Flood Control Distance Vector-Hop (FCDV-Hop) Localization in Wireless Sensor Networks

Zazali, Azyyati Adiah; Subramaniam, Shamala; Zukarnain, Zuriati Ahmad

doi:10.1109/access.2020.3038047

Cited by 12 publications

(8 citation statements)

References 51 publications

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“…The influence of radio asymmetry on the localization method is not described in this paper. Zazali et al (2020) [17] introduce an advanced algorithm aimed at optimizing the spatial allocation of sensor nodes within a network. The main objective of this study is to extend the network's operational lifespan by reducing the energy consumption of sensor nodes while simultaneously minimizing localization errors.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Node Localization on Sensor Internet of Things Networks Using Deep Learning and Virtual Node Simulation

Kanwar,

Aydin

2024

Electronics

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Localization is a primary concern for wireless sensor networks as numerous applications rely on the precise position of nodes. This paper presents a precise deep learning (DL) approach for DV-Hop localization in the Internet of Things (IoT) using the whale optimization algorithm (WOA) to alleviate shortcomings of traditional DV-Hop. Our method leverages a deep neural network (DNN) to estimate distances between undetermined nodes (non-coordinated nodes) and anchor nodes (coordinated nodes) without imposing excessive costs on IoT infrastructure, while DL techniques require extensive training data for accuracy, we address this challenge by introducing a data augmentation strategy (DAS). The proposed algorithm involves creating virtual anchors strategically around real anchors, thereby generating additional training data and significantly enhancing dataset size, improving the efficacy of DNNs. Simulation findings suggest that the proposed deep learning model on DV-Hop localization outperforms other localization methods, particularly regarding positional accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Efficient Node Localization on Sensor Internet of Things Networks Using Deep Learning and Virtual Node Simulation

Kanwar,

Aydin

2024

Electronics

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“…Specific symbols used in Equation ( 5) are described in the paper. 35 Nemer et al 36 studied various range-free localization techniques. They simulated five range-free algorithms (DV-Hop, DV-HopMAx, centroid, amorphous, and APIT) in MATLAB and compared them on different setups and topologies.…”

Section: F I G U R E 1 6 Machine Learning Techniques For Localizationmentioning

confidence: 99%

“…Zalzali et al 35 proposed a method that helps in efficient node deployment in a localization region. The developed algorithm increases the network lifetime and decreases the energy consumption and localization error.…”

Section: Detailed Analysis Of the Literaturementioning

confidence: 99%

“…The algorithm improves the localization error, energy consumption, and accuracy of the sensor nodes; from the distance of the nodes and the nodes with angles calculated, the transmission power is calculated using Equation ().

P_{r} goodbreak= c \frac{P_{\overset{t}{true}}}{{normalⅆ}^{a}} \Leftrightarrow d goodbreak= \sqrt[a]{\frac{P_{t}}{p_{r}}}

Specific symbols used in Equation () are described in the paper 35 …”

Section: Detailed Analysis Of the Literaturementioning

confidence: 99%

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A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

Yadav

Sharma

2022

Int J Communication

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“…The common methods of distance acquisition for the range-based localization scheme include time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), received signal strength (RSS) [15][16][17][18][19], etc. Unlike the range-based localization scheme, the range-free localization scheme does not need distance measurements and finds the relationship between nodes through the network connectivity or hop count, then solves the target node location using the centroid algorithm (CA), distance vector hop (DV-HOP), approximate point-in-triangulation test (APIT) [20][21][22][23], etc. In the marine ecological environment monitoring scenario, a high level of localization accuracy is essential.…”

Section: Introductionmentioning

confidence: 99%

Novel High-Precision and High-Robustness Localization Algorithm for Underwater-Environment-Monitoring Wireless Sensor Networks

Ma,

Xian,

et al. 2023

JMSE

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In marine ecological environment monitoring, the acquisition of node location information is crucial, and the absence of location information can render the collected data meaningless. Compared to the rest of the distance-based localization methods, the received signal strength (RSS)-based localization technique has gained significant interest due to its low cost and the absence of time synchronization. However, the acoustic signal propagates in the complex and changeable aqueous medium, and, in addition to the time-varying path loss factor (PLF), there is often a certain absorption loss, which seriously deteriorates the localization accuracy of the RSS-based technique. To address the above challenges, we propose a novel high-precision and high-robustness localization (NHHL) algorithm that introduces an estimation parameter to conjointly estimate the marine node location and the ambient PLF. Firstly, the original non-convex localization problem is converted into an alternating nonnegative constrained least squares (ANCLS) framework with the unknown PLF and absorption loss, and a two-step localization method based on the primitive dual interior point method and block co-ordinate update method is presented to find the optimal solution. In the first step, the penalty function is utilized to reformulate the localization problem and find an approximate solution. Nevertheless, due to inherent errors, it is unable to approximate the constraint boundary and the global optimum solution. Subsequently, in the second step, the original localization problem is further transformed into a generalized trust region sub-problem (GTRS) framework, and the approximate solution of the interior point method is utilized as the initial estimation, and then iteratively solved by block co-ordinate update to obtain the precise location and PLF conjointly. Furthermore, the closed-form expression of the Cramér–Rao lower bound (CRLB) for the case of the unknown path loss factor and absorption loss is derived to evaluate the our NHHL algorithm. Finally, the simulation results demonstrate the superiority of the presented NHHL algorithm compared with the selected benchmark methods in various marine simulation scenarios.

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Flood Control Distance Vector-Hop (FCDV-Hop) Localization in Wireless Sensor Networks

Cited by 12 publications

References 51 publications

Efficient Node Localization on Sensor Internet of Things Networks Using Deep Learning and Virtual Node Simulation

Efficient Node Localization on Sensor Internet of Things Networks Using Deep Learning and Virtual Node Simulation

A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

Novel High-Precision and High-Robustness Localization Algorithm for Underwater-Environment-Monitoring Wireless Sensor Networks

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