Ocalization in S Ensor N Etwork with N Ystrom a Pproximation

ACM Transactions on Sensor …, 2006

IEEE Transactions on Parallel and Distributed Systems, 2004

We propose an approach that uses connectivity information-who is within communications range of whom-to derive the locations of nodes in a network. The approach can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor nodes, if it is available. It is based on multidimensional scaling (MDS), an efficient data analysis technique that takes Oðn 3 Þ time for a network of n nodes. Unlike previous approaches, MDS takes full advantage of connectivity or distance information between nodes that have yet to be localized. Two methods are presented: a simple method that builds a global map using MDS and a more complicated one that builds small local maps and then patches them together to form a global map. Furthermore, least-squares optimization can be incorporated into the methods to further improve the solutions at the expense of additional computation. Through simulation studies on uniform as well as irregular networks, we show that the methods achieve more accurate solutions than previous methods, especially when there are few anchor nodes. They can even yield good relative maps when no anchor nodes are available.

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

Mobile nodes with sensing, computing and communicating capabilities are usually deployed in indoor environments as wireless sensor networks (WSN) for internet-ofthings applications. Proper interpretation of the data acquired by these sensor networks requires an estimate of their location. Among the currently existing positioning methods for WSN, multidimensional scaling (MDS) is a computationally efficient technique based on empirical measurement or estimation of the ranges between the nodes. This communication describes and evaluates several versions of MDS localization methods, particularly applied to indoor networks based on transmission of radiofrequency signals and measurement of the received signal strengths (RSS). The positioning accuracy, resistance to measurement noise, and computation times are analyzed and commented within a common framework.

IEEE Vehicular Technology Conference, 2006

There are various applications in wireless sensor networks which require knowing the relative or actual position of the sensor nodes. Recently, there have been different localization algorithms proposed in the literature. The algorithms based on classical Multidimensional Scaling (MDS) [1][2] only require 3 or 4 anchor nodes and can provide higher accuracy than some other schemes. In this paper, we propose and analyze another type of MDS (called ordinal MDS) for localization in wireless sensor networks. Ordinal MDS differs from classical MDS by that it only requires a monotonicity constraint between the shortest path distance and the Euclidean distance for each pair of nodes. We conduct simulation studies under square and C-shaped topologies with different connectivity levels and number of anchors. Results show that ordinal MDS provides a lower position estimation error than classical MDS.

IEEE/ACM Transactions on Networking, 2018

Most range-based localization approaches for wireless sensor networks (WSNs) rely on accurate and sufficient range measurements, yet noise and data missing are inevitable in distance ranging. Existing localization approaches often suffer from unsatisfied accuracy in the coexistence of incomplete and corrupted range measurements. In this paper, we propose LoMaC, a noise-tolerant localization scheme, to address this problem. Specifically, we first employ Frobenius-norm and L1norm to formulate the reconstruction of noisy and missing Euclidean Distance Matrix (EDM) as a Norm-Regularized Matrix Completion (NRMC) problem. Secondly, we design an efficient algorithm based on Alternating Direction Method of Multiplier (ADMM) to solve the NRMC problem. Thirdly, based on the completed EDM, we further employ a Multi-Dimension Scaling (MDS) method to localize unknown nodes. Meanwhile, to accelerate our algorithm, we also adopt some acceleration techniques to reduce the computation cost. Finally, extensive experimental results show that, our algorithm not only achieves significantly better localization performance than prior algorithms, but also provides an accurate position prediction of outlier, which are useful for malfunction diagnosis in WSNs. Index Terms-Wireless sensor networks; localization. I. INTRODUCTION L OCALIZATION is a core problem in Wireless Sensor Networks (WSNs) as the location information is critical for many WSN applications, such as environment monitoring, geographical routing, and data gathering [1]. In a WSN, typically only a few nodes are equipped with GPS devices, and they are called anchor nodes. The purpose of a localization algorithm is to determine the positions of all unknown nodes based on anchor nodes positions and inter-node measurements. Existing localization algorithms in WSNs can be classified into Manuscript

Computing Research Repository, 2008

The paper develops DILOC, a distributive, iterative algorithm that locates M sensors in R m , m ≥ 1, with respect to a minimal number of m + 1 anchors with known locations. The sensors exchange data with their neighbors only; no centralized data processing or communication occurs, nor is there centralized knowledge about the sensors' locations. DILOC uses the barycentric coordinates of a sensor with respect to its neighbors that are computed using the Cayley-Menger determinants. These are the determinants of matrices of inter-sensor distances. We show convergence of DILOC by associating with it an absorbing Markov chain whose absorbing states are the anchors.

2008

Nodes localization in Wireless Sensor Networks (WSN) has arisen as a very challenging problem in the research community. Most of the applications for WSN are not useful without a priori known nodes positions. One solution to the problem is by adding GPS receivers to each node. Since this is an expensive approach and inapplicable for indoor environments, we need to find an alternative intelligent mechanism for determining nodes location. In this paper, we propose our cluster-based approach of multidimensional scaling (MDS) technique. Our initial experiments show that our algorithm outperforms MDS-MAP[8], particularly for irregular topologies in terms of accuracy.

Axioms, 2017

This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of the measurement error at one sensor node is limited within a small area of the network around the node. Extensive simulations and comparison with other methods under various representative network settings are carried out, showing the superior performance of the proposed algorithm.

A distributed localization algorithm for determining the position ofnodes in a structured wireless sensor network is proposed. Detailsregarding the implementation of such algorithm are also discussed.Experiments were performed in a testbed area containing anchorand blind nodes deployed in it to characterize the pathlossexponent and to determine the localization error of the algorithm.The algorithm is shown to have localization error of 0.74m which isbetter than4.73m shown by centroid algorithm for three anchornodes.

With the recent development of technology, wireless sensor networks are becoming an important part of many applications such as health and medical applications, military applications, agriculture monitoring, home and office applications, environmental monitoring, etc. Knowing the location of a sensor is important, but GPS receivers and sophisticated sensors are too expensive and require processing power. Therefore, the localization wireless sensor network problem is a growing field of interest. The aim of this paper is to give a comparison of wireless sensor network localization methods, and therefore, multidimensional scaling and semidefinite programming are chosen for this research. Multidimensional scaling is a simple mathematical technique widely-discussed that solves the wireless sensor networks localization problem. In contrast, semidefinite programming is a relatively new field of optimization with a growing use, although being more complex. In this paper, using extensive simulations, a detailed overview of these two approaches is given, regarding different network topologies, various network parameters and performance issues. The performances of both techniques are highly satisfactory and estimation errors are minimal.