원문정보
초록
영어
The estimation error of the least square method in traditional Distance Vector-Hop (DV-Hop) algorithm is too large and the Particle Swarm Optimization (PSO) algorithm is easy to trap into local optimum. In order to overcome the problems, a fusion algorithm of improved particle swarm algorithm and DV-Hop algorithm was presented. Firstly, PSO algorithm was improved from aspects of particle velocity, inertia weight, learning strategy and variation, which enhanced the ability to jump out of local optimum of the algorithm and increased the search speed of the algorithm in later iterative stage. Then, the node localization result was optimized by using the improved PSO algorithm in the third stage of the DV-Hop algorithm. The simulation results show that compared with the traditional DV-Hop algorithm, the improved DV-Hop based on chaotic PSO algorithm and the DV-Hop algorithm based on improved PSO, the proposed algorithm has higher positioning accuracy and better stability, which is suitable for high positioning accuracy and stability requirements scenes.
목차
1. Introduction
2. Theory
2.1. Description of Poisition Problem
2.2. Design of Fitness Function
3. Improved Particle Swarm Optimization
3.1. Standard Particle Swarm Optimization
3.2. Improvement of Speed
3.3. Inertia Weight Improvements
3.4. Learning Strategies
3.5. Mutation
4. Algorithmic Process
5. Simulation and Analysis
5.1. Simulation Environment and Parameters
5.2. Anchor Node Ratio’s Effect on the Positioning Accuracy
5.3. Number of Nodes’ Effect on the Positioning Accuracy
5.4. Communication Radius’ Effect on the Positioning Accuracy
6. Conclusion
Acknowledgment
References