Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Abstract

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the restricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Centroid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust.

Centralized approach for multi-node localization and identification

“…But due to its high cost and in ability to work in indoor environment, a lot of algorithms concerns with localization Centralized approach for multi-node localization and identification according to the type of information used to localize nodes have been studied [9]. Many of those algorithms lay on the range-based schemes which use the ranged measurements strategies such as the received signal strength (RSS), angle of arrival (AOA), time of arrival (TOA) and time difference of arrival (TDOA) to compute either the distance or angle between two nodes [18]. As an example is the algorithm proposed by P. Bahl et al [19] which is a method to convert the RSS to a distance and then uses the triangulation to calculate the node's position.…”

Centralized approach for multi-node localization and identification

“…But due to its high cost and in ability to work in indoor environment, a lot of algorithms concerns with localization Centralized approach for multi-node localization and identification Ola A. Hasan according to the type of information used to localize nodes have been studied [9]. Many of those algorithms lay on the range-based schemes which use the ranged measurements strategies such as the received signal strength (RSS), angle of arrival (AOA), time of arrival (TOA) and time difference of arrival (TDOA) to compute either the distance or angle between two nodes [18]. As an example is the algorithm proposed by P. Bahl et al [19] which is a method to convert the RSS to a distance and then uses the triangulation to calculate the node's position.…”

Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks

“…CS is a promising technique for signal processing, which asserts that far fewer samples than Nyquist rate will suffice for recovering sparse or compressible signals. SLMLU [ 18 ] translates target localization problem into CS problem, and utilizes an LU decomposition preprocessing to make the measurement matrix meet restricted isometry property (RIP) [ 19 ]. Compared to the traditional DFL approaches, the CS-based DFL approach can achieve much higher localization accuracy with much less measurements (or wireless links).…”