In underwater sensor networks, nodes are usually sparsely arranged, and autonomous underwater vehicles (AUVs) are often used to collect data by moving. However, it is a key problem for AUVs to shorten the path as much as possible to guarantee whole-network data acquisition. Aiming to solve this problem, this paper proposes an AUV underwater data collection method based on affinity propagation clustering. First, this clustering theory is used to select the nodes with large residual energy as cluster head nodes, and the clustering results are dynamically adjusted according to the communication radius of nodes to obtain the final clustering results. Finally, the cluster head nodes of the clusters are sequentially traversed using the traveling salesman problem, and an optimal path of AUV is planned to complete data collection for the whole network. A large number of simulation experiments verify that this method can effectively plan the mobile path of AUVs.

Due to the challenges of low bandwidth, high latency, and high communication energy costs in acoustic channel communication, data collection in underwater acoustic sensor networks remains severely restricted. The assistance of multiple autonomous underwater vehicles (AUVs) in data collection brings advantages of low latency and energy-efficient balance. However, existing AUV-assisted data collection schemes preplan paths and assume data transmission from all nodes, which is inefficient in information delay-sensitive monitoring scenarios. Furthermore, most schemes do not consider the influence of ocean currents when studying path planning. To solve these problems, this paper proposes a joint optimization scheme for multi-AUV cooperative data collection based on Stackelberg game, where the task allocation and path planning problems are formulated as the leader and follower of the Stackelberg game, respectively. At the leader level, a sequential game based algorithm is proposed, dynamically allocating cluster heads that require data transmission to different AUVs. At the follower level, the influence of ocean currents is considered, and an improved backtracking method is employed to plan the optimal path for each AUV. Experimental results show that the proposed method significantly improves network performance when compared with other representative underwater data collection methods.

Because the galloping of iced conductors is one of the main disasters in the State Grid, resulting in huge economic and property losses every year, the research on relevant monitoring methods is of great significance. The existing galloping monitoring technology is mainly based on the contact detection method, which presents potential electrical hazards and power supply problems. In this paper, a conductor galloping monitoring method based on the target detection of infrared sources is put forward to overcome the shortcomings of existing methods. In other words, an infrared source label is installed on the conductor spacer, high-definition night vision infrared cameras are installed on electric power towers to take video of the infrared source labels, and the characteristic amplitude of conductor galloping is calculated by an image recognition and tracking algorithm. The practical application results indicate that the method has the advantages of non-contact detection, safety and reliability, and high detection accuracy.

Due to the low bandwidth, high latency, and high communication energy cost of acoustic channel communication, data collection in underwater sensor networks is still severely limited, and it is particularly important to design an efficient and reliable routing protocol. Depth-based opportunistic routing has garnered significant attention due to its lack of reliance on 3-D geographic coordinates. Nevertheless, it suffers from the problems of void region and high energy consumption. In this paper, we propose an opportunistic routing protocol for underwater sensor networks based on the bargaining game. The protocol regards source and relay nodes as buyers and sellers, respectively, and establishes a multi-stage bargaining game model. In the phase of selecting the optimal relay set, the source and relay nodes make the decision of maximum payoff based on subgame perfect equilibrium to balance the energy consumption of the nodes. Subsequently, we introduce a recovery model to effectively mitigate the void region and long detour problems. In addition, an associated dynamic timing forwarding mechanism is set up to calculate timing based on node pricing and packet delivery probability, reducing end-to-end delay. Simulation results show that the proposed method significantly improves the network performance when compared with other representative underwater routing protocols.

To improve the reliability and efficiency of underwater acoustic sensor networks (UASNs) under limited node energy and network attacks, a joint scheme of enhancing robustness and extending network lifetime is proposed in this paper. Scale-free networks exhibit strong robustness against random failures, and because of the lower number of link connections, nodes consume energy more slowly. We first introduce a new scale-free topology evolution model that adjusts the initial number of nodes according to the characteristics of UASNs. This model incorporates factors such as flow load, energy consumption, and distance into the preferential attachment mechanism, balancing the network load and enhancing its resistance to attacks. Further, based on this topology, we propose a network flow adjustment algorithm that features the joint selection of paths and corresponding power levels. Energy consumption is balanced among nodes in proportion to their residual energy, rather than by minimizing the absolute consumed power. Numerical simulations show that different topologies significantly affect network lifetime, which is the longest when the number of links added is 2. The network lifetime of the proposed scheme surpasses the state-of-the-art schemes, such as ETFLA, Initial-BA, and VODA, by up to 37.18%, 46.23%, and 128.80%, respectively.

To address the problems of node deployment schemes in existing underwater sensor networks that lack consideration of network connectivity and high deployment costs, this article constructs an optimization model that maximizes network coverage and minimizes deployment costs while ensuring full connectivity. For the NP-hard property of this optimization model, an improved moth flame optimization node deployment algorithm based on fuzzy operators (IMF ² O ² ) is proposed. First, comprehensively considering the two performance metrics of network coverage and network connectivity, a multi-objective selection mechanism based on fuzzy operators is proposed to improve network coverage while ensuring full connectivity. Second, a fixed number of nodes are used to monitor the target event points, transforming the node deployment of sensors into an optimal problem and proposing an improved moth flame optimization algorithm to solve this problem. Finally, the two metrics of coverage and deployment cost are measured and the fuzzy operator is used to select the optimal number of nodes to be deployed. Numerical results showed that the proposed algorithm improved network coverage rate by 10%, 22%, and 25%, and improved network connectivity rate by 12%, 20%, and 8% as compared to PSSD, RAWS, and VODA, respectively, while ensuring full connectivity.