Abstract
Localization in wireless sensor networks (WSNs) is one of the primary functions of the intelligent Internet of Things (IoT) that offers automatically discoverable services, while the localization accuracy is a key issue to evaluate the quality of those services. In this paper, we develop a framework to solve the Euclidean distance matrix completion problem, which is an important technical problem for distance-based localization in WSNs. The sensor network localization problem is described as a low-rank dimensional Euclidean distance completion problem with known nodes. The task is to find the sensor locations through recovery of missing entries of a squared distance matrix when the dimension of the data is small compared to the number of data points. We solve a relaxation optimization problem using a modification of Newton’s method, where the cost function depends on the squared distance matrix. The solution obtained in our scheme achieves a lower complexity and can perform better if we use it as an initial guess for an interactive local search of other higher precision localization scheme. Simulation results show the effectiveness of our approach.
Highlights
Localization of sensor nodes is a challenging issue in wireless sensor networks (WSNs) for intelligent Internet of Things (IoT)
GPS-based localization systems have a high degree of accuracy and offer global location information
We first formulate the sensor localization problem as a relaxation of semidefinite programming relaxation (SDP), the solution can be numerically obtained via a modification of the Newton’s method as long as the received signal strength (RSS) measurements are valid. This result is mainly used for coarse localization strategy, i.e., reducing the region of interest and computation time for the fine localization stage, where the estimated solutions can be used as a starting point for other fine localization algorithms such as dwMDS and multidimensional scaling (MDS)-MAP, etc
Summary
Localization of sensor nodes is a challenging issue in wireless sensor networks (WSNs) for intelligent Internet of Things (IoT). We use some parametric methods for estimating the sensor positions based on the signal parameters in the first step. GPS-based localization systems have a high degree of accuracy and offer global location information. Alternative solutions for GPS are required, which are cost effective, rapidly deployable and can operate in diverse conditions, especially for indoor or non-line-of-sight environments. For these reasons, more suitable localization algorithms for WSNs need to be investigated. We study sensor network localization problems in embedding dimension, given anchors and the RSS information between sensors. Our goal is to approximate all sensor locations by using only a partial Euclidean distance matrix for the second step. For two arbitrary symmetric matrices A and B, A B means A − B is positive semidefinite. grad f and Hess f denote the gradient and the Hessian vectors representing the first and the second partial derivatives
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