Abstract
In wireless sensor networks, due to the significance of the location information of mobile nodes for many applications, location services are the basis of many application scenarios. However, node state and communication uncertainty affect the distance estimation and position calculation of the range-based localization method, which makes it difficult to guarantee the localization accuracy and the system robustness of the distributed localization system. In this paper, we propose a distributed localization method based on anchor nodes selection and particle filter optimization. In this method, we first analyze the uncertainty of error propagation to the least-squares localization method. According to the proportional relation between localization error and uncertainty propagation, anchor nodes are selected optimally in real-time during the movement of mobile nodes. Then we use the ranging and position of the optimally selected anchor nodes to obtain the location information of the mobile nodes. Finally, the particle filter (PF) algorithm is utilized to gain the optimal estimation of the localization results. The experimental evaluation results verified that the proposed method effectively improves the localization accuracy and the robustness of the distributed system.
Highlights
Wireless sensor networks (WSNs) are applied in numerous application scenarios [1,2,3], such as environmental monitoring, smart cities, disaster relief, and asset tracking, which all require precise location services of nodes, especially moving object tracking
In this paper, considering the uncertainty of error propagation caused by some negative factors, we adopt the minimum standard deviation optimization (MSDO) and minimum error propagation optimization (MEPO) methods, after which we propose the distributed localization method based on the MSDO-particle filter (PF) and MEPO-PF algorithms to optimize the positioning results
To improve the positioning accuracy and robustness of the WSN distributed mobile localization system, this paper deduces the anchor node optimization algorithm based on minimum standard deviation and minimum error propagation by analyzing the error propagation of the range-based positioning algorithm
Summary
Wireless sensor networks (WSNs) are applied in numerous application scenarios [1,2,3], such as environmental monitoring, smart cities, disaster relief, and asset tracking, which all require precise location services of nodes, especially moving object tracking. The Global Positioning System (GPS) and the BeiDou Navigation Satellite System (BDS) provide location services Their positioning accuracy is reduced significantly in buildings, indoors, or canyons [4,5]. To calculate the position of the moving nodes with the absolute distance or angle information between the nodes, range-based localization methods adopt different algorithms, such as trilateration, triangulation, least squares, and maximum likelihood estimation [7]. It is necessary to select reliable anchor nodes for distance estimation and positioning calculations during the process of the distributed localization. To improve the dynamic positioning accuracy in the case of the ranging information changing constantly, a filtering algorithm needs to be used to optimize the initial positioning results after selecting anchor nodes for the range-based positioning method. Combining the MSDO and MEPO criteria with the particle filter algorithm, we propose the distributed localization method based on anchor node optimal selection and particle filter (MSDO-PF and MEPO-PF)
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