Modified Zeroing Neurodynamics Models for Range-Based WSN Localization From AOA and TDOA Measurements

Wang, Lijuan; Su, Dan; Liu, Mei; Du, Xiujuan

doi:10.1109/jsen.2022.3177409

Cited by 17 publications

(8 citation statements)

References 41 publications

(71 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pollen is spread over long distances by pollinators such as birds, insects, bees, or bats. Its spread can be modeled using the Lévy distribution, as shown in Equation (7).…”

Section: Global Pollinationmentioning

confidence: 99%

“…The localization methods can be classified into range-based [ 5 ] and range-free [ 6 ] localization methods based on whether the actual measurement of the distance between nodes is necessary. The former achieves the estimation of the distance between nodes based on the angle of arrival [ 7 ], time of arrival [ 8 ], and time difference of arrival [ 9 ] of the signal or based on the received signal strength. Although this type of method has high positioning accuracy, it requires additional equipment to achieve the corresponding measurement, which is costly and not suitable for monitoring of large areas.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN

Zheng

Yuan

Xie

et al. 2023

Sensors

View full text Add to dashboard Cite

Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness.

show abstract

“…Pollen is spread over long distances by pollinators such as birds, insects, bees, or bats. Its spread can be modeled using the Lévy distribution, as shown in Equation (7).…”

Section: Global Pollinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN

Zheng

Yuan

Xie

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Various localization methods have been proposed for use in WSN and IoT. Some of these methods include distance vector-hop (DV-Hop) [7], angle of arrival (AOA) [8], time-difference-of-arrival (TDOA) [9], artificial neural networks (ANN) [10], and deep learning [11].…”

Section: Introductionmentioning

confidence: 99%

Localization in a wireless sensor network with a geometric approach of Trilateration and mathematical modeling of sine and cosine functions

Ghadami

2024

Preprint

View full text Add to dashboard Cite

Wireless sensor network (WSN) has several applications in agriculture, military, rescue, and environmental applications. In a WSN, a large number of sensor nodes are scattered in an operating environment and gather environmental information. The data collected by the WSN is sent to an application in the cloud computing layer or base station for analysis. Gathering information without having their localization and position is not of high value, and accurate localization and position determination of sensor nodes are required in various applications. One method for localization is to use a Global Positioning System (GPS) and install it on all sensor nodes. localization with GPS costs a lot, and on the other hand, this method consumes a lot of energy for localization. DV-Hop technique is a practical method for positioning by reducing the positioning cost. The challenge of the DV-Hop approach for localization is the significant error in locating sensor nodes without a GPS. In this article, a practical localization method is presented by combining the DV-Hop method and the geometric localization method of three references. In this study, the sensor nodes first estimate their distance from the three guide nodes with the step measurement algorithm and then locate them using the Trilateration method. In the proposed approach, the sine and cosine optimization algorithm (SCA) are applied to minimize the positioning error. The evaluations illustrate that the localization error of the suggested approach is lower than the DV-Hop approach. The proposed positioning algorithm has less positioning error than the whale optimization algorithm (WOA), the Harris Hawk Optimization (HHO) algorithm, and the Jellyfish Search optimizer (JSO). The evaluations show that the proposed method of advanced localization methods based on steps such as GSDV-Hop and MGDV-Hop has fewer errors in localization.

show abstract

“…Therefore, how to improve the node localization accuracy is one of the goals pursued by researchers [5]. Node localization techniques are divided into two categories: range-based [6] and range-free [7], the former relying on whether the distance measurement is accurate enough, and the latter estimating the location of unknown nodes based on node connectivity, signal reception strength and routing information, etc. The Distance Vector Hop method (DV-Hop) [8] is one of the representative methods.…”

Section: Introductionmentioning

confidence: 99%

Gray Wolf Optimizer with Communication Strategy Based on DV-Hopfor Nodes Location ofWireless Sensor Networks

Yuan

Qing

et al. 2023

Preprint

View full text Add to dashboard Cite

Node localization has been one of the critical and significant research problems in wireless sensornetworks (WSN). The DV-Hop method determines the location of an unknown node based onthe anchor node location and the multi-hop forwarding principle, but the method falls far shortof satisfactory localization accuracy. Therefore, an improved Gray Wolf Optimizer (GWO) withcommunication strategy (IGWO-CS) is proposed in this paper. A convergence function based onthe sine function is proposed to eliminate the limitation of the convergence factor 𝑎 on the searchability of the GWO algorithm. A communication strategy based on Lévy flight is designed, which canimprove the efficiency of information interaction between wolves 𝛼, 𝛽 and 𝛿 while also improving thesearch accuracy and convergence speed. Extensive experimental results on 23 well-known benchmarkfunctions and unknown nodes location in WSN demonstrate the effectiveness of the proposed IGWO-CS method and also show sufficient competitiveness compared with other state-of-the-art algorithms,while the proposed IGWO-CS algorithm can achieve satisfactory performance in a short time.

show abstract

Modified Zeroing Neurodynamics Models for Range-Based WSN Localization From AOA and TDOA Measurements

Cited by 17 publications

References 41 publications

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN

Localization in a wireless sensor network with a geometric approach of Trilateration and mathematical modeling of sine and cosine functions

Gray Wolf Optimizer with Communication Strategy Based on DV-Hopfor Nodes Location ofWireless Sensor Networks

Contact Info

Product

Resources

About