A Kernel-Based Node Localization in Anisotropic Wireless Sensor Network

He, Wenxiu; Lu, Fangfang; Chen, Jingjing; Ruan, Yi; Lu, Tingjuan; Zhang, Yi

doi:10.1155/2021/9944358

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…In order to prevent wireless sensor networks from being attacked by malicious and selfish behavior, researchers have proposed many different types of secure routing protocols. However, existing security routing algorithms are often targeted at certain malicious or self-inflicted behavioral attacks [6,7]. Figure 1 shows the wireless sensor security routing protocol.…”

Section: Introductionmentioning

confidence: 99%

A Secure Routing Protocol for Wireless Sensor Energy Network Based on Trust Management

Yuan

2022

Wireless Communications and Mobile Computing

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In order to enhance the ability of wireless sensor networks to resist various security threats and reduce the limitations caused by the characteristics of wireless sensor networks and sensor nodes, this paper proposes a secure routing protocol for wireless sensor networks based on trust management. Combined with the relevant parameters of wireless sensor network, the simulation experiment is carried out with MATLAB. Aiming at the trust management part of the wireless sensor network security protocol proposed in this paper, the malicious attack environment such as sensor node attributes is simulated to verify the resistance of this model to relevant malicious attacks. For the trust management-based wireless sensor network security routing protocol proposed in this paper, the model included in the protocol is compared to the existing security routing model, combining the characteristics of average simulation transmission, network life, and average routing update time. Experiments show that the model has better routing performance and has improved by an average of about 20%. We offer a new solution to solve the problem of wireless routing security.

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Section: Introductionmentioning

confidence: 99%

A Secure Routing Protocol for Wireless Sensor Energy Network Based on Trust Management

Yuan

2022

Wireless Communications and Mobile Computing

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“…The beacon nodes have high-computational data processing and have their own localization that helps for other nodes to estimate and compute their location and positon of the ordinary sensor nodes in the network. Adding machine learning to WSN localization helps increase the precision of range-free node positioning [36]. In particular, the use of artificial neural networks (ANNs) in range-free localization algorithms has significantly improved their accuracy and performance compared to more conventional methods.…”

Section: Position Estimationmentioning

confidence: 99%

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

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Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.

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“…He et al [ 22 ] researched the introduction of kernel regression to node localization of anisotropic WSN [ 22 ]. Simulation results solved location accuracy and stability using radial basis kernel-based G-LSVR and polynomial-kernel-based P-LSVR.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm

Chen

Sackey

Ansere

et al. 2022

Computational Intelligence and Neuroscience

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Finding the location of sensors in wireless sensor networks (WSNs) is a major test, particularly in a wide region. A salient clustering approach is laid out to achieve better performance in such a network using an evolutional algorithm. This paper developed a clustered network called neighborhood grid cluster which has a node assuming the part of a cluster center focused in every grid. Grid-based clustering is less difficult and possesses a lot of benefits compared to other clustering techniques. Besides, we proposed a localization algorithm that centers around assessing the target area by considering the least estimated distance embedded with the genetic algorithm. Performance standards incorporate the energy representation, connectivity stratagem, and distance measure as fitness functions that assess our localization problem to demonstrate its viability. Simulation results confirm that our approach further improves localization accuracy, energy utilization, node lifetime, and localization coverage.

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A Kernel-Based Node Localization in Anisotropic Wireless Sensor Network

Cited by 6 publications

References 34 publications

A Secure Routing Protocol for Wireless Sensor Energy Network Based on Trust Management

A Secure Routing Protocol for Wireless Sensor Energy Network Based on Trust Management

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm

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