Wireless localization using self-organizing maps

Giorgetti, Gianni; Gupta, Sandeep; Manes, G.

doi:10.1145/1236360.1236399

Cited by 55 publications

(59 citation statements)

References 34 publications

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“…The two major methods, multi-dimensional scaling (MDS) based [1][2][3] and neural network based [4,5], achieve the highest localization accuracy and yield coordinates of sensor nodes that preserve the distance matrix between the data points of the input space and the output space (i.e., a 2D plane) as much as possible.…”

Section: Challenges Of Previous Approachesmentioning

confidence: 99%

“…The second method is based on neural networks [4,5], where non-linear mapping techniques and neural network models such as self-organizing map (SOM) are employed for dimension reduction of multidimensional datasets, yielding coordinates of sensor nodes that preserve the distances (also approximated by hop counts) between the data points of the input space and the output space (i.e., a 2D plane) as much as possible.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Scalable and Fully Distributed Localization in Large-Scale Sensor Networks

Jin

Xia

et al. 2017

Axioms

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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.

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Section: Challenges Of Previous Approachesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Scalable and Fully Distributed Localization in Large-Scale Sensor Networks

Jin

Xia

et al. 2017

Axioms

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show abstract

“…Giorgetti et al proposed the SOM (self-organizing maps) algorithm [14], which is based on a neural network formalism known as self-organizing map and generates virtual coordinates that describe the relative positions of nodes [10]. The SOM [14] algorithm estimates the distance between every possible pairs of nodes roughly by using the shortest path algorithm and deduces the pairs of nodes distance by utilizing multidimensional scaling method and locates unknown nodes by using the position of anchor nodes.…”

Section: Related Workmentioning

confidence: 99%

“…The SOM [14] algorithm estimates the distance between every possible pairs of nodes roughly by using the shortest path algorithm and deduces the pairs of nodes distance by utilizing multidimensional scaling method and locates unknown nodes by using the position of anchor nodes.…”

Section: Related Workmentioning

confidence: 99%

An Eliminating Method to Improve Localization Accuracy

Chen

Yan

et al. 2014

International Journal of Distributed Sensor Networks

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Node localization is essential in wireless sensor networks (WSNs). However, due to limited node energy and overhead network, node localization faces many challenges in WSNs. In addition, it is difficult to meet the requirement of localization accuracy and energy consumption at the same time in existing algorithms. In this paper, we proposed a novel eliminating algorithm called NPLA which achieves high localization accuracy and reduces energy consumption effectively. It makes full use of multiple-hop neighbor nodes of unknown node to eliminate the possible location area (PLA) of the unknown node. Simulations show that the localization error of algorithm NPLA can be controlled within 20%.

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“…The method presented by Giorgetti et al [19], which we name CSOM, employs the classical SOM [20] to the localization in which the SOM-winner node updates the weights (estimated locations) of its neighbors when they are found out to be outside its radio range. It uses centralized implementation and requires thousands of learning steps.…”

Section: Introductionmentioning

confidence: 99%

Distributed Hybrid Localization Using RSS Threshold Based Connectivity Information and Iterative Location Update

Maung

Kikuchi

2015

International Journal of Distributed Sensor Networks

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Resource constraints of wireless ad hoc and sensor networks prohibit high accuracy range-based localization schemes which require specialized hardware for ranging. On the other hand, cost-effective range-free schemes offer lower accuracy and grant their applicability only to large-scale networks. This paper proposes an efficient distributed hybrid solution which integrates received signal strength (RSS) based ranging and connectivity-based range-free approaches to improve the localization accuracy without any extra ranging hardware and to be adaptable to any network size. First, it configures the connectivity information using available RSS measurements and a predefined RSS threshold. Optimal RSS threshold value that minimizes the error for a particular network to be localized is derived as a function of the total number of nodes and the network size. And then, localization accuracy is further improved by introducing the use of regulated hop-count values. Finally, locations of the nodes are iteratively updated using both connectivity information and RSS-based distance information between the nodes to get more precise localization accuracy. Effectiveness of the proposed scheme is evaluated with both experiments and simulations, and results show that the proposed scheme achieves significant performance improvement over existing schemes.

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Wireless localization using self-organizing maps

Cited by 55 publications

References 34 publications

Scalable and Fully Distributed Localization in Large-Scale Sensor Networks

Scalable and Fully Distributed Localization in Large-Scale Sensor Networks

An Eliminating Method to Improve Localization Accuracy

Distributed Hybrid Localization Using RSS Threshold Based Connectivity Information and Iterative Location Update

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