【摘要】 为了最大化无人机辅助无线传感网中收集数据的信息价值,提出了一种最大信息价值优先和连续凸近似的无人机航迹规划算法.首先,采用Rician信道模型并将传感器节点分为关键节点和普通节点.其次,将数据收集问题建模为一个混合整数非线性规划问题,并根据节点类型将该优化问题分为2个子优化问题以寻求低复杂度的次优解.最后,提出最大信息价值优先和连续凸近似的无人机航迹规划算法,既能用于目标区域的日常数据收集,也能用于紧急情况时时间敏感的非正常数据收集.仿真结果表明,所提算法收集数据的信息价值比经典的旅行商问题算法和贪婪路径规划算法收集的信息价值提高约15%～30%.更多还原

【Abstract】 In order to maximize the value of information(VoI) of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs), a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA) is proposed. First, the Rician channel model is adopted in the system and sensor nodes(SNs) are divided into key nodes and common nodes. Secondly, the data collection problem is formulated as a mixed integer non-linear program(MINLP) problem. The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity. Finally, the MVF-SCA algorithm for UAV trajectory planning is proposed, which can not only be used for daily data collection in the target area, but also collect time-sensitive abnormal data in time when the exception occurs. Simulation results show that, compared with the existing classic traveling salesman problem(TSP) algorithm and greedy path planning algorithm, the VoI collected by the proposed algorithm can be improved by about 15% to 30%.更多还原