3D weighted centroid algorithm & RSSI ranging model strategy for node localization in WSN based on smart devices

The Weighted Center Localization (WCL), which is a classical localization scheme for the Wireless Sensor Network (WSN), can't decrease the average localization errors and the maximum errors simultaneously due to the fixed weight argument. Improving the localization accuracy of WCL is to decrease the distance measurement errors or to set a proper dynamic weight argument. We have already improved the distance measurement accuracy based on a statistical Gaussian mixed Model (GMM) based on the off-line Received Signal Strength Indication (RSSI) collected under the mine. To improve the localization accuracy further, we provide a dynamic weight argument based on GMM. The attributes of the dynamic weight argument emphasize the effect of the large RSSI and prevent the large RSSI from enlarging the maximum localization errors. The simulation results describe that the method proposed in this paper decreases the average localization errors and the maximum errors simultaneously.

A localization method with dynamic weight argument based on Gaussian mixed Model

The article presents the dynamic estimation method of the path loss exponent parameter in the function of the distance based on the conducted measurements. A specific feature of this solution is its suitability for distance estimation on devices which are characterised by a small amount of resources. The presented method allows to provide an acceptable precision of distance estimation while using a relatively small measurement set. For this purpose, real RSSI (Received Signal Strength Indicator) measurements were used and estimation of the path-loss exponent was performed with the use of a Bayesian particle filter. The article, apart from a detailed demonstration of the algorithms, presents the results of the sensitivity analysis of this method to change the number of inserted particles and of the repetitions of calculations needed to estimate the path loss exponent. Additionally, the results of the model stability study on the size change of the experimental dataset RSSI are presented. The properties and accuracy of the proposed method are verified based on a set of actual measurement data. All the obtained results indicate the utility of the Bayesian filtering method for effective estimation of the path loss exponent and confirm the possibility of using the described method in systems with a limited amount of computing resources.

Application of Particle Filter in Path-loss Modelling

In this paper, a novel method is proposed to estimate the distance between sensor nodes based on a statistical model of GMM, which is established with the offline RSSI values, called RSSI-D. In order to estimate the arguments of RSSI-D with unobserved latent variables, EM is used to calculate the arguments of RSSI-D as accurately as possible in this paper. After RSSI-D established, the probability of sub models of RSSI-D that the online RSSI values obey can be calculated by the posterior probability in Bayesian statistics. Based on the probability of sub models corresponding to the distance segment, the distance between sensor nodes can be estimated by the weighted value of distance segments. In the simulation, the accuracy is improved obviously by RSSI-D and the arguments such as K and N are provided by the simulation results

A Novel Method to Improve the Accuracy of the RSSI Techniques Based on RSSI-D

Tracking moving sensor node is a key point in Wireless Sensor Networks (WSN) However, it's difficult to achieve high tracking accuracy quickly because many problems should be solved After analyzing those fundamental problems, Unscented Kalman Filter (UKF) is established to estimate the moving sensor node's localization and environment argument simultaneously To adapt for the case of colored measurement noise, the UKF model is improved to form a time-lag UKF model with Gauss Noise based on the first-order Auto Regressive (AR) model of the colored noise To decrease the influence of dynamic variance of measurement noise in the time-lag UKF model, Wavelet Transformation (WT) is used to estimate the standard deviation of the measurement noise on-line fast, and the new model is named WT-UKF In simulations, results show that WT-UKF is always better than UKF when the number of the measurement values analyzed by WT in one interval is set properly

Moving sensor nodes' tracking by WT-UKF in Wireless Sensor Networks

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A Novel Method to Improve the Accuracy of the RSSI Techniques Based on RSSI-D

Moving sensor nodes' tracking by WT-UKF in Wireless Sensor Networks