nodes for the rapid detection of forest fires with focus on reducing the power consumption of the remote sensing nodes and providing efficient wireless sensor network-based forest environment monitoring in terms of data transmission, network stability and data acquisition. The experimental result showed that battery life can be extended through the intelligent sampling of remote sensing nodes, and the average accuracy of the measurement of flame detection based on the distance is 44%.

Nodes in MANETs are battery powered which makes energy an invaluable resource. In OLSR, MPRs are special nodes that are selected by other nodes to relay their data/control traffic which may lead to high energy consumption of MPR nodes. Therefore, employing energy efficient MPR selection mechanism is imperative to ensure prolonged network lifetime. However, misbehaving MPR nodes tend to preserve their energy by dropping packets of other nodes instead of forwarding them. This leads to huge energy loss and performance degradation of existing energy efficient MPR selection schemes. This paper proposes an energy efficient secure MPR selection (ES-MPR) technique that takes into account both energy and security metrics for MPR selection. It introduces the concept of 'Composite Eligibility Index' (CEI) to examine the eligibility of a node for being selected as an MPR. CEI is used in conjunction with willingness to provide distinct selection parameters for Flooding and Routing MPRs. Simulation studies reveal the efficiency of ES-MPR in selection of energy efficient secure and stable MPRs, in turn, prolonging the network operational lifetime.

The challenge of penetrating obstacles along with impact from weak multipath effects makes 60GHz signal very difficult to be transmitted in non-line of sight (NLOS) channel. So 60GHz system is vulnerable to obstructions and thus likely results in link interruption. While the application of cooperative technology to solve link blockage problemin 60GHz system should consider the characteristic of directional transmission for 60GHz signal. Therefore in this paper a system is proposed to solve the link blockage problem in 60GHz NLOS communication environment based on the concept of cooperation and also the beamforming technology, which is the basis of directional transmission for 60GHz communication system. The process of anti-blockage solution with cooperative communication is presented in detail, and the fast switching and recovery schemes are well designed. The theoretical values of symbol error rate (SER) using decode and forward (DF) cooperation and amplify and forward (AF) cooperation are presented respectively when the common channel interference exists. Simulation results demonstrate that the performance based on DF cooperation is better than the performance based on AF cooperation when directional transmission is used.

Recently, deploying WiFi access points (APs) for facilitating indoor localization has attracted increasing attention. However, most existing mechanisms in this aspect are typically simulation based and further they did not consider how to jointly utilize pre-existing APs in target environment and newly deployed APs for achieving high localization performance. In this paper, we propose a measurement-based AP deployment mechanism (MAPD) for placing APs in target indoor environment for assisting fingerprint based indoor localization. In the mechanism design, MAPD takes full consideration of pre-existing APs to assist the selection of good candidate positions for deploying new APs. For this purpose, we first choose a number of candidate positions with low location accuracy on a radio map calibrated using the pre-existing APs and then use over-deployment and on-site measurement to determine the actual positions for AP deployment. MAPD uses minimal mean location error and progressive greedy search for actual AP position selection. Experimental results demonstrate that MAPD can largely reduce the localization error as compared with existing work.

Inter-domain routing is the most critical function of the Internet. The routing system is a logical network relying on the physical infrastructure with geographical characteristics. Nature disasters or disruptive accidents such as earthquakes, cable cuts and power outages could cause regional failures which fail down geographically co-located network nodes and links, therefore, affect the resilience of inter-domain routing system. This paper presents a model for regional failures in inter-domain routing system called REFER for the first time. Based on REFER, the resilience of the inter-domain routing system could be evaluated on a finer level of the Internet, considering different routing policies of intra-domain and inter-domain routing systems. Under this model, we perform simulations on an empirical topology of the Internet with geographical characteristics to simulate a regional failure locating at a city with important IXP (Internet eXchange Point). Results indicate that the Internet is robust under a city-level regional failure. The reachability is almost the same after the failure, and the reroutings occur at the edge of the Internet, hardly affecting the core of inter-domain routing system.

Connectivity and trust in social networks have been exploited to propose applications on top of these networks, including routing, Sybil defenses, and anonymous communication systems. In these networks, and for such applications, connectivity ensures good performance of applications while trust is assumed to always hold, so as collaboration and good behavior are always guaranteed. In this paper, we study the impact of differential behavior of users on performance in typical social network-enabled routing applications. We classify users into either collaborative or rational (probabilistically collaborative) and study the impact of this classification and the associated behavior of users on the performance of such applications, including random walk-based routing, shortest path based routing, breadth-first-search based routing, and Dijkstra routing. By experimenting with real-world social network traces, we make several interesting observations. First, we show that some of the existing social graphs have high routing costs, demonstrating poor structure that prevents their use in such applications. Second, we study the factors that make probabilistically collaborative nodes important for the performance of the routing protocol within the entire network and demonstrate that the importance of these nodes stems from their topological features rather than their percentage of all the nodes within the network.

Clustering is one of the key technologies in vehicular networks. Constructing and maintaining stable clusters is a challenging task in high mobility environments. DWCM (Distributed and Weighted Clustering based on Mobility Metrics) is proposed in this paper based on the d-hop dominating set of the network. Each vehicle is assigned a priority that describes the cluster relationship. The cluster structure is determined according to the d-hop dominating set, where the vehicles in the d-hop dominating set act as the cluster head nodes. In addition, cluster maintenance handles the cluster structure changes caused by node mobility. The rationality of the proposed algorithm is proven. Simulation results in the NS-2 and VanetMobiSim integrated environment demonstrate the performance advantages.

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.

Energy is a scarce resource in wireless sensor networks (WSNs). A variety of clustering protocols for WSNs, such as the zone-based stable election protocol-enhanced (ZSEP-E), have been developed for energy optimization. The ZSEP-E is a heterogeneous zone-based clustering protocol that focuses on unbalanced energy consumption with parallel formation of clusters in zones and election of cluster heads (CHs). Most ZSEP-E research has assumed probabilistic election of CHs in the zones by considering the maximum residual energy of nodes. However, studies of the diverse CH election parameters are lacking. We investigated the performance of the ZSEP-E in such scenarios using a fuzzy logic approach based on three descriptors, i.e., energy, density, and the distance from the node to the base station. We proposed an efficient ZSEP-E scheme to adapt and elect CHs in zones using fuzzy variables and evaluated its performance for different energy levels in the zones.

In this paper, we propose a novel saliency detection framework via multiple random walks (MRW) which simulate multiple agents on a graph simultaneously. In the MRW system, two agents, which represent the seeds of background and foreground, traverse the graph according to a transition matrix, and interact with each other to achieve a state of equilibrium. The proposed algorithm is divided into three steps. First, an initial segmentation is performed to partition an input image into homogeneous regions (i.e., superpixels) for saliency computation. Based on the regions of image, we construct a graph that the nodes correspond to the superpixels in the image, and the edges between neighboring nodes represent the similarities of the corresponding superpixels. Second, to generate the seeds of background, we first filter out one of the four boundaries that most unlikely belong to the background. The superpixels on each of the three remaining sides of the image will be labeled as the seeds of background. To generate the seeds of foreground, we utilize the center prior that foreground objects tend to appear near the image center. In last step, the seeds of foreground and background are treated as two different agents in multiple random walkers to complete the process of salient object detection. Experimental results on three benchmark databases demonstrate the proposed method performs well when it against the state-of-the-art methods in terms of accuracy and robustness.

Recently, Peer-to-Peer (P2P) live streaming assisted by the cloud computing has attracted considerable attention to improve the reliability of the P2P such as the resilience to peer churn and the shortage of upload capacity. The cost of cloud-assistance is comprised of the number of requests issued to the cloud and the amount of data fetched from the cloud. In this paper, we propose three techniques to reduce the cost of such a cloud-assistance.More concretely, in the proposed method, 1) each peer which lost its parent in the overlay can find a new parent by referring to the information registered in the cloud, 2) several peers which proactively fetch chunks from the cloud are dynamically invested, and 3) the number of requests issued to the cloud is reduced by allowing peers to fetch a collection of chunks using a single request. The performance of the proposed method is evaluated by simulation. The simulation results indicate that it reduces the cost of conventional scheme by 46% while guaranteeing the quality of live streaming service.

Home security has become the prime concern for everyone in present scenario. In this work an attempt has been made to develop a home security system which is accessible, affordable and yet effective.The proposed system is based on 'Remote Embedded Control System' (RECS) which works both on the web and gsm platform for authentication and monitoring. This system is therefore cost effective as it relies on existing network infrastructure. As PCA is most popular and efficient algorithm for face recognition, it has been usedin this work. Next to it an interface has been developed for communication purpose in the embedded security system through the ZigBee module. Based on this embedded system, automated control of door movement has been implemented through electromagnetic door lock technology. This helps the users to monitor the real-time activities through web services/SMS. The web service consists of either web browser command or e-mail provision. The system establishes the communication between the system and authenticated user. The e-mail received by the system from the authorized person will monitor and control the real-time operation and door lock. The entire control system is reinforced using ARM1176JZF-S microcontroller and tested for actual use in the home environment. The result shows the experimental verification of the proposed system.

In contemporary video streaming systems based on HTTP protocol, video players at the client side are responsible for adjusting video quality to network conditions and user expectations. However, when multiple video clips are streamed simultaneously, an intricate application logic implemented in the video players overlays the TCP mechanism which is responsible for a balanced access to a shared network link. As a result, some video players may not obtain a fair share of network throughput and may be vulnerable to an unstable video bit-rate. Therefore, we propose to simplify the algorithms implemented in the video players, which are responsible for the adjustment of video quality and constrain their functionality only to sending feedback to a server about a state of the player buffer. The main logic of the system is shifted to the server, which is now responsible for bit-rate selection and prioritisation of the video streams transmitted to multiple clients. To verify our proposition, we performed several experiments in a laboratory environment which show that when the server cooperates with the clients, the video players experience fewer quality switches and the system achieves better fairness when allocating network throughput among the video players. However, this comes at the cost of worse utilisation of network bandwidth.

Digital image encryption is widely used for image data security. JPEG standard compresses image with great performance on reducing file size. Thus, to encrypt an image in JPEG format we should keep the quality of original image and reduced size. This paper proposes a JPEG image encryption scheme based on quantized DC and non-zero AC coefficients inner category scrambling. Instead of coefficient value encryption, the address of coefficient is encrypted to get the address of cipher text. Then 8*8 blocks are shuffled. Chaotic iteration is employed to generate chaotic sequences for address scrambling and block shuffling. Analysis of simulation shows the proposed scheme is resistant to common attacks. Moreover, the proposed method keeps the file size of the encrypted image in an acceptable range compared with the plain text. To enlarge the cipher text possible space and improve the resistance to sophisticated attacks, several additional procedures are further developed. Contrast experiments verify these procedures can refine the proposed scheme and achieve significant improvements.

Local Binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments show that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.

The GMM is a conventional approach which has been recently applied in many face recognition studies. However, the question about how to deal with illumination changes while ensuring high performance is still a challenge, especially with real-world databases. In this paper, we propose a Visual Observation Confidence (VOC) measure for robust face recognition for illumination changes. Our VOC value is a combined confidence value of three measurements: Flatness Measure (FM), Centrality Measure (CM), and Illumination Normality Measure (IM). While FM measures the discrimination ability of one face, IM represents the degree of illumination impact on that face. In addition, we introduce CM as a centrality measure to help FM to reduce some of the errors from unnecessary areas such as the hair, neck or background. The VOC then accompanies the feature vectors in the EM process to estimate the optimal models by modified-GMM training. In the experiments, we introduce a real-world database, called KoFace, besides applying some public databases such as the Yale and the ORL database. The KoFace database is composed of 106 face subjects under diverse illumination effects including shadows and highlights. The results show that our proposed approach gives a higher Face Recognition Rate (FRR) than the GMM baseline for indoor and outdoor datasets in the real-world KoFace database (94% and 85%, respectively) and in ORL, Yale databases (97% and 100% respectively).

High dynamic range (HDR) images can present the perfect real scene and rich color information. A commonly encountered problem in practical applications is how to well visualize HDR images on standard display devices. In this paper, we propose a multi-scale decomposition method using guided filtering for HDR image tone mapping. In our algorithm, HDR images are directly decomposed into three layers：base layer, coarse scale detail layer and fine detail layer. We propose an effective function to compress the base layer and the coarse scale detail layer. An adaptive function is also proposed for detail adjustment. Experimental results show that the proposed algorithm effectively accomplishes dynamic range compression and maintains good global contrast as well as local contrast. It also presents more image details and keeps high color saturation.

Card-Not-Present (CNP) transactions taking place remotely over the Internet are becoming more prevalent. Cardholder authentication should be provided to prevent the CNP fraud resulting from the theft of stored credit card numbers. To address the security problems associated with CNP transactions, the use of a virtual card number derived from the transaction details for the payment has been proposed, instead of the real card number. Since all of the virtual card number schemes proposed so far are based on a password shared between the cardholder and card issuer, transaction disputes due to the malicious behavior of one of the parties involved in the transaction cannot be resolved. In this paper, a new virtual card number scheme is proposed, which is associated with the cardholder's public key for signature verification. It provides strong cardholder authentication and non-repudiation of the transaction without deploying a public-key infrastructure, so that the transaction dispute can be easily resolved. The proposed scheme is analyzed in terms of its security and usability, and compared with the previously proposed schemes.

In video coding, the goal of rate control (RC) is not only to avoid the undesirable fluctuation in bit allocation, but also to provide a good visual perception. In this paper, a novel frame-level rate control algorithm for High Efficiency Video Coding (HEVC) is proposed. Firstly a model that reveals the relationship between bit per pixel (bpp), the bitrate of the intra frame and the bitrate of the subsequent inter frames in a group of pictures (GOP) is established, based on which the target bitrate of the first intra frame is well estimated. Then a novel frame-level bit allocation algorithm is developed, which provides a robust bit balancing scheme between the intra frame and the inter frames in a GOP to achieve the visual quality smoothness throughout the whole sequence. Our experimental results show that when compared to the RC scheme for HEVC encoder HM-16.0, the proposed algorithm can produce reconstructed frames with more consistent objective video quality. In addition, the objective visual quality of the reconstructed frames can be improved with less bitrate.

Mobile social network is becoming more and more popular with respect to the development and popularity of mobile devices and interpersonal sociality. As the amount of social data increases in a great deal and cloud computing techniques become developed, the architecture of mobile social network is evolved into cloud-based that mobile clients send data to the cloud and make data accessible from clients. The data in the cloud should be stored in a secure fashion to protect user privacy and restrict data sharing defined by users. Ciphertext-policy attribute-based encryption (CP-ABE) is currently considered to be a promising security solution for cloud-based mobile social network to encrypt the sensitive data. However, its ciphertext size and decryption time grow linearly with the attribute numbers in the access structure. In order to reduce the computing overhead held by the mobile devices, in this paper we propose a new Outsourcing decryption and Match-then-decrypt CP-ABE algorithm (OM-CP-ABE) which firstly outsources the computation-intensive bilinear pairing operations to a proxy, and secondly performs the decryption test on the attributes set matching access policy in ciphertexts. The experimental performance assessments show the security strength and efficiency of the proposed solution in terms of computation, communication, and storage. Also, our construction is proven to be replayable choosen-ciphertext attacks (RCCA) secure based on the decisional bilinear Diffie-Hellman (DBDH) assumption in the standard model.

Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0-255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

Jitterbug is a passive network covert timing channel supplying reliable stealthy transmission. It is also the basic manner of some improved covert timing channels designed for higher undetectability. The existing entropy-based detection scheme based on training sample binning may suffer from model mismatching, which results in detection performance deterioration. In this paper, a new detection method based on the feature of Jitterbug covert channel traffic is proposed. A fixed binning strategy without training samples is used to obtain bins distribution feature. Coefficient of variation (CV) is calculated for several sets of selected bins and the weighted mean is used to calculate the final CV value to distinguish Jitterbug from normal traffic. Furthermore, the timing window parameter of Jitterbug is estimated based on the detected traffic. Experimental results show that the proposed detection method can achieve high detection performance even with interference of network jitter, and the parameter estimation method can provide accurate values after accumulating plenty of detected samples.

The Digital Video Broadcasting-Common Scrambling Algorithm is an ETSI-designated algorithm designed for protecting MPEG-2 signal streams, and it is universally used. Its structure is a typical hybrid symmetric cipher which contains stream part and block part within a symmetric cipher, although the entropy is 64 bits, there haven't any effective cryptanalytic results up to now. This paper studies the security level of CSA against impossible differential cryptanalysis, a 20-round impossible differential for the block cipher part is proposed and a flaw in the cipher structure is revealed. When we attack the block cipher part alone, to recover 16 bits of the initial key, the data complexity of the attack is O(2^44.5), computational complexity is O(2^22.7) and memory complexity is O(2^10.5) when we attack CSA-BC reduced to 21 rounds. According to the structure flaw, an attack on CSA with block cipher part reduced to 21 rounds is proposed, the computational complexity is O(2^21.7), data complexity is O(2^43.5) and memory complexity is O(2^10.5), we can recover 8 bits of the key accordingly. Taking both the block cipher part and stream cipher part of CSA into consideration, it is currently the best result on CSA which is accessible as far as we know.