Yu He

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance indoor wireless communication and sensing performance. However, the construction of reliable received signal strength (RSS)-based fingerprint databases for RIS-assisted indoor positioning remains an open challenge due to the lack of realistic and spatially consistent channel modeling methods. In this paper, we propose a novel method with open-source code for generating RIS-assisted RSS fingerprint databases. Our method captures the complex RIS-assisted multipath behaviors by extended cluster-based channel modeling and the physical and electromagnetic properties of RIS and transmitter (Tx). And the spatial consistency is incorporated when simulating the fingerprint data collection across neighboring positions. Moreover, an effective sorting algorithm is proposed to solve the online synchronization issue, a closed-form RIS phase configuration strategy is proposed to improve the localization accuracy, and the modeling method of mutual coupling (MC) effect is provided. Extensive simulations are conducted to evaluate the fingerprint database generated by the proposed method. And the positioning performance on the database using different algorithms is analyzed, providing valuable insights for the system design.

Autonomous underwater vehicle (AUV) swarms are increasingly vital for large-scale underwater data collection. However, they are vulnerable to both external and internal attacks, including identity spoofing and selfish behaviors. To address these attacks, this paper proposes a novel trust evaluation mechanism, named PRLTE, which integrates Physical Layer Authentication (PLA) with Reinforcement Learning (RL). The mechanism comprises three core components: 1) trust calculation. Sink nodes collect multi-source trust evidence, including communication trust and energy trust. Furthermore, “work trust” evaluating data quality and quantity is introduced to mitigate the issue of insufficient historical trust evidence; 2) identity assessment. PLA under the Bellhop channel model is performed to authenticate agent identities and derive “identity trust;” and 3) trust evaluation. An RL-based trust evaluation mechanism is deployed to adaptively optimize trust component weights for agents based on identity trust. Simulation results demonstrate that PRLTE outperforms existing mechanisms in detecting malicious agents, with superior performance across both dense and sparse deployment scenarios.

Underwater acoustic sensor networks (UASNs) are extensively utilized in various sectors, including aquaculture, naval operations, and oceanic disaster alert systems. The protection of UASNs, with a specific focus on internal threats, has become an increasing priority. Attacks originating from within the network, involving compromised legitimate nodes, can be more harmful and covert compared to external threats, such as communication interception, data decryption, and identity impersonation. Trust models, which serve as mechanisms for detecting internal threats through interaction data, have proven effective in enhancing UASN security. However, traditional trust models often face scalability issues, particularly in environments characterized by mobile underwater devices, diverse network conditions, and evolving attack strategies. To address these challenges, this work presents a novel trust management scheme based on attention-weighted federated deep reinforcement learning (AFRTM). The AFRTM overcomes the limitations of existing approaches by first improving the evidence quantification methods—encompassing both environmental and behavioral evidence—to better adapt to the uncertainty of underwater scenarios. Subsequently, the acquired trust evidence is input into the respective deep reinforcement learning (DRL)-driven local trust framework to achieve trust estimation and model development. Finally, the model’s parameters are periodically aggregated and updated using an attention-weighted federated learning method, ensuring adaptability to changing conditions. The experimental findings demonstrate that the suggested approach delivers commendable outcomes in enhancing trust estimation precision and energy efficiency, and further providing a robust solution to the security challenges faced by UASNs.

With the rapid advancement of underwater networking and multi-agent reinforcement learning (MARL) technologies, autonomous underwater vehicle (AUV) cluster networks have emerged as a promising framework for enabling smart underwater missions, particularly in cooperative target encirclement. However, training MARL policies in such environments faces a critical challenge: acquiring large-scale interaction data is infeasible due to the severely constrained communication bandwidth, especially for online MARL frameworks. Therefore, achieving fast policy convergence with limited data collection is essential for practical deployment. This article proposes an LLM-Empowered Hybrid Training (LLM-EHT) architecture for MARL, which leverages the reasoning and generative capabilities of large language models (LLMs) to facilitate the transition from online to offline MARL under limited-sample conditions. Specifically, the proposed architecture first collects a small amount of online interaction data. It then employs an LLM to synthesize an offline dataset, guided by a dedicated alignment loss that enforces consistency between policy actions and LLM-generated references. This process significantly accelerates MARL policy convergence. Building upon LLM-EHT, we further develop a smart and cooperative underwater target encirclement scheme, incorporating a scalable state space representation method and a smart target encirclement policy to ensure robust, efficient, and scalable operation. Evaluation results showcase that the proposed scheme significantly reduces the required sample size while achieving faster convergence and maintaining stable encirclement effectiveness.

Autonomous Underwater Vehicles (AUVs) in wireless cluster networks have shown great promise for ocean exploration, particularly in multi-target tracking, with Critical applications in both military and civilian purposes such as environmental monitoring and underwater resource exploration. This paper proposes a novel framework for smart underwater AUV wireless cluster networks by integrating Software-Defined Networking (SDN) and Multi-Agent Reinforcement Learning (MARL) to achieve efficient, scalable multi-target tracking in dynamic underwater environments. Specially, this paper introduces a Software-Defined Multi-Teacher-Student Reinforcement Learning (SD-TSRL) architecture that synergizes SDN’s centralized control with MARL’s adaptive decision-making, enabling intelligent communication and dynamic resource management. To further enhance learning efficiency, a reciprocal teacher-student mechanism is proposed, which optimizes resource allocation and communication during training. On account of the mechanism, this paper presents the Reciprocal Teacher-Student-Inspired Centralized (RTSIC) MARL algorithm, which improves both communication and computation resource utilization in AUV wireless cluster network. Experimental results demonstrate that the proposed approach significantly enhances tracking accuracy and network performance compared to existing methods, validating the effectiveness of SDN-MARL integration for advanced underwater wireless networks.

The underwater acoustic sensor network (UASN) is a pivotal paradigm within the underwater Internet of Things, where multi-hop forwarding-based underwater data routing is essential for information acquisition. However, the dynamic nature of underwater network topology and the instability of underwater acoustic communication pose significant challenges to achieving efficient and reliable data transmission. In light of unreliable underwater environments and potential malicious attacks, studying trusted routing strategies for UASNs is crucial. This study introduces a context-aware trust routing scheme (CADTR) based on deep reinforcement learning (DRL), which integrates real-time environmental state perception with AI-driven routing decisions, thereby enhancing the reliability and robustness of data routing in dynamic and potentially hostile underwater scenarios. Firstly, a unified trust evidence framework is developed to strengthen the support of evidence experience for subsequent trust decisions by mapping multi-dimensional trust evidence to a unified scale. This framework is tightly coupled with the DRL agent, allowing the agent to evaluate and update trust levels based on real-time evidence. Secondly, a dynamic topology perception model and an underwater acoustic communication perception model are constructed to enable real-time perception of the interactive experience context. These models provide continuous input to the DRL agent, enabling it to adapt to topological changes and communication conditions dynamically. This facilitates priority experience sampling during the training process of the routing decision model, indirectly boosting model training efficiency and decision accuracy. Finally, the DRL agent learns optimal routing policies by interacting with the environment, leveraging the trust evidence and perception models to make informed decisions. Experimental results demonstrate that the proposed CADTR algorithm significantly improves the overall performance of the routing strategy in terms of packet delivery rate, energy utilization efficiency, and data transmission delay compared to the benchmark algorithms.

The Internet of Underwater Things (IoUT) has garnered significant interest due to its potential applications in monitoring underwater environments. However, the unique characteristics of acoustic communication, such as long propagation delays and high attenuation, present considerable obstacles for achieving efficient and dependable data transmission. Opportunistic routing is a crucial technique for enhancing packet delivery ratios by selecting a set of forwarding nodes and utilizing their cooperative forwarding to boost network throughput. Nevertheless, choosing an excessive number of forwarding nodes can lead to wasteful energy usage and extended communication delays. Moreover, the overlooked trustworthiness of forwarded nodes in most research works can undermine the effectiveness of opportunistic routing. Therefore, this study presents a novel trust opportunistic routing scheme that employs reinforcement learning to achieve resilience in constantly changing underwater settings. The combination of reinforcement learning and trust management enables the proposed opportunistic routing scheme to adapt to the unstable underwater environment and unknown malicious attacks. Initially, a method is introduced for measuring environmental fitness by considering multiple trust factors, including communication success rate, data reliability, and location dynamics. The proposed scheme then uses reinforcement learning to develop a reliable opportunistic routing method based on quantified state information. This component employs the obtained state to formulate action strategies and obtains reward values from environmental inputs. The reward update equation integrates these qualities to optimize the deployment of superior action strategies, finally achieving trust opportunistic routing for underwater data collection. Fundamental experimental results demonstrate that the proposed protocol performs exceptionally well in demanding underwater conditions, outperforming existing methods in packet transmission rate, energy efficiency, and end-to-end delay.

Deploying multiple unmanned aerial vehicles (UAVs) to locate a signal-emitting source covers a wide range of military and civilian applications like rescue and target tracking. It is well known that the UAVs-source (sensors-target) geometry, namely geometric configuration, significantly affects the final localization accuracy. This paper focuses on the geometric configuration optimization for received signal strength difference (RSSD)-based passive source localization by drone swarm. Different from prior works, this paper considers a general measuring condition where the spread angle of drone swarm centered on the source is constrained. Subject to this constraint, a geometric configuration optimization problem with the aim of maximizing the determinant of Fisher information matrix (FIM) is formulated. After transforming this problem using matrix theory, an alternating direction method of multipliers (ADMM)-based optimization framework is proposed. To solve the subproblems in this framework, two global optimal solutions based on the Von Neumann matrix trace inequality theorem and majorize-minimize (MM) algorithm are proposed respectively. Finally, the effectiveness as well as the practicality of the proposed ADMM-based optimization algorithm are demonstrated by extensive simulations.

Constructing underwater acoustic sensor networks (UASNs) for data collection has gradually become an effective ocean exploration and exploitation method. However, the interference of the underwater environment and the limited capacity of underwater communication equipment increase the difficulty of information interaction, posing a challenge to secure data transmission strategies for UASNs. Therefore, this study proposes a safe and reliable data transmission scheme based on reinforcement learning-aided two-stage trust evaluation (RLTST) to overcome the problems mentioned above. This article proposes a distinct self-trust concept, different from traditional trust mechanisms. A node self-trust evaluation method based on Q-learning is designed in the first stage, which defects compromised nodes actively. In the second stage, the trustworthiness of data is calculated based on the real data received, followed by backtracking the transmission path of untrustworthy data to identify malicious nodes. Finally, the results show that our proposed scheme is more effective in malicious node detection and improves data collection reliability.

Underwater acoustic sensor networks (UASNs) have been widely deployed in many areas, such as marine ranching, naval applications, and marine disaster warning systems. The security of UASNs, particularly insider threats, is of growing concern. Internal attacks carried out via compromised normal nodes are more damaging and stealthy than external attacks, such as signal stealing, data decryption, and identity forgery. As a security mechanism for internal threat detection based on interaction data, trust models have proven to enhance the security of UASNs. However, traditional trust models lack sufficient scalability when faced with movable underwater devices, heterogeneous network environments, and variable attack patterns. Therefore, in this paper, a novel trust model based on federated deep reinforcement learning is proposed for UASNs. First, the evidence acquisition mechanism, including communication, energy, and data evidence, is improved based on existing ones to better accommodate the topological dynamics of UASNs. Second, acquired trust evidence is fed into the corresponding deep reinforcement learning-based local trust model to accomplish trust prediction and model training. Finally, a federated learning-based update method periodically aggregates and updates the parameters of the local models. The experimental results prove that the proposed scheme exhibits satisfactory performance in terms of improving trust prediction accuracy and energy efficiency.