Fuzzy Weighted Centroid Localization With Virtual Node Approximation in Wireless Sensor Networks

Phoemphon, Songyut; So-In, Chakchai; Leelathakul, Nutthanon

doi:10.1109/jiot.2018.2811741

Cited by 46 publications

(17 citation statements)

References 28 publications

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“…1 illustrates the framework of the proposed algorithm. The centroid algorithm is to calculate the centroid of the particle population [29] by applying the momentum moment theorem [14], [25], [30]. Combining the centroid with the average particle velocity, the scalar representation is transformed into the momentum.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Zhang

et al. 2020

IEEE Access

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The localization problem of target nodes remains unresolved, especially in large-scale and complex environments. In this paper, we propose a particle centroid drift (PCD) algorithm to reduce the distance errors between nodes and obtain the particle aggregation region by using the drift vector. First, we use the particle quality prediction function to obtain the particles in a high-likelihood region. The high-quality particles have high probability in the calculation, which can increase the number of effective particles and enable avoiding particle degradation. Then, the centroid drift vector is used to make the particle distribution similar to the actual reference distribution. Experiments are conducted on state-space models: the local movement where 55% nodes are moving and the globe movement where 100% nodes are moving. The results show that the proposed algorithm has low estimation errors, a good tracking effect and an acceptable time complexity.

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Section: Proposed Algorithmmentioning

confidence: 99%

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Zhang

et al. 2020

IEEE Access

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“…The typical range-free location algorithms are multidimensional scaling-map (MDS-MAP), distance vector-hop (DV-Hop), centroid, and convex position estimation (CPE) [26][27][28][29]. The CPE algorithm transforms the WSN positioning problem into a convex optimization planning problem.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

Fang

Chen

2020

Sensors

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The Global Positioning System (GPS) is unable to provide precise localization services indoors, which has led to wireless sensor network (WSN) localization technology becoming a hot research issue in the field of indoor location. At present, the ranging technology of wireless sensor networks based on received signal strength has been extensively used in indoor positioning. However, wireless signals have serious multipath effects in indoor environments. In order to reduce the adverse influence of multipath effects on distance estimation between nodes, a multi-channel ranging localization algorithm based on signal diversity is herein proposed. In real indoor environments, the parameters used for multi-channel localization algorithms are generally unknown or time-varying. In order to increase the positioning accuracy of the multi-channel location algorithm in a multipath environment, we propose an optimal multi-channel trilateration positioning algorithm (OMCT) by establishing a novel multi-objective evolutionary model. The presented algorithm utilizes a three-edge constraint to prevent the traditional multi-channel localization algorithm falling into local optima. The results of a large number of practical experiments and numerical simulations show that no matter how the channel number and multipath number change, the positioning error of our presented algorithm is always smaller compared with that of the state-of-the-art algorithm.

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“…Phoemphon et al [7] by the technique of fine-tuning with F-based centroid localization method the location inaccuracies by FLS has been improved. The utilization of the virtual anchor has been proposed for the coverage of unknown nodes.…”

Section: Related Workmentioning

confidence: 99%

“…For the estimation of location and area, where directions and location value are not known at first. It should be possible by the assistance of the absolute position values of a couple of sensors with its respective estimations [7]. The sensor whose coordinate's information is known then it is referred as reference nodes or anchor nodes [8] [9].…”

Section: Introductionmentioning

confidence: 99%

H-Best Particle Swarm Optimization Based Localization Algorithm for Wireless Sensor Network

kamal,

Sharma,

Vandana

2019

IJEAT

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Discussion of the work, which proposed the idea of virtual anchor nodes for the localization of the sensor nodes, with having the movement of single sensor node in the circular movement with being optimized by the HPSO. For the ranging the RSSI model has been proposed in the algorithm. As a reference node, single anchor node has been used for the localization of whole network. As of the random deployment of the sensor nodes (target nodes), when the target nodes fall under the range of the mobile anchor node, the Euclidean distance between the target node and mobile anchor node is being calculated. After the calculation of the Euclidean distance the two anchor nodes are being deployed with a difference of 600 angle. Using the directional information the projecting of virtual anchor nodes is done, to which the virtual anchor nodes helps in the calculation of the 2D coordinates. While the calculation the mobile sensor node follow ups the circular path. The mobile sensor node considering at a center of the area marks up distance of its maximum range, and with that distance as a radius its goes for other circular path movement if all sensor nodes don’t fell to its range. With its movement at constant velocity the algorithm runs again and again. The performance of the algorithm are done on the factors of the average localization error and convergence time. The problem as of the LoS, with the virtual anchor nodes have been minimized.

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Fuzzy Weighted Centroid Localization With Virtual Node Approximation in Wireless Sensor Networks

Cited by 46 publications

References 28 publications

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

H-Best Particle Swarm Optimization Based Localization Algorithm for Wireless Sensor Network

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