On the adaptive data forwarding in opportunistic underwater sensor networks using GPS-free mobile nodes

摘要

Opportunistic underwater sensor networks (OUSNs) are developed for a set of underwater applications, including the tracking of underwater creatures and tactical surveillance. The data forwarding objectives of OUSNs differ significantly from those in wireless sensor networks or delay-tolerant networks due to multifarious factors of underwater environments, such as the large energy consumption and the long transmission delay. As a result of the elusive moving intentions of nodes and the sophisticated external forces, the underwater movements of nodes are usually much more irregular, which accordingly makes the contacts among nodes become more stochastic. In addition, GPS signal is not available because of the limitations of satellite coverage and the underwater obstructions, and thus the real-time positions of nodes cannot be obtained and served for the future trajectory prediction. In this paper, we study the OUSNs data forwarding problem where the nodes are not equipped with GPS devices. Especially, the forwarding probabilities of data holders are exploited, and three vital metrics (movement speed, time slot sequence and distribution uniformity) are investigated for setting the probabilities to minimize the energy consumption. Then, we propose an adaptive GPS-free data forwarding approach (GDFA), in which the data holders probabilistically decide whether to forward the carried data packets or not, according to their movement speeds and the current time slot sequences rather than the node positions. Furthermore, the number of forwarded copies is determined by the number of neighboring data holders. Finally, the GDFA performance is evaluated through simulation experiments under an underwater mobility model. Simulation results indicate the satisfactory energy saving, delivery ratio and transmission delay obtained from GDFA. It is also shown that GDFA has a good scalability, and it is especially suitable for densely deployed OUSNs. (C) 2018 Elsevier Inc. All rights reserved.