Energy-Efficient Data Collection Maximization for UAV-Assisted Wireless Sensor Networks

The accelerated development of the Internet of Things (IoT) incurs a great demand for data acquired from Wireless Sensor Networks (WSNs), leading to considerable attention on data collection of WSNs in recent years. With the high agility, mobility and flexibility, the Unmanned Aerial Vehicle (UAV) is widely considered as a promising technology for data collection in WSNs. Along with the Orthogonal Frequency Division Multiple Access (OFDMA) technique, the UAV is capable to collect data from multiple sensors simultaneously within its communication range (referred to as the one-to-many data collection scheme), which improves data collection efficiency significantly. In this paper, we focus on the improvement of the data collection efficiency in WSNs under the one-to-many data collection scheme via the trajectory finding of a UAV for data collection. To this end, we first formulate a novel data collection maximization problem in WSNs via deploying an energy-constrained UAV and show the NP-hardness of the problem. We then devise an efficient algorithm for the problem by investigating the impact of UAV hovering locations on the data collection. We finally evaluate the performance of the devised algorithm through experimental simulations. Simulation results demonstrate that the proposed algorithm is promising, and outperforms the other heuristics significantly.

Automated summary

The accelerated development of IoT has led to a high demand for data collection from WSNs, with UAVs considered a promising technology to improve efficiency. This paper focuses on improving data collection efficiency in WSNs using UAV trajectory finding, formulating a novel maximization problem and devising an efficient algorithm to address it. Simulation results show the proposed algorithm outperforms other heuristics significantly.