Critical node identification is essential for Underwater Acoustic Sensor Networks (UASNs) to ensure network connectivity and reliability. Existing methods identify critical nodes by evaluating their contributions to network connectivity and node communication count. However, these methods identify critical nodes inaccurately due to neglecting the influence of packet collisions, leading to unreliable network. Packet collisions disrupt connected links and cause communication failures, resulting in unreliable network connectivity and improper communication count. To this end, we propose the Collision-Aware Critical Node Identification Algorithm (CCNIA), which accounts for the impact of packet collisions to improve the accuracy of critical node identification and enhance network reliability. CCNIA identifies critical nodes with high connectivity, large collision probability, and heavy network load, through building the three following interdependent models. Specifically, Topological Connectivity Model (TCM) evaluates link reachability by analyzing connectivity and density within a node's local network. Based on TCM, Collision Probability Model (CPM) further ensures packet reliability by quantifying the impact of packet collisions on critical node identification. Through CPM's reliable packet transmissions, Network Load Model (NLM) assesses network efficiency by analyzing node occurrence count within global end-to-end communication paths. Experiments show that CCNIA outperforms existing methods across diverse network configurations, enhancing network reliability in terms of packet delivery ratio, delay, and energy efficiency.

Internet of Underwater Thing (IoUT) stands as promising technology facilitating diverse underwater applications. Nevertheless, IoUT across vast marine regions is challenged by highly diverse and fluctuating channel environments, which results in unreliable point-to-point (PTP) transmissions. Moreover, its multi-hop nature exacerbates severe unreliable end-to-end (ETE) transmissions. Existing methods utilize routing protocols to address the above challenges by independently power control for PTP reliability or multi-path transmission for ETE reliability. However, these methods ignore the interdependencies between power control and multi-path transmission, which fail to guarantee high energy-efficient reliability in resource-constrained and harsh underwater environments. To this end, we propose a joint power Control And Multi-Path routing (CAMP) protocol for IoUTs in varying environments. Specifically, we develop PTP and ETE reliability models by analyzing the interrelation between power control and multi-path routing, incorporating historical, current, and predictive information. A hybrid routing strategy is designed based on the reliability models to accommodate changing environmental conditions, residual energy, and link quality. This strategy initiates multi-path routing at the source and single-path forwarding at relay nodes, combined with power control. Extensive simulations demonstrate that CAMP achieves superior reliability (packet delivery rate) and energy efficiency, while simultaneously improving network performance in terms of latency and throughput.

Increasing demands for versatile applications have spurred the rapid development of Unmanned Underwater Vehicle (UUV) networks. Nevertheless, multi-UUV movements exacerbates the spatial-temporal variability, leading to serious intermittent connectivity of underwater acoustic channel. Such phenomena challenge the identification of reliable paths for high-dynamic network routing. Existing routing protocols overlook the effects of UUV movements on forwarding path, typically selecting forwarders based solely on the current network state, which lead to instability in packet transmission. To address these challenges, we propose a Routing protocol based on Spatial-Temporal Graph model with Q-learning for multi-UUV networks (STGR), achieving high reliable and energy effective transmission. Specifically, a distributed Spatial-Temporal Graph model (STG) is proposed to depict the evolving variation characteristics (neighbor relationships, link quality, and connectivity duration) among underwater nodes over periodic intervals. Then we design a Q-learning-based forwarder selection algorithm integrated with STG to calculate reward function, ensuring adaptability to the ever-changing conditions. We have performed extensive simulations of STGR on the Aqua-Sim-tg platform and compared with the state-of-the-art routing protocols in terms of Packet Delivery Rate (PDR), latency, energy consumption and energy balance with different network settings. The results show that STGR yields 24.32 percent higher PDR on average than them in multi-UUV networks.

Underwater wireless sensor networks (UWSNs) can provide services to the ocean. The deployment is one of the key problems in UWSNs. Optimizing networks power consumption and coverage has always been a huge challenge in deployment. Existing deployment models do not consider the asymmetric-link phenomenon and power control. In this paper, a new multi-objective optimization deployment model with power control in UWSNs is proposed to obtain a deployment scheme, which reduces the power consumption, and the asymmetric-link phenomenon is considered. At the same time, due to the unsatisfactory optimization performance and stability of some current algorithms, this paper proposes an algorithm named Crow-Colony Search Optimization Algorithm (C-CSOA) to optimize the deployment scheme. In this algorithm, we adopt the framework of Colony Search Optimization Algorithm (CSOA). In addition, we combine the advantages of Crow Search Algorithm (CSA) and CSOA to improve the optimization performance of the algorithm. We conducted simulation experiments, and the results indicate that: First, the model we proposed is feasible. Second, an efficient deployment scheme can be obtained by C-CSOA. Third, when reaching the predetermined network coverage, the total power consumption of UWSNs is significantly reduced (nearly 23.93% in average). By comparing with various advanced optimization algorithms, it is showed that C-CSOA has advantages of the good optimization performance and the small standard deviation.

A high-performance network deployment strategy supports fundamental network services, such as topology controls, protocol designs, and boundary detections in underwater wireless sensor networks (UWSNs). Existing deployment methods treat nodes within the communication range as connected. However, in addition to internode distance, packet errors and collisions are also significant factors for point-to-point connectivity. Furthermore, when allocating node locations, deployment strategies focus on maximizing coverage, ignoring the tradeoff between coverage and network performance (reliability, latency, and energy efficiency). To this end, an efficient deployment scheme with network performance modeling (EDNPM) is proposed, to provide reliable data transmission in a time-aware and energy-efficient way for UWSNs. Specifically, we first explore sensor locations’ impact on communication and network factors, to improve the point-to-point connectivity and network performance. A network performance evaluation model (NPEM) is established to quantify performance metrics for guiding network deployment. Based on NPEM, network deployment is formulated as a multiobjective optimization problem, and we propose a novel network connection-constraint particle swarm optimization (NCPSO) algorithm to solve this problem. Notably, EDNPM is a unified network deployment framework for various underwater applications. Extensive experiments demonstrate that EDNPM outperforms other deployment algorithms in terms of network performance, and robustness with different network settings.