Range Free Localization for Three Dimensional Wireless Sensor Networks Using Multi Objective Particle Swarm Optimization

Kanwar, Vivek; Kumar, Ashok

doi:10.1007/s11277-020-07902-1

Cited by 27 publications

(15 citation statements)

References 23 publications

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“…Due to the large number of parameters and related equations in the operation of this method, the calculation difficulty and algorithm complexity are increased during the calculation. In Kanwar and Kumar [35] firstly, the localization of the target node is corrected by the hyperbolic method. Then, the optimal target node localization was obtained by iterating through single-objective and multiobjective functions, respectively, which improved the robustness of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

Gou

et al. 2022

Wireless Communications and Mobile Computing

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Acquiring precise localization information of sensor nodes is very important in wireless sensor networks. The 3DDV-hop localization algorithm suffers from large localization errors and high energy consumption. In order to improve positioning accuracy and reduce energy consumption, a 3DDV-hop node localization algorithm (3D-HCSSA) based on hop size correction and improved sparrow search optimization is proposed. The algorithm redefines the amendment factor and reduces the cumulative error caused by the hop counts in the traditional algorithm. A maximum distance similar link method based on a similar path search is proposed to find the most similar known node path pair from the target node to the noncoplanar known node link and correct the hop size between multihop counts. The sparrow search algorithm is improved by using the k -means clustering and sine cosine search strategy, which solves the problem that the traditional sparrow algorithm is easy to fall into the local optimum, accelerates the convergence speed, corrects the position deviation of the target node, and improves the positioning accuracy. Experiments demonstrate that the 3D-HCSSA algorithm can improve positioning accuracy and reduce energy consumption. Compared with the 3DDV-hop algorithm, 3D-GAIDV-hop algorithm, and HCLSO-3D algorithm, the 3D-HCSSA positioning accuracy is significantly improved.

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Section: Related Workmentioning

confidence: 99%

Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

Gou

et al. 2022

Wireless Communications and Mobile Computing

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“…The proposed NN localization approach demonstrated higher accuracy compared to the centroid localization algorithm [14]. showcased that the localization errors obtained by the multi-objective PSO outperformed those achieved with a single-objective PSO approach [16]. Sharma et al utilized fuzzy logic, a soft computing tool, to enhance localization accuracy in 3D-WSNs.…”

Section: Related Workmentioning

confidence: 98%

Improved Tree Dimensional DV-Hop Protocol for Large Scale Range-Free Wireless Sensors Network

Liouane

Amdouni

Hajjaji

et al. 2023

Preprint

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Location plays a crucial role in many applications of Wireless Sensor Networks (WSNs), and accurate sensor localization is an important aspect of the acquired data. While connectivity algorithms are commonly used for localizing multi-hop WSNs because their simplicity and acceptable accuracy, their effectiveness can be limited in two-dimensional (2D) or three-dimensional (3D) environments. An analytic model that incorporates hop size quantization and the Recursive Least Squares (RLS) method can be advantageous for Range-Free 3D wireless sensor networks (WSNs) in localization. This approach reduces computational complexity, memory requirements, and localization errors. The third dimension significantly impacts localization accuracy, necessitating the development of effective self-localization algorithms for 3D WSNs. This article introduces a novel probabilistic quantization technique for hop sizes in 3D-WSNs, specifically designed to address the uniform distribution of sensor nodes. The RLS method is employed as an adaptive filtering algorithm to recursively estimate the positions of sensor nodes in the system by minimizing the sum of squared errors between actual measured values and predicted values. Through extensive simulations conducted in isotropic settings under various conditions, the proposed algorithms are evaluated based on their average localization error performance. The simulation data clearly demonstrate that the suggested localization algorithm outperforms previous 3D-DV-Hop heuristics in terms of accuracy. The proposed localization method for 3D-WSNs successfully decreases the average localization error of nodes and achieves superior location accuracy when utilizing predicted hop quantization for hop-size estimation and the RLS algorithm for position estimation compared to competing approaches.

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“…Reference [16] proposed a concept of continuous jump values and derives its calculation method, which improves the localization accuracy of the algorithm to some extent, but does not consider the influence of the average jump distance on the localization accuracy of the algorithm. Reference [17] proposed a multi-objective particle swarm algorithm based on the optimization of the 3D DV-Hop algorithm, which replaces the original single objective function with a multi-objective function. In addition, it uses a particle swarm algorithm to solve for the unknown node coordinates, reducing the positioning error of the algorithm, but ignoring the errors generated in the first two stages and the problem that the particle swarm algorithm tends to fall into local optimum solutions.…”

Section: Related Workmentioning

confidence: 99%

A Three-Dimensional DV-Hop Algorithm Based on the Improved Grey Wolf Algorithm for Wireless Sensor Networks

Shuai

Liu

2023

Preprint

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Location technology has become one of the core technologies of the Internet of Things, especially for wireless sensor networks. The DV-Hop algorithm is used in various situations as one of the representative algorithms. Aiming at the large positioning error of the DV-Hop algorithm, this paper proposes a three-dimensional DV-Hop algorithm based on the improved Grey Wolf algorithm (TDIGW). First of all, the algorithm refines the number of hops through a multi-communication radius strategy, which reduces errors caused by the number of node hops. Then, mean square error is introduced to optimize the average hop distance of the beacon nodes. Finally, the grey wolf algorithm with dynamic weight factor optimization is used instead of least squares to calculate the coordinate positions of the unknown nodes. The simulation results show that compared with the traditional 3D-DVHop algorithm and other algorithms. the average localization error of this algorithm is reduced by 20.13%, 12.62% and 8.98% respectively. The algorithm proposed in this paper reduces the localization error effectively.

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Range Free Localization for Three Dimensional Wireless Sensor Networks Using Multi Objective Particle Swarm Optimization

Cited by 27 publications

References 23 publications

Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

Improved Tree Dimensional DV-Hop Protocol for Large Scale Range-Free Wireless Sensors Network

A Three-Dimensional DV-Hop Algorithm Based on the Improved Grey Wolf Algorithm for Wireless Sensor Networks

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Range Free Localization for Three Dimensional Wireless Sensor Networks Using Multi Objective Particle Swarm Optimization

Cited by 27 publications

References 23 publications

Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search

Improved Tree Dimensional DV-Hop Protocol for Large Scale Range-Free Wireless Sensors Network

​A Three-Dimensional DV-Hop Algorithm Based on the Improved Grey Wolf Algorithm for Wireless Sensor Networks

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A Three-Dimensional DV-Hop Algorithm Based on the Improved Grey Wolf Algorithm for Wireless Sensor Networks