Abstract
One of the main challenges faced by wireless sensor network (WSN) localization is the positioning accuracy of the WSN node. The existing algorithms are arduous to use for dealing with the pulse noise that is universal and ineluctable in practical considerations, resulting in lower positioning accuracy. Aimed at this problem and introducing Bregman divergence, we propose in this paper a novel WSN localization algorithm via matrix completion (LBDMC). Based on the natural low-rank character of the Euclidean Distance Matrix (EDM), the problem of EDM recovery is formulated as an issue of matrix completion in a noisy environment. A regularized matrix completion model is established, smoothing the pulse noise by leveraging -norm and the multivariate function Bregman divergence is defined to solve the model to obtain the EDM estimator. Furthermore, node localization is available based on the multi-dimensional scaling (MDS) method. Multi-faceted comparison experiments with existing algorithms, under a variety of noise conditions, demonstrate the superiority of LBDMC to other algorithms regarding positioning accuracy and robustness, while ensuring high efficiency. Notably, the mean localization error of LBDMC is about ten times smaller than that of other algorithms when the sampling rate reaches a certain level, such as >30%.
Highlights
Wireless sensor networks (WSNs) are widely used in monitoring, target tracking, and other fields [1,2], with the premise of providing accurate location information
This paper mainly examines range-based localization technology, which utilizes the a priori physical position coordinates of beacon nodes and the distance measurement between node pairs to locate the unknown nodes in a WSN
In order to evaluate the efficacy of our proposed LBDMC, the Euclidean Distance Matrix (EDM) recovery errors, mean localization errors, localization errors variance, and localization errors’ cumulative distribution were selected as evaluation indicators and compared with IALM [20], OptSpace [22], and ScGrassMC [23]
Summary
Wireless sensor networks (WSNs) are widely used in monitoring, target tracking, and other fields [1,2], with the premise of providing accurate location information. Only a few beacon nodes in a WSN can implement their positioning by configuring GPS devices. In this case, the location information of unknown nodes can be achieved, employing the prior position coordinates of the beacon nodes as well as the physical measurements between the node pairs. The other uses range-free localization technology, in which coarse-grained location information is acquired by using the connectivity between unknown nodes and beacon nodes [4]. The positioning accuracy is one of the main challenges in WSNs. Localization methods based on multi-dimensional scaling (MDS) [5,6,7], maximum likelihood (ML) [8], fingerprint [9,10], and semi-definite programming (SDP) [11] have been proposed
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