Abstract
The Underwater Acoustic Sensor Network (UASN) effectively enhances the reliability of long-distance underwater transmission through multi-hop transmission among multiple nodes. However, due to the limited energy of acoustic nodes and the high end-to-end transmission delay, the load of UASN becomes uneven, leading to a short lifespan. This paper proposes a dynamic hierarchical routing protocol (BPDLR) based on Back Propagation Neural Network (BP-NN) to address the above issues. In this scheme, considering factors such as node topology, remaining energy, and communication environment, UASN is vertically divided into top, middle, and bottom layers. The routing design for each layer is different and is predicted through the BP-NN routing training model. Additionally, we compare the proposed BPDLR protocol with existing static layered routing (SLR) protocols and deep-based routing (DBR) protocols in terms of network node survival, end-to-end average delay, and packet loss rate. Simulation results show that the proposed BPDLR protocol effectively alleviates the energy hole problem in UASN and extends the network’s lifespan.
Research Highlights
Conclusion
To address the inevitable energy holes, dynamic topology, and complex underwater environmental conditions in UASN, this paper proposes a dynamic hierarchical routing BPDLR protocol based on the BP-NN artificial intelligence algorithm and hierarchical routing concepts. This method demonstrates significant advantages in alleviating the energy hole problem and extending network lifespan. From the perspective of node survival and packet transmission interruptions, simulation results prove the reliability and superiority of the proposed BPDLR protocol, which improves energy load balancing while maintaining communication quality and reducing latency, effectively extending the overall network’s survival time.
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