Unmanned Aerial Vehicle (UAV) networks have been widely used in both military and civilian scenarios. When users are interested in the statistical information of the historical sensory data in a certain region during a certain time period, they will send an aggregation query request with a spatial-temporal constraint to target UAVs which store the qualified data. Then, the target UAVs will return the query results to users. Meanwhile, the query results can be aggregated within the network during transmission to save energy and bandwidth resources, which are typically scarce in UAV networks. However, due to the unique characteristics of UAV networks, it is difficult to perform efficient in-network aggregation of query results without the sacrifice of the user query delay. To the best of our knowledge, there is no research on spatial-temporal range aggregation query in UAV networks. In this paper, we propose an Efficient Spatial-Temporal range Aggregation query processing (ESTA) algorithm for UAV networks. First, a topology change graph is constructed based on the pre-planned trajectory information. Meanwhile, an efficient shortest path algorithm is proposed to obtain the user query delay. Then, on the basis of ensuring the user query delay, ESTA transforms the aggregation processing of query results into recursively solving the set cover problem, thereby constructing a spatial-temporal aggregation tree (STAT), based on which an efficient in-network aggregation routing path for query results can be found. Through extensive simulation, we demonstrate that ESTA can save more than 50% of the energy consumption compared with the baseline algorithm.

翻译：无人机网络已在军事和民用场景中广泛应用。当用户对特定时空范围内的历史感知数据统计信息感兴趣时，会向存储相关数据的无人机发送带时空约束的聚合查询请求，目标无人机将查询结果返回用户。为节省无人机网络中稀缺的能耗与带宽资源，查询结果可在传输过程中完成网络内聚合。然而，受无人机网络特殊性质限制，在不牺牲用户查询延迟的前提下实现高效的网络内查询结果聚合存在困难。据我们所知，目前尚无针对无人机网络时空范围聚合查询的研究。本文提出一种面向无人机网络的高效时空范围聚合查询处理（ESTA）算法。首先，基于预规划轨迹信息构建拓扑变化图，同时设计高效的最短路径算法以获取用户查询延迟；其次，在确保用户查询延迟的基础上，ESTA将查询结果聚合处理转化为递归求解集合覆盖问题，进而构建时空聚合树（STAT），并据此确定查询结果的高效网络内聚合路由路径。大规模仿真实验表明，与基线算法相比，ESTA可节省超过50%的能耗。