摘要
无人机(Unmanned aerial vehicle,UAV)作为移动的数据收集平台,凭其优越的机动性在无线传感器网络(Wireless sensor network,WSN)领域中具有重要的应用前景。为了解决无线传感器网络能耗大的问题,本研究面向传感器位置信息共享场景,提出了一种无人机-车协同数据收集优化方法以实现无人机非定点起降下的传感器能耗最小化的优化目标。该方法考虑了各传感器的调度问题,通过引入基于块坐标下降技术的凸优化算法迭代求解轨迹和唤醒策略交替优化中存在的混合整数非凸问题,在满足数据收集需求的同时最小化传感器节点(Sensor nodes,SNs)的能耗。仿真结果表明,本方法在典型传感器分布下能够有效降低无线传感器网络的能耗,展现了其在更复杂环境中的潜力,具有显著的适应性和扩展性。
As a mobile data collection platform,unmanned aerial vehicle(UAV)has significant application prospects in the field of wireless sensor network(WSN)due to its superior mobility.To solve the problem of high energy consumption in wireless sensor networks,this study proposes a UAV-vehicle collaborative data collection optimization method for the sensor location information sharing scenario to achieve the optimization goal of minimizing sensor energy consumption under non-fixed take-off and landing of UAVs.This method considers the scheduling problem of each sensor,specifically,by introducing a convex optimization algorithm based on block coordinate descent techniques to iteratively solve the mixed integer non-convex problem in the alternating optimization of trajectory and wake-up strategy.This approach ensures that the data collection needs are met while minimizing the energy consumption of the sensor nodes(SNs).Simulation results demonstrate that this method can effectively reduce the energy consumption of wireless sensor networks in typical sensor distributions,showing its potential in more complex environments and its significant adaptability and scalability.
作者
赵月
田文
丁徐飞
戴跃伟
ZHAO Yue;TIAN Wen;DING Xufei;DAI Yuewei(School of Electronics and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《数据采集与处理》
北大核心
2025年第4期1011-1022,共12页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(U21B2003,62072250)
国家重点研发计划(2021QY0700)
江苏省高等学校基础科学(自然科学)研究面上项目(23KJB120007)
江苏省科技计划专项资金(基础研究计划自然科学基金)项目(BK20230415)。
关键词
无人机
无线传感器网络
轨迹优化
唤醒策略
自动驾驶汽车
unmanned aerial vehicle(UAV)
wireless sensor network(WSN)
trajectory optimization
wake-up strategy
autonomous vehicles
