Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN

Parras, Juan; Zazo, Santiago; Álvarez, Irina Argüelles; González, José Luis Sanz

doi:10.3390/s19163530

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…The reason is the movement of the mobile anchor towards the curve path is not easy. The mobile robot needed to program with the current path so that it can complete a random walk to cover the entire network area [12].…”

Section: Graphical Scenarios: (Topology Constraints)mentioning

confidence: 99%

Performance Analysis of Wireless Sensor Network Localization algorithms

Kevin¹,

viely²,

Samarakoon³

2019

IJCNA

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In recent years, the use of Wireless Sensor Networks (WSNs) has been increasing. Like WSNs protocols development, localization is the main issue and needed to address several areas including 3D and mobile anchor-based localization algorithms. WSNs are being used widely in different fields like environment, disaster relief, target tracking and several other applications like the SCADA system for the high voltage of electricity. In many applications, node localization is very important in which the system needed to know the exact location of the event. This paper addresses the main ideas and techniques published in recent years. We highlight the error performance analysis between the algorithms stated static 3D localization.

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Section: Graphical Scenarios: (Topology Constraints)mentioning

confidence: 99%

Performance Analysis of Wireless Sensor Network Localization algorithms

Kevin¹,

viely²,

Samarakoon³

2019

IJCNA

View full text Add to dashboard Cite

show abstract

“…In general, the range-based method has higher positioning accuracy and higher complexity than the range-free method. There exist many range-based positioning methods, such as those based on time of arrival (TOA) [6,7], time difference of arrival (TDOA) [8][9][10], received signal strength indicator (RSSI) and so on [11,12]. As for range-based localization methods, they consist of two steps in the positioning process: distance estimation and position calculation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Localization Algorithm Based on RSSI and Multilateration for Indoor Environments

Yuan

Yue

et al. 2022

Electronics

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Indoor localization algorithms based on the received signal strength indicator (RSSI) in wireless sensor networks (WSNs) have higher localization accuracy than other range-free methods. This paper considers indoor localization based on multilateration and averaged received signal strength indicator (RSSI). We propose an approach called weighted three minimum distances method (WTM) to deal with the poor accuracy of distances deduced from RSSI. Using a practical localization system, an experimental channel model is deduced to assess the performance of the proposed localization algorithm in realistic conditions. Both simulated data and measured data are used to verify the proposed method. Compared with nonlinear least squares (NLS), Levenberg–Marquardt algorithm (LM) and semidefinite programming method (SDP), simulations show that the proposed method exhibits better localization accuracy but consumes more calculation time.

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Localization in WSN‐Assisted IoT Networks Using Machine Learning Techniques for Smart Agriculture

Singh,

Yadav,

Rishiwal

et al. 2024

Int J Communication

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It has been observed that the agriculture sector has picked exponential growth with the help of the integration of advanced technology like wireless sensor networks (WSNs), the Internet of Things (IoT), and machine learning (ML). They have not only created new opportunities but also boosted the efficiency and productivity in the sector. Out of so many significant hurdles, accurately pinpointing the positions of the sensor nodes and agricultural assets like crops, livestock, and machinery within the field is one major challenge. Merging ML with a WSN‐assisted IoT (WIoT) network has offered a promising solution to overcome the localization challenge for smart agriculture. This integration will greatly benefit agricultural practices, such as precision agriculture, fast decision‐making, accurate positioning of sensor nodes, monitoring, and managing real‐time data effectively. This paper comprehensively discusses state‐of‐the‐art techniques and methodologies employed in localization within WIoT networks for smart agriculture. It explores various ML algorithms, which include reinforcement learning, supervised learning, and unsupervised learning, to perform the localization activity precisely. Moreover, it explores how fusion technologies like WSN, IoT, ML, and sensors will enhance localization accuracy and reliability in various agriculture activities. Additionally, the paper discusses the application of localization in the agriculture sector, such as crop monitoring, livestock management, precision agriculture, environmental conditions, and crop monitoring. It also discusses the challenges and obstacles in the path domain of various evaluation parameters like energy efficiency, scalability, and robustness, along with research and societal implications. Overall, the paper provides valuable guidance and outlines potential directions for future research in ML‐driven localization in WIoT for smart agriculture, offering a clear roadmap for researchers.

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Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN

Cited by 9 publications

References 30 publications

Performance Analysis of Wireless Sensor Network Localization algorithms

Performance Analysis of Wireless Sensor Network Localization algorithms

A Novel Localization Algorithm Based on RSSI and Multilateration for Indoor Environments

Localization in WSN‐Assisted IoT Networks Using Machine Learning Techniques for Smart Agriculture

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