基于无人驾驶飞行器的压缩数据采集,用于WSN中的环境监测
Cuicui Lv1, Linchuang Yang1, Xinxin Zhang2
1School of Computer and Control Engineering, Yantai University, Yantai 264005, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了一种使用数据压缩和压缩传感 (CS) 的无人机辅助算法,以减少无线传感器网络 (WSN) 的能源消耗. 该方法有效地收集和重建环境数据,降低传感器节点的电力需求.
科学领域:
- 环境监测环境监测环境监测
- 无线传感器网络 (WSN) 是一种无线传感器网络.
- 无人驾驶飞行器 (UAV) 技术 无人驾驶飞行器 (UAV) 技术
背景情况:
- 随着环境数据量不断增加,WSN的存储和电池功率有限.
- 数据传输的高能耗是WSN中的一个关键挑战.
- 高效的数据处理和减少能源消耗对于可持续的WSN运行至关重要.
研究的目的:
- 为面对大量数据量的WSN开发一个节能数据采集算法.
- 通过数据压缩和优化数据收集,降低传感器节点的能耗.
- 利用无人机作为移动基站,在WSN中有效收集数据.
主要方法:
- 采用数据压缩来消除冗余的环境数据,减少传感器节点的能源使用.
- 引入了压力传感 (CS) 来降低网络数据量.
- 开发了一个无人机辅助的压缩数据采集算法,使用了优化的贪路由策略.
主要成果:
- 拟议的无人机辅助算法显示,与现有方法相比,能源消耗显著降低.
- 实验结果证实了该算法的有效性,减少了WSN数据传输的能源需求.
- 测试了各种数据重建算法,在更短的时间内显示了高效的数据恢复.
结论:
- 将数据压缩和CS与基于无人机的数据收集集成为节能WSN提供了可行的解决方案.
- 优化的贪算法提高了无人机数据收集效率.
- 该方法有效地平衡了数据量减少与精确的感官数据重建.
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