Device-to-device (D2D) communication underlaying cellular networks can bring significate benefits for improving the performance of mobile services. However, it hinges on elaborate resource sharing scheme to coordinate interference between cellular users and D2D pairs. We formulate a joint mode selection, link allocation and power control optimization problem for D2D communication sharing uplink resources in a multi-user cellular network and consider the efficiency and the fairness simultaneously. Due to the non-convex difficulty, we propose a three-step scheme: firstly, we conduct mode selection for D2D pairs based on a minimum distance metric after an admission control and obtain some cellular candidates for them. And then, a cellular candidate will be paired to each D2D pair based on fairness. Finally, we use Lagrangian Algorithm to formulate a joint power control strategy for D2D pairs and their reused cellular users and a closed-form of solution is derived. Simulation results demonstrate that our proposed algorithms converge in a short time. Moreover, both the sum rate of D2D pairs and the energy efficiency of cellular users are improved.

With proliferation of diverse network access technologies, users demands are also increasing and service providers are offering a Quality of Service (QoS) to satisfy their customers. In roaming, a mobile node (MN) traverses number of available networks in the heterogeneous wireless networks environment and a single operator is not capable to fulfill the demands of user. It is crucial task for MN for selecting a best network from the list of networks at any time anywhere. A MN undergoes a network selection situation frequently when it is becoming away from the home network. Multiple Attribute Group Decision (MAGD) method will be one of the best ways for selecting target network in heterogeneous wireless networks (4G). MAGD network selection process is predominantly dependent on two steps, i.e., attribute weight, decision maker's (DM's) weight and aggregation of opinion of DMs. This paper proposes Multi-Attribute Intuitionistic Fuzzy Group Decision Method (MAIFGDM) using TOPSIS for the selection of the suitable candidate network. It is scalable and is able to handle any number of networks with large set of attributes. This is a method of lower complexity and is useful for real time applications. It gives more accurate result because it uses Intuitionistic Fuzzy Sets (IFS) with an additional parameter intuitionistic fuzzy index or hesitant degree. MAIFGDM is simulated in MATLAB for its evaluation. A comparative study of MAIFDGM is also made with TOPSIS and Fuzzy-TOPSIS in respect to decision delay. It is observed that MAIFDGM have low values of decision time in comparison to TOPSIS and Fuzzy-TOPSIS methods.

With device-to-device (D2D) communications, an inactive user terminal can be utilized as a relay node to support multi-hop communication so that connective experience of the cell-edge user as well as the capacity of the whole system can be significantly improved. In this paper, we investigate the spectrum sharing for a cooperative relay assisted D2D communication underlying a cellular network. We formulate a joint relay selection and channel assignment problem to maximize the throughput of the system while guaranteeing the quality of service (QoS) requirements of cellular users (CUs) and D2D users (DUs). By exploiting coalition formation game theory, we propose two algorithms to solve the problem. The first algorithm is designed based on merge and split rules while the second one is developed based on single user's movement. Both of them are proved to be stable and convergent. Simulation results are presented to show the effectiveness of the proposed algorithms.

On large-scale data analysis platforms deployed on cloud infrastructures over the Internet, the instability of the data transfer time and the dynamics of the processing rate require a more sophisticated data distribution scheme which maximizes parallel efficiency by achieving the balanced load among participated computing elements and by eliminating the idle time of each computing element. In particular, under the constraints that have the real-time and limited data buffer (in-memory storage) are given, it needs more controllable mechanism to prevent both the overflow and the underflow of the finite buffer. In this paper, we propose an auto regulated data provisioning model based on receiver-driven data pull model. On this model, we provide a synchronized data replenishment mechanism that implicitly avoids the data buffer overflow as well as explicitly regulates the data buffer underflow by adequately adjusting the buffer resilience. To estimate the optimal size of buffer resilience, we exploits an adaptive buffer resilience control scheme that minimizes both data buffer space and idle time of the processing elements based on directly measured sample path analysis. The simulation results show that the proposed scheme provides allowable approximation compared to the numerical results. Also, it is suitably efficient to apply for such a dynamic environment that cannot postulate the stochastic characteristic for the data transfer time, the data processing rate, or even an environment where the fluctuation of the both is presented.

With the growth of the massive amount of data, the failure probability of the cloud storage node is becoming more and more big. A single fault tolerance strategy, such as replication and erasure codes, has some unavoidable disadvantages, which can not meet the needs of the today's fault tolerance. Therefore, according to the file access frequency and size, an adaptive hybrid redundant fault tolerance strategy is proposed, which can dynamically change between the replication scheme and erasure codes scheme throughout the lifecycle. The experimental results show that the proposed scheme can not only save the storage space(reduced by 32% compared with replication), but also ensure the fast recovery of the node failures(increased by 42% compared with erasure codes).

In wireless communication, the multiple-input multiple-output (MIMO) system is a well-known approach to improve the reliability as well as the data rate. In MIMO systems, channel state information (CSI) is typically required at the receiver to detect transmitted signals; however, in practical systems, the CSI is imperfect and contains errors, which affect the overall system performance. In this paper, we propose a novel maximum likelihood (ML) scheme for MIMO systems that is robust to the CSI errors. We apply an optimization method to estimate an instantaneous covariance matrix of the CSI errors in order to improve the detection performance. Furthermore, we propose the employment of the list sphere decoding (LSD) scheme to reduce the computational complexity, which is capable of efficiently finding a reduced set of the candidate symbol vectors for the computation of the covariance matrix of the CSI errors. An iterative detection scheme is also proposed to further improve the detection performance.

In vehicle-to-vehicle (V2V) networks, where selfishness degrades node activity, countermeasures for collaboration enforcement must be provided to enable application of a sage and efficient network environment. Because vehicular networks feature both high mobility and various topologies, selfish behavior judgment and establishment of a stable routing protocol become intensely challenging. In this paper, a two-phase-based generous cooperative routing protocol (called GEC) is presented for V2V networks to provide resistance to selfishness. To detect selfish behaving vehicles, a packet forwarding watchdog and an average connection rate based on the multipath weight method are used, where evidence is gathered from different watchdogs. Then, multihop relay decisions are made using a generous cooperative algorithm based on game theory. Finally, through buffering of the multiple end-to-end paths and judicious choice of optimal cooperative routes, route maintenance phase is capable of dealing with congestion and rapidly exchanging traffic. Specifically, it is proved that the GEC is theoretically subgame perfect. Simulation results show that for V2V networks with inherently selfish nodes, the proposed method isolates uncooperative vehicles and is capable of accommodating both the mobility and congestion circumstances by facilitating information dissemination and reducing end-to-end delay.

Considering the tradeoff between energy consumption and outage behavior in buffer-aided relay selection, a novel energy-efficient buffer-aided optimal relay selection scheme with power adaptation and Inter-Relay Interference (IRI) cancellation is proposed. In the proposed scheme, energy consumption minimization is the objective with the consideration of relay buffer state, outage probability and relay power control, in order to eliminate IRI. The proposed scheme selects a pair of optimal relays from multiple candidate relays, denoted as optimal receive relay and optimal transmit relay respectively. Source-relay and relay-destination communications can be performed within a time-slot, which performs as Full-Duplex (FD) relaying. Markov chain model is applied to analyze the evolution of relay buffer states. System steady state outage probability and achievable diversity order are derived respectively. In addition, packet transmission delay and power reduction performance are investigated with a specific analysis. Numerical results show that the proposed scheme outperforms other relay selection schemes in terms of outage behavior with power adaptation and IRI cancellation in the same relay number and buffer size scenario. Compared with Buffer State relay selection method, the proposed scheme reduces transmission delay significantly with the same amount of relays. Average transmit power reduction can be implemented to relays with the increasing of relay number and buffer size, which realizes the tradeoff between energy-efficiency, outage behavior and delay performance in green cooperative communications.

Estimation of signal-to-noise ratio (SNR) is an important problem in wireless communication systems. It has been studied for various constellation types and channels using different estimation techniques. Maximum likelihood estimation is a technique which provides efficient and in most cases unbiased estimators. In this paper, we have applied maximum likelihood estimation for systems employing square or cross QAM signals which are undergoing slow flat Rayleigh fading. The problem has been considered under various scenarios like data-aided (DA), non-data-aided (NDA) and partially data-aided (PDA) and the performance of each type of estimator has been evaluated and compared. It has been observed that the performance of DA estimator is best due to usage of pilot symbols, with the drawback of greater bandwidth consumption. However, this can be catered for by using partially data-aided estimators whose performance is better than NDA systems with some extra bandwidth requirement.

Currently, the quantize-and-forward (QF) scheme with high order modulation and quantization has rather high complexity and it is thus impractical, especially in multiple relay cooperative communications. To overcome these deficiencies, an improved low complex QF scheme is proposed by the combination of the low order binary phase shift keying (BPSK) modulation and the 1-bit and 2-bit quantization, respectively. In this scheme, the relay selection is optimized by the best relay position for best bit-error-rate (BER) performance, where the relays are located closely to the destination node. In addition, an optimal power allocation is also suggested on a total power constraint. Finally, the BER and the achievable rate of the low order 1-bit, 2-bit and 3-bit QF schemes are simulated and analyzed. Simulation results indicate that the 3-bit QF scheme has about 1.8~5 dB, 4.5~7.5 dB and 1~2.5 dB performance gains than those of the decode-and-forward (DF), the 1-bit and 2-bit QF schemes, at BER of $10^{-2}$, respectively. For the 2-bit QF, the scheme of the normalized Source-Relay (S-R) distance with 0.9 has about 5dB, 7.5dB, 9dB and 15dB gains than those of the distance with 0.7, 0.5, 0.3 and 0.1, respectively, at BER of $10^{-3}$. In addition, the proposed optimal power allocation saves about 2.5dB much more relay power on an average than that of the fixed power allocation. Therefore, the proposed QF scheme can obtain excellent features, such as good BER performance, low complexity and high power efficiency, which make it much pragmatic in the future cooperative communications.

Femtocells are envisioned as a key solution to embrace the ever-increasing high data rate and thus are extensively deployed. However, the dense and random deployments of femtocell access points (FAPs) induce severe intercell inference that in turn may degrade the performance of spectral efficiency. Hence, unrestrained proliferation of FAPs may not acquire a net throughput gain. Besides, given that numerous FAPs deployed in ultra-dense networks (UDNs) lead to significant energy consumption, the amount of FAPs deployed is worthy of more considerations. Nevertheless, little existing works present an analytical result regarding the optimal FAP density for a given User Equipment (UE) density. This paper explores the realistic scenario of randomly distributed FAPs in UDN and derives the coverage probability via Stochastic Geometry. From the analytical results, coverage probability is strictly increasing as the FAP-to-UE ratio increases, yet the growing rate of coverage probability decreases as the ratio grows. Therefore, we can consider a specific FAP-to-UE ratio as the point where further increasing the ratio is not cost-effective with regards to the requirements of communication systems. To reach the optimal FAP density, we can deploy FAPs in line with peak traffic and randomly switch off FAPs to keep the optimal ratio during off-peak hours. Furthermore, considering the unbalanced nature of traffic demands in the temporal and spatial domain, dynamically and carefully choosing the locations of active FAPs would provide advantages over randomization. Besides, with a huge FAP density in UDN, we have more potential choices for the locations of active FAPs and this adds to the demand for a strategic sleeping policy.

Enhancing the performance of computing systems has been an important topic since the invention of computers. The leading-edge technologies of multicore and virtualization dramatically influence the development of current IT systems. We study performance attributes of response time (RT), throughput, efficiency, and scalability of a virtualized Web system running on a multicore server. We build virtual machines (VMs) for a Web application, and use distributed stress tests to measure RTs and throughputs under varied combinations of virtual cores (VCs) and VM instances. Their gains, efficiencies and scalabilities are also computed and compared. Our experimental and analytic results indicate: 1) A system can perform and scale much better by adopting multiple single-VC VMs than by single multiple-VC VM. 2) The system capacity gain is proportional to the number of VM instances run, but not proportional to the number of VCs allocated in a VM. 3) A system with more VMs or VCs has higher physical CPU utilization, but lower vCPU utilization. 4) The maximum throughput gain is less than VM or VC gain. 5) Per-core computing efficiency does not correlate to the quality of VCs or VMs employed. The outcomes can provide valuable guidelines for selecting instance types provided by public Cloud providers and load balancing planning for Web systems.

A Wireless Sensor Network (WSN) is composed of several sensor nodes which are severely restricted to energy and memory. Energy is the lifeblood of sensors and thus energy conservation is a critical necessity of WSN. This paper proposes a clustering algorithm namely Energy Efficient Trustworthy Clustering algorithm (EETCA), which focuses on three phases such as chief node election, chief node recycling process and bi-level trust computation. The chief node election is achieved by Dempster-Shafer theory based on trust. In the second phase, the selected chief node is recycled with respect to the current available energy. The final phase is concerned with the computation of bi-level trust, which is triggered for every time interval. This is to check the trustworthiness of the participating nodes. The nodes below the fixed trust threshold are blocked, so as to ensure trustworthiness. The system consumes lesser energy, as all the nodes behave normally and unwanted energy consumption is completely weeded out. The experimental results of EETCA are satisfactory in terms of reduced energy consumption and prolonged lifetime of the network.

In this article, we model the distributed relay assignment network as a many-to-one matching market with peer effects. We discuss two scenarios for throughput optimization of relay networks: the scenario of aggregate throughput optimization and the scenario of fairness performance optimization. For the first scenario, we propose a Mutual Benefit-based Deferred Acceptance (MBDA) algorithm to increase the aggregate network throughput. For the second scenario, instead of using the alternative matching scheme, a non-substitution matching algorithm (NSA) is designed to solve the fairness problem. The NSA improves the fairness performance. We prove that both two algorithms converge to a globally stable matching, and discuss the practical implementation. Simulation results show that the performance of MBDA algorithm outperforms existing schemes and is almost the same with the optimal solution in terms of aggregate throughput. Meanwhile, the proposed NSA improves fairness as the scale of the relay network expands.

Energy efficiency of resource-constrained wireless sensor networks is critical in applications such as real-time monitoring/surveillance. To improve the energy efficiency and reduce the energy consumption, the time series data can be compressed before transmission. However, most of the compression algorithms for time series data were developed only for single variate scenarios, while in practice there are often multiple sensor nodes in one application and the collected data is actually multivariate time series. In this paper, we propose to compress the time series data by the Lasso (least absolute shrinkage and selection operator) approximation. We show that, our approach can be naturally extended for compressing the multivariate time series data. Our extension is novel since it constructs an optimal projection of the original multivariates where the best energy efficiency can be realized. The two algorithms are named by ULasso (Univariate Lasso) and MLasso (Multivariate Lasso), for which we also provide practical guidance for parameter selection. Finally, empirically evaluation is implemented with several publicly available real-world data sets from different application domains. We quantify the algorithm performance by measuring the approximation error, compression ratio, and computation complexity. The results show that ULasso and MLasso are superior to or at least equivalent to compression performance of LTC and PLAMlis. Particularly, MLasso can significantly reduce the smooth multivariate time series data, without breaking the major trends and important changes of the sensor network system.

An improved optimal weights based on parallel interference cancellation algorithm has been proposed to cancel for interference induced by multi-user access satellite to ground uplink system. Due to differences in elevation relative motion between the user and the satellite, as well as access between users, resulting in multi-user access interference (Multi-user Access Interference, MUI), which significantly degrade system performance when multi-user access. By steepest gradient method, it obtained based on the MMSE criterion, parallel interference cancellation adjust optimal weights to obtain the maximum SINR. Compared to traditional parallel interference cancellation (Parallel Interference Cancellation, PIC) algorithm or serial interference cancellation ( Successive interference Cancellation, SIC), the accuracy of which is not high and too many complex iterations, we establish the multi-user access to the satellite to ground up link system to demonstrate that the improved WPIC algorithm could be provided with high accuracy and relatively low number of iterations.

Doppler Effect is a prominent obstacle in vehicular networks, which dramatically increase the Bit-Error-Rate (BER). This problem is accompanied with the presence of the Orthogonal Frequency Division Multiplexing (OFDM) systems in which the Doppler shift interrupts the subcarriers orthogonality. Additionally, Inter-Symbol Interference (ISI) and high Peak-to-Average Power Ratio (PAPR) are likely to occur which corrupt the received signal. In this paper, the single-carrier combined with the frequency domain equalizer (SC-FDE) is utilized as an alternative to the OFDM over the IEEE 802.11p uplink vehicular channels. The Minimum Mean Squared Error (MMSE) and Zero-Forcing (ZF) are employed in order to study the impact of these equalization techniques along with the SC-FDE on the propagation medium. In addition, we aim to enhance the BER, improve the transmitted signal quality and achieve ISI and PAPR mitigation. The proposed schemes are investigated and we found that the MMSE outperforms the ZF equalization under different Doppler shift effects and modulations.

Automatic analysis and understanding of human activities is a challenging task in computer vision, especially for the surveillance scenarios which typically contains crowds, complex motions and occlusions. To address these issues, a Bag-of-words representation of videos is developed by leveraging information including crowd positions, motion directions and velocities. We infer the crowd activity in a motion field using Category Constrained Correlated Topic Model (CC-CTM) with latent topics. We represent each video by a mixture of learned motion patterns, and predict the associated activity by training a SVM classifier. The experiment dataset we constructed are from Crowd_PETS09 bench dataset and UCF_Crowds dataset, including 2000 documents. Experimental results demonstrate that accuracy reaches 90%, and the proposed approach outperforms the state-of-the-arts by a large margin.

JPEG-LS is an international standard for lossless or near-lossless image-compression algorithms. In this paper, a simple method is proposed to improve the performance of the lossless JPEG-LS algorithm. With respect to JPEG-LS and its supplementary explanation, Golomb-Rice (GR) coding is mainly used for entropy coding, but it is not used for long codewords. The proposed method replaces a set of long codewords with a set of shorter location map information. This paper shows how efficiently the location map guarantees reversibility and enhances the compression rate in terms of performance. Experiments have also been conducted to verify the efficiency of the proposed method.

Parametric min-cuts is an object proposal algorithm, which can be used for accurate image segmentation. In parametric min-cuts, foreground seeds generation plays an important role since the number and quality of foreground seeds have great effect on its efficiency and accuracy. To improve the performance of parametric min-cuts, this paper proposes a new framework for foreground seeds generation. First, to increase the odds of finding objects, saliency detection at multiple scales is used to generate a large set of diverse candidate seeds. Second, to further select good-quality seeds, a two-stage cascaded ranking classifier is used to filter and rank the candidates based on their appearance features. Experimental results show that parametric min-cuts using our seeding strategy can obtain a relative small pool of proposals with high accuracy.

In this paper, we present a novel image operator to extract textual information in natural scene images. First, a powerful refiner called the Stroke Color Extension, which extends the widely used Stroke Width Transform by incorporating color information of strokes, is proposed to achieve significantly enhanced performance on intra-character connection and non-character removal. Second, a character classifier is trained by using gradient features. The classifier not only eliminates non-character components but also remains a large number of characters. Third, an effective extractor called the Character Color Transform combines color information of characters and geometry features. It is used to extract potential characters which are not correctly extracted in previous steps. Fourth, a Convolutional Neural Network model is used to verify text candidates, improving the performance of text detection. The proposed technique is tested on two public datasets, i.e., ICDAR2011 dataset and ICDAR2013 dataset. The experimental results show that our approach achieves state-of-the-art performance.

We propose a face recognition method that utilizes the LCP face descriptor. The proposed method applies a LoG mask to extract a face contour response, and employs the LCP algorithm to produce a binary pattern representation that ensures high recognition performance even under the changes in illumination, noise, and aging. The proposed LCP algorithm produces excellent noise reduction and efficiency in removing unnecessary information from the face by extracting a face contour response using the LoG mask, whose behavior is similar to the human eye. Majority of reported algorithms search for face contour response information. On the other hand, our proposed LCP algorithm produces results expressing major facial information by applying the threshold to the search area with only 8 bits. However, the LCP algorithm produces results that express major facial information with only 8-bits by applying a threshold value to the search area. Therefore, compared to previous approaches, the LCP algorithm maintains a consistent accuracy under varying circumstances, and produces a high face recognition rate with a relatively small feature vector. The test results indicate that the LCP algorithm produces a higher facial recognition rate than the rate of human visual's recognition capability, and outperforms the existing methods.

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

Security models for key exchange protocols have been researched for years, however, lots of them only focus on what secret can be compromised but they do not differentiate the timing of secrets compromise, such as the extended Canetti-Krawczyk (eCK) model. In this paper, we propose a new security model for key exchange protocols which can not only consider what keys can be compromised as well as when they are compromised. The proposed security model is important to the security proof of the key exchange protocols with forward secrecy (either weak forward secrecy (wFS) or strong forward secrecy (sFS)). In addition, a new kind of key compromise impersonation (KCI) attacks which is called strong key compromise impersonation (sKCI) attack is proposed. Finally, we provide a new one-round key exchange protocol called mOT+ based on mOT protocol. The security of the mOT+ is given in the new model. It can provide the properties of sKCI-resilience and sFS and it is secure even if the ephemeral key reveal query is considered.

Participatory sensing applications rely on recruiting appropriate participants to share their surrounding conditions with others, and have been widely used in many areas like environmental monitoring, health care, and traffic congestion monitoring, etc. In such applications, how to ensure the privacy of a participant is important, since incentive mechanisms are used to maintain their enthusiasm for sustainable participation by offering certain amount of reward. In this paper, we propose a pseudonym-based privacy protection scheme, that takes both privacy protection and user incentives into consideration. The proposed scheme uses the pseudonym mechanism and one-way hash function to achieve user incentives, while protecting their identity. We also show extensive analysis of the proposed scheme to demonstrate that it can meet the security and performance the requirement of a participatory sensing application.

Fast IDentity Online(FIDO) supports biometric authentications in an online environment without transmitting biometric templates over the network. For a given FIDO client, the "Fuzzy Vault" securely stores biometric templates, houses additional biometric templates, and unlocks private keys via biometrics. The Fuzzy Vault has been extensively researched and some vulnerabilities have been discovered, such as brute force, correlation, and key inversions attacks. In this paper, we propose a simple fingerprint Fuzzy Vault for FIDO clients. By using the FIDO feature, a simple minutiae alignment, and point-to-point matching, our Fuzzy Vault provides a secure algorithm to combat a variety of attacks, such as brute force, correlation, and key inversions. Using a case study, we verified our Fuzzy Vault by using a publicly available fingerprint database. The results of our experiments show that the Genuine Acceptance Rate and the False Acceptance Rate range from 48.89% to 80% and from 0.02% to 0%, respectively. In addition, our Fuzzy Vault, compared to existing similar technologies, needed fewer attempts.