Data collection and event detection in the deep sea with delay minimization

As special applications of delay tolerant networks (DTNs), efficient data collection and event detection in the deep sea pose some unique challenges, due to the need of timely data reporting and the delay of acoustic transmission in the ocean. Since underwater communications suffer from a significant signal attenuation, autonomous underwater vehicles (AUVs) deployed in the deep sea are used to surface frequently to transmit collected data and events to the surface stations. However, extra delay is introduced at each resurfacing, since AUVs are usually operated in the deep sea. In this paper, we want to minimize the average data and event 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. Finally, experiments in both the synthetic and real traces validate the efficiency and effectiveness of the proposed algorithms.