Research Of Link Quality Estimation Parameters Optimization Model For Wireless Sensor Networks

Wireless sensor network is a multi-hop Ad Hoc network system formed by a large number of micro sensor nodes through wireless radio frequency. It has a great application prospect in many fields. Node energy problem is the key factor that restricts its development. Routing efficiency decides node energy consumption in the application of WSN. And the design of upper layer routing protocol is related to node communication link. Reliable estimation of link quality can not only reduce packet re-transmission but also increase throughput of WSN, leading to prolonging network lifetime. An effective link quality parameter play a key role in the evaluation and prediction of link quality. This thesis is supported by the National Natural Science Fund. It focuses on link quality estimation parameter optimization model for WSN,aiming to provide effectively link quality estimation parameter.This thesis analyzes the causes of link’s instability, including link volatility,environmental sensitivity, irregularity and asymmetry. It discusses research status of link quality estimation parameters, which fall into three categories: hardware-based,software-based and combine-based. It can get conclusion that the combine-based parameter is better than single parameter in estimation performance.Considering stability and agility of link quality estimation, this paper proposes a parameter optimization model for link quality parameter based on dynamic intuitive fuzzy multiple attribute decision making. The optimization model takes multiple link decision information, which is calculated on several link levels, as preferred input. It sorts candidate parameter set based on TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution) decision algorithm. At the same time, this thesis selects and combines candidate parameter set based on parameter closeness degree.And then it obtains link quality parameter DMD-LQE(Link Quality Estimator Based on Dynamic Multiple Attribute Decision Making) based on optimization model.Experiment shows that optimization model is of better stability and agility and can effectively sort out prefered link quality parameters. Compared with the existing evaluation methods, the evaluation method based on DMD-LQE parameter has similar accuracy with F-LQE. And it is obviously better than F-LQE in agility, because DMD-LQE can capture link’s sudden change more rapidly when evaluation value change in slight fluctuations. It’s stability is better than SPRR parameter, but slightly worse than F-LQE.