Fuhong Lin

Steiner tree based optimal resource caching scheme in fog computing

Fog Computing is a new platform that can serve mobile devices in the local area. In Fog Computing, the resources need to be shared or cached in the widely deployed Fog clusters. In this paper, we propose a Steiner tree based caching scheme, in which the Fog servers, when caching resources, first produce a Steiner tree to minimize the total path weight (or cost) such that the cost of resource caching using this tree could be minimized. Then we give a running illustration to show how the Fog Computing works and we compare the traditional shortest path scheme with the proposed one. The outcome shows that the Steiner tree based scheme could work more efficiently.

A Security Differential Game Model for Sensor Networks in Context of the Internet of Things

A particular challenging problem in designing Internet of Things is that how to detect and prevent internal attacks, because all nodes try their best to save their limited network resource. So it is difficult to achieve optimal objectives simultaneously, game theory provides an appropriate tool. In this paper, we propose a non-cooperative differential game model, which allows all nodes to choose the optimal amount of network resource to invest in information security contingent upon the state of game. In our model, we specifically consider how the vulnerability of information and the potential loss from such vulnerability affects the optimal amount of resources that should be devoted to securing that information. In the paper, the optimal strategies of selfish nodes and malicious nodes are obtained respectively. The simulation results show that our game model has a good performance in stability of the probability that the selfish nodes discover the malicious nodes under the optimal strategies of the selfish and the malicious nodes.

A optimal power control strategy based on network wisdom in wireless networks

Abstract In this paper, the power control problem in wireless networks is investigated and a dynamic power control scheme, namely wisdom power control, is proposed. Wisdom power control is a process in which network nodes can accurately forecast their optimal transmission powers to get profit maximization, and a trade-off between minimum interference level and a desired transmission quality is obtained from the equilibrium of the game.

Novel Pre-pushing Scheme for Peer-Assisted Streaming Network based on Multi-leader Multi-follower Stackelberg Model

Peer-assisted streaming network plays an important role in people’s daily lives, for users can watch multimedia resources anywhere, anytime. In this kind of network, servers need pre-pushing certain resources to users for caching which can be further retrieved by other users, and then the network performance can be improved. A key design algorithm in pre-pushing scheme is that which user should be selected to be pushed and how to price to keep the effectiveness and fairness of this action. To address this issue, we turn to multi-leader multi-follower Stackelberg model which is a hierarchical game model dealing with the resource competing among leaders and followers. In our model, we set that users are leaders who own bandwidth and define the price of a unit amount of bandwidth. Servers are followers and each of them selects a best user for pushing according to leaders’ equilibrium results. We build utility functions for each server and user, respectively, and maximize them according to the built Stackelberg model. The numerical simulation demonstrates the effectiveness and fairness of our proposed pushing scheme. Further, the comparison between traditional pre-pushing scheme and our proposed one is simulated which also shows that our proposed one can make more payoffs.

Multi-objective Optimization of Resource Scheduling in Fog Computing Using an Improved NSGA-II

In conventional cloud computing technology, cloud resources are provided centrally by large data centers. For the exponential growth of cloud users, some applications, such as health monitoring and emergency response with the requirements of real-time and low-latency, cannot achieve efficient resource support. Therefore, fog computing technology has been proposed, where cloud services can be extended to the edge of the network to decrease the network congestion. In fog computing, the idle resources within many distributed devices can be used for providing services. An effective resource scheduling scheme is important to realize a reasonable management for these heterogeneous resources. Therefore, in this paper, a two-level resource scheduling model is proposed. In addition, we design a resource scheduling scheme among fog nodes in the same fog cluster based on the theory of the improved non-dominated sorting genetic algorithm II (NSGA-II), which considers the diversity of different devices. MATLAB simulation results show that our scheme can reduce the service latency and improve the stability of the task execution effectively.

Research on Multicast Routing in Deep Space Sensor Network

In recent years, with the progress and development of human society, the needs of extraterrestrial energy exploration and solar living space development has become more and more urgent. Deep Space Sensor Network has a unique conditions and technical advantages in deep space exploration due to its characteristics of low power, low cost, distribution and self-organization. Multicast routing protocol of Deep Space Sensor Network based on the architecture of Delay and Disruption Tolerant Networks is the focus of work. A new multicast routing protocol named MPDSSN is proposed in this paper. At last, after setting simulation environment and parameters, the author use NS2 simulation tool to simulate MPDSSN. Simulation results show that MPDSSN has more superior performance in the average delay, energy saving, survival time and packet loss ratio.

A Hierarchical Name System Based on Hybrid P2P for Data-Centric Networks

Data-centric network plays an important part in the next generation network architecture. And name system is one of basic elements in data-centric network. This paper describes an approach which achieves the name system design criterion. In naming, a combination of three entities is used to achieve security, scalability, and flexibility. In name resolution, a three-layer structure is proposed to achieve precise and semantic resolutions.

Multi-layer Resources Fair Allocation in Big Data with Heterogeneous Demands

The resource fair allocation is very important for the most systems. This paper focuses on the resource allocation in a big data system. This system has the characteristics of multiple resources owning and heterogeneous resource demanding. Firstly, a basic allocation scheme is proposed. Some allocation properties are introduced including sharing incentive, strategy proofness, envy-freeness, Pareto optimality, resource efficiency, single-layer multi-resource fairness, multi-layer single-resource fairness. Secondly, a single layer dominant and max–min fair allocation (SDMMF) is proposed. The basic idea is that the resources are firstly allocated using dominant resource fairness scheme. If some resources are not fully allocated, using max–min fairness scheme allocate the surplus ones. The SDMMF is theoretically proved satisfying all the hierarchical resource allocation properties. Thirdly, a Multi-layer resource allocation scheme, namely multi-layer dominant and max–min fair allocation (MDMMF) is proposed. The basic idea is that a multi-layer resource demand hierarchy can be divided into a series of single layer resource demand. So a resource demand vector can be built in the hierarchy from down to top. Then, the resource allocation of first layer is calculated using SDMMF allocation. The result of such allocation is further allocating in the third layer. Recursively do the MDMMF allocation until the bottom layer nodes obtain resource allocation shares. Lastly, The MDMMF is also theoretically proved satisfying all the hierarchical resource allocation properties.

Modeling the Channel Time Variation Inside Moving Vehicle

In this letter, we propose a new stochastic model for the time variation of wireless channel inside the moving vehicle. In the classical Jakes’ model, the receiver is assumed to move at a certain speed relative to the surrounding reflectors. However, when the receiver is inside a moving vehicle, the receiver is relatively static to the surrounding reflectors, while the reflectors (as well as the receiver) move at a certain speed relative to the ground. A novel stochastic model for this kind of channel time variation is proposed in this letter, which can be employed to model the channel time variation inside the moving vehicle in a more accurate manner.

Parameter Based Channel Estimation for OFDM Systems Over Time-Varying Channels

This paper investigates the parameter-based (PB) channel estimation for orthogonal frequency division multiplexing over time-varying channels. The time-varying channel impulse response (CIR) can be represented using limited number of channel parameters. Instead of estimating the CIR directly, the PB approach estimates these parameters, from which, the CIR can be regenerated. To estimate these parameters, a differential scheme, utilizing the repetitive structure of the training signal, is proposed in this paper. When expressing the received signal in time–frequency-representation form, the repetitive structure can cause a phase difference between consecutive signal branches. This provides the chance for parameter estimation. Space domain sampling using multiple receive antennas are also employed to gain unique solution for parameter estimation. The effectiveness of the proposed algorithm is demonstrated by computer simulations.