Data acquisition in large-scale wireless sensor networks using multiple mobile sinks: a hierarchical clustering approach

Srinivas, Madana; Amgoth, Tarachand

doi:10.1007/s11276-021-02845-2

Cited by 7 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the detection process of GPR, for different polarization measurement channels, the detection area and the working frequency band of the system are the same, but the difference is only the reflectivity of the detection scene [19]. erefore, for XX, XY, YX, and YY polarization channels, δ l has a common sparse support set, namely, the positions of the non-zero elements of δ l remain unchanged in the measurement, while the positions of the non-zero elements of δ l correspond to different element values [20].…”

Section: Polarization Gpr Imaging Based On the MMV Modelmentioning

confidence: 99%

Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

Kang

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to solve the problem of low positioning accuracy of mechanical fault diagnosis, a polarization GPR imaging reconstruction algorithm based on the MMV model was proposed. The algorithm was mainly based on the joint processing of the measured data of multiple polarization channels to achieve the reconstruction of the reflectance of the detection scene corresponding to each polarization channel. The simulation data processing results based on FDTD showed that compared with the traditional SMV model polarization imaging algorithm, the proposed imaging algorithm could improve the accuracy of target location reconstruction and the ability of background clutter suppression significantly. Compared with the SMV model, TCR obtained by the MMV model increased by 30%. As for the imaging results at different noise ratios, TCR obtained by the MMV model was 10% higher than that obtained by the SMV model. And when the ratio of available real data samples decreased to 25%, the sample data generation based on the adversarial generation network could greatly improve the classification accuracy of the fault diagnosis model. It could realize the detection of the target better, so as to locate faults accurately.

show abstract

Section: Polarization Gpr Imaging Based On the MMV Modelmentioning

confidence: 99%

Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

Kang

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In addition, related scholars have proposed specifically threshold-based decomposition strategies: decomposition of decision variables by pre-setting the relevant thresholds for subgroups. Examples include RDG (Sun et al, 2018), RDG3 (Sun, 2019), and DGSC (Srinivas and Amgoth, 2022). RDG3 solves the overlapping LSGO problem by setting a threshold to specify the size of subcomponents.…”

Section: Decomposition Strategymentioning

confidence: 99%

Large-Scale Evolutionary Optimization Approach Based on Decision Space Decomposition

Chang²,

Yu³

2022

Front. Energy Res.

View full text Add to dashboard Cite

The identification of decision variable interactions has a crucial role in the final outcome of the algorithm in the large-scale optimization domain. It is a prerequisite for decomposition-based algorithms to achieve grouping. In this paper, we design a recognition method with higher efficiency and grouping accuracy. It is based on the decomposition strategy of min hash to solve large-scale global optimization (LSGO) problems, called MHD. Our proposed method focuses on discovering the interactions of decision variables through min hash and forming subcomponents with a principle that the interdependencies between these subcomponents are maintained at a minimal level. This is described as follows: first, the min hash performs several permutations of the vector composed of decision variables. Second, the index value of the first non-zero row of the vector after rearrangement is found to obtain the new feature vector. Third, the probability of identical data at each position is calculated based on the new feature vector to decide whether there are some certain interactions between the decision variables. The advantages of min hash are: simpler computation and greater efficiency improvement than comparison between two or two decision variables; ability to find similar decision variables very quickly; and ability to cluster decision variables in a simple way. Therefore, the efficiency as well as the reliability of MHD is guaranteed. On the accuracy aspect, the proposed algorithm performs well in various types of the large-scale global optimization benchmark test function. Finally, the experimental results analysis and summarize the performance competitiveness of our proposed MHD algorithm from several aspects when it is used within a co-evolutionary framework.

show abstract

Energy-aware mobile sink visiting nodes selection using a mean-shift clustering strategy for data accumulation in WSNs

Sulakshana

Kamatam

2023

Telecommun Syst

View full text Add to dashboard Cite

Data acquisition in large-scale wireless sensor networks using multiple mobile sinks: a hierarchical clustering approach

Cited by 7 publications

References 31 publications

Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

Large-Scale Evolutionary Optimization Approach Based on Decision Space Decomposition

Energy-aware mobile sink visiting nodes selection using a mean-shift clustering strategy for data accumulation in WSNs

Contact Info

Product

Resources

About