Research On The Optimization Methods Of Localization In Wireless Sensor Networks

The application of wireless sensor networks is increasing,and the requirements on sensor location information and the accuracy are also improving.How to implement precise localization in wireless sensor networks has attracted much attention in the literature.The existing studies were mainly focused on the following aspects:optimizing the deployment of anchor nodes(whose location is a priori known);developing advanced ranging techniques or devices;implementing precise localization algorithms.The previous two aspects will be dealt with in this thesis.Firstly,optimal anchor placement is studied from a novel perspective.On the one hand,unlike existing studies relying on ideal independent identically-distributed(I.I.D.)noises in distance measurements,namely distance-independent noises,a more realistic distance-dependent noises model is used in this thesis.On the other hand,other than evaluating the localization performance for sensors at deterministic locations,a statistical approach is adopted by assuming a uniform and random distribution for the sensor location and evaluating the mean value of the associated Fisher information matrix(FIM)determinant as the optimality metric for anchor placement.Through this metric,the optimal anchor placement is investigated from both theoretical and simulative perspectives.On these grounds,the statement that it is optimal to have anchors equally spaced on the boundary is shown to be incorrect.Secondly,the device-free RSS-based and connectivity-based distance estimation methods are fused based on the maximum-likelihood estimator(MLE).Both simulations and experiments based on practical measurements are carried out,and demonstrate that the proposed method outperforms both of these two methods and approaches to the Cramer-Rao lower bound(CRLB).In conclusion,two key issues in relation to sensor network localization are thoroughly investigated in this thesis,e.g.the optimal anchor deployment and the improved distance estimation method,and the outcomes will contribute to future research on optimal sensor localization.