Sequential opening multi‐jammers localisation in multi‐hop wireless network

Wang, Tongxiang; Liang, Tao; Jiang, Fan; Wei, Xianglin

doi:10.1049/iet-ifs.2017.0346

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…For the characteristics of the highly nonlinear nodes in the model that are not easy to solve, the sensor network communication characteristic algorithm is designed using the theoretical method of optimization calculation to solve it, and the convergence of the algorithm is proved by a random process to further obtain the optimal solution. However, the complexity of the algorithm is greatly affected by the prediction operation before positioning [22].…”

Section: Related Workmentioning

confidence: 99%

Nonuniform Clustering of Wireless Sensor Network Node Positioning Anomaly Detection and Calibration

Lu¹,

Liu²

2021

Journal of Sensors

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In order to detect and correct node localization anomalies in wireless sensor networks, a hierarchical nonuniform clustering algorithm is proposed. This paper designs a centroid iterative maximum likelihood estimation location algorithm based on nonuniformity analysis, selects the nonuniformity analysis algorithm, gives the flowchart of node location algorithm, and simulates the distribution of nodes with MATLAB. Firstly, the algorithm divides the nodes in the network into different network levels according to the number of hops required to reach the sink node. According to the average residual energy of nodes in each layer, the sink node selects the nodes with higher residual energy in each layer of the network as candidate cluster heads and selects a certain number of nodes with lower residual energy as additional candidate cluster heads. Then, at each level, the candidate cluster heads are elected to produce the final cluster heads. Finally, by controlling the communication range between cluster head and cluster members, clusters of different sizes are formed, and clusters at the level closer to the sink node have a smaller scale. By simulating the improved centroid iterative algorithm, the values of the optimal iteration parameters α and η are obtained. Based on the analysis of the positioning errors of the improved centroid iterative algorithm and the maximum likelihood estimation algorithm, the value of the algorithm conversion factor is selected. Aiming at the problem of abnormal nodes that may occur in the process of ranging, a hybrid node location algorithm is further proposed. The algorithm uses the ℓ 2 , 1 norm to smooth the structured anomalies in the ranging information and realizes accurate positioning while detecting node anomalies. Experimental results show that the algorithm can accurately determine the uniformity of distribution, achieve good positioning effect in complex environment, and detect abnormal nodes well. In this paper, the hybrid node location algorithm is extended to the node location problem in large-scale scenes, and a good location effect is achieved.

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Section: Related Workmentioning

confidence: 99%

Nonuniform Clustering of Wireless Sensor Network Node Positioning Anomaly Detection and Calibration

Lu¹,

Liu²

2021

Journal of Sensors

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“…For multi-jammers scenario, Cheng et al put forward the M-clusters and X-ray to estimate jammers' positions, respectively [15]. Wang et al proposed the k-mean cluster algorithm based on the neighbor nodes' information to estimate the positions of the jammers [16].…”

Section: Related Workmentioning

confidence: 99%

“…Assume that there is a locating node, which is in charge of jammer localization, in the wireless network chosen from the unaffected nodes through some kind of voting algorithm. To realize the localization (1) if a node does not detect jamming attack then (2) This node is an unaffected node; (3) else (4) if this node does not lose any neighbor and its received SNRs from most of its neighbors' do not decrease more than a percent then (5) This node is an unaffected node; (6) else (7) if this node loses more than percent of its neighbors then (8) This node is a jammed node; (9) else (10) if more than percent of its neighbors' SNRs decrease more than percent then (11) This node is a jammed node; (12) else (13) This node is a boundary node; (14) end if (15) end if (16) end if (17) end if Algorithm 1: Node state determination algorithm. process, it has to firstly collect information from wireless nodes and then execute the localization algorithm.…”

Section: Information Collectionmentioning

confidence: 99%

Jammer Localization in Multihop Wireless Networks Based on Gravitational Search

Wang

Wei²,

Jiang³

et al. 2018

Security and Communication Networks

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Multihop Wireless Networks (MHWNs) can be easily attacked by the jammer for their shared nature and open access to the wireless medium. The jamming attack may prevent the normal communication through occupying the same wireless channel of legal nodes. It is critical to locate the jammer accurately, which may provide necessary message for the implementation of antijamming mechanisms. However, current range-free methods are sensitive to the distribution of nodes and parameters of the jammer. In order to improve the localization accuracy, this article proposes a jammer localization method based on Gravitational Search Algorithm (GSA), which is a heuristic optimization evolutionary algorithm based on Newton’s law of universal gravitation and mass interactions. At first, the initial particles are selected randomly from the jammed area. Then, the fitness function is designed based on range-free method. At each iteration, the mass and position of the particles are updated. Finally, the position of particle with the maximum mass is considered as the estimated jammer’s position. A series of simulations are conducted to evaluate our proposed algorithms and the simulation results show that the GSA-based localization algorithm outperforms many state-of-the-art algorithms.

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“…In addition, for multijammer scenarios, Cheng et al designed two methods to calculate the localization of jammers based on M-clusters and X-rays [31]. Wang et al utilized the k-means clustering algorithm to estimate the location of the jammer according to the location information of neighboring nodes [32].…”

Section: Introductionmentioning

confidence: 99%

Jammer Location‐Aware Method in Wireless Sensor Networks Based on Fibonacci Branch Search

Yang

Shu

et al. 2023

Journal of Sensors

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Due to the sharing and open-access characteristics of the wireless medium, wireless sensor networks (WSNs) can be easily attacked by jammers. To mitigate the effects of a jamming attack, one reliable solution is to locate and remove the jammer from the deployed area within the WSN. To realize the jammer’s localization in the WSN, many range-free methods have been proposed. However, most of these methods are sensitive to the distribution of nodes and the parameters of the jammer. For this reason, a jammer location-aware method based on Fibonacci branch search (FBS) is proposed in this article. First, the interference region is estimated by using the interference region mapping service of sensors in wireless sensor networks. Then, the search point is selected in the jamming area and the fitness function is designed according to the average distance from the search point to the boundary sensor. According to the basic branch structure and interactive search rules, the global optimal solution is obtained in the jamming area. Finally, the position of the search point with the best fitness value is used as the estimation of the jammer position. Compared with the existing typical range-free methods, rich simulation experiments demonstrate that the FBS algorithm is superior in the location-aware method for jammers with a higher precision and a lower sensitivity to the distribution of nodes and the parameters of the jammer, respectively.

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Sequential opening multi‐jammers localisation in multi‐hop wireless network

Cited by 5 publications

References 22 publications

Nonuniform Clustering of Wireless Sensor Network Node Positioning Anomaly Detection and Calibration

Nonuniform Clustering of Wireless Sensor Network Node Positioning Anomaly Detection and Calibration

Jammer Localization in Multihop Wireless Networks Based on Gravitational Search

Jammer Location‐Aware Method in Wireless Sensor Networks Based on Fibonacci Branch Search

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