Future mobile communications face enormous challenges as traditional voice services are replaced with increasing mobile multimedia and data services. To address the vast data traffic volume and the requirement of user Quality of Experience (QoE) in the next generation mobile networks, it is imperative to develop efficient content distribution technique, aiming at significantly reducing redundant data transmissions and improving content delivery performance. On the other hand, in recent years cloud computing as a promising new content-centric paradigm is exploited to fulfil the multimedia requirements by provisioning data and computing resources on demand. In this paper, we propose a cooperative caching framework which implements State based Content Distribution (SCD) algorithm for future mobile networks. In our proposed framework, cloud service providers deploy a plurality of cloudlets in the network forming a Distributed Cloud Service Network (DCSN), and pre-allocate content services in local cloudlets to avoid redundant content transmissions. We use content popularity and content state which is determined by content requests, editorial updates and new arrivals to formulate a content distribution optimization model. Data contents are deployed in local cloudlets according to the optimal solution to achieve the lowest average content delivery latency. We use simulation experiments to validate the effectiveness of our proposed framework. Numerical results show that the proposed framework can significantly improve content cache hit rate, reduce content delivery latency and outbound traffic volume in comparison with known existing caching strategies.

This paper investigates the traffic offloading over unlicensed bands for two-tier multi-mode small cell networks. We formulate this problem as a Stackelberg game and apply a hierarchical learning framework to jointly maximize the utilities of both macro base station (MBS) and small base stations (SBSs). During the learning process, the MBS behaves as a leader and the SBSs are followers. A pricing mechanism is adopt by MBS and the price information is broadcasted to all SBSs by MBS firstly, then each SBS competes with other SBSs and takes its best response strategies to appropriately allocate the traffic load in licensed and unlicensed band in the sequel, taking the traffic flow payment charged by MBS into consideration. Then, we present a hierarchical Q-learning algorithm (HQL) to discover the Stackelberg equilibrium. Additionally, if some extra information can be obtained via feedback, we propose an improved hierarchical Q-learning algorithm (IHQL) to speed up the SBSs' learning process. Last but not the least, the convergence performance of the proposed two algorithms is analyzed. Numerical experiments are presented to validate the proposed schemes and show the effectiveness.

Cloud computing has become a new paradigm by enabling on-demand provisioning of applications, platforms or computing resources for clients. Workflow scheduling has always been treated as one of the most challenging problems in clouds. Commercial clouds have been widely used in scientific research, such as biology, astronomy and weather forecasting. Certainly, it is very important for a cloud service provider to pursue the profits for the commercial essence of clouds. This is also significantly important for the case of providing services to workflow tasks. In this paper, we address the issues of workflow scheduling in commercial clouds. This work takes the communication into account, which has always been ignored. And then, a topology-based workflow-scheduling algorithm named Resource Auction Algorithm (REAL) is proposed in the objective of getting more profits. The algorithm gives a good performance on searching for the optimum schedule for a sample workflow. Also, we find that there exists a certain resource amount, which gets the most profits to help us get more enthusiasm for further developing the research. Experimental results demonstrate that the analysis of the strategies for most profits is reasonable, and REAL gives a good performance on efficiently getting an optimized scheme with low computing complexity.

The femtocell network, which is designed for low power transmission and consists of consumer installed small base stations, coexists with macrocells to exploit spatial reuse gain. For its realization, cross-tier interference mitigation is an important issue. To solve this problem, we propose a joint access and power control scheme that requires limited information exchange between the femto and macro networks. Our objective is to maximize the network throughput while satisfying each user's quality of service (QoS) requirement. To accomplish this, we first introduce two distributed interference detection schemes, i.e., the femto base station and macro user equipment based schemes. Then, the proposed scheme dynamically adjusts the transmission power and makes a decision on the access mode of each femto base station. Through extensive simulations, we show that the proposed scheme outperforms earlier works in terms of the throughput and outage probability.

Distinguished with traditional strategies which offload an application's computation to a single server, parallel computation offloading can promote the performance by simultaneously delivering the computation to multiple computing resources around the mobile terminal. However, due to the variability of communication and computation environments, static application component multi-partitioning algorithms are difficult to maintain the optimality of their solutions in time-varying scenarios, whereas, over-frequent algorithm executions triggered by changes of environments may bring excessive algorithm costs. To this end, an adaptive application component mapping algorithm for parallel computation offloading in variable environments is proposed in this paper, which aims at minimizing computation costs and inter-resource communication costs. It can provide the terminal a suitable solution for the current environment with a low incremental algorithm cost. We represent the application component multi-partitioning problem as a graph mapping model, then convert it into a pathfinding problem. A genetic algorithm enhanced by an elite-based immigrants mechanism is designed to obtain the solution adaptively, which can dynamically adjust the precision of the solution and boost the searching speed as transmission and processing speeds change. Simulation results demonstrate that our algorithm can promote the performance efficiently, and it is superior to the traditional approaches under variable environments to a large extent.

The concept of Internet of Things (IoT) has shed new light on WSN technologies. MAC protocol issues improving the network performance are important in WSNs because of the increase in demand for various applications to secure spectrum resources. Cognitive radio (CR) technology is regarded as a solution to the problems in this future wireless network. In recent years, energy efficiency has become an issue in CR networks. However, few relevant studies have been conducted. In this paper, an energy-efficient non-overlapping channel MAC (ENC-MAC) for CR-enabled sensor networks (CRSNs) is proposed. Applying the dedicated control channel approach, ENC-MAC allows the SUs to utilize channels in a non-overlapping manner, and thus spectrum efficiency is improved. Moreover, the cooperative spectrum sensing that allows an SU to use only two minislots in the sensing phase is addressed to en-hance energy efficiency. In addition, an analytical model for evaluating the performance, such as saturation throughput, average packet delay, and network lifetime, is developed. It is shown in our results that ENC-MAC remarkably outperforms existing MAC protocols.

The energy-efficient design of sensing-based spectrum sharing of a multi-input and multi-output (MIMO) cognitive radio (CR) system with imperfect multiple antenna spectrum sensing is investigated in this study. Optimal resource allocation strategies, including sensing time and power allocation schemes, are studied to maximize the energy efficiency (EE) of the secondary base station under the transmit power and interference power constraints. EE problem is formulated as a nonlinear stochastic fractional programming of a nonconvex optimal problem. The EE problem is transformed into its equivalent nonlinear parametric programming and solved by one-dimension search algorithm. To reduce searching complexity, the search range was founded by demonstration. Furthermore, simulation results confirms that an optimal sensing time exists to maximize EE, and shows that EE is affected by the spectrum detection factors and corresponding constraints.

Different network provides different service. To maximize the profit, heterogeneous networks form a whole, which may either compete or cooperate with each other. In this paper, the healthcare monitor network architecture is introduced to build the competitive and cooperative mechanisms of heterogeneous networks which contain three networks, namely, cellular network, WLAN and WMAN. This paper considers the natural growth rate of the network with competitive and cooperative effects. Then, the stability of the proposed model and its equilibrium points are analyzed by the ordinary differential principle. Finally, simulation results show that the natural growth rate cannot increase the profit of the network, but effective cooperative among heterogeneous networks can increase the profit of each network, and competitive may decrease the profit of each network.

The use of an efficient packet scheduling scheme for a forward link in satellite communication networks is very important to support fairness for each return channel satellite terminal (RCST) and the service differentiations for user traffics. To support fairness and QoS for each RCST with service-level agreement (SLA), the Adaptive Coding and Modulation (ACM) system in a satellite hub has to process packets with considering modulation and coding (MODCOD) and packet types. Although a DVB-S2 system with ACM scheme has higher transmission efficiency, it cannot offer fairness or quality of service (QoS) to RCSTs. Because the data are transmitted with high MODCOD in regions with clear skies, while data are transmitted using low MODCOD in regions experiencing rain events. In this paper, we propose a two-step scheduling scheme offering fairness and QoS to RCSTs, while minimizing a decrease in throughput. The proposed scheme is carried out performance evaluations using a computer simulation. As results of this simulation, the proposed scheduler was shown to support bandwidth fairness to an individual RCST, and provide a level of QoS differentiation for user traffics.

The IEEE 802.15.6 standard is introduced to satisfy all the requirements for monitoring systems operating in, on, or around the human body. In this paper, analytical models are developed for evaluating the performance of the IEEE 802.15.6 CSMA/CA-based medium access control protocol for wireless body area networks (WBAN) under unsaturation condition. We employ a three-dimensional Markov chain to model the backoff procedure, and an M/G/1/K queuing system to describe the packet queues in the buffer. The throughput and delay performances of WBAN operating in the beacon mode are analyzed in heterogeneous network comprised of different user priorities. Simulation results are included to demonstrate the accuracy of the proposed analytical model.

This paper investigates optimization-based base station (BS) placement. An optimization model is defined and the BS placement problem is transformed to a lexicographical stratified programming (LSP) model for a given trajectory, according to different accuracy requirements. The feasible region for BS deployment is obtained from the positioning system requirement, which is also solved with signal coverage problem in BS placement. The LSP mathematical model is formulated with the average geometric dilution of precision (GDOP) as the criterion. To achieve an optimization solution, a tolerant factor based complete stratified series approach and grid searching method are utilized to obtain the possible optimal BS placement. Because of the LSP model utilization, the proposed algorithm has wider application scenarios with different accuracy requirements over different trajectory segments. Simulation results demonstrate that the proposed algorithm has better BS placement result than existing approaches for a given trajectory.

In this paper, we investigate the outage performance of a two-way decode-and-forward relaying network in an interference-limited Nakagami-m fading environment. More specifically, assuming the presence of Nakagami-m faded multiple co-channel interferers at the source/destination terminals, the closed-form approximate expression for the outage probability is derived by using moment-based estimators attaining the appropriate Nakagami-m fading parameter. Simulation results demonstrate that our analytical result is in excellent agreement with the Monte Carlo simulation.

In this paper, we investigate a security transmission scheme at the physical layer for cooperative wireless relay networks in the presence of a passive eavesdropper. While the security scheme has been previously investigated with perfect channel state information(CSI) in the presence of a passive eavesdropper, this paper focuses on researching the robust cooperative relay beamforming mechanism for wireless relay networks which makes use of artificial noise (AN) to confuse the eavesdropper and increase its uncertainty about the source message. The transmit power used for AN is maximized to degrade the signal-to-interference-plus-noise-ratio (SINR) level at the eavesdropper, while satisfying the individual power constraint of each relay node and worst-case SINR constraint at the desired receiver under a bounded spherical region for the norm of the CSI error vector from the relays to the destination. Cooperative beamforming weight vector in the security scheme can be obtained by using S-Procedure and rank relaxation techniques. The benefit of the proposed scheme is showed in simulation results.

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the "divide-conquer" strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

Hypertext Transfer Protocol (HTTP) adaptive streaming has become a new trend in video delivery. An HTTP adaptive streaming client needs to effectively estimate resource availability and demand. However, due to the bitrate of the video encoded in variable bitrate (VBR) mode, a bitrate mismatch problem occurs. With the rising demand for mobile devices, the likelihood of cases where two or more HTTP adaptive streaming clients share the same network bottleneck and competing for available bandwidth will increase. These mismatch and competition issues lead to network congestion, which adversely affects the Quality of Experience (QoE). To solve these problem, we propose a video rate adaptation scheme for the HTTP video streaming to guarantee and optimize the QoE. The proposed scheme estimates the available bandwidth according to the bitrate of each segment and also schedules the segment request time to expedite the response to the bandwidth variation. We used a multi-client simulation to prove that our scheme can effectively cope with drastic changes in the connection throughput and video bitrate.

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.

Dynamic texture (DT) recognition is a challenging problem in numerous applications. In this study, we propose a new algorithm for DT recognition based on group sparsity structure in conjunction with chaotic feature vector. Bag-of-words model is used to represent each video as a histogram of the chaotic feature vector, which is proposed to capture self-similarity property of the pixel intensity series. The recognition problem is then cast to a group sparsity model, which can be efficiently optimized through alternating direction method of multiplier algorithm. Experimental results show that the proposed method exhibited the best performance among several well-known DT modeling techniques.

The traditional feature extraction algorithms for recognition of communication signals can hardly realize the balance between computational complexity and signals' interclass gathered degrees. They can hardly achieve high recognition rate at low SNR conditions. To solve this problem, a novel feature extraction algorithm based on Holder coefficient was proposed, which has the advantages of low computational complexity and good interclass gathered degree even at low SNR conditions. In this research, the selection methods of parameters and distribution properties of the extracted features regarding Holder coefficient theory were firstly explored, and then interval gray relation algorithm with improved adaptive weight was adopted to verify the effectiveness of the extracted features. Compared with traditional algorithms, the proposed algorithm can more accurately recognize signals at low SNR conditions. Simulation results show that Holder coefficient based features are stable and have good interclass gathered degree, and interval gray relation classifier with adaptive weight can achieve the recognition rate up to 87% even at the SNR of -5dB.

Video streaming services make up a large proportion of Internet traffic on both fixed and mobile access throughout the world. Adaptive streaming allows for dynamical adaptation of the bitrate with varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput as it varies widely over time. In this paper, we first evaluate the throughput estimation techniques and show that the method that we have used offers stable response to throughput fluctuations while maintaining a stable playback buffer. Then, we propose an adaptive bitrate scheme that intelligently selects the video bitrates based on the estimated throughput and buffer occupancy. We show that the proposed scheme improves viewing experience by achieving a high video rate without taking unnecessary risks and by minimizing the frequency of changes in the video quality. Furthermore, we show that it offers a stable response to short-term fluctuations and responds swiftly to large fluctuations. We evaluate our algorithm for both constant bitrate (CBR) and variable bitrate (VBR) video content by taking into account the segment sizes and show that it significantly improves the quality of video streaming.

It is not easy to reconstruct the geometrical characteristics of the distorted images captured by the devices. One of the most popular optimization methods is fast iterative shrinkage/ thresholding algorithm. In this paper, to deal with its approximation error and the turbulence of the decrease process, an adaptive proximal conjugate gradient (APCG) framework is proposed. It contains three stages. At first stage, a series of adaptive penalty matrices are generated iterate-to-iterate. Second, to trade off the reconstruction accuracy and the computational complexity of the resulting sub-problem, a practical solution is presented, which is characterized by solving the variable ellipsoidal-norm based sub-problem through exploiting the structure of the problem. Third, a correction step is introduced to improve the estimated accuracy. The numerical experiments of the proposed algorithm, in comparison to the favorable state-of-the-art methods, demonstrate the advantages of the proposed method and its potential.

This paper considers physical-layer security protocols in multicast cognitive radio (CR) networks. In particular, we propose dual-hop cooperative decode-and-forward (DF) and randomize-and-forward (RF) schemes using partial relay selection method to enhance secrecy performance for secondary networks. In the DF protocol, the secondary relay would use same codebook with the secondary source to forward the source's signals to the secondary destination. Hence, the secondary eavesdropper can employ either maximal-ratio combining (MRC) or selection combining (SC) to combine signals received from the source and the selected relay. In RF protocol, different codebooks are used by the source and the relay to forward the source message secretly. For each scheme, we derive exact and asymptotic closed-form expressions of secrecy outage probability (SOP), non-zero secrecy capacity probability (NzSCP) in both independent and identically distributed (i.i.d.) and independent but non-identically distributed (i.n.i.d.) networks. Moreover, we also give a unified formula in an integral form for average secrecy capacity (ASC). Finally, our derivations are then validated by Monte-Carlo simulations.

Wireless sensor networks are often organized in the form of clusters leading to the new framework of WSN called cluster or hierarchical WSN where each cluster head is responsible for its own cluster and its members. These hierarchical WSN are prone to various routing layer attacks such as Black hole, Gray hole, Sybil, Wormhole, Flooding etc. These routing layer attacks try to spoof, falsify or drop the packets during the packet routing process. They may even flood the network with unwanted data packets. If one cluster head is captured and made malicious, the entire cluster member nodes beneath the cluster get affected. On the other hand if the cluster member nodes are malicious, due to the broadcast wireless communication between all the source nodes it can disrupt the entire cluster functions. Thereby a scheme which can detect both the malicious cluster member and cluster head is the current need. Abnormal energy consumption of nodes is used to identify the malicious activity. To serve this purpose a learning based energy prediction algorithm is proposed. Thus a two level energy prediction based intrusion detection scheme to detect the malicious cluster head and cluster member is proposed and simulations were carried out using NS2-Mannasim framework. Simulation results achieved good detection ratio and less false positive.

Currently, the leakage of internal information has emerged as one of the most significant security concerns in enterprise computing environments. Especially, damage due to internal information leakage by insiders is more serious than that by outsiders because insiders have considerable knowledge of the system's identification and password (ID&P/W), the security system, and the main location of sensitive data. Therefore, many security companies are developing internal data leakage prevention techniques such as data leakage protection (DLP), digital right management (DRM), and system access control, etc. However, these techniques cannot effectively block the leakage of internal information by insiders who have a legitimate access authorization. The security system does not easily detect cases which a legitimate insider changes, deletes, and leaks data stored on the server. Therefore, we focused on the insider as the detection target to address this security weakness. In other words, we switched the detection target from objects (internal information) to subjects (insiders). We concentrated on biometrics signals change when an insider conducts abnormal behavior. When insiders attempt to leak internal information, they appear to display abnormal emotional conditions due to tension, agitation, and anxiety, etc. These conditions can be detected by the changes of biometrics signals such as pulse, temperature, and skin conductivity, etc. We carried out experiments in two ways in order to verify the effectiveness of the emotional recognition technology based on biometrics signals. We analyzed the possibility of internal information leakage detection using an emotional recognition technology based on biometrics signals through experiments.

Broadcast encryption is an efficient way to distribute confidential information to a set of receivers using broadcast channel. It allows the broadcaster to dynamically choose the receiver set during each encryption. However, most broadcast encryption schemes in the literature haven't taken into consideration the receiver's privacy protection, and the scanty privacy preserving solutions are often less efficient, which are not suitable for practical scenarios. In this paper, we propose an efficient dynamic anonymous broadcast encryption scheme that has the shortest ciphertext length. The scheme is constructed over the composite order bilinear groups, and adopts the Lagrange interpolation polynomial to hide the receivers' identities, which yields efficient decryption algorithm. Security proofs show that, the proposed scheme is both secure and anonymous under the threat of adaptive adversaries in standard model.

A Mobile Ad hoc Network (MANET) consists of mobile nodes which co-operate to forward each other's packets without the presence of any centralized authority. Due to this lack of centralized monitoring authority, MANETs have become vulnerable to various kinds of routing misbehaviour. Sometimes, nodes exhibit non-cooperating behaviour for conserving their own resources and exploiting others' by relaying their traffic. A node may even drop packets of other nodes in the guise of forwarding them. This paper proposes an efficient Reputation and Collaboration technique through route-request for handling such misbehaving nodes. It lays emphasis not only on direct observation but also considers the opinion of other nodes about misbehaving nodes in the network. Unlike existing schemes which generate separate messages for spreading second-hand information in the network, nodes purvey their opinion through route-request packet. Simulation studies reveal that the proposed scheme significantly improves the network performance by efficiently handling the misbehaving nodes in the network.

Proper management on user-driven virtual circuits (VCs) is essential for seamless operation of virtual networks. The Network Provisioning System (NPS) is useful software for creating user-driven VCs automatically and must take fault management into account for physical layer impairments on user-driven VCs. This paper addresses a user-driven and trusty protection management in an NPS with an open standard Network Service Interface (NSI), as a contribution to show how to implement the user-driven and trusty protection management required for user-driven VCs. In particular, it provides a RESTful web service Interface for Configuration and Event management (RICE) that enable management of a distinguished data and control plane VC status between Network Service Agents (NSAs) in the event of a node or link fault and repair in a domain. This capability represents a contribution to show how network and protection events in a domain can be monitored between NSAs (NPSs with the NSI) in multiple domains. The implemented NPS controls and manages both the primary and backup VC with disjoint path in a user-driven manner. A demonstration to verify RICE API's capability is addressed for the trusty protection in the dynamic VC network.