A fuzzy C means and hierarchical voting based RSSII quantity localization method for wireless sensor network
Abstract : With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy.
For the localization methods based on WSN, the four main measurement methods to locate the mobile node consist of received signal strength (RSS) [20], angle of arrival (AOA) [21], time of arrival (TOA) [22] or time difference of arrival (TDOA) [23]. If there is direct propagation, also known as line-of-sight (LOS), between the beacon nodes and the mobile node, we can obtain the accurate position of the mobile node through the filtering algorithms. However, one of the major challenges in wireless positioning technology is the non-line of sight (NLOS) problem [24], which occurs when direct line-of-sight is blocked between the beacon and mobile nodes. In the case of NLOS, the propagation time of the signal is increased because the radio waves are reflected by the scatter or penetrate the blocking object [25]. Therefore, the WSN based localization in the complex NLOS environment is still a challenging problem.
As a resource limited framework, WSN drains a con-severable percentage of its energy budget to predict the accurate hypothesis and extract the consensus relation-ship among data samples. Thus, the designers should consider the trade-off between the algorithm’s computational requirements and the learned model’s accuracy. Specifically, the higher the required accuracy, the higher the computational requirements, and the higher energy consumptions. Otherwise, the developed systems might be employed with centralized and resource capable computational units to perform the learning task.
Generally speaking, learning by examples requires large data set of samples to achieve the intended generalization capabilities (i.e., fairly small error bounds), and the algorithm’s designer will not have the full control over the knowledge formulation process.
In this paper, we propose a hierarchical voting based mixed filter (HVMF) localization algorithm to mitigate the NLOS error, which is suitable for a 2D scenario. We firstly use the hierarchical voting method to obtain the initial position estimation of a mobile node, and the probability of including the NLOS errors is obtained. Then, a mixed square root unscented Kalman filter (SRUKF) and particle filter (PF) method based on the probability is proposed to filter the larger measurement error. Finally, the convex optimization and maximum likelihood estimation method is proposed to estimate the position of the mobile node. The main contributions of this paper are given as follows:
(1) The proposed condition detection and distance correction method based on hierarchical voting does not require identification of the propagation state, and it is independent of the physical measurement ways.
(2) A mixed SRUKF and PF method is proposed to filter the larger measurement error. The proposed method only needs the parameter of measurement noise in the LOS condition. It does not require any prior information about the NLOS errors. Therefore, the proposed method can be widely used in other wireless localization methods
Increases Network Lifetime, Energy saving due to aggregation by CHs
Connection setup delay is less Routes are established on demand
Topological changes are localized
Increases Network Lifetime Helps in balancing energy consumption.
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