Optimal Placement for Barrier Coverage in Bistatic Radar Sensor Networks

Gong, Xiaowen; Zhang, Junshan; Cochran, Douglas; Xing, Kai

doi:10.1109/tnet.2014.2360849

Cited by 52 publications

(32 citation statements)

References 28 publications

(78 reference statements)

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“…One of the main objectives of vulnerability detection and assessment is to analyze and understand various security risks that may exist in the whole system development process, and to provide scientific basis and intention for how to protect the security and development of the whole system. The whole system vulnerability here can be just one device as a service, or just the device stored on the network as a computer, or the entire computer network [11][12]. shows an example of an attack tree in which the attacker's goal is to get a free lunch.…”

Section: ) Network Unit Vulnerabilitymentioning

confidence: 99%

Key Technology of Computer Network Vulnerability Assessment Based on Neural Network

Wang¹

2020

Preprint

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With the wide application of computer network, network security has attracted more and more attention. The main reason why all kinds of attacks on the network can pose a great threat to the network security is the vulnerability of the computer network system itself. Introducing neural network technology into computer network vulnerability assessment can give full play to the advantages of neural network in network vulnerability assessment. The purpose of this article is by organizing feature map neural network and the combination of multilayer feedforward neural network, the training samples using SOM neural network clustering, the result of clustering are added to the original training samples and set a certain weight, based on the weighted iterative update ceaselessly, in order to improve the convergence speed of BP neural network. On the BP neural network algorithm for LM algorithm was improved, the large matrix inversion in the LM algorithm using the parallel algorithm method is improved for solving system of linear equations, and use of computer network vulnerability assessment as the computer simulation and analysis on the actual example, design a set of computer network vulnerability assessment scheme, finally the vulnerability is lower than 0.75, which is beneficial to research on related theory and application to provide the reference and help.

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Section: ) Network Unit Vulnerabilitymentioning

confidence: 99%

Key Technology of Computer Network Vulnerability Assessment Based on Neural Network

Wang¹

2020

Preprint

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“…Sobhani et al proposed a tracking algorithm based on low-complexity particle filtering specifically tailored to radar sensor networks with one transmitter and several receivers [23]. Through studying the coverage problem of a bistatic radar sensor network, Gong et al reported valuable insights into the placement of bistatic radars for barrier coverage [24]. Liang and Cheng proposed a knowledge-based persistent sensor network for estimating parameters in target radar cross section [25].…”

Section: Microstrip Antenna Designmentioning

confidence: 99%

Tuning rectangular microstrip antenna through scan and zoom algorithm

Zhang

2018

J Wireless Com Network

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Microstrip antennas have been widely studied and can be applied to a large number of wide frequency domain in wireless communication. Microstrip antennas are featured with light weight, small volume, and low cost advantages. Planar or spatial structures could substantially affect the frequency reliability and sidelobe level of microstrip antennas. Designing high performance microstrip antennas requires the tuning of the length and width of microstrip patch, the equivalent permittivity, the feed position of the coaxial line, and so on. Although electromagnetic theory could provide a solution for antenna design, such solution is probably not optimal solution due to complex environment and the affection of multiple factors. This paper attempts to tuning the design of microstrip antennas by using scan and zoom algorithm. It does not require derivative information of antenna model. This algorithm consists of search space scan and search domain zoom stages. The effectiveness of the scan and zoom algorithm is firstly tested on mathematical functions. The algorithm is then verified on rectangular microstrip antenna design. Compared with theoretical solution, the novel algorithm attains better design solution. The algorithm can also be easily extended to tackle other antenna design problems.

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“…Barrier coverage problem has been extensively studied in [4,5,7,10,11,14,18,19]. A centralized optimal algorithm using sleep-wakeup model is proposed in [10] to achieve globally strong barrier coverage.…”

Section: Related Workmentioning

confidence: 99%

Chain-based barrier coverage in WSNs: toward identifying and repairing weak zones

et al. 2015

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Barrier coverage constructs a sensing barrier for detecting intruders crossing a belt region. Recent studies mostly focus on efficient algorithms to guarantee barrier coverage, with little consideration on the collaboration between individual nodes. Observing that in many situations, sensors naturally fall into several clusters (or components), for example when the sensors are deployed uniformly at random with a relatively low density, or when random sensors go down as a result of energy exhaustion, we propose to use chain as a basic scheduling unit for sensing and communication. A chain is a set of sensors whose sensing areas overlap with each other, and it can be extracted from a cluster. We present a distributed algorithm, named BARRIER, to provide barrier coverage with a low communication overhead for the wireless sensor networks (WSNs). The algorithm is able to detect weak zones that are often found in an initial deployment of a WSN, and repair them by adding an appropriate number of sensors. Theoretic analysis and simulations show that, compared with a representative previous algorithm, BARRIER significantly reduces the communication overhead and reparation cost in terms of number of sensors.

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Optimal Placement for Barrier Coverage in Bistatic Radar Sensor Networks

Cited by 52 publications

References 28 publications

Key Technology of Computer Network Vulnerability Assessment Based on Neural Network

Key Technology of Computer Network Vulnerability Assessment Based on Neural Network

Tuning rectangular microstrip antenna through scan and zoom algorithm

Chain-based barrier coverage in WSNs: toward identifying and repairing weak zones

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