集群和轨迹的优化,以尽量减少无人机辅助移动边缘计算网络中的信息时代
Huicong Shen1, Die Wang1, Zhen Huang1
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
本研究介绍了一个无人机 (UAV) 辅助的移动边缘计算 (MEC) 网络,以改善物联网 (IoT) 的数据收集. 拟议的AOI-MATP算法通过优化无人机路径和收集点来最大限度地降低数据的年龄.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 物联网 (IoT) 网络产生大量的传感器数据,需要及时处理.
- 陆地多跳网络面临能源漏洞问题,影响数据传输效率.
- 无人驾驶飞行器 (UAV) 提供了一种灵活的解决方案,用于在具有挑战性的环境中收集数据.
研究的目的:
- 解决无人机辅助移动边缘计算 (MEC) 网络中的数据收集挑战.
- 尽量减少信息时代 (AoI) 的峰值,以此衡量数据的新鲜度.
- 开发一个高效的算法,以优化无人机数据收集策略.
主要方法:
- 使用信息时代 (AoI) 建立了一个数学模型来表示数据的新鲜性,考虑数据收集和无人机飞行时间.
- 制定了一个混合整数非凸的优化问题,以最大限度地减少峰值AOI.
- 提出了一种代的两步算法,AoI最小化的关联和轨迹规划 (AoI-MATP),利用亲和力传播集群和改进的精英遗传算法.
主要成果:
- AoI-MATP算法通过代地确定最佳的传感器节点-收集点关联和UAV轨迹,有效地解决了优化问题.
- 该算法优化了一个关键参数 (ε),以平衡数据收集时间和无人机飞行时间.
- 与现有方法相比,模拟结果显示,峰值AoI显著降低.
结论:
- 拟议的AOI-MATP算法有效地减少了无人机辅助MEC网络的峰值AOI.
- 这种方法确保了对时间敏感的物联网应用程序 (如火灾监控) 的信息新鲜度高.
- 这项工作为优化动态无线网络数据收集提供了强大的框架.
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