Efficient target detection in maritime search and rescue wireless sensor network using data fusion

Wu, Huafeng; Xian, Jiangfeng; Mei, Xiaojun; Zhang, Yuanyuan; Wang, Jim; Cao, Jiannong; Mohapatra, Prasant

doi:10.1016/j.comcom.2019.01.002

Cited by 23 publications

(14 citation statements)

References 32 publications

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“…e wireless sensor network (WSN) functions by broadcasting a large number of sensor nodes in the interest area and rapidly forming a wireless network through self-organization. Using the additional information of these physical quantities, relying on the collaborative perception and computing capabilities of each node in the wireless sensor network, the location of the target object can be measured [5][6][7]. At present, the detection of moving targets using sensor networks has become a hot topic in the field of computer vision research.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

2021

Complexity

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Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video sequence and the background image are channel-merged to construct a deep full convolutional network model. Finally, the network model is used to learn the deep features of the video scene and output the pixel-level classification results to achieve moving target detection. This method not only can adapt to complex video scenes of different sizes but also has a simple background extraction method, which effectively improves the detection speed.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

2021

Complexity

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“…Jan et al [8] presented a novel technique by using a hybrid algorithm for clustering and cluster member selection in the wireless multi-sensor system, and the proposed scheme efficiently reduced the blind broadcast messages but also decreases the signal overhead as the result of cluster formation. To reduce the intensity of correlation for WSNs, in [9], the author proposed in-node data aggregation technique that eliminates redundancy in the sensed data in an energy-efficient manner, meanwhile adopted a novel datadriven approach to perform in-node data aggregation using an underlying cluster-based hierarchical network. In order to design both local and global data fusion rules based on the likelihood of ratio test statistics using a Neyman-Pearson lemma and Bayesian approach, Zhang et al [10] proposed a novel mobile target detection algorithm (NMTDA) based on information theory.…”

Section: A Cluster-based Hierarchical Data Fusion Algorithmmentioning

confidence: 99%

A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

et al. 2020

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In order to effectively reduce the redundant information transmission in the network, a data fusion algorithm based on extreme learning machine optimized by bat algorithm for mobile heterogeneous wireless sensor networks is proposed. In this paper, the data fusion process of mobile heterogeneous wireless sensor networks is mainly studied, and regards the nodes of wireless sensor networks as neurons in the neural network of extreme learning machines. The neural network of the extreme learning machine extracts the sensory data collected by mobile heterogeneous wireless sensor network and combines the collected sensor data with the clustering route to greatly reduce the amount of network data sent to the sink node. Aiming at the problem that the extreme learning machine randomly generates the input layer weight and the hidden layer threshold before training, the output result is unstable, affecting the data fusion efficiency and the long delay, a new method of data fusion for mobile heterogeneous wireless sensor networks based on extreme learning machine optimized by bat algorithm is proposed. Simulation experiments are carried out from two aspects: mobile heterogeneous wireless sensor networks and heterogeneous mobile heterogeneous wireless sensor networks. The simulation results show that compared with the traditional SEP algorithm, BP neural network algorithm and ELM algorithm, the proposed BAT-ELM-based data fusion algorithm can effectively reduce network traffic, save network energy, improve network work efficiency, and significantly prolong network's lifetime. INDEX TERMS Mobile heterogeneous wireless sensor networks, data fusion, extreme learning machine, bat algorithm, energy efficient, reliability.

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“…Applications of WSNs are traffic management, healthcare, military surveillance, fire detection in forest, flood warning, habitat monitoring, agriculture, industries, smart home, volcano monitoring, security, military surveillance maritime search and rescue etc. [2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic K-means Based Clustering Algorithm Using Fuzzy Logic for CH Selection and Machine Learning Based Data Transmission

Choudhary¹,

Badholia

Sharma

et al. 2021

Preprint

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Clustering is effective method to increase network lifetime, energy efficiency, and connectivity of Sensor nodes in wireless sensor network. An energy efficient clustering algorithm has been proposed in this paper. Sensor nodes are clustered using K-means algorithm which dynamically forms number of clusters in accordance with number of alive nodes. Selection of suitable CH is done by fuzzy inference system by choosing three fuzzy input variable such as residual energy of Sensor node, its distance from cluster center and base station. Amount of data transmitted by member nodes to CH is reduced by machine learning that classify similar data at regular interval. The simulation results show that proposed algorithm outperforms other cluster based algorithms in terms of data received by base station, number of alive node per round, time of first node, middle node and last node to die for various density of sensor nodes and scalable conditions.

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Efficient target detection in maritime search and rescue wireless sensor network using data fusion

Cited by 23 publications

References 32 publications

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

A Dynamic K-means Based Clustering Algorithm Using Fuzzy Logic for CH Selection and Machine Learning Based Data Transmission

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