基于SDN的骨干网关键技术研究

本文提出了一种基于SDN的骨干网解决方案,通过转控解耦的软件定义思想,将底层网络资源虚拟化,结合网络遥测、智能算路等关键技术,实现网络的智能感知、智能分析、智能控制。同时也为未来新型骨干网络架构不断演进提供重要的理论依据和实践基础。

Real-time Threshold Routing Algorithm Based on Wireless Sensor Network

[Objective] This study was to design an intelligent greenhouse real-time monitoring system based on the core technology of Internet of Things in order to meet the needs of agricultural informatization and intellectualization. [Method] Based on the application characteristics of Wireless Sensor Network （WSN）, the intelligent greenhouse monitoring system was designed. And for the incompleteness strategy of load balancing in the Low-Energy Adaptive Clustering Hierarchy （LEACH）, a Real- time Threshold Routing Algorithm （RTRA） was proposed. [Result] The performance of network lifetime and network delay of RTRA were tested in MATLAB and found that, within the same testing environment, RTRA can save nodes energy consumption, prolong network lifetime, and had better real-time performance than LEACH. The al- gorithm satisfies the crops＇ requirements on real-time and energy efficiency in the greenhouse system. [Conclusion] For the good performance on real-time, the de- signed intelligent greenhouse real-time monitoring system laid the foundation for the research and development of agricultural informatization and intellectualization.

Coordinated Path Planning for UAVs Based on Sheep Optimization

Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle these.Firstly,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population ideas.Then an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative paths.The simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of multi-UAVs.Compared with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved.

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.

Adaptive swarm-based routing in communication networks

Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.

Method of Dynamic Optimal Rout Calculation in the ＂Smart Clean City＂ Project

A solution of the optimization garbage removal problem in the large cities is suggested. In this paper there is described a system architecture to find time-optimal dynamic route for garbage trucks within ＂Smart Clean City＂ project which unites an approach to put special electronic devices on the garbage containers with the developed software responsible for the detecting the filled up containers and building the optimal way to collect the garbage. There is proposed a formal mathematical model of the task of dynamic optimal route and formal the optimization criterion for time-optimal garbage collection of all waste from landfills. The system includes the knowledge base which contains the rule describing the expert knowledge of the city traffic situation.

Intelligent computing budget allocation for on-road tra jectory planning based on candidate curves

In this paper, on-road trajectory planning is solved by introducing intelligent computing budget allocation(ICBA) into a candidate-curve-based planning algorithm, namely, ordinal-optimization-based differential evolution(OODE). The proposed algorithm is named IOODE with ‘I' representing ICBA. OODE plans the trajectory in two parts: trajectory curve and acceleration profile. The best trajectory curve is picked from a set of candidate curves, where each curve is evaluated by solving a subproblem with the differential evolution(DE) algorithm. The more iterations DE performs, the more accurate the evaluation will become. Thus, we intelligently allocate the iterations to individual curves so as to reduce the total number of iterations performed. Meanwhile, the selected best curve is ensured to be one of the truly top curves with a high enough probability. Simulation results show that IOODE is 20% faster than OODE while maintaining the same performance in terms of solution quality. The computing budget allocation framework presented in this paper can also be used to enhance the efficiency of other candidate-curve-based planning methods.