Relay-assisted D2D communication extends the communication range of the D2D pairs and helps users to form D2D pairs effectively. However, due to the introduction of the multi-hop relaying, the D2D communication has to occupy extra transmission time, which may decrease the efficiency of the communication system. In this paper, we propose a scheme to make node receive D2D signal and BS signal at overlapping time to improve the spectrum efficiency according to ZigZag decoding and successive-interference-cancellation (SIC). In this way, more data can be delivered during the same duration, thus the network throughput can be further improved. Numerical results verify the performance improvement of the proposed scheme when compared with a baseline scheme. Moreover, we expand the proposed scheme from one-hop relay scenario to multi-hop relay scenario.

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

In this paper, a mechanism for spectrum allocation in overlay cognitive radio networks is proposed. In overlay cognitive radio networks, the secondary users (SUs) must first sense the activity of primary users (PUs) to identify unoccupied spectrum bands. Based on their different contributions for the spectrum sensing, the SUs get payoffs that are computed by the fusion center (FC). The unoccupied bands will be auctioned and SUs are asked to bid using payoffs they earned or saved. Coalitions are allowed to form among SUs because each SU may only need a portion of the bands. We formulate the coalition forming process as a coalition forming game and analyze it by game theory. In the coalition formation game, debtor-creditor relationship may occur among the SUs because of their limited payoff storage. A debtor asks a creditor for payoff help, and in return provides the creditor with a portion of transmission time to relay data for the creditor. The negotiations between debtors and creditors can be modeled as a Bayesian game because they lack complete information of each other, and the equilibria of the game is investigated. Theoretical analysis and numerical results show that the proposed auction yields data rate improvement and certain fairness among all SUs.

In this paper, we analyze the connectivity of cognitive radio ad-hoc networks in a log-normal shadow fading environment. Considering secondary user and primary user's locations and primary user's active state are randomly distributed according to a homogeneous Poisson process and taking into account the spectrum sensing efficiency of secondary user, we derive mathematical models to investigate the connectivity of cognitive radio ad-hoc networks in three aspects and compare with the connectivity of ad-hoc networks. First, from the viewpoint of a secondary user, we study the communication probability of that secondary user. Second, we examine the possibility that two secondary users can establish a direct communication link between them. Finally, we extend to the case of finding the probability that two arbitrary secondary users can communicate via multi-hop path. We verify the correctness of our analytical approach by comparing with simulations. The numerical results show that in cognitive radio ad-hoc networks, high fading variance helps to remarkably improve connectivity behavior in the same condition of secondary user's density and primary user's average active rate. Furthermore, the impact of shadowing on wireless connection probability dominates that of primary user's average active rate. Finally, the spectrum sensing efficiency of secondary user significantly impacts the connectivity features. The analysis in this paper provides an efficient way for system designers to characterize and optimize the connectivity of cognitive radio ad-hoc networks in practical wireless environment.

Wireless sensor network is an important research topic that has attracted a lot of attention in recent years. However, most of the interest has focused on wireless sensor network to gather scalar data such as temperature, humidity and vibration. Scalar data are insufficient for diverse applications such as video surveillance, target recognition and traffic monitoring. However, if we use camera sensors in wireless sensor network to collect video data which are vast in information, they can provide important visual information. Video sensor networks continue to gain interest due to their ability to collect video information for a wide range of applications in the past few years. However, how to efficiently store the massive data that reflect environmental state of different times in video sensor network and how to quickly search interested information from them are challenging issues in current research, especially when the sensor network environment is complicated. Therefore, in this paper, we propose a fast algorithm for extracting key frames from video and describe the design and implementation of key frame extraction and sharing in Android for wireless video sensor network.

Recently, several approaches to share road conditions and/or digital contents through VANETs have been proposed, and such approaches have generally considered the radial distance from the information source as well as the TTL to provision an ephemeral, geographically-limited information sharing service. However, they implement general MANETs and have not been tailored to the constrained movement of vehicles on roads that are mostly linear. In this paper, we propose a novel application-level mechanism that can be used to share road conditions, including accidents, detours and congestion, through a VANET. We assign probabilities to roads around each of the intersections in the neighborhood road network. We then use the graph representation of the road network to build a spanning tree of roads with the information source as the root node. Nodes below the root represent junctions, and the edges represent inter-connecting road segments. Messages propagate along the branches of the tree, and as the information propagates down the branches, the probability of replication decreases. The information is replicated until a threshold probability has been reached, and our method also ensures that messages are not delivered to irrelevant vehicles, independently of their proximity to the source. We evaluated the success rate and performance of this approach using NS-3 simulations, and we used IDM car following and MOBIL lane change models to provide realistic modeling of the vehicle mobility.

Sensors used in Wireless Sensor Networks (WSN) have mostly limited capacity which affects the performance of their applications. One of the data-gathering methods is to use mobile sinks to visit these sensors so that they can save their limited battery energies from forwarding data packages to static sinks. The main disadvantage of employing mobile sinks is the delay of data collection due to relative low speed of mobile sinks. Since sensors have very limited memory capacities, whenever a mobile sink is too late to visit a sensor, that sensor's memory would be full, which is called a 'memory overflow', and thus, needs to be purged, which causes loss of collected data. In this work, a method is proposed to generate mobile sink tours, such that the number of overflows and the amount of lost data are minimized. Moreover, the proposed method does not need either the sensor locations or sensor memory status in advance. Hence, the overhead stemmed from the information exchange of these requirements are avoided. The proposed method is compared with a previously published heuristic. The simulation experiment results show the success of the proposed method over the rival heuristic with respect to the considered metrics under various parameters.

This paper considers the effect of co-channel interference on hybrid satellite-terrestrial relay network. In particular, we investigate the problem of amplify-and-forward (AF) relaying in hybrid satellite-terrestrial link, where the relay is interfered by multiple co-channel interferers. The direct link between satellite and terrestrial destination is not available due to masking by surroundings. The destination node can only receive signals from satellite with the assistance of a relay node situated at ground. The satellite-relay link is assumed to follow the shadowed Rice fading, while the channels of interferer-relay and relay-destination links experience Nakagami-m fading. For the considered AF relaying scheme, we first derive the analytical expression for the moment generating function (MGF) of the output signal-to-interference-plus-noise ratio (SINR). Then, we use the obtained MGF to derive the average symbol error rate (SER) of the considered scenario for M-ary phase shift keying (M-PSK) constellation under these generalized fading channels.

Interference can cause serious problems in our daily life. Traditional ways in localizing a target can't work well when it comes to the source of interference for it may take an uncooperative or even resistant attitude towards localization. To tackle this issue, we take the BPSN (Binary Proximity Sensor Networks) and consider a passive way in this paper. No cooperation is needed and it is based on simple sensor node suitable for large-scale deployment. By dividing the sensing field into different patches, when enough patches are formed, good localization accuracy can be achieved with high resolution. Then we analyze the relationship between sensing radius and localization error, we find that in a finite region where edge effect can't be ignored, the trend between sensing radius and localization error is not always consistent. Through theoretical analysis and simulation, we explore to determine the best sensing radius to achieve high localization accuracy.

A broadcast operation is the fundamental transmission technique in mobile ad-hoc networks (MANETs). Because a broadcast operation can cause a broadcast storm, only selected forwarding nodes have the right to rebroadcast a broadcast message among the one-hop and two-hop neighboring nodes of a sender. This paper proposes the maximum intersection self-pruning (MISP) algorithm to minimize broadcasting redundancy. Herein, an example is given to help describe the main concept of MISP and upper bounds of forward node have been derived based on induction. A simulation conducted demonstrated that when conventional blind flooding (BF), self-pruning (SP), an optimized link state routing (OLSR) multipoint relay (MPR) set, and dominant pruning (DP), are replaced with the MISP in executing Strong duplicate address detection (DAD), the performances in terms of the energy consumption, upper bounds of the number of forward nodes, and message complexity have been improved. In addition, to evaluate the performance in reference to the link error probability, P_e, an enhancement was achieved by computing a proposed retransmission limit, S, for error recovery based on this probability. Retransmission limit control is critical for efficient energy consumption of MANET nodes operating with limited portable energy where Strong DAD reacts differently to link errors based on the operational procedures.

In this paper, we present a multi-chain based hierarchical topology control algorithm (MCHTC) for wireless sensor networks. In this algorithm, the topology control process using static clustering is divided into sensing layer that is composed by sensor nodes and multi-hop data forwarding layer that is composed by leader nodes. The communication cost and residual energy of nodes are considered to organize nodes into a chain in each cluster, and leader nodes form a tree topology. Leader nodes are elected based on the residual energy and distance between themselves and the base station. Analysis and simulation results show that MCHTC outperforms LEACH, PEGASIS and IEEPB in terms of network lifetime, energy consumption and network energy balance.

One of the challenging tasks in Mobile Ad hoc Network is to discover precise optimal routing solution due to the infrastructure-less dynamic behavior of wireless mobile nodes. Ant Colony Optimization, a swarm Intelligence technique, inspired by the foraging behaviour of ants in colonies was used in the past research works to compute the optimal path. In this paper, we propose a Recurrent Ant Colony Optimization (RECACO) that executes the actual Ant Colony Optimization iteratively based on recurrent value in order to obtain an optimal path convergence. Each iteration involves three steps: Pheromone tracking, Pheromone renewal and Node selection based on the residual energy in the mobile nodes. The novelty of our approach is the inclusion of new pheromone updating strategy in both online step-by-step pheromone renewal mode and online delayed pheromone renewal mode with the use of newly proposed metric named ELD (Energy Load Delay) based on energy, Load balancing and end-to-end delay metrics to measure the performance. RECACO is implemented using network simulator NS2.34. The implementation results show that the proposed algorithm outperforms the existing algorithms like AODV, ACO, LBE-ARAMA in terms of Energy, Delay, Packet Delivery Ratio and Network life time.

Data accuracy can be increased by detecting and removing the incorrect data generated in wireless sensor networks. By increasing the data accuracy, network lifetime can be increased parallel. Network lifetime or operational time is the time during which WSN is able to fulfill its tasks by using microcontroller with on-chip memory radio transceivers, albeit distributed sensor nodes send summary of their data to their cluster heads, which reduce energy consumption gradually. In this paper a powerful algorithm using proactive fuzzy system is proposed and it is a mixture of fuzzy logic with comparative correlation techniques that ensure high data accuracy by detecting incorrect data in distributed wireless sensor networks. This proposed system is implemented in two phases there, the first phase creates input space partitioning by using robust fuzzy c means clustering and the second phase detects incorrect data and removes it completely. Experimental result makes transparent of combined correlated fuzzy system (CCFS) which detects faulty readings with greater accuracy (99.21%) than the existing one (98.33%) along with low false alarm rate.

This paper investigates and analyzes the default block placement strategy of HDFS. HDFS is a typical representative distributed file system to stream vast amount of data effectively at high bandwidth to user applications. However, the default HDFS block placement policy assumes that all nodes in the cluster are homogeneous, and places blocks with a simple RoundRobin strategy without considering any nodes' resource characteristics, which decreases self-adaptability of the system. The primary contribution of this paper is the proposition of a network load sensitive block placement strategy. We have implemented our algorithm and justify it through extensive simulations and comparison with similar existing studies. The results indicate that our work not only performs much better in the data distribution but also improves write performance more significantly than the others.

This paper addresses the problem of signal acquisition with a sparse representation in a given orthonormal basis using fewer noisy measurements. The authors formulate the problem statement for randomly measuring with strong signal noise. The impact of white Gaussian signals noise on the recovery performance is analyzed to provide a theoretical basis for the reasonable design of the measurement matrix. With the idea that the measurement matrix can be adapted for noise suppression in the adaptive CS system, an adapted selective compressive sensing (ASCS) scheme is proposed whose measurement matrix can be updated according to the noise information fed back by the processing center. In terms of objective recovery quality, failure rate and mean-square error (MSE), a comparison is made with some nonadaptive methods and existing CS measurement approaches. Extensive numerical experiments show that the proposed scheme has better noise suppression performance and improves the support recovery of sparse signal. The proposed scheme should have a great potential and bright prospect of broadband signals such as biological signal measurement and radar signal detection.

This article investigates the topology control problem in the space information network (SIN) using a hierarchical autonomous system (AS) approach. We propose an AS network topology control (AS-TC) algorithm to minimize the time delay in the SIN. Compared with most existing approaches for SIN where either the purely centralized or the purely distributed control method is adopted, the proposed algorithm is a hybrid control method. In order to reduce the cost of control, the control message exchange is constrained among neighboring sub-AS networks. We prove that the proposed algorithm achieve logical k-connectivity on the condition that the original physical topology is k-connectivity. Simulation results validate the theoretic analysis and effectiveness of the AS-TC algorithm.

Recent advancements in broadband wireless communication technologies enable mobile users to receive video streaming services with various smart devices. The long term evolution (LTE) network provides high bandwidth and low latency for several emerging mobile applications. This paper proposes the buffer aware scheduling (BAS) approach to schedule the downlink video traffic in LTE network. The proposed BAS scheme applies the weighting function to heuristically adjust the scheduling priority by considering the buffer status and channel condition of UE so as to reduce the time that UE stays in the connected state without receiving data. Both of 1080P and 2160P resolution video streaming sources were applied for exhaustive simulations to examine the performance of the proposed scheme by comparing to that of the fair bandwidth (FB) and the best channel quality indicator (CQI) schemes. The simulation results indicate that the proposed BAS scheme not only achieves better performance in power saving, streaming delivery time, and throughput than the FB scheme while maintaining the similar performance as the best CQI scheme in light traffic load. Specifically, the proposed scheme reduces streaming delivery time and generates less signaling overhead than the best CQI scheme when the traffic load is heavy.

In this paper, we build a theoretical framework for quantitatively measuring and graphically representing the degrees of closeness centralization among performers assigned to enact a workflow procedure. The degree of closeness centralization of a workflow-performer reflects how near the performer is to the other performers in enacting a corresponding workflow model designed for workflow-supported organizational operations. The proposed framework comprises three procedural phases and four functional transformations, such as discovery, analysis, and quantitation phases, which carry out ICN-to-WsoN, WsoN-to-SocioMatrix, SocioMatrix-to-DistanceMatrix, and DistanceMatrix-to-CCV transformations. We develop a series of algorithmic formalisms for the procedural phases and their transformative functionalities, and verify the proposed framework through an operational example. Finally, we expatiate on the functional expansion of the closeness centralization formulas so as for the theoretical framework to handle a group of workflow procedures (or a workflow package) with organization-wide workflow-performers.

With the rapid development of smart devices and mobile Internet, the video application plays an increasingly important role on mobile devices. Understanding user behavior patterns is critical for optimized operation of mobile live streaming systems. On the other hand, volume based billing models on cloud services make it easier for video service providers to scale their services as well as to reduce the waste from oversized service capacities. In this paper, the watching behaviors of a commercial mobile live streaming system are studied in a content-centric manner. Our analysis captures the intrinsic correlation existing between popularity and watching intensity of programs due to the synchronized watching behaviors with program schedule. The watching pattern is further used to estimate traffic volume generated by the program, which is useful on data volume capacity reservation and billing strategy selection in cloud services. The traffic range of programs is estimated based on a naive popularity prediction. In cross validation, the traffic ranges of around 94% of programs are successfully estimated. In high popularity programs (>20000 viewers), the overestimated traffic is less than 15% of real happened traffic when using upper bound to estimate program traffic.

In this paper, we develop a novel object tracking method based on sparse representation. First, we propose a relaxed sparse representation model, based on which the tracking problem is casted as an inverse sparse representation process. In this process, the target template is able to be sparsely approximated by all candidate samples. Second, we present an objective function that combines the sparse representation process of different fragments, the relaxed representation scheme and a weight reference prior. Based on some propositions, the proposed objective function can be solved by using an iteration algorithm. In addition, we design a tracking framework based on the proposed representation model and a simple online update manner. Finally, numerous experiments are conducted on some challenging sequences to compare our tracking method with some state-of-the-art ones. Both qualitative and quantitative results demonstrate that the proposed tracking method performs better than other competing algorithms.

Recently, the standardization of backward compatible JPEG image coding for high dynamic range (HDR) image has been undertaken to establish an international standard called "JPEG XT." The JPEG XT consists of two layers: the base layer and the residual layer. The base layer contains tone mapped low dynamic range (LDR) image data and the residual layer contains the error signal used to reconstruct the HDR image. This paper gives the result of a study to evaluate the overall performance of tone mapping operators (TMOs) for this standard. The evaluation is performed using five HDR image datasets and six TMOs for profiles A, B, and C of the proposed JPEG XT standard. The Tone Mapped image Quality Index (TMQI) and no reference image quality assessment (NR IQA) are used for measuring the LDR image quality. The peak signal to noise ratio (PSNR) is used to evaluate the overall compression performance of JPEG XT profiles A, B, and C. In TMQI and NR IQA measurements, TMOs using display adaptive tone mapping and adaptive logarithmic mapping each gave good results. A TMO using adaptive logarithmic mapping gave good PSNRs.

To avoid influences caused by pose, illumination and facial expression variations, we propose a robust three-step algorithm based on ASM and POEM for facial landmark localization. Firstly, Model Selection Factor is utilized to achieve a pose-free initialized shape. Then, we use the global shape model of ASM to describe the whole face and the texture model POEM to adjust the position of each landmark. Thirdly, a second localization is presented to discriminatively refine the subtle shape variation for some organs and contours. Experiments are conducted in four main face datasets, and the results demonstrate that the proposed method accurately localizes facial landmarks and outperforms other state-of-the-art methods.

A system for the recognition of machine printed Arabic script is proposed. The Arabic script is shared by three languages i.e., Arabic, Urdu and Farsi. The three languages have a descent amount of vocabulary in common, thus compounding the problems for identification. Therefore, in an ideal scenario not only the script has to be differentiated from other scripts but also the language of the script has to be recognized. The recognition process involves the segregation of Arabic scripted documents from Latin, Han and other scripted documents using horizontal and vertical projection profiles, and the identification of the language. Identification mainly involves extracting connected components, which are subjected to Principle Component Analysis (PCA) transformation for extracting uncorrelated features. Later the traditional K-Nearest Neighbours (KNN) algorithm is used for recognition. Experiments were carried out by varying the number of principal components and connected components to be extracted per document to find a combination of both that would give the optimal accuracy. An accuracy of 100% is achieved for connected components >=18 and Principal components equals to 15. This proposed system would play a vital role in automatic archiving of multilingual documents and the selection of the appropriate Arabic script in multi lingual Optical Character Recognition (OCR) systems.

3D reconstruction based on binocular data is significant to machine vision. In our method, we propose a new and high efficiency 3D reconstruction approach by using a consumer camera aiming to: 1) address the configuration problem of dual camera in the binocular reconstruction system; 2) address stereo matching can hardly be done well problem in both time computing and precision. The kernel feature is firstly proposed in calibration stage to rectify the epipolar. Then, we segment the objects in the camera into background and foreground, for which system obtains the disparity by different method: local window matching and kernel feature-based matching. Extensive experiments demonstrate our proposed algorithm represents accurate 3D model.

Sybil attack is a special kind of attack which is difficult to be detected in Wireless Sensor Network (WSN). So a lightweight detection mechanism based on LEACH-RSSI-ID (LRD) is proposed in this paper. Due to the characteristic of Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, none of nodes can be the cluster head forever.

Many sorts of image processing software facilitate image editing and also generate a great number of doctored images. Forensic technology emerges to detect the unintentional or malicious image operations. Most of forensic methods focus on the detection of single operations. However, a series of operations may be used to sequentially manipulate an image, which makes the operation detection problem complex. Forensic investigators always want to know as much exhaustive information about a suspicious image's entire processing history as possible. The detection of the operation chain, consisting of a series of operations, is a significant and challenging problem in the research field of forensics. In this paper, based on the histogram distribution uniformity of a manipulated image, we propose an operation chain detection scheme to identify histogram equalization (HE) followed by the dither-like operation (DLO). Two histogram features and a local spatial feature are utilized to further determine which DLO may have been applied. Both theoretical analysis and experimental results verify the effectiveness of our proposed scheme for both global and local scenarios.