A novel DV-Hop method based on coupling algorithm used for wireless sensor network localisation

Wang, Yechuang; Wang, Penghong; Zhang, Jiangjiang; Cai, Xingjuan; Li, Wuchao; Ma, Yanyan

doi:10.1504/ijwmc.2019.099027

Cited by 7 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The suggested approach's simulation and experimental findings are discussed. To improve the WSN localization accuracy, Wang et al [11] proposed a new coupling algorithm based on Bacterial Foraging Algorithm (BFA) and Glowworm Swarm Optimisation (GSO) (BFO-GSO). The algorithm has good convergence speed and the optimization performance is verified by CEC2013 benchmarks.…”

Section: Literature Reviewmentioning

confidence: 99%

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

Walia¹,

Singh²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Location information plays an important role in most of the applications in Wireless Sensor Network (WSN). Recently, many localization techniques have been proposed, while most of these deals with two Dimensional applications. Whereas, in Three Dimensional applications the task is complex and there are large variations in the altitude levels. In these 3D environments, the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level. For such applications, 2D localization models are not reliable. Due to this, the design of 3D localization systems in WSNs faces new challenges. In this paper, in order to find unknown nodes in Three-Dimensional environment, only single anchor node is used. In the simulation-based environment, the nodes with unknown locations are moving at middle & lower layers whereas the top layer is equipped with single anchor node. A novel soft computing technique namely Adaptive Plant Propagation Algorithm (APPA) is introduced to obtain the optimized locations of these mobile nodes. These mobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity (Degree of Irregularity (DOI)) value set to 0.01. The simulation results present that proposed APPA algorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error, computational time, and the located sensor nodes.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

Walia¹,

Singh²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Wang et al [25] proposed an effective integration framework to solve multi-objective optimization problems (MOPs), which combined different evolutionary operators with selection mechanisms, and the performance of the algorithm was maintained through cooperation and competition among populations. Wang et al [26] adopted different integration ideas and integrated eight different bat algorithm (BA) search modes to solve the single-objective complex DV-Hop location problem. Fan et al [27] proposed a multi-objective integration algorithm based on parameter and mutation strategy, which was suitable for different stages of evolution and solving different optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A many-objective evolutionary algorithm based on integrated strategy for skin cancer detection

2022

KSII TIIS

View full text Add to dashboard Cite

Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.

show abstract

Localization in Wireless Sensor Networks Using a Mobile Anchor and Subordinate Nodes

Kaushik

Lobiyal

2021

Applied Soft Computing and Communication Networks

View full text Add to dashboard Cite

A novel DV-Hop method based on coupling algorithm used for wireless sensor network localisation

Cited by 7 publications

References 0 publications

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

Three Dimensional Optimum Node Localization in Dynamic Wireless Sensor Networks

A many-objective evolutionary algorithm based on integrated strategy for skin cancer detection

Localization in Wireless Sensor Networks Using a Mobile Anchor and Subordinate Nodes

Contact Info

Product

Resources

About