Abstract
Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN). DBOD assigns density values to each point used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms. It is shown that the proposed algorithm performs better than these algorithms even when the anchor geometry about an unlocalized node is poor.
Highlights
The process of finding the spatial location of nodes in a wireless network has been called localization, positioning, geolocation, and self-organizing in the literature
We propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining
Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms
Summary
The process of finding the spatial location of nodes in a wireless network has been called localization, positioning, geolocation, and self-organizing in the literature. Position accuracy is not constant across the area of coverage, and poor geometry of the unlocalized nodes relative to the anchor nodes can lead to high geometric dilution of precision (GDOP). An approach from data mining called density-based outlier detection (DB OD) [9] is employed which uses the distance to the Knearest neighbours (KNN) to select the best (candidate) points, and these are averaged to get the estimated location of the unlocalized node. Another version of GDOP is the generalized geometry of dilution precision GGDOP. The angle i is the orientation of the ith anchor or localized node relative to the node (a)
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