A Novel Utility Function for Energy-Efficient Power Control Game in Cognitive Radio Networks

Spectrum scarcity is a major challenge in wireless communications systems requiring efficient usage and utilization. Cognitive radio network (CRN) is found as a promising technique to solve this problem of spectrum scarcity. It allows licensed and unlicensed users to share the same licensed spectrum band. Interference resulting from cognitive radios (CRs) has undesirable effects on quality of service (QoS) of both licensed and unlicensed systems where it causes degradation in received signal-to-noise ratio (SIR) of users. Power control is one of the most important techniques that can be used to mitigate interference and guarantee QoS in both systems. In this paper, we develop a new approach of a distributed power control for CRN based on utility and pricing. QoS of CR user is presented as a utility function via pricing and a distributed power control as a non-cooperative game in which users maximize their net utility (utility-price). We define the price as a real function of transmit power to increase pricing charge of the farthest CR users. We prove that the power control game proposed in this study has Nash Equilibrium as well as it is unique. The obtained results show that the proposed power control algorithm based on a new utility function has a significant reduction in transmit power consumption and high improvement in speed of convergence.     

AUI&GIV: Recommendation with Asymmetric User Influence and Global Importance Value

The user-based collaborative filtering (CF) algorithm is one of the most popular approaches for making recommendation. Despite its success, the traditional user-based CF algorithm suffers one serious problem that it only measures the influence between two users based on their symmetric similarities calculated by their consumption histories. It means that, for a pair of users, the influences on each other are the same, which however may not be true. Intuitively, an expert may have an impact on a novice user but a novice user may not affect an expert at all. Besides, each user may possess a global importance factor that affects his/her influence to the remaining users. To this end, in this paper, we propose an asymmetric user influence model to measure the directed influence between two users and adopt the PageRank algorithm to calculate the global importance value of each user. And then the directed influence values and the global importance values are integrated to deduce the final influence values between two users. Finally, we use the final influence values to improve the performance of the traditional user-based CF algorithm. Extensive experiments have been conducted, the results of which have confirmed that both the asymmetric user influence model and global importance value play key roles in improving recommendation accuracy, and hence the proposed method significantly outperforms the existing recommendation algorithms, in particular the user-based CF algorithm on the datasets of high rating density.     

Adaptive Packet-size Control for Improved Throughput in Dynamic Access Networks

Cluster Computing - Cognitive radio (CR) is a new intelligent wireless technology that aims at improving spectrum utilization by allowing opportunistic access to the underutilized licensed...     

Modeling and Assessing Variability in Energy Consumption During the Use Stage of Online Multimedia Services

In this study, we use an improved, more accurate model to analyze the energy footprint of content downloaded from a major online newspaper by means of various combinations of user devices and access networks. Our results indicate that previous analyses based on average figures for laptops or desktop personal computers predict national and global energy consumption values that are unrealistically high. Additionally, we identify the components that contribute most of the total energy consumption during the use stage of the life cycle of digital services. We find that, depending on the type of user device and access network employed, the data center where the news content originates consumes between 4% and 48% of the total energy consumption when news articles are read and between 2% and 11% when video content is viewed. Similarly, we find that user devices consume between 7% and 90% and 0.7% and 78% for articles and video content, respectively, depending on the type of user device and access network that is employed. Though increasing awareness of the energy consumption by data centers is justified, an analysis of our results shows that for individual users of the online newspaper we studied, energy use by user devices and the third‐generation (3G) mobile network are usually bigger contributors to the service footprint than the datacenters. Analysis of our results also shows that data transfer of video content has a significant energy use on the 3G mobile network, but less so elsewhere. Hence, a strategy of reducing the resolution of video would reduce the energy footprint for individual users who are using mobile devices to access content by the 3G network.     

Predicting Positive and Negative Relationships in Large Social Networks

In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers'' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.     

Two-Level Scheduling for Video Transmission over Downlink OFDMA Networks

This paper presents a two-level scheduling scheme for video transmission over downlink orthogonal frequency-division multiple access (OFDMA) networks. It aims to maximize the aggregate quality of the video users subject to the playback delay and resource constraints, by exploiting the multiuser diversity and the video characteristics. The upper level schedules the transmission of video packets among multiple users based on an overall target bit-error-rate (BER), the importance level of packet and resource consumption efficiency factor. Instead, the lower level renders unequal error protection (UEP) in terms of target BER among the scheduled packets by solving a weighted sum distortion minimization problem, where each user weight reflects the total importance level of the packets that has been scheduled for that user. Frequency-selective power is then water-filled over all the assigned subcarriers in order to leverage the potential channel coding gain. Realistic simulation results demonstrate that the proposed scheme significantly outperforms the state-of-the-art scheduling scheme by up to 6.8 dB in terms of peak-signal-to-noise-ratio (PSNR). Further test evaluates the suitability of equal power allocation which is the common assumption in the literature.     

Isolation of a single carboxyl-carboxylate proton binding site in the pore of a cyclic nucleotide-gated channel.

The pore of the catfish olfactory cyclic nucleotide-gated (CNG) channel contains four conserved glutamate residues, one from each subunit, that form a high-affinity binding site for extracellular divalent cations. Previous work showed that these residues form two independent and equivalent high-pKa (approximately 7.6) proton binding sites, giving rise to three pH-dependent conductance states, and it was suggested that the sites were formed by pairing of the glutamates into two independent carboxyl-carboxylates. To test further this physical picture, wild-type CNG subunits were coexpressed in Xenopus oocytes with subunits lacking the critical glutamate residue, and single channel currents through hybrid CNG channels containing one to three wild-type (WT) subunits were recorded. One of these hybrid channels had two pH-dependent conductance states whose occupancy was controlled by a single high-pKa protonation site. Expression of dimers of concatenated CNG channel subunits confirmed that this hybrid contained two WT and two mutant subunits, supporting the idea that a single protonation site is made from two glutamates (dimer expression also implied the subunit makeup of the other hybrid channels). Thus, the proton binding sites in the WT channel occur as a result of the pairing of two glutamate residues. This conclusion places these residues in close proximity to one another in the pore and implies that at any instant in time detailed fourfold symmetry is disrupted.     

Access Point Selection Game with Mobile Users Using Correlated Equilibrium

One of the most important issues in wireless local area network (WLAN) systems with multiple access points (APs) is the AP selection problem. Game theory is a mathematical tool used to analyze the interactions in multiplayer systems and has been applied to various problems in wireless networks. Correlated equilibrium (CE) is one of the powerful game theory solution concepts, which is more general than the Nash equilibrium for analyzing the interactions in multiplayer mixed strategy games. A game-theoretic formulation of the AP selection problem with mobile users is presented using a novel scheme based on a regret-based learning procedure. Through convergence analysis, we show that the joint actions based on the proposed algorithm achieve CE. Simulation results illustrate that the proposed algorithm is effective in a realistic WLAN environment with user mobility and achieves maximum system throughput based on the game-theoretic formulation.     

Proxy based seamless connection management method in mobile cloud computing

Nowadays, complex smartphone applications are developed that support gaming, navigation, video editing, augmented reality, and speech recognition which require considerable computational power and battery lifetime. The cloud computing provides a brand new opportunity for the development of mobile applications. Mobile Hosts (MHs) are provided with data storage and processing services on a cloud computing platform rather than on the MHs. To provide seamless connection and reliable cloud service, we are focused on communication. When the connection to cloud server is increased explosively, each MH connection quality has to be declined. It causes several problems: network delay, retransmission, and so on. In this paper, we propose proxy based architecture to improve link performance for each MH in mobile cloud computing. By proposed proxy, the MH need not keep connection of the cloud server because it just connected one of proxy in the same subnet. And we propose the optimal access network discovery algorithm to optimize bandwidth usage. When the MH changes its point of attachment, proposed discovery algorithm helps to connect the optimal access network for cloud service. By experiment result and analysis, the proposed connection management method has better performance than the 802.11 access method.     

A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks

Local access networks (LAN) are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower’s problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach.     

Knapsack - TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network

In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay.     

Characteristics and needs of different user groups in the urban forest of Stuttgart

In the urban forest of Stuttgart the forest roads are used by a growing number of people for recreational and leisure activities. To manage and channel the different user groups within an optimised forest road network needs a lot of information: Number and composition of visitors, demands of different user groups concerning standard of roads and trails they use for their activities, conflicts that may rise between different users using the same road at the same time, dedication of roads to special activities (e.g. fitness trail, hiking trail).

A relatively new method of video monitoring was used to collect long-term information about number and composition of visitors, personal interviews with “experts” were made to obtain information about the specific demands of the different user groups, and analysis of road network was done with the help of GIS. The results show that these tools complement one another quite well and the combination of obtained data may help to channel visitor flows and to minimise conflicts between different user groups.     

Control of neural synchrony using channelrhodopsin-2: a computational study

In this paper, we present an optical stimulation based approach to induce 1:1 in-phase synchrony in a network of coupled interneurons wherein each interneuron expresses the light sensitive protein channelrhodopsin-2 (ChR2). We begin with a transition rate model for the channel kinetics of ChR2 in response to light stimulation. We then define "functional optical time response curve (fOTRC)" as a measure of the response of a periodically firing interneuron (transfected with ChR2 ion channel) to a periodic light pulse stimulation. We specifically consider the case of unidirectionally coupled (UCI) network and propose an open loop control architecture that uses light as an actuation signal to induce 1:1 in-phase synchrony in the UCI network. Using general properties of the spike time response curves (STRCs) for Type-1 neuron model (Ermentrout, Neural Comput 8:979-1001, 1996) and fOTRC, we estimate the (open loop) optimal actuation signal parameters required to induce 1:1 in-phase synchrony. We then propose a closed loop controller architecture and a controller algorithm to robustly sustain stable 1:1 in-phase synchrony in the presence of unknown deviations in the network parameters. Finally, we test the performance of this closed-loop controller in a network of mutually coupled (MCI) interneurons.     

Two Types of Well Followed Users in the Followership Networks of Twitter

In the Twitter blogosphere, the number of followers is probably the most basic and succinct quantity for measuring popularity of users. However, the number of followers can be manipulated in various ways; we can even buy follows. Therefore, alternative popularity measures for Twitter users on the basis of, for example, users'' tweets and retweets, have been developed. In the present work, we take a purely network approach to this fundamental question. First, we find that two relatively distinct types of users possessing a large number of followers exist, in particular for Japanese, Russian, and Korean users among the seven language groups that we examined. A first type of user follows a small number of other users. A second type of user follows approximately the same number of other users as the number of follows that the user receives. Then, we compare local (i.e., egocentric) followership networks around the two types of users with many followers. We show that the second type, which is presumably uninfluential users despite its large number of followers, is characterized by high link reciprocity, a large number of friends (i.e., those whom a user follows) for the followers, followers'' high link reciprocity, large clustering coefficient, large fraction of the second type of users among the followers, and a small PageRank. Our network-based results support that the number of followers used alone is a misleading measure of user''s popularity. We propose that the number of friends, which is simple to measure, also helps us to assess the popularity of Twitter users.     

Multi-level on-demand access control for flexible data sharing in cloud

The exponential growth of data storage and sharing in cloud demands an efficient access control mechanism for flexible data sharing. Attribute-Based Encryption (ABE) is a promising cryptographic solution to share data among users in the cloud. But it suffers from user revocation, attribute revocation, forward secrecy and backward secrecy issues. Communication and computation overhead is more due to the linear variation in the size of ciphertext and the secret key with respect to the number of attributes. In this paper, we investigate an on-demand access control for flexible sharing of secure data among randomly selected users. It is a tunable access control mechanism for the flexible sharing of ciphertext classes in the cloud. It delegates the decryption rights of any set of ciphertext classes among the users only if their attributes are satisfied with the access policy associated with ciphertext and if they should possess a compact key corresponding to the intended set of ciphertext classes. It produces a constant size ciphertext and a compact secret key to efficiently utilize the storage space and reduce the communication cost. The compact key aggregates the power of secret keys used to encrypt the outsourced data. This method flexibly shares the ciphertext classes among the randomly selected users with a specific set of attributes. All other ciphertext classes outside the set remain confidential. It allows dynamic data updates by verifying the data manipulation privilege of users with the help of claim policy. The proposed scheme provides access control of varying granularity, at user-level, at file-level, and attribute-level. Granularity levels can be chosen based on applications and user demands. Hence, it is a multi-level, tunable access control over the shared data. It is very useful for secure data storage. This scheme tackles user revocation and attribute revocation problems so that, it allows the data owner to revoke a specific user or a group of users. It prevents forward and backward secrecy issues.

     

Software Review of Risk*Assistant (Version 1.1) and RiskEZ (Version 1), both for Windows

RiskEZ and Risk*Assistant both make site risk assessment more accessible to many users and provide a relatively efficient means for performing screening level risk assessments. The programs perform basic site risk assessment calcu lations for users and provide point estimates of incremental individual cancer risks and non cancer hazard quotients following standard Environmental Pro tection Agency risk assessment guidance documents. Risk*Assistant allows the user to input raw data from which it can perform some dispersion modeling and calculate exposure point concentrations, while RiskEZ takes the exposure point concentrations as inputs. RiskEZ includes features that allow multiple network users to work on the same site and to deal with radioactive contami nants as well as chemical ones, while Risk*Assistant limits access to a site to one user at a time and it includes non radioactive substances. Neither program currently has the capability to perform probabilistic risk analyses, although Risk*Assistant allows the user to peform some sensitivity analysis. This review highlights differences between the programs and demonstrates how selection of a particular program might influence the results.     

Efficient chameleon hashing-based privacy-preserving auditing in cloud storage

Cloud storage is an important application service in cloud computing, it allows data users to store and access their files anytime, from anywhere and with any device. To ensure the security of the outsourced data, data user needs to periodically check data integrity. In some cases, the identity privacy of data user must be protected. However, in the existing preserving identity privacy protocols, data tag generation is mainly based on complex ring signature or group signature. It brings a heavy burden to data user. To ensure identity privacy of data user, in this paper we propose a novel identity privacy-preserving public auditing protocol by utilizing chameleon hash function. It can achieve the following properties: (1) the identity privacy of data user is preserved for cloud server; (2) the validity of the outsourced data is verified; (3) data privacy can be preserved for the auditor in auditing process; (4) computation cost to produce data tag is very low. Finally, we also show that our scheme is provably secure in the random oracle model, the security of the proposed scheme is related to the computational Diffie–Hellman problem and hash function problem.     

The pattern of polytene chromosome conjugation and crossing-over in interspecific hybrids of Drosophila

Summary The pairing of polytene chromosomes was investigated in the hybrids between three closely related species of Drosophila belonging to the virilis species group. It was found that within the same hybrid different chromosome bands lost the ability to pair by differing degrees. Furthermore, the same chromosome sections paired with different frequencies depending on the hybrid involved. This study revealed that poor polytene chromosome pairing in the hybrids is not due to specific genetic interaction in the hybrids, but depends solely on the properties of the homologous loci themselves. It was also of interest to find whether the pattern of polytene chromosome somatic pairing resembled in some way the picture of chromosome synapsis during meiosis. To obtain evidence for this, crossing-over in the hybrid 5th chromosome was analyzed both genetically and cytologically (from salivary gland chromosome observations). It was found that the sections of the fifth chromosome which were characterized by a high frequency of conjugation in the salivary glands of hybrids also exhibited a high frequency of crossing-over in hybrid females. It may be concluded that sections of the polytene chromosome characterized by a low frequency of conjugation behave in the same manner in meiosis, and thus rarely take part in genetic recombination.     

An Improvement of Robust Biometrics-Based Authentication and Key Agreement Scheme for Multi-Server Environments Using Smart Cards

In multi-server environments, user authentication is a very important issue because it provides the authorization that enables users to access their data and services; furthermore, remote user authentication schemes for multi-server environments have solved the problem that has arisen from user’s management of different identities and passwords. For this reason, numerous user authentication schemes that are designed for multi-server environments have been proposed over recent years. In 2015, Lu et al. improved upon Mishra et al.’s scheme, claiming that their remote user authentication scheme is more secure and practical; however, we found that Lu et al.’s scheme is still insecure and incorrect. In this paper, we demonstrate that Lu et al.’s scheme is vulnerable to outsider attack and user impersonation attack, and we propose a new biometrics-based scheme for authentication and key agreement that can be used in multi-server environments; then, we show that our proposed scheme is more secure and supports the required security properties.