Minimizing deep sea data collection delay with autonomous underwater vehicles

As a special application of delay tolerant networks (DTNs), efficient data collection in the deep sea poses some unique challenges, due to the need for timely data reporting and the delay of acoustic transmission in the ocean. Autonomous underwater vehicles (AUVs) are deployed in the deep sea to surface frequently to transmit collected data from sensors (in a 2-dimensional or 3-dimensional search space) to the surface stations. However, additional delay occurs at each resurfacing. In this paper, we want to minimize the average data reporting delay, through optimizing the number and locations of AUV resurfacing events. We also study the AUV trajectory planning using an extended Euler circuit, where the search space is a set of segments (e.g., oil pipes) in the deep sea. To further reduce the data reporting delay, several schemes, which schedules multiple AUVs cooperatively, are also explored. Finally, experiments in both the synthetic and real traces validate the efficiency and effectiveness of the proposed algorithms.