Suppr超能文献

用于大面积监测的无人机-无线传感器网络的协作

A Collaborative UAV-WSN Network for Monitoring Large Areas.

机构信息

Department of Control Engineering and Industrial Informatics, University POLITEHNICA of Bucharest, București 060042, Romania.

出版信息

Sensors (Basel). 2018 Nov 30;18(12):4202. doi: 10.3390/s18124202.

Abstract

Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and to better overall performance. This paper presents a hybrid UAV-WSN network which is self-configured to improve the acquisition of environmental data across large areas. A prime objective and novelty of the heterogeneous multi-agent scheme proposed here is the optimal generation of reference trajectories, parameterized after inter- and intra-line distances. The main contribution is the trajectory design, optimized to avoid interdicted regions, to pass near predefined way-points, with guaranteed communication time, and to minimize total path length. Mixed-integer description is employed into the associated constrained optimization problem. The second novelty is the sensor localization and clustering method for optimal ground coverage taking into account the communication information between UAV and a subset of ground sensors (i.e., the cluster heads). Results show improvements in both network and data collection efficiency metrics by implementing the proposed algorithms. These are initially evaluated by means of simulation and then validated on a realistic WSN-UAV test-bed, thus bringing significant practical value.

摘要

近年来,大规模监测系统得到了快速发展。由数千个传感、计算和通信节点组成的无线传感器网络(WSN)构成了这些系统的骨干。与无人机(UAV)的集成可提高监测区域并提高整体性能。本文提出了一种混合 UAV-WSN 网络,该网络可自配置以改善大面积环境数据的采集。所提出的异构多智能体方案的主要目标和新颖性是最佳生成参考轨迹,这些轨迹根据行间和列间距离进行参数化。主要贡献是轨迹设计,旨在避免被禁止的区域,靠近预定义的航点,保证通信时间,并最小化总路径长度。混合整数描述被应用于相关的约束优化问题中。第二个新颖之处是传感器定位和聚类方法,考虑到 UAV 和一组地面传感器(即簇头)之间的通信信息,以实现最佳的地面覆盖。结果表明,通过实施所提出的算法,网络和数据收集效率指标都得到了提高。这些算法最初通过仿真进行评估,然后在现实的 WSN-UAV 测试平台上进行验证,从而带来了显著的实际价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cec/6308614/8c2bc6d0047c/sensors-18-04202-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验