The blockchain-driven federated learning (FL) framework, leveraging its decentralized and immutable characteristics, enhances the security and credibility of IoT and edge computing. However, the inherent limitations in transaction throughput and high latency of the blockchain have posed substantial challenges to the efficiency of FL systems. Furthermore, FL itself is vulnerable to security threats such as poisoning attacks initiated by malicious participants and privacy inference attacks, which can compromise the security and confidentiality of the FL process. To address these challenges, this paper introduces a novel secure and trustworthy FL framework based on a hierarchical sharding blockchain structure, named HSChainFL. The design of HSChainFL framework centers around a meticulously crafted hierarchical sharding blockchain architecture. This structure facilitates interaction and collaboration between the main chain and multiple sub-chains, effectively reducing consensus overhead and thereby significantly improving the overall system efficiency. Moreover, HSChainFL incorporates a confidence evaluation method based on discrete cosine transform. This method utilizes the low-frequency components of the gradient vector to effectively identify malicious gradients in differential privacy scenarios, thereby providing strong resistance to poisoning attacks. Extensive experiments demonstrate that the HSChainFL framework exhibits superior performance in resisting various types of participant poisoning attacks while simultaneously reducing system overhead.

In ocean environments, underwater sensor nodes (USNs) are susceptible to failure due to various factors, such as seawater corrosion, hardware failure, depleted battery, harsh deployment scenarios, and intentional sabotage. This article focuses on the topology restoration problem of disconnected subnetworks (TR-DSNs) caused by large-scale USN failures in underwater wireless sensor networks (UWSNs). The existing research cannot be well adapted to dynamic ocean environments because they ignore the effects of underwater communication channel and current movement on the cost and reliability of network restoration. It would consequently lead to high restoration cost and unreliable data transmission for UWSNs. To solve the mentioned problem, we first build a reliability evaluation model of topology restoration that considers the link quality, network connectivity, and data transmission of UWSNs in dynamic ocean environments. Then, a reliability-driven topology restoration strategy (called RDTRS) based on underwater relay node (URN) placement is designed. RDTRS comprises three key algorithms: 1) URN placement path generation; 2) URN location determination; and 3) URN location adjustment. By RDTRS, the number of URNs can be reduced on the premise of ensuring the restoration reliability of UWSNs. In the end, we validate the performance of RDTRS in terms of network restoration cost, packet delivery ratio, and transmission latency.

In Underwater Acoustic Sensor Networks (UASNs), the robustness of network is greatly affected by complex marine environments when implementing multi-hop data transmission. Factors such as the underwater acoustic channel and dynamic topological changes induced by multi-layered oceanic vortices exacerbate this influence. However, there is currently a research gap in the specific area of robustness optimization for UASNs. Existing studies on robustness optimization are unsuitable for UASNs as they neglect the considerations of the marine environment and node characteristics (e.g., residual energy). In this work, we propose PRobust, a percolation-based robustness optimization model for UASNs. PRobust consists of two distinct phases: percolation modeling and bottleneck optimization. In the percolation modeling phase, we incorporate both node and edge features, considering the physical and topological properties, and introduce a novel approach for calculating link quality. In the bottleneck optimization phase, we devise a graph theory-based method to identify bottlenecks, leveraging the flow information recorded by nodes to improve the accuracy of bottleneck discovery. Moreover, we integrated time slots and a current movement model into the proposed model, allowing its applicability to dynamically changing UASNs. Extensive simulation results indicate that, compared to existing methods, PRobust significantly enhances network robustness and performance with the same overhead after bottleneck optimization.

In recent years, multimodal underwater wireless sensor networks (M-UWSNs) have attracted widespread concern in academia. Due to the complex underwater communication environment and the increasing marine applications, it is a crucial issue for M-UWSNs to design an energy-efficient routing strategy that can satisfy multiple transmission latency requirements of different marine applications. Reinforcement learning (RL) approaches with distributed dynamic optimization ability provide a prospective way to solve the aforementioned problem. Therefore, we propose an improved RL framework and then design an energy-efficient multilevel routing strategy (MLRS-RL) based on this framework for multiple transmission latency requirements. In MLRS-RL, a method of model knowledge collection based on the time backoff principle is proposed to preliminarily learn the network environment information before network operates. The convergence speed of the RL framework can be accelerated by utilizing the model knowledge. Then, underwater nodes use the improved RL model to calculate the transmission rewards that data packets with different transmission latency requirements are sent to different candidate relay nodes. Finally, a cooperative transmission strategy using multiple relay nodes is designed to further improve the reliability of data transmission. We verify the effectiveness of the MLRS-RL strategy in terms of packet delivery ratio, transmission latency, energy efficiency, network lifetime, and delivery quantity.

This article focuses on the topology optimization problem to decrease transmission delay and prolong network lifetime on the premise of reliable transmission in underwater wireless sensor networks (UWSNs). This is extremely challenging owing to dynamic ocean current movement, harsh underwater communication channel, and increasingly demanding application requirements. With the development of software-defined network architectures, the centralized topology control (TC) strategy with a global perspective in UWSNs is expected to become a more effective way to tackle the above challenges compared with the existing distributed and heuristic TC strategies involving local network state information. Therefore, we first transform the topology optimization problem of UWSNs into an integer nonlinear programming (INLP) model and design a centralized TC framework to solve the INLP model. In this framework, a TC center is built to periodically generate the network topology for UWSNs according to the current network state information. Further, an efficient topology generation algorithm based on deep reinforcement learning (TGA-DRL) is proposed in the TC center. In TGA-DRL, to reduce computing overhead and improve operational efficiency, we formulate an action-space narrowed Markov decision process suitable for network topology generation and solve it with the aid of the rainbow algorithm which is a deep reinforcement learning model. Finally, the performance of our centralized TC framework is verified in terms of the node out-degree, algorithm convergence, and optimization effect.

This paper focuses on the anti-interference transmission strategy for underwater acoustic sensor networks(UASNs). The interference existing between nodes communicating in a shared channel may significantly decrease the communication quality and increase the energy consumption of nodes. However, existing researches on transmission strategies for UASNs either do not consider inter-node interference or ignore the effect of acoustic channels. To solve the above problems, we propose to characterize the interference communication problem of nodes as an ordinal potential game model. Furthermore, a deep reinforcement learning (DRL)- based algorithm is designed to solve the problem, which selects the transmission power for nodes by learning historical information of signal-to-interference-plus-noise ratio(SINR) to minimize network interference. Finally, we verify that the DRL-based anti-interference transmission strategy proposed in this paper can obtain the optimal transmission strategy from variable underwater environment through extensive simulations, and show the feasibility of the algorithm under large-scale networks.

In underwater wireless sensor networks (USWNs), autonomous underwater vehicle (AUV)-assisted data collection has received significant attention for its characteristics of low energy consumption and long network lifetime. However, the existing AUV-assisted data collection schemes ignore the communication range of sensor nodes and the kinematics of AUVs. In this way, the path planned is inefficient and difficult to be applied in practice. To address the problems mentioned above, this paper proposes an AUV-assisted data collection scheme based on Fermat’s spiral (FS-PPS), which considers path planning within and outside the communication range of sensor nodes. Firstly, an improved firefly algorithm is used to determine the traversal order of nodes. Based on the determined sequence, we plan the path by adjusting the turning point and the steering angle of Fermat’s spiral. It can shorten the data collection path length. significantly while meeting the requirements. In the simulation, compared with two other schemes, FS-PPS can reduce data collection time by 17% and 6% on average.

This paper discusses the data transmission strategy based on underwater multimodal communication for marine applications in underwater wireless sensor networks (UWSNs). Underwater data required by various applications have different values of information (VoIs) depending on the type and timeliness of events. These data should be transmitted in different time latency according to their VoIs to accommodate the both application requirements and network performance. Our objective is to design a multi-level transmission strategy by using underwater multimodal communication system so that multiple paths with different transmission latency and energy consumption are provided for underwater data in UWSNs. For this purpose, we first define a minimum cost flow (MCF) model for the design of transmission strategy that considers transmission latency, energy efficiency, and transfer load. Then a distributed multilevel transmission strategy EMTS is proposed based on time backoff method for large-scale UWSNs. Finally, we compared the transmission latency, energy efficiency and network lifetime obtained by our EMTS strategy to those of the optimum solution of the MCF model, and a transmission algorithm based on greedy strategy. Although the latency of EMTS is slightly higher than that of other algorithms, our average network lifetime can reach 88.7% of that of the optimum solution of the MCF model.

The development of the smart ocean requires that various features of the ocean be explored and understood. The Underwater Internet of Things (UIoT), an extension of the Internet of Things (IoT) to the underwater environment, constitutes powerful technology for achieving the smart ocean. This article provides an overview of the UIoT with emphasis on current advances, future system architecture, applications, challenges, and open issues. The UIoT is enabled by the most recent developments in autonomous underwater vehicles, smart sensors, underwater communication technologies, and underwater routing protocols. In the coming years, the UIoT is expected to bridge diverse technologies for sensing the ocean, allowing it to become a smart network of interconnected underwater objects that has self-learning and intelligent computing capabilities. This article first provides a horizontal overview of the UIoT. Then, we present a five-layer system architecture for the future UIoT, which consists of a sensing, communication, networking, fusion, and application layer. Finally, we suggest the current challenges and the future UIoT research trends, in which cloud computing, fog computing, and artificial intelligence are combined.

For the development of the Underwater Internet of Things, reliable transmission of underwater wireless sensor networks to monitor the marine environment is important. However, for ocean monitoring, the reliability of data transmission is difficult to guarantee because of node mobility. In addition, energy consumption must be reduced during data transmission because node energy is limited. To entirely address these problems, this paper proposes a self-organising routing algorithm based on a joint clustering and routing strategy for ocean monitoring (JCR-OM) to increase reliable data transmission in underwater wireless sensor networks. Firstly, the reliable communication distance of the node is calculated in a multilayer current model by using a force analysis of the anchor node. Then, in cluster head selection, the reliable transmission distance and a backoff strategy are introduced to improve the impact of node mobility on data transmission. In intercluster routing selection, a greedy strategy is used to construct a routing strategy with minimum communication cost. The simulation results verify that JCR-OM can improve data transmission and prolong network lifetime.