Research on DV-Hop improved algorithm based on dual communication radius

Li, Tuochen; Wang, Changzhu; Qi, Na

doi:10.1186/s13638-020-01711-7

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…This section analyzes the performance of the improved sparrow search algorithm with 20 benchmark functions; as shown in Table 2, most of the benchmark functions come from the literatures [20,42]. 3.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

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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: Simulation Results and Analysismentioning

confidence: 99%

“…Set the perfect hop count. Suppose the real distance between known nodes i and j is d ij ; the ratio of real distance between known nodes i, j and the maximum communication radius R is defined as the perfect hop count H ij [42]:…”

Section: D-hcssa Localization Algorithmmentioning

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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“…Many researchers put forward different ideas to enhance the localization accuracy of the algorithm: Tang et al used TDOA location feature to quantify the hop value of nodes in DV-Hop location algorithm to achieve a stable effect [ 32 ]. Li et al suggested increasing the communication radius to update the minimum hop-count value of unknown nodes to a smaller hop-count value, which solves the problem that the actual distance difference of the same hop-count value to a certain extent [ 33 ]. Cai et al proposed a new algorithm (N2-3DDV-Hop) to modify the average distance per hop and to optimize the error combined with NSGA-II algorithm [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Huang

Han

Cui

et al. 2021

Sensors

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In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

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“…fit fit P provide sufficient information to narrow the possible locations down to two. An additional circle that passes through the node may narrow the possibilities down to one unique location [17]. The principle of trilateration algorithm is shown in Figure 1 below.…”

Section: Trilateration Algorithmmentioning

confidence: 99%

“…Here, the NN is introduced to predict and correct the ranging error, and then the ABC algorithm is employed to optimize the NN parameters; the distance error was estimated by equation (11) and the prediction error is denoted as △E'. Thus, we have the positioning error correction equation: (17) During the NN-prediction of positioning error, the error of measured distance was determined by connection weights and thresholds. These weight and thresholds were optimized by the ABC algorithm.…”

Section: Error Correctionmentioning

confidence: 99%

A Novel Wireless Sensor Node Positioning Algorithm Based on Ant Colony Optimization Algorithm and Neural Network

Chen

2018

Int. J. Onl. Eng.

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<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">This paper aims to enhance the positioning accuracy of wireless sensor network (WSN) nodes. For this purpose, a WSN node positioning algorithm was proposed based on artificial bee colony (ABC) algorithm and the neural network (NN). First, the parameters between three anchor nodes and the target node were measured. Then, the ABC and NN were introduced to simulate and predict the ranging error, and the weight was determined according to the results. In the proposed algorithm, the cluster structure was effectively combined with the NN model. The weight of backpropagation NN was optimized by the ant colony optimization (ACO) algorithm. Then, the ACO-optimized NN was used to fuse the data collected by WSN nodes. The simulation results show that the proposed algorithm can improve the positioning accuracy of WSN nodes and reduce the time of the search. The research findings shed new light on the positioning of WSN nodes.</span>

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Research on DV-Hop improved algorithm based on dual communication radius

Cited by 14 publications

References 19 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

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

A Novel Wireless Sensor Node Positioning Algorithm Based on Ant Colony Optimization Algorithm and Neural Network

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