Localization with beacon based support vector machine in Wireless Sensor Networks

Livinsa, Z. Mary; Jayashri, S.

doi:10.1109/race.2015.7097295

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…Support Vector Regression (SVR) [16, 21, 22], dedicated to regression problems, is a variant of the well‐known Support Vector Machine (SVM) technique. SVR uses the same principle as SVM [23, 24] for classification, mapping the data into a high dimensional feature space using non‐linear transformations; linear regression is then executed in this space. Kernel functions perform the non‐linear transformation of the data into higher dimensional feature space that then enables the linear separation.…”

Section: Kernel‐based Localisationmentioning

confidence: 99%

LoRaWAN‐implemented node localisation based on received signal strength indicator

Aqeel

Iorkyase

Zangoti

et al. 2022

IET Wireless Sensor Systems

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Long Range Wireless Area Network (LoRaWAN) provides desirable solutions for Internet of Things (IoT) applications that require hundreds or thousands of actively connected devices (nodes) to monitor the environment or processes. In most cases, the location information of the devices arguably plays a critical role and is desirable. In this regard, the physical characteristics of the communication channel can be leveraged to provide a feasible and affordable node localisation solution. This paper presents an evaluation of the performance of LoRaWAN Received Signal Strength Indicator (RSSI)-based node localisation in a sandstorm environment. We employ machine learning algorithms -Support Vector Regression (SVR) and Gaussian Process Regression (GPR), which turns the high variance of RSSI due to frequency hopping feature of LoRaWAN to advantage; creating unique signatures representing different locations. In this work, the RSSI features are used as input location fingerprints into the machine learning models. The proposed method reduces node localisation complexity when compared to GPS-based approaches whilst provisioning more extensive connection paths. Furthermore, the impact of LoRa spreading factor and kernel function on the performance of the developed models have been studied. Experimental results show that the SVR-enhanced fingerprint yields the most significant improvement in node localisation performance.

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Section: Kernel‐based Localisationmentioning

confidence: 99%

LoRaWAN‐implemented node localisation based on received signal strength indicator

Aqeel

Iorkyase

Zangoti

et al. 2022

IET Wireless Sensor Systems

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“…Yu et al 21 studied the indoor localization based on SVM and locate the fingerprint library. The localization of beacon-based SVM in WSN is proposed in Livinsa and Jayashri, 22 mainly by awakening the learning concept of SVM and improving the localization accuracy. A fast SVM-based localization algorithm proposed in large-scale WSN is proposed in Zhu and Wei; 23 the algorithm constructs a minimum span by introducing a similarity measure and divides the support vectors into groups according to the maximum similarity in the feature space.…”

Section: Related Workmentioning

confidence: 99%

A node localization algorithm based on Voronoi diagram and support vector machine for wireless sensor networks

Hao

Dang

Yan

et al. 2021

International Journal of Distributed Sensor Networks

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For wireless sensor network, the localization algorithm based on Voronoi diagram has been applied. However, the location accuracy node position in wireless sensor network needs to be optimized by the analysis of the literature, a node location algorithm based on Voronoi diagram and support vector machine is proposed in this article. The basic idea of the algorithm is to first divide the region into several parts using Voronoi diagram and anchor node in the localization region. The range of the initial position of the target node is obtained by locating the target node in each region and then the support vector machine is used to optimize the position of the target node accurately. The localization performance of the localization algorithm is analyzed by simulation and real-world experiments. The experimental results show that the localization algorithm proposed in this article is better than the optimal region selection strategy based on Voronoi diagram-based localization scheme and Weighted Voronoi diagram-based localization scheme localization algorithms in terms of localization accuracy. Therefore, the performance of the localization algorithm proposed in this article is verified.

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“…SVMs have been widely used for a wide range of applications in science, medicine, and engineering with excellent empirical performance. For example, several classes of support vector classifier (SVC) and support vector regression (SVR) have been used successfully in localization by digital identifiers [166], [167].…”

Section: ) Svmsmentioning

confidence: 99%

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Ali¹,

Liu²,

He³

et al. 2021

IEEE Access

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Object identification and localization in indoor and outdoor environments are paramount issues in object-human interaction. Recent advancements in the data fusion capabilities of multi-sensor systems have paved the way for research on emerging object identification and positioning techniques. This review describes techniques and methods used in positioning technologies. State-of-the-art localization technologies are classified into range-based, range-free and AI-based categories. An in-depth analysis of localization approaches based on laser range finder, radio-frequency identification, ultra-wideband, inertial measurement unit, etc., are presented by providing a detailed comparison based on range, accuracy, measurement method, advantages, disadvantages, and their applications. Furthermore, we investigate stateof-the-art multimodal data fusion techniques that utilize probabilistic methods for the precise estimation of object identification in motion and its localization.INDEX TERMS Indoor and outdoor positioning systems; identification and localization; unstructured environment; multi-sensor system; multi-object; positioning and localization techniques.

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Localization with beacon based support vector machine in Wireless Sensor Networks

Cited by 13 publications

References 9 publications

LoRaWAN‐implemented node localisation based on received signal strength indicator

LoRaWAN‐implemented node localisation based on received signal strength indicator

A node localization algorithm based on Voronoi diagram and support vector machine for wireless sensor networks

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

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