An Energy-Efficient Reliable Data Transmission Scheme for Complex Environmental Monitoring in Underwater Acoustic Sensor Networks

TLDR

Underwater acoustic sensor networks face significant challenges in efficiency and reliability due to complex 3D topology changes, high propagation delay, node mobility, and density, especially for applications such as monitoring submarine oil pipelines. The study proposes an energy‑efficient data transmission scheme, EGRC, to reduce energy consumption in UASNs. EGRC models the network as a 3D cube divided into small cubes (clusters), duty‑cycles nodes at the MAC layer, selects cluster‑heads based on residual energy and location, and chooses next‑hop nodes using residual energy, location, and end‑to‑end delay to maintain reliability. Simulations demonstrate that EGRC outperforms representative algorithms in energy efficiency, reliability, and end‑to‑end delay.

Abstract

As an extension of wireless sensor network in underwater environment, underwater acoustic sensor networks (UASNs) have caused widespread concern of academia. In UASNs, the efficiency and reliability of data transmission are very challenging due to the complex underwater environment in variety of ocean applications, such as monitoring abnormal submarine oil pipelines. Motivated by the importance of energy consumption in many deployments of UASNs, we therefore propose an energy-efficient data transmission scheme in this paper, called energy-efficiency grid routing based on 3D cubes (EGRCs) in UASNs, considering the complex properties of underwater medium, such as 3D changing topology, high propagation delay, node mobility and density, as well as rotation mechanism of cluster-head nodes. First, the whole network model is regarded as a 3D cube from the grid point of view, and this 3D cube is divided into many small cubes, where a cube is seen as a cluster. In the 3D cube, all the sensor nodes are duty-cycled in the media access control layer. Second, in order to make energy efficient and extend network lifetime, the EGRC shapes an energy consumption model considering residual energy and location of sensor nodes to select the optimal cluster-heads. Moreover, the EGRC utilizes residual energy, locations, and end-to-end delay for searching for the next-hop node to maintain the reliability of data transmission. Simulation validations of the proposed algorithm are carried out to show the effectiveness of EGRC, which performs better than the representative algorithms in terms of energy efficiency, reliability, and end-to-end delay.