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In this paper, we propose a hierarchical data collection scheme, towards the realization of unmanned aerial vehicles (UAV)-aided industrial wireless sensor networks (IWSN). The particular application is that of agricultural monitoring. For that, we propose the use of hybrid compressed sampling (CS) through exact and greedy approaches. With the exact approach -to model the energy-optimal formulation -it is utilized an improved linear programming formulation of the minimum cost flow problem. The greedy approach is based on a proposed balance factor parameter, consisting of data sparsity and the distance from the cluster head to the normal nodes. To improve the node clustering efficiency, a hierarchical data collection scheme is implemented, by which, the nodes in different layers are adaptively clustered, and the UAV can be scheduled to perform energy-efficient data collection. Simulation results show that our method can effectively collect the data and plan the path for the UAV at a low energy cost. Index Terms-artificial intelligence, unmanned aerial vehicles (UAV), industrial wireless sensor network (IWSN), agricultural monitoring system, intelligent signal processing.

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