In-network caching is one of the most important issues in content centric networking (CCN), which may extremely influence the performance of the caching system. Although much work has been done for in-network caching scheme design in CCN, most of them have not addressed the multiple network attribute parameters jointly during caching algorithm design. Hence, to fill this gap, a new in-network caching based on grey relational analysis (GRA) is proposed. The authors firstly define two newly metric parameters named request influence degree (RID) and cache replacement rate, respectively. The RID indicates the importance of one node along the content delivery path from the view of the interest packets arriving. The cache replacement rate is used to denote the caching load of the node. Then combining hops a request traveling from the users and the node traffic, four network attribute parameters are considered during the in-network caching algorithm design. Based on these four network parameters, a GRA based in-network caching algorithm is proposed, which can significantly improve the performance of CCN. Finally, extensive simulation based on ndnSIM is demonstrated that the GRA-based caching scheme can achieve the lower load in the source server and the less average hops than the existing the betweeness (Betw) scheme and the ALWAYS scheme.

Various cognitive network technologies are developed rapidly. In the article, the power and spectrum allocation in multi-hop cognitive radio network (CRN) with linear topology is investigated. The overall goal is to minimize outage probability and promote spectrum utility, including total reward and fairness, while meeting the limits of total transmit power and interference threshold to primary user simultaneously. The problem is solved with convex optimization and artificial bee colony (ABC) algorithm jointly. Simulation shows that the proposed scheme not only minimizes outage probability, but also realizes a better use of spectrum.

In order to reveal the intrinsic properties of scientific collaboration networks, a new local-world evolution model on a scientific collaboration network is proposed by analysing the network growth mechanism. The act degree as the measurement of preferential attachment is taken, and the local-world information of nodes is taken into account. Analysis and simulation show that the node degree and the node strength obey the power-law distribution. Low average path length and high clustering coefficient are approved. Experiment indicates that the model can depict efficiently the topological structure and statistical characteristics of real-life scientific collaboration networks.

It is known that the social network is an excellent source for gathering the emotions of people. There are thousands of micro-blogs posted in every second and every micro-blog that may contain a variety of user’s emotions. The users’ collective emotional behaviors are with great impacts on today’s societies, so it is good to find groups for society management based on users’ emotional behavior. This article focuses on analyzing multivariate emotional behavior of users in social network and the goal is to cluster the users from a fully new perspective-emotions. The following tasks are completed: firstly, the multivariate emotion of Chinese micro-blog with vector is analyzed, and multivariate time series to describe the user’s emotional behavior are constructed. Seconedly, considering principal component analysis (PCA) in similarity and distance similarity, the similarity of the multivariate emotion time series is measured. The contribution could be summarized as follows: groups of users though different emotions in social network are discovered. The emotional fluctuation and intensity of users are considered as well. Experiment in clustering effectively illustrates the emotional behavior characteristics of the users in different groups.

As one promising technology for indoor coverage and service offloading from the conventional cellular networks, femtocells have attracted considerable attention in recent years. However, most of previous work are focused on resource allocation during the access period, and the backhaul involved resource allocation is seriously ignored. The authors studied the backhaul resource allocation in the wireless backhaul based two-tier heterogeneous networks (HetNets), in which cross-tier interference control during access period is jointly considered. Assuming that the macrocell base station (MBS) protects itself from interference by pricing the backhaul spectrum allocated to femtocells, a Stackelberg game is formulated to work on the joint utility maximization of the macrocell and femtocells subject to a maximum interference tolerance at the MBS. The closed-form expressions of the optimal strategies are obtained to characterize the Stackelberg equilibriums for the proposed games, and a backhaul spectrum payment selection algorithm with guaranteed convergence is proposed to implement the backhaul resource allocation for femtocell base stations (FBSs). Simulations are presented to demonstrate the Stackelberg equilibrium (SE) is obtained by the proposed algorithm and the proposed scheme is effective in backhaul resource allocation and macrocell protection in the spectrum-sharing HetNets.

In this paper, the problem of inter symbol interference (ISI) sparse channel estimation in wireless communication with the application of compressed sensing is investigated. However, smoothed L0 norm algorithm (SL0) has ‘notched effect’ due to the negative iterative gradient direction. Moreover, the property of continuous function in SL0 is not steep enough, which results in inaccurate estimations and low convergence. Afterwards, we propose the Lagrange multipliers as well as Newton method to optimize SL0 algorithm in order to obtain a more rapid and efficient signal reconstruction algorithm, improved smoothed L0 (ISL0). ISI channel estimation will have a direct effect on the performance of ISI equalizer at the receiver. So, we design a pre-filter model which with no considerable loss of optimality and do analyses of the equalization methods of the sparse multi-path channel. Real-time simulation results clearly show that the ISL0 algorithm can estimate the ISI sparse channel much better in both signal noise ratio (SNR) and compression levels. In the same channel conditions, ISL0 algorithm has been greatly improved when compared with the SL0 algorithm and other compressed-sensing algorithms.

The localization of multiple mobile terminals (MTs) is an encouraging paradigm of applications in wireless networks. Peer-to-peer communication between MTs facilitates the cooperative localization of multiple MTs. For sake of low complexity and high robustness, investigations often focus on the distributed algorithm in cooperative localization. However, the impact from position uncertainty of cooperative MT lacks of analysis in related works. A new distributed location model, as well as corresponding algorithm, is devised when considering both the distance measurement error and the position uncertainty of MTs, which is more judicious than the traditional model for distributed cooperative localization scenario. In addition, the performance of proposed algorithm is analyzed through Cramér-Rao lower bound (CRLB). Simulations indicate that the algorithm outperforms traditional methods in terms of accuracy and robustness.

A time domain designing method is proposed for discrete Fourier transform (DFT) modulated filter banks (DFT-FBs) for application in multi-carrier transceiver systems. Instead of using the time-reversed pair limitation between the transmitting /receiving filter pair, the receiving filters in the proposed filter banks are derived from transmitting filters in accordance with the Moore-Penrose generalized inverse matrix. It can be freely obtained to design the transmitting prototype filter, which mainly affects the level of spectral containment. Furthermore, the symbol error rate (SER) performance of the proposed filter bank based trans-multiplexer with one tap equalizer is investigated in ideal channel and multi-path channel environments respectively. Simulation shows that the proposed approach can achieve significant SER reductions when square root raised cosine (RRC) prototype filter is used for comparing with the orthogonal frequency division multiplexing (OFDM) and the general DFT-FBs based applications.

Although the medium access control (MAC) signaling has been well-defined in the 3rd generation partnership project (3GPP) long term evolution (LTE) specifications, the scheduling algorithm crucial to guarantee QoS performance, still remains as open issues. In this article, a traffic-based queue-aware scheduling (TQS) algorithm is proposed for evolved nodeB’s (eNB’s) MAC scheduler in 3GPP LTE broadband wireless networks. The proposed TQS is divided into three sub-algorithms: firstly, the authors propose a traffic model construction (TMC) algorithm which can construct a discrete-time Markov-modulated Poisson process (dMMPP) to represent each flow. Secondly, a newly traffic state estimation (TSE) algorithm is designed to obtain the queue’s analytical statistics. Thirdly, based on the derived results of TSE and the channel states, a scheduling action decision (SAD) algorithm is presented that can adaptively allocate bandwidth to flows by considering both queue states and spectrum efficiency. Simulation shows that the TMC and TSE algorithm can capture the fluctuation of traffic and queue accurately. Moreover, compared with a widely accepted traffic-based scheduling algorithm, the proposed TQS has better average queue length and overflow probability performance.

Punctured convolution codes (PCCs) have a lot of applications in modern communication system. The efficient way to search for best PCCs with longer constraint lengths is desired since the complexity of exhaustive search becomes unacceptable. An efficient search method to find PCCs is proposed and simulated. At first, PCCs’ searching problem is turned into an optimization problem through analysis of PCCs’ judging criteria, and the inefficiency to use pattern search (PS) for many local optimums is pointed out. The simulated annealing (SA) is adapted to the non-convex optimization problem to find best PCCs with low complexity. Simulation indicates that SA performs very well both in complexity and success ratio, and PCCs with memories varying from 9 to 12 and rates varying from 2/3 to 4/5 searched by SA are presented.

The data of online social network (OSN) is collected currently by the third party for various purposes. One of the problems in such practices is how to measure the privacy breach to assure users. The recent work on OSN privacy is mainly focus on privacy-preserving data publishing. However, the work on privacy metric is not systematic but mainly focus on the traditional datasets. Compared with the traditional datasets, the attribute types in OSN are more diverse and the tuple is relevant to each other. The retweet and comment make the graph character of OSN notably. Furthermore, the open application programming interfaces (APIs) and lower register barrier make OSN open environment, in which the background knowledge is more easily achieved by adversaries. This paper analyzes the background knowledge in OSN and discusses its characteristics in detail. Then a privacy metric model faces OSN background knowledge based on kernel regression is proposed. In particular, this model takes the joint attributes and link knowledge into consideration. The effect of different data distributions is discussed. The real world data set from weibo.com has been adopted. It is demonstrated that the privacy metric algorithm in this article is effective in OSN privacy evaluation. The prediction error is 30% lower than that of the work mentioned above

The author puts forward an integrated intrusion detection (ID) model based on artificial immune (IIDAI), a vaccination strategy based on the significance degree of genes and a method to generate initial memory antibodies with rough set (RS). IIDAI integrates two kinds of intrusion detection mode: misuse detection and anonymous detection. Misuse detection and anonymous detection are applied to detect the known and the unknown attacks, respectively. On the basis of IIDAI model, an ID algorithm is presented. Simulation shows that the IIDAI has better performance than traditional ID methods in feasibility and effectiveness. It is very prone to achieve a higher convergence rate by using the vaccination strategy. Moreover, RS can remove the redundancy attributes and increase the detection speed. It can also increase detection rate by applying the integrated method.

Path-oriented test case generation is in essence a constraint satisfaction problem (CSP) solved by search strategies, among which backtracking algorithms are widely used. In this article, the backtracking algorithm branch and bound (BB) is introduced to generate path-oriented test cases automatically. A model based on state space search is proposed to construct the search tree dynamically. The BB is optimized from two perspectives. Variable permutation with a heuristic rule to break ties is adopted for the branching operation, and interval computation with analysis on the monotony of branching conditions is utilized for the bounding operation. Empirical experiments show that the proposed method performs well with linear complexity, and reaches 100% coverage on some benchmark programs with an advantage over some static and dynamic algorithms.

In the article, an improved variational inference (VI) framework for learning finite Beta-Liouville mixture models (BLM) is proposed for proportional data classification and clustering. Within the VI framework, some non-linear approximation techniques are adopted to obtain the approximated variational object functions. Analytical solutions are obtained for the variational posterior distributions. Compared to the expectation maximization (EM) algorithm which is commonly used for learning mixture models, underfitting and overfitting events can be prevented. Furthermore, parameters and complexity of the mixture model (model order) can be estimated simultaneously. Experiment shows that both synthetic and real-world data sets are to demonstrate the feasibility and advantages of the proposed method.

In this article, multiple receiver effects in a non-line-of-sight (NLOS) ultraviolet (UV) communication system is studied. The idea of using multiple receivers for diversity reception is known as a practical, effective and widely applied technique in wireless communications. The current approach is to use multiple antennas at the receiver in order to improve the quality of the received signal. A method of modeling and simulation is proposed to depict the principle and feasibility of the multiple receiver adopted in UV communication. The study provides an insight to the channel characteristics and achievable capabilities of ultraviolet communication systems with multiple receivers. It provides guidelines for practical system design with discussions on trade off between the receiver gain and the additional cost.