Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach

In this article, we propose a hierarchical data collection scheme, toward the realization of unmanned aerial vehicle (UAV)-aided industrial wireless sensor networks. The particular application is that of agricultural monitoring. For that, we propose the use of hybrid compressed sampling through exact and greedy approaches. With the exact approach-to model the energy-optimal formulation-an improved linear programming formulation of the minimum cost flow problem was utilized. The greedy approach is based on a proposed balance factor parameter, consisting of data sparsity, and distance from cluster head to normal nodes. To improve node clustering efficiency, a hierarchical data collection scheme is implemented, by which 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.

Automated summary

This article proposes a hierarchical data collection scheme for applications like agricultural monitoring, utilizing hybrid compressed sampling through exact and greedy approaches. By improving node clustering efficiency and implementing energy-optimal formulations, the method can effectively collect data and plan paths for UAVs at a low energy cost.