Soft Computing Paradigms Based Clustering in Wireless Sensor Networks: A Survey

Sharma, Richa; Vashisht, Vasudha; Singh, Umang

doi:10.1007/978-981-15-0372-6_11

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…It is the process of converting energy from the environment into electrical energy. Several obstacles may arise in the efective integration of energy harvesting technologies into WSNs [15][16][17][18][19][20].…”

Section: Energy Harvestingmentioning

confidence: 99%

Neural Network Clustering and Swarm Intelligence‐Based Routing Protocol for Wireless Sensor Networks: A Machine Learning Perspective

Balobaid

Ahamed

Shamsudheen

et al. 2023

Computational Intelligence and Neuroscience

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With no requirement for an established network infrastructure, wireless sensor networks (WSNs) are well suited for applications that call for quick network deployment. Military training and emergency rescue operations are two prominent uses of WSNs. The individual network nodes must carry out routing and intrusion detection because there is no predetermined routing or intrusion detection in a wireless network. WSNs can only manage a certain volume of data, and doing so requires a significant amount of energy to process, transmit, and receive. Since sensors have a modest energy source and a constrained bandwidth, they cannot transmit all of their data to a base station for processing and analysis. Therefore, machine learning (ML) techniques are needed for WSNs to facilitate data transmission. Other current solutions have drawbacks as well, such as being less reliable, more susceptible to environmental changes, converging more slowly, and having shorter network lifetimes. This study addressed problems with wireless sensor networks and devised an efficient clustering and routing algorithm based on machine learning. Results from simulations demonstrate that the proposed system beats previous state-of-the-art models on a variety of metrics, including accuracy, specificity, and sensitivity (0.93, 0.93, and 0.92 respectively).

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Section: Energy Harvestingmentioning

confidence: 99%

Neural Network Clustering and Swarm Intelligence‐Based Routing Protocol for Wireless Sensor Networks: A Machine Learning Perspective

Balobaid

Ahamed

Shamsudheen

et al. 2023

Computational Intelligence and Neuroscience

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“…With the rapid development of Internet of Things (IoT) technologies, various types of IoT devices (e.g., sensor nodes) can be deployed in large dam engineering to measure the different physical quantities and sense their changes in various regions of the structure, such as deformation, stress, pressure, etc. [1][2][3][4]. In the dam safety monitoring systems, the massive monitoring data are generated from the deployed sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

“…(2) Encoding. Multiple convolutional layers and maximum pooling layers are stacked alternately to compress the input data and obtain the feature representation FT.(3) Decoding.…”

mentioning

confidence: 99%

Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Chen

Mao

Wang

et al. 2020

Sensors

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Many various types of sensors have been installed to monitor the deformation and stress in the dam structure. It is difficult to directly evaluate the operation status of the dam structure based on the massive monitoring data. The sensor network is divided into multiple regions according to the design specifications, simulation data, and engineering experiences. The local results from sub-regions are integrated to achieve overall evaluation. However, it ignores the spatial distribution of sensors and the variation of time series, which cannot meet the real-time evaluation for the dam safety monitoring. If the network partitions can provide the preliminary foundation for analyzing the dynamic change laws of the dam’s working conditions in a real-way, we should consider the similarity of structure and stresses in the local region of the dam and the correlation among the monitoring data. A time-series denoising autoencoder (TSDA) is proposed to represent the spatial and temporal features of the nodes by compressing high-dimensional monitoring data. Then, a network partitioning algorithm (NPA) based on spatial-temporal features based on the TSDA is presented. The NPA ensures that the partition results can support the analysis of the physical change laws by introducing the auxiliary objective variable to optimize the network partition objective function. Experimental results on the public datasets and a real dataset from an arch dam demonstrate that the proposed network partition algorithm NPA can achieve better partition performance than TSDA+K-Means and TSDA+GMM. The NPA can improve the silhouette coefficient by 45.1% and 58.4% higher than the TSDA+K-Means and TSDA+GMM, respectively. The NPA can increase the Calinski-Harabaz Index by 30.8% and 61.6%, respectively.

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