Collaborative Data Collection in UAV-Enabled Maritime IoT With Constrained Aerial Base Stations

Unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) systems are considered an effective solution for maritime environmental monitoring. However, in the maritime data collection scenarios, the coverage of the maritime sensor network is significantly larger and changes over time. This makes existing UAV-enabled IoT solutions unable to meet the performance requirements of maritime environmental monitoring, especially in terms of data transmission latency. Therefore, we design a UAV-enabled maritime IoT (MIoT) data collection architecture with constrained aerial base stations (ABSs). Based on this architecture, we propose a UAV-enabled MIoT collaborative data collection (UMCDC) scheme. In this scheme, we first introduce a distributed adaptive large neighborhood search (ALNS) algorithm to generate flight trajectories of UAVs. On this basis, we present a diffusion-based relay pairing method and a centripetal-based flight trajectory assignment method to establish relay pairing relationships between UAVs and assign flight trajectories to UAVs, respectively. Finally, a reselection method is developed to extend the lifetime of the maritime sensor network by optimizing the hover points of UAVs and data collection nodes of subnets. Extensive experimental results have shown that the UMCDC scheme can achieve low data transmission latency and significantly extend the lifetime of the maritime sensor network.