Dynamic time window based full-view coverage maximization in CSNs

IF 0.9 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jingfang Su, Zeqing Li, Hongwei Du, Shengxin Liu
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引用次数: 0

Abstract

In order to maximize full-view coverage of moving targets in Camera Sensor Networks (CSNs), a novel method known as “group set cover” is presented in this research. Choosing the best camera angles and placements to accomplish full-view coverage of the moving targets is one of the main focuses of the research in CSNs. Discretize the target into multiple views of [0, 2\(\pi \)], use a set of views of targets to represent the sensing direction of the camera sensor, and use a group set of views of targets to represent the position of the camera sensor. The total number of targets in a dynamic time window that is visible in full view is calculated. A mixed integer linear programming formulation is employed, which is then approximated using a random rounding method. This approximation approach offers a global estimation of local optimality, particularly for non-submodular optimization problems. Two methods for maximizing overall full-view coverage within a dynamic time window are proposed TSC-FTC-DTW and FTC-TW-DTW. Finally, the proposed methods are verified through experiments.

CSN 中基于动态时间窗口的全视角覆盖最大化
为了在摄像机传感器网络(CSN)中最大限度地实现移动目标的全视角覆盖,本研究提出了一种称为 "组集覆盖 "的新方法。选择最佳摄像机角度和位置以实现对移动目标的全视角覆盖是 CSN 研究的重点之一。将目标离散化为[0, 2\(\pi \)]的多个视图,用一组目标的视图代表摄像机传感器的感应方向,用一组目标的视图代表摄像机传感器的位置。在一个动态时间窗口中,计算在全视角下可见的目标总数。该方法采用混合整数线性规划公式,然后使用随机舍入法进行逼近。这种近似方法提供了对局部最优性的全局估计,尤其适用于非次模块优化问题。此外,还提出了 TSC-FTC-DTW 和 FTC-TW-DTW 两种在动态时间窗口内最大化整体全视角覆盖的方法。最后,通过实验验证了所提出的方法。
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来源期刊
Journal of Combinatorial Optimization
Journal of Combinatorial Optimization 数学-计算机:跨学科应用
CiteScore
2.00
自引率
10.00%
发文量
83
审稿时长
6 months
期刊介绍: The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering. The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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