A Nash–Stackelberg equilibrium model for internet and network service providers in the demand market—a scenario-based approach

This paper evaluates the optimal pricing for two Internet service providers and two network providers; all are competing on price, which is based on quality. To find the optimal prices of service and network providers and to determine optimal scenarios, a two-stage competition is modeled. In the first stage, network providers compete on market prices by setting the quality in four scenarios. At this stage, we found the equilibrium prices in the market. In the second stage, by obtaining market prices, service providers compete on network prices. Finally, the equilibrium solutions are compared with each other by considering the intensity of market competition in price and quality. It is shown that equilibrium never occurs in the case when the smaller service provider has a higher Internet quality than the other (scenario 2) and the more significant service provider offers a higher Internet quality (scenario 4). Besides, when both Internet service providers offer low-quality Internet (scenario 1) and high-quality Internet (scenario 3), the companies make the most profit. By increasing and decreasing the competition in quality, equilibrium would still exist for the first scenario, and the third scenario, respectively. The intensity of market competition in price behaves oppositely as quality.

     

Securing Wireless Sensor Networks Against Denial‐of‐Sleep Attacks Using RSA Cryptography Algorithm and Interlock Protocol

Wireless sensor networks (WSNs) have been vastly employed in the collection and transmission of data via wireless networks. This type of network is nowadays used in many applications for surveillance activities in various environments due to its low cost and easy communications. In these networks, the sensors use a limited power source which after its depletion, since it is non‐renewable, network lifetime ends. Due to the weaknesses in sensor nodes, they are vulnerable to many threats. One notable attack threating WSN is Denial of Sleep (DoS). DoS attacks denotes the loss of energy in these sensors by keeping the nodes from going into sleep and energy‐saving mode. In this paper, the Abnormal Sensor Detection Accuracy (ASDA‐RSA) method is utilized to counteract DoS attacks to reducing the amount of energy consumed. The ASDA‐RSA schema in this paper consists of two phases to enhancement security in the WSNs. In the first phase, a clustering approach based on energy and distance is used to select the proper cluster head and in the second phase, the RSA cryptography algorithm and interlock protocol are used here along with an authentication method, to prevent DoS attacks. Moreover, ASDA‐RSA method is evaluated here via extensive simulations carried out in NS‐2. The simulation results indicate that the WSN network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), network lifetime, detection ratio, and average residual energy.     

Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis

Accurate estimation of the thermal conductivity of nanofluids plays a key role in industrial heat transfer applications. Currently available experimental and empirical relationships can be used to estimate thermal conductivity. However, since the environmental conditions and properties of the nanofluids constituents are not considered these models cannot provide the expected accuracy and reliability for researchers. In this research, a robust hybrid artificial intelligence model was developed to accurately predict wide variety of relative thermal conductivity of nanofluids. In the new approach, the improved simulated annealing (ISA) was used to optimize the parameters of the least-squares support vector machine (LSSVM-ISA). The predictive model was developed using a data bank, consist of 1800 experimental data points for nanofluids from 32 references. The volume fraction, average size and thermal conductivity of nanoparticles, temperature and thermal conductivity of base fluid were selected as influent parameters and relative thermal conductivity was chosen as the output variable. In addition, the obtained results from the LSSVM-ISA were compared with the results of the radial basis function neural network (RBF-NN), K-nearest neighbors (KNN), and various existing experimental correlations models. The statistical analysis shows that the performance of the proposed hybrid predictor model for testing stage (R = 0.993, RMSE = 0.0207) is more reliable and efficient than those of the RBF-NN (R = 0.970, RMSE = 0.0416 W/m K), KNN (R = 0.931, RMSE = 0.068 W/m K) and all of the existing empirical correlations for estimating thermal conductivity of wide variety types of nanofluids. Finally, robustness and convergence analysis were conducted to evaluate the model reliability. A comprehensive sensitivity analysis using Monte Carlo simulation was carried out to identify the most significant variables of the developed models affecting the thermal conductivity predictions of nanofluids.

     

Scalable video traffic offloading for streaming services in 5G HetNets

Multimedia Tools and Applications - In this paper, by combining scalable video coding (SVC) and traffic offloading, we propose a scalable video traffic offloading (SVO) approach to provide video...     

An efficient contrast enhancement method using repulsive force of edges

Multidimensional Systems and Signal Processing - This paper presents a novel algorithm which employs edge information to automatically improve the quality of low contrast images. This algorithm...     

A Model to Create Organizational Value with Big Data Analytics

Value creation is a major factor not only in the sustainability of organizations but also in the maximization of profit, customer retention, business goals fulfillment, and revenue. When the value is intended to be created from Big Data scenarios, value creation entails being understood over a broader range of complexity. A question that arises here is how organizations can use this massive quantity of data and create business value? The present study seeks to provide a model for creating organizational value using Big Data Analytics (BDA). To this end, after reviewing the related literature and interviewing experts, the BDA-based organizational value creation model is developed. Accordingly, five hypotheses are formulated, and a questionnaire is prepared. Then, the respective questionnaire is given to the research statistical population (i.e., IT managers and experts, particularly those specializing in data analysis) to test the research hypotheses. In next phase, connections between model variables are scrutinized using the structural equation modeling (measurement and structural models). The results of the study indicate that investigating the infrastructures of the Big Data Analytics, as well as the capabilities of the organization and those of Big Data Analytics is the initial requirement to create organizational value using BDA. Thereby, the Big Data Analytics strategy is formulated, and ultimately, the organizational value is created as well.     

RMRL: improved regret minimisation techniques using learning automata

Game theory as one of the most progressive areas in AI in last few years originates from the same root as AI. The unawareness of the other players and their decisions in such incomplete-information problems, make it necessary to use some learning techniques to enhance the decision-making process. Reinforcement learning techniques are studied in this research; regret minimisation (RM) and utility maximisation (UM) techniques as reinforcement learning approaches are widely applied to such scenarios to achieve optimum solutions. In spite of UM, RM techniques enable agents to overcome the shortage of information and enhance the performance of their choices based on regrets, instead of utilities. The idea of merging these two techniques are motivated by iteratively applying UM functions to RM techniques. The main contributions are as follows; first, proposing some novel updating methods based on UM of reinforcement learning approaches for RM; the proposed methods refine RM to accelerate the regret reduction, second, devising different procedures, all relying on RM techniques, in a multi-state predator-prey problem. Third, how the approach, called RMRL, enhances different RM techniques in this problem is studied. Estimated results support the validity of RMRL approach comparing with some UM and RM techniques.     

Hybrid Touch/Tangible Spatial 3D Data Selection

We discuss spatial selection techniques for three‐dimensional datasets. Such 3D spatial selection is fundamental to exploratory data analysis. While 2D selection is efficient for datasets with explicit shapes and structures, it is less efficient for data without such properties. We first propose a new taxonomy of 3D selection techniques, focusing on the amount of control the user has to define the selection volume. We then describe the 3D spatial selection technique Tangible Brush, which gives manual control over the final selection volume. It combines 2D touch with 6‐DOF 3D tangible input to allow users to perform 3D selections in volumetric data. We use touch input to draw a 2D lasso, extruding it to a 3D selection volume based on the motion of a tangible, spatially‐aware tablet. We describe our approach and present its quantitative and qualitative comparison to state‐of‐the‐art structure‐dependent selection. Our results show that, in addition to being dataset‐independent, Tangible Brush is more accurate than existing dataset‐dependent techniques, thus providing a trade‐off between precision and effort.     

Token-based incentive mechanism for peer-to-peer video streaming networks

The Journal of Supercomputing - We consider the problem of live video streaming in peer-to-peer networks consisting of selfish peers aiming at maximizing peers’ individual utilities. We use...     

No-but-semantic-match: computing semantically matched xml keyword search results

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.