No abstract

The advancement of communication, computing, and the Internet of Underwater Things (IoUT) led to the blooming of marine applications. However, due to the constrained resources of underwater nodes and highly dynamic underwater environment, traditional Underwater Acoustic Sensor Networks (UASNs) become incompetent for such enormous demands. Software-Defined N etworking ( SDN) i s a p romising approach to improve the flexibility a nd r eliability o f U ASN. T his paper introduces an SDN-enabled UASN with multi-controllers called SM-UASN. Firstly, a hierarchical framework of SM-UASN is offered. Then the energy consumption model and communication process of SM-UASN are established. Finally, we implement a simulation platform for SM-UASN by integrating Mininet and the NS-3 underwater acoustic network (UAN) module. SM-UASN is compared with the traditional single-sink-based UASN (TS-UASN), the traditional multi-sink-based UASN (TM-UASN), and the SDN-based UASN with a single controller (SS-UASN). The result reveals that our proposed architecture significantly prolongs the network's lifetime and increases the throughput.

With the advancements in wireless sensor networks and the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which has also been widely used in marine engineering exploration and disaster prevention. However, UASNs still face many challenges, including high propagation latency, limited bandwidth, high energy consumption, and unreliable transmission, influencing the good quality of service (QoS). In this paper, we propose a routing protocol based on the on-site architecture (SROA) for UASNs to improve network scalability and energy efficiency. The on-site architecture adopted by SROA is different from most architectures in that the data center is deployed underwater, which makes the sink nodes closer to the data source. A clustering method is introduced in SROA, which makes the network adapt to the changes in the network scale and avoid single-point failure. Moreover, the Q-learning algorithm is applied to seek optimal routing policies, in which the characteristics of underwater acoustic communication such as residual energy, end-to-end delay, and link quality are considered jointly when constructing the reward function. Furthermore, the reduction of packet retransmissions and collisions is advocated using a waiting mechanism developed from opportunistic routing (OR). The SROA realizes opportunistic routing to choose candidate nodes and coordinate packet forwarding among candidate nodes. The scalability of the proposed routing protocols is also analyzed by varying the network size and transmission range. According to the evaluation results, with the network scale ranging from 100 to 500, the SROA outperforms the existing routing protocols, extensively decreasing energy consumption and end-to-end delay.