Three-dimensional WSN Node Localization Method Based on LSSVR Optimized by DE Algorithm

2014 ◽  
Vol 7 (2) ◽  
pp. 123-129 ◽  
Author(s):  
Lieping Zhang ◽  
Wenjun Ji ◽  
Ming Chen
Keyword(s):  
Three Dimensional ◽  
Node Localization ◽  
Localization Method ◽  
De Algorithm

Intelligent Three-dimensional Node Localization Algorithm Using Dynamic Path Planning

2021 ◽  
Vol 14 ◽  
Author(s):  
Songhao Jia ◽  
Cai Yang ◽  
Xing Chen ◽  
Yan Liu ◽  
Fangfang Li
Keyword(s):  
Path Planning ◽  
Dynamic Stiffness ◽  
Three Dimensional ◽  
Localization Algorithm ◽  
Node Localization ◽  
Positioning Accuracy ◽  
Dynamic Path Planning ◽  
Anchor Nodes ◽  
Simulation Results ◽  
Dynamic Path

Background: In the applications of wireless sensor network technology, three-dimensional node location technology is crucial. The process of node localization has some disadvantages, such as the uneven distribution of anchor nodes and the high cost of the network. Therefore, the mobile anchor nodes are introduced to effectively solve accurate positioning. Objective: Considering the estimated distance error, the received signal strength indication technology is used to optimize the measurement of the distance. At the same time, dynamic stiffness planning is introduced to increase virtual anchor nodes. Moreover, the bird swarm algorithm is also used to solve the optimal location problem of nodes. Method: Firstly, the dynamic path is introduced to increase the number of virtual anchor nodes. At the same time, the improved RSSI distance measurement technology is introduced to the node localization. Then, an intelligent three-dimensional node localization algorithm based on dynamic path planning is proposed. Finally, the proposed algorithm is compared with similar algorithms through simulation experiments. Results: Simulation results show that the node coordinates obtained by the proposed algorithm are more accurate, and the node positioning accuracy is improved. The execution time and network coverage of the algorithm are better than similar algorithms. Conclusion: The proposed algorithm significantly improves the accuracy of node positioning. However, the traffic of the algorithm is increased. A little increase in traffic in exchange for positioning accuracy is worthy of recognition. The simulation results show that the proposed algorithm is robust and can be implemented and promoted in the future.

Inverse modeling of transient three-dimensional core-scale two-phase flows

2021 ◽  
Author(s):  
Andrea Manzoni ◽  
Aronne Dell'Oca ◽  
Martina Siena ◽  
Alberto Guadagnini
Keyword(s):  
Capillary Pressure ◽  
Random Noise ◽  
Three Dimensional ◽  
Population Based ◽  
Model Parameters ◽  
Reference Scenario ◽  
Two Phase ◽  
De Algorithm ◽  
Continuum Scale ◽  
Available Information

<p>We consider transient three-dimensional (3D) two-phase (oil and water) flows, taking place at the core-scale. In this context, we aim at exploiting the full information content associated with available information of (i) the 3D distribution of oil saturation and (ii) the overall pressure difference across the rock sample, to estimate the set of model parameters. We consider a continuum-scale description of the system behavior upon relying on the widely employed Brooks-Corey model for the characterization of relative permeabilities and on the capillary pressure correlation introduced by Skjaeveland et al. (2000). To provide a transparent way of assessing the results of the inversion, we rely on a synthetic reference scenario. The latter is intended to mimic having at our disposal 3D and section-averaged distributions of (time-dependent) oil saturations of the kind that can be acquired during typical laboratory experiments. These are in turn corrupted by way of a random noise, to address the influence of experimental uncertainties. We focus on diverse scenarios encompassing imbibition and drainage conditions. We employ two population-based optimization algorithms, i.e., (i) the particle swarm optimization (PSO); and (ii) the differential evolution (DE), which enable one to effectively tackle the high-dimensionality parameters space (i.e., 12 dimensions in our setting) we consider. Model calibration results are of satisfactory quality for the majority of the tested scenarios, whereas the DE algorithm is associated with highest effectiveness.</p><p><strong>References</strong></p><p>S.M. Skjaeveland; L.M. Siqveland; A. Kjosavik; W.L. Hammervold Thomas; G.A. Virnovsky (2000). Capillary Pressure Correlation for Mixed-Wet Reservoirs SPE Res Eval & Eng 3 (01): 60–67. https://doi.org/10.2118/60900-PA</p>

scholarly journals Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 343 ◽  
Author(s):  
Dezhi Han ◽  
Yunping Yu ◽  
Kuan-Ching Li ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Random Mutation ◽  
Localization Accuracy ◽  
De Algorithm ◽  
Distance Vector

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

EODL: Energy Optimized Distributed Localization Method in three-dimensional underwater acoustic sensors networks

2018 ◽  
Vol 141 ◽  
pp. 179-188 ◽  
Author(s):  
Zhuo Wang ◽  
Xiaoning Feng ◽  
Guangjie Han ◽  
Yancheng Sui ◽  
Hongde Qin
Keyword(s):  
Three Dimensional ◽  
Acoustic Sensors ◽  
Underwater Acoustic ◽  
Localization Method

scholarly journals Genetic Algorithm Approach to the 3D Node Localization in TDOA Systems

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3880 ◽  
Author(s):  
Javier Díez-González ◽  
Rubén Álvarez ◽  
David González-Bárcena ◽  
Lidia Sánchez-González ◽  
Manuel Castejón-Limas ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Sensor Deployment ◽  
Selection Strategy ◽  
Ground Vehicles ◽  
Node Localization ◽  
Heuristic Solution ◽  
Positioning Systems ◽  
Genetic Algorithm Approach

Positioning asynchronous architectures based on time measurements are reaching growing importance in Local Positioning Systems (LPS). These architectures have special relevance in precision applications and indoor/outdoor navigation of automatic vehicles such as Automatic Ground Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs). The positioning error of these systems is conditioned by the algorithms used in the position calculation, the quality of the time measurements, and the sensor deployment of the signal receivers. Once the algorithms have been defined and the method to compute the time measurements has been selected, the only design criteria of the LPS is the distribution of the sensors in the three-dimensional space. This problem has proved to be NP-hard, and therefore a heuristic solution to the problem is recommended. In this paper, a genetic algorithm with the flexibility to be adapted to different scenarios and ground modelings is proposed. This algorithm is used to determine the best node localization in order to reduce the Cramér-Rao Lower Bound (CRLB) with a heteroscedastic noise consideration in each sensor of an Asynchronous Time Difference of Arrival (A-TDOA) architecture. The methodology proposed allows for the optimization of the 3D sensor deployment of a passive A-TDOA architecture, including ground modeling flexibility and heteroscedastic noise consideration with sequential iterations, and reducing the spatial discretization to achieve better results. Results show that optimization with 15% of elitism and a Tournament 3 selection strategy offers the best maximization for the algorithm.

scholarly journals Research on Algorithm of Three-Dimensional Wireless Sensor Networks Node Localization

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jiang Minlan ◽  
Luo Jingyuan ◽  
Zou Xiaokang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Optimization Method ◽  
Wireless Sensor ◽  
Minimum Problem ◽  
Localization Algorithm ◽  
Node Localization ◽  
Anchor Nodes

This paper proposes a three-dimensional wireless sensor networks node localization algorithm based on multidimensional scaling anchor nodes, which is used to realize the absolute positioning of unknown nodes by using the distance between the anchor nodes and the nodes. The core of the proposed localization algorithm is a kind of repeated optimization method based on anchor nodes which is derived from STRESS formula. The algorithm employs the Tunneling Method to solve the local minimum problem in repeated optimization, which improves the accuracy of the optimization results. The simulation results validate the effectiveness of the algorithm. Random distribution of three-dimensional wireless sensor network nodes can be accurately positioned. The results satisfy the high precision and stability requirements in three-dimensional space node location.

Three Tiered Self-Localization of Two Position Estimation Using Three Dimensional Environment Map and Gyro-Odometry

2014 ◽  
Vol 26 (2) ◽  
pp. 196-203 ◽  
Author(s):  
Kazuya Okawa ◽  
Keyword(s):  
Three Dimensional ◽  
Position Estimation ◽  
Map Matching ◽  
Localization Method ◽  
Challenge Course ◽  
Environment Map ◽  
Improve Accuracy ◽  
Environment Maps ◽  
Using Data ◽  
Map Data

As in the Tsukuba Challenge, any robot that autonomously moves around outdoors must be capable of accurate self-localization. Among many existing methods for robot self-localization, the most widely used is for the robot to estimate its position by comparing it with prior map data actually acquired using its sensor while it moves around. Although we use such a self-localization method in this study, this paper proposes a new method to improve accuracy in robot self-localization. In environments with few detected objects, a lack of acquired data very likely will lead to a failure in map matching and to erroneous robot self-localization. Therefore, a method for robot self-localization that uses three-dimensional environment maps and gyro-odometry depending on the situation is proposed. Moreover, the effectiveness of the proposed method is confirmed by using data from the 2013 Tsukuba Challenge course.

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