Underwater Acoustic Sensor Networks (UASNs) have attracted significant research interest due to their broad range of marine applications. However, challenges such as limited battery replacement capability and unstable underwater communication severely constrain their reliability and efficiency, with the routing void problem emerging as a key bottleneck. To address this issue, this study presents VPNC, a Void-avoidance and Particle Swarm Optimization (PSO)-based Non-uniform Dynamic Clustering routing protocol. The proposed method embeds void node evaluation into the PSO fitness function, jointly optimizing cluster-head selection and void avoidance by considering residual energy, node depth, and node degree. A cooperative inter-cluster transmission mechanism between cluster heads and gateway nodes ensures balanced load distribution and extends network lifetime. Moreover, an accelerated Dijkstra algorithm incorporating hop count, residual energy, and distance metrics is developed, along with a traversal termination principle to enhance routing efficiency. Simulation results show that VPNC achieves substantial improvements over existing clustering protocols, reducing the proportion of void nodes and improving network lifetime and throughput by at least 20.40% and 37.38%, respectively. These results confirm the effectiveness and robustness of VPNC in maintaining energy efficiency and long-term stability under challenging underwater acoustic environments.

Underwater Acoustic Sensor Networks (UASNs) have attracted considerable attention due to their broad application prospects. However, the challenges of limited battery replacement and unstable communication in underwater environments make it difficult for traditional routing protocols to balance stability and energy consumption under dynamic topologies. To address these challenges, we propose a Dynamic Virtual Heat Field-based Void-Avoidance Opportunistic Routing (DVHVOR) protocol. Inspired by the diffusion of heat from sources to cooler regions in nature, the protocol constructs a virtual heat field to guide efficient data forwarding without relying on precise three-dimensional localization. Specifically, it integrates hop count, residual energy, and depth information into a heat-value metric. The Sink periodically broadcasts packets, forming a dynamic and adaptive virtual heat field across the network. During data transmission, each node selects the neighbor with the highest heat value as the relay and employs a concurrent forwarding mechanism to improve both energy efficiency and adaptability to topology changes. Compared with conventional greedy routing strategies, the virtual heat field provides stable and tunable multi-path guidance.Simulation results demonstrate that, relative to existing protocols, the proposed approach reduces the variance of residual energy by an average of 35.9%, decreases transmission delay by approximately 10.9%, and achieves superior performance across multiple metrics including network lifetime, thereby validating its feasibility and advantages in dynamic and network sparse scenarios.

Underwater acoustic sensor networks (UASNs) have extensive application prospects in the marine Internet of Things, where routing protocols play a critical role in determining network energy efficiency and lifespan. Traditional clustering routing protocols typically employ a weighted approach to convert multi-objective optimization problems into single-objective ones. However, their reliance on predefined weights restricts generalization, as these weights may fail to adapt to dynamic underwater environments and can become invalid when application scenarios change. To address this issue, this paper proposes a Non-dominated Sorting Genetic Algorithm II (NSGA-II)-based multi-objective optimization cluster routing protocol (MO-CRP) for UASNs, aiming to jointly optimize multiple performance aspects of the network. Specifically, the protocol formulates cluster head selection as a multi-objective optimization problem of four key factors: energy consumption, cluster head residual energy, energy consumption balance, and network coverage rate. The NSGA-II algorithm generates the Pareto optimal solution set, thereby eliminating the subjectivity involved in weight assignment. Furthermore, this paper proposes an inter-cluster multi-hop routing mechanism, a non-uniform clustering structure, and a constraint repair mechanism to enhance the validity and rationality of the clustering results. Simulation results demonstrate that MO-CRP achieves a well-balanced trade-off among various optimization objectives: Compared to UCPSO, MDCSFLA, QHUC and LEACH, the optimal compromise solution of MO-CRP effectively extends the network lifetime by an average of 20.55%, 6.09%, 36.54% and 67.36% across different node counts, respectively, while maintaining good network coverage.

Underwater acoustic (UWA) communication is until now the only effective means for long distance underwater wireless communication, and hence it is the key foundation for Internet of Underwater Things (IoUT). However, in ocean environment, impulsive noise (IN) generated by natural and human factors usually seriously affects the performance of UWA communication. In this article, utilizing the powerful capability of deep learning, a 1-D convolutional neural network based on multiattention mechanism (1DCNN-MAM) for IN mitigation in UWA orthogonal frequency division multiplexing (OFDM) systems is proposed. To enhance the generalization performance of the network, it utilizes minimization of the energy on null subcarriers as an auxiliary task for network training. Furthermore, to adapt to specific environment quickly and reduce the amount of real data required for training, it adopts a network-based deep transfer learning approach for fine-tuning. To verify the performance of the proposed scheme, a sea trial has been carried out along with simulations, and both demonstrate that the proposed scheme can effectively suppress the IN in UWA OFDM systems.

The clustering-based routing protocol for underwater acoustic networks (UANs) in ocean Internet of Things (IoT) effectively optimizes network energy consumption and extends network lifespan. However, the traditional equal clustering method may lead to premature node failure due to heavy load, thereby degrading network performance. In this article, we propose a particle swarm optimization (PSO)-based unequal clustering routing protocol for the underwater acoustic cooperative networks (PUCRC) in ocean IoT. In the context of underwater acoustic cooperative networks, we consider factors, such as node energy, transmission energy consumption, and the number of isolated nodes during cluster head selection. We use PSO to select cluster heads that minimize transmission energy consumption and reduce the number of isolated nodes unable to reliably communicate with cluster heads. During the clustering stage, different competition radii are set for cluster heads. Noncluster heads join different clusters based on their competitive radius and distance to form an unequal cluster structure, balancing the energy consumption of each cluster head and preventing premature death of certain nodes. To better approximate real-world underwater acoustic channels, we enhance the cooperative communication model in traditional underwater acoustic cooperative network routing protocols by introducing the Ekman drift model to simulate dynamic marine environments for ocean IoT. Simulation results demonstrate that our proposed method effectively reduces network transmission energy consumption, mitigates isolated node generation, improves energy balance within clusters, and extends entire network lifecycle.

Due to its relatively lower propagation loss, acoustic wave is until now the only option for medium and long range underwater wireless communication. However, the low velocity of acoustic wave in water can easily lead to significant Doppler effect. Especially in the case of communication between underwater platforms with high mobility, the Doppler effect could even be time varying during signal transmission. In this paper, an iterative Doppler tracking algorithm based on Kalman filter is proposed for non-coherent frequency hopping frequency shift keying underwater acoustic communication (UWA) systems suffered from varying Doppler effect. Since the duration of a UWA communication signal is usually short, the change in relative motion between transmitter and receiver is not too drastic.Therefore the motion during this period is modeled by constant acceleration model or constant jerk model. Then, a receiver algorithm iteratively estimates and refines the time-varying Doppler is proposed, in which the refinement is carried out based on Kalman filter. Utilizing the filtered Doppler scale factors, this algorithm adaptively performs symbol synchronization and Doppler frequency shift correction in a symbol-by-symbol fashion, which effectively compensates the signal distortion induced by varying Doppler effect. Simulation results demonstrate the effectiveness of the proposed algorithm in different maneuvering scenes.