Improve Performance of Wireless Sensor Network Clustering Using Mobile Relay

Gupta, Nidhi; Pawar, Pranav M.; Jain, Satbir

doi:10.1007/s11277-019-06769-1

Cited by 5 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, in the traditional wireless communication data transmission method, compressive sensing can ensure that the model as a whole completes compressive sampling of data at a lower cost, but compressive sensing also affects the sensitive characteristics of the measurement matrix. It reduces the data reconstruction accuracy and leads to its limited application prospects [19]. In addition, the data transmission frame structure in wireless communication networks is shown in Fig.…”

Section: Construction Of Data Transmission Model For Wireless Communi...mentioning

confidence: 99%

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

Juan Wang

2023

Journal of Computers

View full text Add to dashboard Cite

<p>With the maturity and development of new generation communication technology, the demand for wireless network communication quality is getting higher and higher. However, the existing wireless network data transmission models are greatly affected by the signal quality in the data transmission process, which seriously affects the quality of data transmission. To address the problem of signal perturbation affecting communication quality in wireless network communication, this study designs an improved perturbation compression reconstruction algorithm that can be used to process block-structured data based on the block sparse structure in the original signal, and constructs a single-user wireless network communication data transmission model based on this algorithm. The performance simulation test of the model shows that the MSE values of the model are lower than those of the conventional and perfect schemes used as comparisons under the same noise conditions and the transmitted compressed data length conditions. When the variance of Gaussian white noise is 0.125, the MSEs of the research design model, the conventional scheme and the perfect scheme are 0.031, 0.048, and 0.047, respectively. Experimental data show that the improved disturbance compressed sensing reconstruction algorithm can better reconstruct the compressed information and reduce the information loss caused by the disturbance in the compression process on the premise of removing the signal disturbance. This research result has reference significance for improving the signal quality of wireless network communication.</p> <p> </p>

show abstract

Section: Construction Of Data Transmission Model For Wireless Communi...mentioning

confidence: 99%

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

Juan Wang

2023

Journal of Computers

View full text Add to dashboard Cite

show abstract

“…In 1997, Shor proposed quantum algorithms for solving decompositions of large integers and discrete logarithms and demonstrated that the time complexity of their algorithmic operations was polynomial in the level [20]. With the development of quantum computing and quantum computers, it has gradually become apparent that the hard problems currently used in traditional asymmetric cryptographic regimes will likely no longer be secure [21][22][23][24][25][26][27][28][29]. 2009 saw the development of the first programmable general-purpose quantum computer, and then asymmetric ciphers based on the hard problem of solving the decomposition of large integers and discrete logarithms will likely no longer be secure in the quantum computer environment.…”

Section: Introductionmentioning

confidence: 99%

New Lattice-Based Broadcast Authentication Protocol for Wireless Sensor Networks

Yang

Wang

2022

Security and Communication Networks

View full text Add to dashboard Cite

At present, wireless sensor networks have become one of the indispensable infrastructures in people’s lives. Considering the frequent mobility of wireless sensor network nodes, an efficient lattice-based random broadcast authentication protocol is proposed. The security of the proposed protocol is based on the SIS problem which helps to resist against quantum attacks. In this paper, we propose two forms of broadcast authentication, that is, one-to-many broadcast authentication and one-to-one broadcast authentication. The proposed protocol satisfies the broadcast authentication property. Moreover, the resistance against man in the middle attack, antireplay attack, unforgeability, and scalability properties are achieved. Moreover, its efficiency has certain advantages with other lattice-based studies.

show abstract

Machine learning‐based data fusion method for wireless sensor networks

Liang,

Yao

2024

Adv Control Appl

View full text Add to dashboard Cite

For wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non‐replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto‐Encoder (DSAE) model is constructed and trained using a layer‐wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto‐Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back‐Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.

show abstract

Improve Performance of Wireless Sensor Network Clustering Using Mobile Relay

Cited by 5 publications

References 24 publications

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

New Lattice-Based Broadcast Authentication Protocol for Wireless Sensor Networks

Machine learning‐based data fusion method for wireless sensor networks

Contact Info

Product

Resources

About