A practical drone trajectory control approach for energy and data management in wireless sensor networks

In wireless sensor networks (WSNs), drones can be utilized for wireless charging and data collection. In this research work, we employ a single drone in WSNs to simultaneously and wirelessly recharge and collect data from ground sensors. By considering a drone's lifespan, we aim to enhance both performance metrics simultaneously. First, we present the power consumption models utilized by the drone for wireless charging and data collection operations, namely the traveling power consumption model, the hovering power consumption model, and the wireless power transfer consumption model. We also discuss the trade-off between data collection and wireless charging tasks. The drone trajectory is then determined by formulating an optimization problem that jointly maximizes the data collected from sensors and the energy transferred to them, under realistic system constraints. We demonstrate the NP-completeness of this optimization problem using the prize-collecting traveling salesman problem (PCTSP). Then we propose a heuristic algorithm to solve the optimization problem due to its intractability, namely, the energy-weighted nearest neighbor algorithm (EWNNA), which is inspired by the nearest-neighbor algorithm (NNA) used to find an approximate solution for the traveling salesman problem (TSP). We validate the performance of our algorithm by performing extensive simulations. The results show that the EWNNA outperforms the NNA in terms of visiting high-priority sensors, where the EWNNA allocates about 33% more energy consumption for data collection and energy transfer tasks of high-priority sensors compared to the NNA.