Optimal Overbooking Mechanism in Data Plan Sharing

For mobile devices users, the inconvenience caused by the fixed data plans becomes a popular issue. With the help of personal hotspot (pH), however, the smartphone users can share network connection with other users nearby. In this paper a data plan sharing platform with reservation mechanism is proposed. The users on this platform can sell their surplus data plan traffic to make profit. A Markov decision process model was formulated and analyzed for data plan sharing platform revenue management through overbooking mechanism. The results of experiment and simulation show that the platform revenue has a considerable growth using this model.

An Availability Evaluation Method of Web Services Using Improved Grey Correlation Analysis with Entropy Difference and Weight

Web services is one of the basic network services, whose availability evaluation is of great significance to the promotion of users’ experience. This paper focuses on the problem of availability evaluation of Web services and proposes a method for availability evaluation of Web services using improved grey correlation analysis with entropy difference and weight (EWGCA).This method is based on grey correlation analysis, and use entropy difference to illustrate the changes of availability, set weight to quantize availability requirements of different operations or transactions in services. Through simulation experiment in high load scenarios for Web services, the experiment result shows that our method can realize hierarchical description and overall evaluation for availability of Web services accurately in the case of smaller test sample volumes or uncertain data even in the field of big data.

An Energy Efficient Random Walk Based Expanding Ring Search Method for Wireless Networks

Wireless networks generate large amount of data. It is important to design energy efficient data search method since power of wireless nodes is finite. Expanding Ring Search (ERS) is a data search technique exploring for targets progressively, which is widely used to locate destinations or information in wireless networks. The existing studies on improving the energy efficiency of ERS cannot work without positioning systems. In this paper, we combine the technique of random walk with ERS, and propose a random walk based expanding ring search method (RWERS) for large-scale wireless networks. RWERS can work without using positioning systems, and improve the energy efficiency of ERS by preventing each node from transmitting the same request more than once using the technique of random walk. We compare RWERS with the optimal ERS strategy and CERS in networks with various shapes of terrains. The simulation results show that RWERS decreases the energy cost by 50% without decreasing in success rate compared with ERS, and has twice the success rate of CERS when the network is sparse. RWERS can be applied to various shapes of terrains better compared with CERS.

A Distributed Strategy for Defensing Objective Function Attack in Large-scale Cognitive Networks

Most of existed strategies for defending OFA (Objective Function Attack)are centralized, only suitable for small-scale networks and stressed on the computation complexity and traffic load are usually neglected. In this paper, we pay more attentions on the OFA problem in large-scale cognitive networks, where the big data generated from the network must be considered and the traditional methods could be of helplessness. In this paper, we first analyze the interactive processes between attacker and defender in detail, and then a defense strategy for OFA based on differential game is proposed, abbreviated as DSDG. Secondly, the game saddle point and optimal defense strategy have proved to be existed simultaneously. Simulation results show that the proposed DSDG has a less influence on network performance and a lower rate of packet loss.More importantly, it can cope with the large range

An effective method for service components selection based on micro-canonical annealing considering dependability assurance

Distributed virtualization changes the pattern of building software systems. However, it brings some problems on dependability assurance owing to the complex social relationships and interactions between service components. The best way to solve the problems in a distributed virtualized environment is dependable service components selection. Dependable service components selection can be modeled as finding a dependable service path, which is a multiconstrained optimal path problem. In this paper, a service components selection method that searches for the dependable service path in a distributed virtualized environment is proposed from the perspective of dependability assurance. The concept of Quality of Dependability is introduced to describe and constrain software system dependability during dynamic composition. Then, we model the dependable service components selection as a multiconstrained optimal path problem, and apply the Adaptive Bonus-Penalty Microcanonical Annealing algorithm to find the optimal dependable service path. The experimental results show that the proposed algorithm has high search success rate and quick converges.

V2V Online Data Offloading Method Based on Vehicle Mobility

As people are accustomed to getting information in the vehicles,mobile data offloading through Vehicular Ad Hoc Networks(VANETs)becomes prevalent nowadays.However,the impacts caused by the vehicle mobility(such as the relative speed and direction between vehicles)have great effects on mobile data offloading.In this paper,a V2V online data offloading method is proposed based on vehicle mobility.In this mechanism,the network service process was divided into continuous and equal-sized time slots.Data were transmitted in a multicast manner for the sake of fairness.The data offloading problem was formalized to maximize the overall satisfaction of the vehicle users.In each time slot,a genetic algorithm was used to solve the maximizing problem to obtain a mobile data offloading strategy.And then,the performance of the algorithm was enhanced by improving the algorithm.The experiment results show that vehicle mobility has a great effect on mobile data offloading,and the mobile data offloading method proposed in the paper is effective.

Near-Optimal Transmission Optimization of Cache-Based SVC Vehicle Video

A problem of video streaming of scalable video coding(SVC)is studied in vehicular networks.To improve the performance of the video streaming services and alleviate the pressure on backhaul links,the small cell base stations(SBS)is proposed with caching ability to assist the content delivery.In this paper,it introduced the problem of joint optimization of caching strategy and transmission path in SBS cache cluster.An integer programming problem was formulated to maximize the average quality of experience.In order to obtain the globally optimal solution,the primal problem was first relaxed,then an adaptive algorithm was used based on the joint KKT condition,and the branch definition algorithm was applied.Extensive simulations were performed to demonstrate the efficiency of our proposed caching strategy.