Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

Abukhalaf, Hala; Wang, Jianxin; Zhang, Shigeng

doi:10.14257/ijfgcn.2016.9.7.07

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Likewise, the papers proposed by Subir Halder [19] Hala Abukhalaf [20] Yuxuan Long [21] and Ketan Sabale [22,23] explained mobile anchor localization and path planning with a very brief review. In these five papers, there is no information about how the helicopter view is related to this topic with other fields.…”

Section: Related Workmentioning

confidence: 99%

Research Trend Topic Area on Mobile Anchor Localization

Indah Hapsari,

Munadi,

Erfianto

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Localization in a dynamic environment is one of the challenges in WSN localization involving dynamic sensor nodes or anchor nodes. Mobile anchors can be an efficient solution for the number of anchors in a 3-dimensional environment requiring more local anchors. The reliability of a localization system using mobile anchors is determined by various parameters such as energy efficiency, coverage, computational complexity, and cost. Various methods have been proposed by researchers to build a reliable mobile anchor localization system. This certainly shows the many research opportunities that can be carried out in mobile anchor localization. The many opportunities in this topic will be very confusing for researchers who want to research in this field in choosing a topic area early. However, until now there is still no paper that discusses systematic mapping studies that can provide information on topic areas and trends in the field of mobile anchor localization. A systematic Mapping Study (SMS) was conducted to determine the topic area and its trends, influential authors, and produce modeling topics and trends from the resulting modeling topics. This SMS can be a solution for researchers who are interested in research in the field of mobile anchor localization in determining the research topics they are interested in for further research. This paper gives information on the mobile anchor research area, the author who has influenced mobile anchor localization research, and the topic modeling and trend that potentially promissing research in the future. The SMS includes a chronology of publications from 2017-2022, bibliometric co-occurrence, co-author analysis, topic modeling, and trends. The results show that the development of mobile anchor localization publications is still developing until 2022. There are 10 topic models with 6 of them included in the promising topic. The results of this SMS can be used as preliminary research from the literacy stage, namely Systematic Literature Review (SLR).

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Section: Related Workmentioning

confidence: 99%

Research Trend Topic Area on Mobile Anchor Localization

Indah Hapsari,

Munadi,

Erfianto

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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“…In this type of dataset, a distance can be defined between items. A relevant example is a wireless sensor network, where localization is based on the distances between nodes and the presence of outliers in data results in localization inaccuracy [32,33]. Abukhalaf et al [34] presents a comprehensive survey of outlier detection techniques for localization in wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Outlier Mining Methods Based on Graph Structure Analysis

2019

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Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms. While many outlier mining algorithms have been proposed in the literature, they tend to be valid or efficient for specific types of datasets (time series, images, videos, etc.). Here we propose two methods that can be applied to generic datasets, as long as there is a meaningful measure of distance between pairs of elements of the dataset. Both methods start by defining a graph, where the nodes are the elements of the dataset, and the links have associated weights that are the distances between the nodes. Then, the first method assigns an outlier score based on the percolation (i.e., the fragmentation) of the graph. The second method uses the popular IsoMap non-linear dimensionality reduction algorithm, and assigns an outlier score by comparing the geodesic distances with the distances in the reduced space. We test these algorithms on real and synthetic datasets and show that they either outperform, or perform on par with other popular outlier detection methods. A main advantage of the percolation method is that is parameter free and therefore, it does not require any training; on the other hand, the IsoMap method has two integer number parameters, and when they are appropriately selected, the method performs similar to or better than all the other methods tested.

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“…In this type of dataset, a distance can be defined between items. A relevant example is a wireless sensor network, where localization is based on the distances between nodes and the presence of outliers in data results in localization inaccuracy [102,103]. Abukhalaf et al [104] presents a comprehensive survey of outlier detection techniques for localization in wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Machine learning methods for the characterization and classification of complex data

Amil Marletti

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This thesis work presents novel methods for the analysis and classification of medical images and, more generally, complex data. First, an unsupervised machine learning method is proposed to order anterior chamber OCT (Optical Coherence Tomography) images according to a patient's risk of developing angle-closure glaucoma. In a second study, two outlier finding techniques are proposed to improve the results of above mentioned machine learning algorithm, we also show that they are applicable to a wide variety of data, including fraud detection in credit card transactions. In a third study, the topology of the vascular network of the retina, considering it a complex tree-like network is analyzed and we show that structural differences reveal the presence of glaucoma and diabetic retinopathy. In a fourth study we use a model of a laser with optical injection that presents extreme events in its intensity time-series to evaluate machine learning methods to forecast such extreme events. El presente trabajo de tesis desarrolla nuevos métodos para el análisis y clasificación de imágenes médicas y datos complejos en general. Primero, proponemos un método de aprendizaje automático sin supervisión que ordena imágenes OCT (tomografía de coherencia óptica) de la cámara anterior del ojo en función del grado de riesgo del paciente de padecer glaucoma de ángulo cerrado. Luego, desarrollamos dos métodos de detección automática de anomalías que utilizamos para mejorar los resultados del algoritmo anterior, pero que su aplicabilidad va mucho más allá, siendo útil, incluso, para la detección automática de fraudes en transacciones de tarjetas de crédito. Mostramos también, cómo al analizar la topología de la red vascular de la retina considerándola una red compleja, podemos detectar la presencia de glaucoma y de retinopatía diabética a través de diferencias estructurales. Estudiamos también un modelo de un láser con inyección óptica que presenta eventos extremos en la serie temporal de intensidad para evaluar diferentes métodos de aprendizaje automático para predecir dichos eventos extremos. Aquesta tesi desenvolupa nous mètodes per a l’anàlisi i la classificació d’imatges mèdiques i dades complexes. Hem proposat, primer, un mètode d’aprenentatge automàtic sense supervisió que ordena imatges OCT (tomografia de coherència òptica) de la cambra anterior de l’ull en funció del grau de risc del pacient de patir glaucoma d’angle tancat. Després, hem desenvolupat dos mètodes de detecció automàtica d’anomalies que hem utilitzat per millorar els resultats de l’algoritme anterior, però que la seva aplicabilitat va molt més enllà, sent útil, fins i tot, per a la detecció automàtica de fraus en transaccions de targetes de crèdit. Mostrem també, com en analitzar la topologia de la xarxa vascular de la retina considerant-la una xarxa complexa, podem detectar la presència de glaucoma i de retinopatia diabètica a través de diferències estructurals. Finalment, hem estudiat un làser amb injecció òptica, el qual presenta esdeveniments extrems en la sèrie temporal d’intensitat. Hem avaluat diferents mètodes per tal de predir-los.

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Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

Abstract: Accurate location information is critical to many applications in wireless sensor networks (WSNs)

Cited by 3 publications

References 27 publications

Research Trend Topic Area on Mobile Anchor Localization

Research Trend Topic Area on Mobile Anchor Localization

Outlier Mining Methods Based on Graph Structure Analysis

Machine learning methods for the characterization and classification of complex data

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