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.

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.

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.

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.

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.

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.

In the process of data acquisition of underwater acoustic sensor networks (UASNs), the safety of the network is threatened by the disclosure of source node location information. So how to protect the security and privacy of source node location is the main challenge faced by UASN security. To realize this taeget, a hierarchical structure-based algorithm for protecting source location privacy (HSSLP) is proposed in this paper. Firstly, it is proposed to divide UASNs into dynamic and static layers based on Ekman drift model. Location privacy protection schemes suitable for source nodes located in different layers have been proposed separately. In the static layer, k-means clustering separates the nodes into groups, and the source node’s location privacy is protected using fake source node and phantom nodes, while auxiliary cluster head and sleep scheduling mechanism are used to save node energy. Nodes in the dynamic layer, whose positions are prone to change, are no longer clustered. The source node makes use of inducing nodes to take adversaries away from the source node, enhancing the privacy and security of the source node with minimal energy expenditure. Finally, autonomous underwater vehicles (AUV) need to support the cluster head in collecting data combined in the static layer and data uploaded in the dynamic layer. Based on the communication range of AUV, the network is segmented into areas, and when the AUV receives warning messages while traveling, it changes its route to lead the adversary to an area remote from the source node. Simulation results show that the proposed algorithm owns the capacity to balance the relationship between network security, transmission delay, and node energy consumption. To be more specific, the HSSLP algorithm improves the safety time by about 50$\%$, reduces the delay by about 20$\%$and saves the node energy by about 36$\%$as compared to the DIS-PLP algorithm.

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.

The massive growth in interconnected devices from a multiplicity of networks goes hand-in-hand with the emergence of the Internet of Things (IoT) paradigm. As a critical component of the IoT, sensor networks have become ubiquitous and widely used in various application domains. However, open-ended wireless communication brings severe threats to user privacy and security. Attackers from outside the network can trace back along the data stream to capture the source node, which poses a significant threat to the privacy of the data source. A feasible defense method is to interfere with the attacker’s tracking process through forged data streams. However, the related traditional solutions generally have shortcomings in terms of balancing security and efficiency. Therefore, this article proposes a pseudopacket scheduling algorithm (PPSA), which aims at reasonably regulating the process of pseudopacket generation to interfere with the adversary’s tracking to the data source. The algorithm comprises three phases. First, the sink node performs geographic information acquisition and neighbor node discovery with a flood-based method. Then, the sink node uses a self-adapting proxy selection method to construct backbone routes with both randomness and low latency to receive actual packets. Finally, the nodes on both sides of the backbone routes follow a pseudopacket scheduling strategy to interfere with the adversary’s tracking of the source locations. The experimental results showcase that our proposed scheme effectively controls the additional energy consumption and transmission delays within acceptable ranges while ensuring adequate location privacy.

Underwater acoustic sensor networks (UASNs) have been widely applied in marine scenarios, such as offshore exploration, auxiliary navigation and marine military. Due to the limitations in communication, computation, and storage of underwater sensor nodes, traditional security mechanisms are not applicable to UASNs. Recently, various trust models have been investigated as effective tools towards improving the security of UASNs. However, the existing trust models lack flexible trust update rules, particularly when facing the inevitable dynamic fluctuations in the underwater environment and a wide spectrum of potential attack modes. In this study, a novel trust update mechanism for UASNs based on reinforcement learning (TUMRL) is proposed. The scheme is developed in three phases. First, an environment model is designed to quantify the impact of underwater fluctuations in the sensor data, which assists in updating the trust scores. Then, the definition of key degree is given; in the process of trust update, nodes with higher key degree react more sensitively to malicious attacks, thereby better protecting important nodes in the network. Finally, a novel trust update mechanism based on reinforcement learning is presented, to withstand changing attack modes while achieving efficient trust update. The experimental results prove that our proposed scheme has satisfactory performance in improving trust update efficiency and network security.

In recent years, developments in data collection schemes based on multipleautonomous underwater vehicles(AUVs) are facilitating the realization of the so-calledunderwater acoustic sensor networks(UASNs). As yet, the lack of suitable collaboration mechanisms among multiple AUVs, which are based on functional or resource distributions, prevents effective information sharing and yields increased data collection delays, thus reducing the capacity of the networks. In this article, to address these shortcomings, we propose astate prediction-based data collection(SPDC) algorithm for UASNs. The principle of operation is as follows. First, some cluster pairs named observation clusters obtain and exchange the state information about AUVs between the adjacent subregions. Based on the shared information, the AUVs predict each other’s status and adjust their data collection areas. Then, the AUVs use a heuristic strategy to complete the path planning based on the updated access area. Finally, a scheduling data forwarding mechanism reduces the diving number of the AUVs, by reasonably allocating the overlapped data unloading intervals between the AUVs and a mobile sink. Experimental results prove that the proposed algorithm shows satisfactory performance in reducing data collection delays and in improving the total network lifetime.

Poisonous pollutants produced in chemical, plastics, or nuclear power industry are easy to leak and result in a large-scale hazardous event region. Recently, industrial wireless sensor networks (IWSNs) are intended to provide situational awareness in industry site and thus hold the promise of profiling the event region. However, low-cost nodes in IWSNs are prone to fail due to prolonged exposure to harsh environment. This article targets the detection of hazardous event region for IWSNs with faulty nodes. A fault-tolerant event region detection algorithm named TPE-FTED is proposed to formulate faulty nodes identification as a trajectory pattern extraction problem. Through online learning of probabilistic model, each node characterizes the distribution of sensing values under different sensing states. A specific set of probabilistic models can be formed as a trajectory which indicates something special happens. Based on the implicit knowledge from generated trajectories, TPE-FTED conducts pattern matching and checks spatiotemporal constraint to identify the declaration of faulty nodes. Simulation results demonstrate that TPE-FTED achieves low false alarm rate as well as high detection accuracy.

The advent of underwater acoustic sensor networks (UASNs) has enhanced marine environmental monitoring, auxiliary navigation, and marine military defense. One of the core functions of UASNs is data collection. However, current underwater data collection schemes generally encounter problems such as high energy consumption and high latency. Furthermore, the application of multiple autonomous underwater vehicles (AUVs) has contributed to more problems of task assignment and load balancing. This leads to significant failure in data collections and controlling of spontaneous emergencies. To address these problems, a district partition-based data collection algorithm with event dynamic competition in UASNs has been proposed. In this algorithm, the value of information of the packet determines the priority of its transmission to the cluster head. The navigation position of the mobile sink and the area under the responsibility of each AUV are determined by the spatial region division. The path of the AUV in the subregion is then planned using reinforcement learning. Subsequently, the dynamic competition of multiple AUVs is used to handle emergency tasks. The simulation demonstrates that our proposed algorithm significantly reduces energy consumption to guarantee load balancing while reducing end-to-end transmission delay.

An exciting network called smart IoT has great potential to improve the level of our daily activities and the communication. Source location privacy is one of the critical problems in the wireless sensor network (WSN). Privacy protections, especially source location protection, prevent sensor nodes from revealing valuable information about targets. In this paper, we first discuss about the current security architecture and attack modes. Then we propose a scheme based on cloud for protecting source location, which is named CPSLP. This proposed CPSLP scheme transforms the location of the hotspot to cause an obvious traffic inconsistency. We adopt multiple sinks to change the destination of packet randomly in each transmission. The intermediate node makes routing path more varied. The simulation results demonstrate that our scheme can confuse the detection of adversary and reduce the capture probability.