Efficient Drone-Based Mobile Wireless Sensor Network for Spatio-Temporal Environmental Monitoring

The technological advancements in wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) or drones have made significant contributions in various fields, benefiting human society. Although WSNs have been widely used for environmental monitoring, their utility is constrained by inherent limitations, which make them suitable only for small-scale sensing tasks. On the other hand, UAVs provide quick access to large areas, including challenging terrains, allowing faster data collection that is both less labor-intensive and more cost-effective. In this paper, we present an efficient method to collect data for environmental monitoring using a drone-based mobile WSN system. The framework discretizes the region to be monitored into a 3D spatial-temporal field, where the field is further divided into sub-grids for the deployment of drones in each sub-grid. The drones utilize their built-in sensors to move around the designated region in a pre-defined trajectory to collect spatio-temporal data. In order to obtain parameter values at intermediate points within the surveyed region, interpolation techniques such as inverse distance weighting (IDW) and nearest neighbors (NN) are applied, ensuring comprehensive data coverage. To validate the feasibility of our proposed system, experiments are performed for temperature modeling applications using a simulated dataset, a real-world NYC weather dataset, and a real-time REST API dataset. The obtained results demonstrate that the proposed model is well-suited for large-scale sensing, even in remote areas, exhibiting cost-effectiveness and substantial reductions in labor and resource requirements, providing a robust and efficient solution for real-time environmental data collection and analysis.