Support Vector Regression for Mobile Target Localization in Indoor Environments

Jondhale, Satish R.; Mohan, V.; Sharma, Bharat Bhushan; Lloret, Jaime; Athawale, Shashikant V.

doi:10.3390/s22010358

Cited by 34 publications

(17 citation statements)

References 28 publications

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“…In localization techniques utilizing RSS, it is essential to consider the precision and variability of the signal across different fading models. However, the majority of localization studies in WSN assume a propagation model where the received power is related to distance by a path loss model with zero-mean Gaussian noise, such as in [ 18 , 19 , 20 ]. An anchor free algorithm for one-hop nodes is proposed in [ 21 ], where it computes the inter-node distances based on RSS and the centroid techniques.…”

Section: Related Workmentioning

confidence: 99%

An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network

Zaarour,

Hakem,

Kandil

2024

Sensors

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Sensor localization remains a crucial function within the context of wireless sensor networks (WSNs) and is a delicate concern that has attracted many researchers’ attention. Undoubtedly, a good distance estimation between different wireless sensors allows us to estimate their accurate locations in the network well. In this article, we present a simple but very effective anchor-free localization scheme for wireless sensor networks called the contextual received signal strength approach (CRSSA) localization scheme. We use the received signal strength (RSS) values and the contextual network connectivity within an anchor-free WSN. We present and thoroughly analyze a novel joint estimation methodology for determining the range, path loss exponent (PLE), and inter-node distances in a composite fading model that addresses small-scale multipath fading and large-scale path loss shadowing effects. We formulate analytical expressions for key parameters, the node’s communication range and the PLE value, as functions of the sensor’s number, the network’s connectivity, and the network density. Once these parameters are estimated, we estimate the inter-node distances and the positions of nodes, with relatively high accuracy, based on the assumed propagation model in a two-dimensional anchor-free WSN. The effectiveness of the CRSSA is evaluated through extensive simulations assuring its estimation accuracy in anchor-free localization.

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Section: Related Workmentioning

confidence: 99%

An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network

Zaarour,

Hakem,

Kandil

2024

Sensors

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“…Jondhale et al [90] evaluate the use of Support Vector Regression (SVR) in RSSI-based indoor positioning systems. Authors compare the proposed SVR scheme to traditional trilateration and GRNN.…”

Section: B Sound Based Localizationmentioning

confidence: 99%

“…Authors report that schemes such as neural networks (NN) and regression trees (RT), used to improve AoA estimation, have a computational time of maximum 7 ms [87]. In case of RSSI-based estimations using schemes like GRNN and SVR combined with KF researchers report 4 ms computational time [90].…”

Section: Complexitymentioning

confidence: 99%

Constrained Localization: A Survey

Savić¹,

Vilajosana

Watteyne³

2022

IEEE Access

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Indoor localization techniques have been extensively studied in the last decade. The well-established technologies enable the development of Real-Time Location Systems (RTLS). A good body of publications emerged, with several survey papers that provide a deep analysis of the research advances. Existing survey papers focus on either a specific technique and technology or on a general overview of indoor localization research. However, there is a need for a use case-driven survey on both recent academic research and commercial trends, as well as a hands-on evaluation of commercial solutions. This work aims at helping researchers select the appropriate technology and technique suitable for developing low-cost, low-power localization system, capable of providing centimeter level accuracy. The article is both a survey on recent academic research and a hands-on evaluation of commercial solutions. We introduce a specific use case as a guiding application throughout this article: localizing low-cost low-power miniature wireless swarm robots. We define a taxonomy and classify academic research according to five criteria: Line of Sight (LoS) requirement, accuracy, update rate, battery life, cost. We discuss localization fundamentals, the different technologies and techniques, as well as recent commercial developments and trends. Besides the traditional taxonomy and survey, this article also presents a hands-on evaluation of popular commercial localization solutions based on Bluetooth Angle of Arrival (AoA) and Ultra-Wideband (UWB). We conclude this article by discussing the five most important open research challenges: lightweight filtering algorithms, zero infrastructure dependency, low-power operation, security, and standardization.

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“…On the time of online estimation, the model needs three RSS measurements to estimate the locations of a mobile target. After testing the model using different experiments, it is shown that the proposed algorithm provide better performance in comparing of other indoor localization techniques such as trilateration- and GRNN-based localization [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Hybrid asset localization using light fidelity and Bluetooth Low Energy

Albraheem

Alshathri²,

Alsheddi³

et al. 2022

PLoS ONE

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Recently, there has been increasing interest in the field of indoor localization. This field of research can facilitate building and asset management. Although there are different technologies that can be used for localization, there are many limitations that need to be improved, and therefore there is a need to explore new technologies and alternatives that can improve indoor localization. It has been proven that visible light can be used to transfer data. A German physicist, Harald Haas, introduced the term “Li-Fi”, which stands for “light fidelity”, as a new technology that uses light as a medium to deliver data. Accordingly, in this study, we have proposed a hybrid asset localization system using Li-Fi and Bluetooth Low Energy (BLE). This system utilizes light-emitting diodes (LEDs) and BLE tags to detect the locations of assets in a smart building with the support of crowdsourcing technology. The system can make the management, maintenance, and localization process of equipment inside the buildings more easier. To achieve the required, the paper provides a comparison between different applications that have been developed for indoor localization using Li-Fi technology in order to highlight the limitations that need more improvement. The proposed system consists of a web-based administrator panel that allows the administrator to manage maps, assets, tags, LED lamps, and maintenance requests, as well as a mobile application that enables the user to locate, search and view asset information. In addition, the mobile application performs the process of crowdsourcing to update the assets’ locations. We experimentally explore the system’s functionalities and the results show that the system can accurately localize assets, and can detect Li-Fi signals from 55 lx and above within a range of 1.5 m. In addition, the BLE stickers can be detected up to 7 meters away, however, the crowdsourcing process to update the asset location is performed if the distance between the mobile application and the asset is less than or equal 1 m which gives accurate results.

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Support Vector Regression for Mobile Target Localization in Indoor Environments

Cited by 34 publications

References 28 publications

An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network

An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network

Constrained Localization: A Survey

Hybrid asset localization using light fidelity and Bluetooth Low Energy

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