BOOM: Bottleneck-Aware Opportunistic Multicast Strategy for Cooperative Maritime Sensing.

With the advancements in sensing technologies, maritime sensing has become indispensable in various domains, including logistics, weather forecasting, and marine ranching. However, transmitting large volumes of sensing data faces many challenges in the maritime environment. Firstly, the transmissions purely depend on satellite links often costly and suffer from long propagation latency. On the other hand, traditional unicast transmission results in data duplication, wasting valuable marine communication resources. With the increasing density of sensing devices, the communication distance between maritime sensors has become closer, enabling the deployment of maritime opportunistic networks consisting of device-to-device links. Rather than using unicast transmission over satellite links, employing multicast with opportunistic routing enables simultaneous data transmission to multiple destinations and saves communication resources. Even though the multicast method can avoid redundancy, conducting multicast without considering the maritime characteristics (i.e., the dynamics and the distribution of sensors) may lead to inefficient data delivery. Through real-world experiments, we observe that devices located on the edges of the network have a relatively low receiving rate compared with internal ones and tend to be the bottleneck of the overall multicast progress. Based on this observation, we propose BOOM, a bottleneck-aware opportunistic multicast strategy aiming at reducing multicast latency, taking into account the influence of the bottleneck node and broadcasting rate. Prominently, within maritime scenarios challenged by extreme conditions such as storms, typhoons, and tsunamis, BOOM’s emphasis encompasses the adaptability of multicast strategies, which necessitates dynamic adjustments in response to equipment failures and shifts in network topology. Through mathematical analysis, we prove the formation of opportunistic multicast is an NP-hard problem and further design a heuristic algorithm based on the convex-hull method to reduce the computational cost in strategy generation. We compare BOOM with four other algorithms using real-world maritime vessel trajectories in various scenarios. The simulation result illustrates that the BOOM achieves a significant reduction in transmission latency, which reduces 36% when sensors are sparsely located in water areas, and the reduction could reach up to 59% when sensors are more dense. Furthermore, in extreme environmental testing conditions, BOOM continues to outperform other algorithms in terms of completion time, with performance improvements of up to 39% and 49% in sparse and dense topology environments respectively.