Carrier-Based Coverage Augmentation in Wireless Sensor and Robot Networks

R. Falcon, Xu Li, A. Nayak
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引用次数: 9

Abstract

Carrier-based sensor placement involves mobile robots carrying and dropping (static) sensors for optimal coverage formation. Existing solutions target at traditional area coverage problem and unrealistically assume that robots carry sensors all together (ignoring the physical dimension of sensors and the limit of robot capacity). In this paper, we consider realistic scenarios that robots have to repeatedly reload sensors and address the FOCUSED coverage (F-coverage) problem [10]in an unknown two-dimensional environment. In F-coverage, sensors are required to surround a point of interest (POI), maximizing coverage radius. We propose a Carrier-Based Coverage Augmentation protocol (CBCA). Robots enter the environment from fixed locations, called base points, and move toward the POI. As soon as they get in touch with already deployed sensors, they search (by communication) along the network border for best sensor placement points with respect to F-coverage optimization, and move to drop sensors at the discovered locations. Border nodes store locations of failed sensors (if any exists) inside the network as well as adjacent available deployment spots outside the network, and recommend them to robots during the search process. Robots return to base points for reloading, after finishing with currently loaded sensors, and re-enter the environment to augment existing F-coverage. At the end o the paper, we propose an optimization technique to reduce augmentation delay and save robot energy.
无线传感器和机器人网络中基于载波的覆盖增强
基于载波的传感器放置涉及移动机器人携带和投放(静态)传感器以获得最佳覆盖编队。现有的解决方案针对传统的区域覆盖问题,不切实际地假设机器人将传感器一起携带(忽略了传感器的物理尺寸和机器人容量的限制)。在本文中,我们考虑了机器人必须反复加载传感器的现实场景,并在未知的二维环境中解决聚焦覆盖(F-coverage)问题[10]。在f覆盖中,传感器需要围绕感兴趣点(POI),以最大化覆盖半径。我们提出了一种基于载波的覆盖增强协议(CBCA)。机器人从固定的位置(称为基点)进入环境,并向POI移动。一旦他们与已经部署的传感器取得联系,他们就会(通过通信)沿着网络边界搜索关于f覆盖优化的最佳传感器放置点,并移动到发现的位置放置传感器。边界节点存储网络内故障传感器的位置(如果存在)以及网络外相邻的可用部署点,并在搜索过程中将它们推荐给机器人。在完成当前加载的传感器后,机器人返回基点重新加载,并重新进入环境以扩大现有的f覆盖范围。在论文的最后,我们提出了一种优化技术,以减少增强延迟,节省机器人的能量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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