Abstract
Unmanned Aerial Vehicles (UAVs) have demonstrated significant potential as mobile nodes for data collection in sensor networks. However, the environmental conditions in remote areas often fail to offer stable and reliable energy supply for energy-constrained UAVs and sensing nodes (SNs). Consequently, efficient energy utilization strategies is crucial for maintaining the long-term and stable operation of such networks. A novel sparse compressive sensing (CS) sampling framework for UAV-assisted data collection is proposed in this work. To minimize the overall system energy consumption in each round, we design an energy optimization scheme that decomposes the energy minimization problem into two sub-problems. The first sub-problem involves the node selection under the CS reconstruction accuracy constraint. We develop a CS-based Node Selection (CSNS) algorithm that optimizes UAV flight distance while balancing network energy consumption. The second sub-problem focuses on jointly optimizing the SNs scheduling, UAV flight duration and UAV trajectory. We design a two-stage joint optimization (TSJO) algorithm that convert the non-convex issue into a convex formulation by employing the successive convex approximation (SCA), variable substitution and variable relaxation. Extensive simulation results not only validate the effectiveness of the CSNS and TSJO algorithms, but also demonstrate that our proposed scheme has a considerable energy consumption advantage compared to the benchmark schemes.