Hidden Markov Model based channel selection framework for cognitive radio network

Cognitive Radio Network (CRN) is an intelligent wireless communication system that adapts itself to variations in the incoming radio frequency stimuli by modifying the operating parameters. Using the spectrum sensing techniques, the idle channels are detected, and allocated to the Secondary Users (SUs). The existing cooperative spectrum sensing techniques such as centralized sensing technique, Distributed sensing technique, and External sensing technique exploit efficient prediction models for allocating the frequency spectrum to SUs. For an optimal assignment of the channel using channel parameters, the channel estimation techniques such as pilot-assisted channel estimation, blind and semi blind estimation technique, and decision directed channel estimation technique are analyzed. The flexible nature of the CRN introduces various security attacks such as Primary User Emulation Attack, Objective Function Attack, Jamming Attack, Spectrum Sensing Data Falsification (SSDF), Control Channel Saturation DoS Attack (CCSD), Selfish Channel Negotiation (SCN), Sinkhole Attacks, HELLO Flood Attacks and Lion Attack. From the surveyed results, it is observed that the existing spectrum sensing, and prediction-based techniques consume more energy, and minimal data transmission rate for detecting the idle channel. Further, the end-to-end delay, energy consumption, end-to-end delay, and bandwidth are not minimized by the existing techniques.

A review of channel estimation and security techniques for CRNS

Study the road traffic carbon emission and accurately predict the problems, the road traffic carbon emission has the complex systems of chaos and nonlinearity, the traditional method ignores the chaos of the road traffic carbon emission change, and it is so difficult to precisely control the rules of the road traffic carbon emission change that the precision of the of traffic carbon emission prediction is lower. In this study, it proposes the road traffic carbon emission prediction model based on the chaos theory and neural network and improves the prediction precision of the road traffic carbon emission time sequence. First of all, it reconstructs the time sequence data of the road traffic carbon emission change through the space, and sorts out the chaos change rules hidden in the time sequence data and then uses the BP neural network to study and carry out the modeling of the time sequence data of the road traffic carbon emission, and optimize the neural network parameter in order to improve the prediction precision of the road traffic carbon emission time sequence. The simulation result shows that, Chao-BPNN has overcome the deficits of the traditional method and could precisely and comprehensively reflect the change rules of the road traffic carbon emission time sequence, and effectively improved the prediction precision of the road traffic carbon emission.

Prediction Simulation Study of Road Traffic Carbon Emission Based on Chaos Theory and Neural Network

There’re huge numbers of users and various tasks need to be handled in the cloud computing environment, the high effective task scheduling algorithm is one of the crucial problems that the cloud computing need to solve Aiming to the model structure of cloud computing, in this article it introduces the Particle Swarm Optimization algorithm (PSO) and Ant Colony Optimization algorithm (ACO) to combine with optimized task scheduling algorithm First it takes the particle swarm optimization algorithm to generate the initial scheduling results, and introduces the random inertia weight to improve the scheduling ability of the algorithm, then to take the generated results of improved particle swarm optimization algorithm as the initial pheromones of the ant colony algorithm to find out the optimal scheduling scheme, and use the elitist strategy and crossover operator in the genetic algorithm to improve the ant colony algorithm, among the algorithms to use multistage optimization algorithm to improve the operating efficiency The experimental results show that under the same conditions, the total task completion time of improved algorithm has been reduced and its performance advantage are getting more obvious with the increased task measures

http://www.sersc.org/journals/IJGDC/vol9_no4/28.pdf

The Intelligent Task Scheduling Algorithm in Cloud Computing with Multistage Optimization

In urban real-time traffic monitoring, due to the difficult of delay time acquisition at intersection, complicated calculation model, lead to inaccurate traffic discriminant. Aiming at this problem, it proposed a real-time traffic discriminant method based on the simplified network model. In traffic data collection various types of intersections are abstracted as a node in this method, and the delay time at intersection are included in the pasting time of connected roads, then conduct back-stepping calculated according to different types of nodes at monitoring, obtained the traffic conditions of regular road, rotary road and intersection, thus realize real-time monitoring. Finally, to verify the validity of the method through simulation of urban real-time traffic monitoring results.

Urban Real-Time Traffic Monitoring Method Based on the Simplified Network Model

To reduce the end-to-end average delay of algorithm in wireless network, this paper proposes the real-time routing algorithm in spectrum network. It is analyzed that the dynamic changes of the radio network model and routing algorithm in spectrum network. Through using Markov state transition and adjusting the router with scaling factor, the high-quality resources in the network can be obtained and fully utilized, and then these can reduce the transmission time latency rate and timely adjust the route. After that the tendency of spectrum network and specific real-time algorithm are given. Finally, by using the network simulation NS-2, simulation experiments are used to estimate the performance test. Experimental results show that compared with the traditional algorithm, the proposed algorithm can obtain a lower end-to-end average delay and improves network throughput and the steady and reliability of the link connection.

Ganping Yan

Papers

Routing Algorithm Based on Delay Rate in Wireless Cognitive Radio Network