Achieving Crossed Strong Barrier Coverage in Wireless Sensor Network

Perimeter barriers can provide intrusion detection for a closed area. It is efficient for practical applications, such as coastal shoreline monitoring and international boundary surveillance. Perimeter barrier coverage construction in some regions of interest with irregular boundaries can be represented by its minimum circumcircle and every point on the perimeter can be covered. This paper studies circle barrier coverage in Bistatic Radar Sensor Network (BRSN) which encircles a region of interest. To improve the coverage quality, it is required to construct a circle barrier with a predefined width. Firstly, we consider a BR deployment problem to constructing a single BR circular barrier with minimum threshold of detectability. We study the optimized BR placement patterns on the single circular ring. Then the unit costs of the BR sensor are taken into account to derive the minimum cost placement sequence. Secondly, we further consider a circular BR barrier with a predefined width, which is wider than the breadth of Cassini oval sensing area with minimum threshold of detectability. We propose two segment strategies to efficiently divide a circular barrier to several adjacent sub-ring with some appropriate width: Circular equipartition strategy and an adaptive segmentation strategy. Finally, we propose approximate optimization placement algorithms for minimum cost placement of BR sensor for circular barrier coverage with required width and detection threshold. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

Section: Related Workmentioning

confidence: 99%

Minimum Cost Deployment of Bistatic Radar Sensor for Perimeter Barrier Coverage

Zhao

et al. 2019

“…This section briefly reviews the research work of barrier coverage in wireless sensor networks. The concept of barrier coverage originates from the requirement of multi-robot long-distance communication [3], and is gradually extended to the research and application of wireless sensor networks [4,5,6]. We summarize the main research contents and methods of barrier coverage, which not only provide preliminary knowledge in this field, but also point out the direction for further research on dynamic-barrier coverage.…”

Section: Related Workmentioning

confidence: 99%

“…Coverage is one of the most important issues in wireless sensor networks [4]. It determines the detection area of sensor nodes and the monitoring degree of the target.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Barrier Coverage in a Wireless Sensor Network for Smart Grids

Fan

et al. 2018

The development of engineering technology such as inspection robots (IR) for transmission lines and wireless sensor networks (WSN) are widely used in the field of smart grid monitoring. However, how to integrate inspection robots into wireless sensor networks is still a great challenge to form an efficient dynamic monitoring network for transmission lines. To address this problem, a dynamic barrier coverage (DBC) method combining inspection robot and wireless sensor network (WSN) is proposed to realize a low-cost, energy-saving and dynamic smart grid-oriented sensing system based on mobile wireless sensor network. To establish an effective smart grid monitoring system, this research focuses on the design of an effective and safe dynamic network coverage and network nodes deployment method. Multiple simulation scenarios are implemented to explore the variation of network performance with different parameters. In addition, the dynamic barrier coverage method for the actual scene of smart grid monitoring considers the balance between network performance and financial costs.

“…In this study, multiple visual sensors are distributed around the surveillance area, and no-blind area coverage for surveillance is realized in a cooperative manner, so as to build an intelligent visual surveillance system for discovering the approaching UAV in time [15,16]. However, due to the optical properties and image resolution of visual sensors, it is limited to detect a target as far as possible.…”

Section: Introductionmentioning

confidence: 99%

Flying Small Target Detection for Anti-UAV Based on a Gaussian Mixture Model in a Compressive Sensing Domain

Wang

Sun

et al. 2019

Addressing the problems of visual surveillance for anti-UAV, a new flying small target detection method is proposed based on Gaussian mixture background modeling in a compressive sensing domain and low-rank and sparse matrix decomposition of local image. First of all, images captured by stationary visual sensors are broken into patches and the candidate patches which perhaps contain targets are identified by using a Gaussian mixture background model in a compressive sensing domain. Subsequently, the candidate patches within a finite time period are separated into background images and target images by low-rank and sparse matrix decomposition. Finally, flying small target detection is achieved over separated target images by threshold segmentation. The experiment results using visible and infrared image sequences of flying UAV demonstrate that the proposed methods have effective detection performance and outperform the baseline methods in precision and recall evaluation.