Distributed on-line multidimensional scaling for self-localization in wireless sensor networks

Morral, Gemma; Bianchi, Pascal

doi:10.1016/j.sigpro.2015.08.014

Cited by 19 publications

(9 citation statements)

References 38 publications

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“…Distributed localization methods are required for a wide range of applications. Therefore, fully distributed MDS based localization methods have been recently developed [186]- [188]. In distributed MDS based localization methods every node calculates range measurements to its neighbors and updates its location estimate by minimizing a local cost function [189]- [192].…”

Section: Distributed Mds Based Localizationmentioning

confidence: 99%

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Saeed

Nam

Al-Naffouri

et al. 2019

IEEE Commun. Surv. Tutorials

128

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Current and future wireless applications strongly rely on precise real-time localization. A number of applications such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/ positioning system is the Global Positioning System (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrestrial wireless networks, wireless sensor networks (WSNs) and wireless local area networks (WLANs). Also, there exist localization techniques which fuse two or more technologies to find out the location of the user, also called signal of opportunity based localization. Most of the localization techniques require ranging measurements such as time of arrival (ToA), time difference of arrival (TDoA), direction of arrival (DoA) and received signal strength (RSS). There are also range-free localization techniques which consider the proximity information and do not require the actual ranging measurements. Dimensionality reduction techniques are famous among the range free localization schemes. Multidimensional scaling (MDS) is one of the dimensionality reduction technique which has been used extensively in the recent past for wireless networks localization. In this paper, a comprehensive survey is presented for MDS and MDS based localization techniques in WSNs, Internet of Things (IoT), cognitive radio networks, and 5G networks. 2 MDS is much popular among all these techniques because of its simplicity and many application areas. MDS analysis finds the spatial map for objects given that the similarity or dissimilarity information between the objects is available [16].In the recent past, MDS is widely used for localization and mapping of wireless sensor networks (WSNs) and the internet of things (IoT). In [17] a proximity information based sensor network localization is proposed, where the main idea is to construct a local configuration of sensor nodes by using classical MDS (CMDS). The MDS based localization algorithms in [17] and [18] are centralized with higher computational complexity [7]. Semi-centralized (or clustered) MDS techniques are developed to compute local coordinates of nodes, which then are refined to find the final position of the nodes [19], [20]. In [21], [22] and [23] the authors proposed manifold learning to estimate the sensor nodes position in wireless sensor networks. In [24] the authors proposed Nystrom approximation for the proximity information matrix in MDS to reduce its size for better localization accuracy in sensor networks. Distributed MDS based localization algorithm is proposed in [25] with noisy range measurements, where the authors assume that the distances are corrupted with independent Gaussian random noise. MDS methods with different refinement schemes have also been proposed in the literature t...

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Section: Distributed Mds Based Localizationmentioning

confidence: 99%

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Saeed

Nam

Al-Naffouri

et al. 2019

IEEE Commun. Surv. Tutorials

128

View full text Add to dashboard Cite

show abstract

“…where a i are the Lagrange multipliers which satisfy 0\a Ã i \C, x i are the support vectors whose a i is not 0, n is the number of support vectors, and b Ã is the bias value. Equation (5) shows that decision function depends on support vectors. That means optimal hyperplane is constructed by these support vectors.…”

Section: Basic Theory Of Svmmentioning

confidence: 99%

“…One is the localization scheme in the centralized manner, such as semi-definite programming 2,3 and multidimensional scaling. 4,5 The other category is the localization scheme in the distributed manner, such as DV-Hop, 6,7 MDS-MAP, 8,9 and MDL. 10 The localization algorithm in distributed manner has lower computation and communication costs, so it is suitable for large-scale WSNs.…”

Section: Introductionmentioning

confidence: 99%

Localization algorithm for large-scale wireless sensor networks based on FCMTSR-support vector machine

Fang

Wei

2016

International Journal of Distributed Sensor Networks

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Sensor node localization is one of research hotspots in the applications of wireless sensor network field. A localization algorithm is proposed in this article which is based on improved support vector machine for large-scale wireless sensor networks. For a large-scale wireless sensor network, localization algorithm based on support vector machine faces to the problem of the large-scale learning samples. The large-scale training samples will lead to high burden of the training calculation, over learning, and low classification accuracy. In order to solve these problems, this article proposed a novel scale of training sample reduction method (FCMTSR). FCMTSR takes the training sample as point set, get the potential support vectors, and remove the non-boundary outlier data immixed by analyzing relationships between points and set. To reduce the calculation load, fuzzy C-means clustering algorithm is applied in the FCMTSR. By the FCMTSR, the training time is reduced and the localization accuracy is improved. Through the simulations, the performance of localization based on FCMTSR-support vector machine is evaluated. The results prove that the localization precision is improved 2%, the training time is reduce 55% than existing localization algorithm based on support vector machine without FCMTSR. FCMTSR-support vector machine localization algorithm also addresses the border problem and coverage hole problem effectively. Finally, the limitation of the proposed localization algorithm is discussed and future work is present.

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“…Most of the existing work on sensor registration is based on two approaches. In the first approach, called cooperative localization, each sensor is provided with direct measurements relative to positions of its neighbors [15]- [22]. Conversely, the second approach is based on exploiting some reference nodes of known positions (also called anchors) in the global coordinate system [23]- [27].…”

Section: Introductionmentioning

confidence: 99%

Distributed Joint Sensor Registration and Multitarget Tracking via Sensor Network

Gao

Battistelli

Chisci

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarget set is modeled as an independent and identically distributed (i.i.d.) cluster random finite set (RFS), and a consensus cardinality probability hypothesis density (CPHD) filter is run over the network to recursively compute in each node the posterior RFS density. Then a suitable cost function, expressing the discrepancy between the local posteriors in terms of averaged Kullback-Leibler divergence, is minimized with respect to the drift and orientation parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registraton and multitarget tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.

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Distributed on-line multidimensional scaling for self-localization in wireless sensor networks

Cited by 19 publications

References 38 publications

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Localization algorithm for large-scale wireless sensor networks based on FCMTSR-support vector machine

Distributed Joint Sensor Registration and Multitarget Tracking via Sensor Network

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