The brain-inspired collaborative Simultaneous Localization And Mapping technology simulates the spatial navigation cells of biological brains to process the detection information of leader-follower Autonomous Underwater Vehicles (AUVs), which can obtain global map of underwater world. However, the capacity of underwater acoustic communication is limited, and the communication content is affected by environmental noise, which can affect the performance of collaborative navigation. To address the above issues, inspired by the granule cells and time cells of biological brains, this paper proposes a Bio-inspired Collaborative Navigation Method of leader-follower AUVs based on composite spatial navigation cells (BCNM). Firstly, this method discusses a brain-inspired collaborative navigation scenario of AUVs. Next, this method designs a granular cell based on Spiking Neural Networks (SNN) to achieve feature point selection and the compression and aggregation of local descriptors, which are used for data transmission and loop closure detection of AUVs. In addition, this method designs a time cell based on sliding window Gaussian Process Regression (GPR) and Improved Walrus Optimizer algorithm (IWO) to correct the position information of AUVs and obtain the transition matrix of local maps, which are used to achieve the map fusion of AUVs. Finally, the pose cells are used to encode the motion postures of AUVs, and the experience map is used to represent the paths of AUVs. To validate the performance of BCNM, this paper establishes an underwater collaborative SLAM dataset. Experimental results show that BCNM has good adaptability in the underwater SLAM datasets, and outperforms other methods in terms of trajectory error.

The brain-inspired Simultaneous Localization And Mapping technology (SLAM) in Internet of Underwater Things enables real-time location of multi-Autonomous Underwater Vehicle (AUV) with low computational overhead. However, in actual scenarios, the movement of AUV swarm interferes with visual images, which affects the performance of SLAM seriously. To deal with the above problem, a Brain-inspired Navigation Method based on Composite Spatial Navigation Cell model is proposed to achieve the autonomous navigation of AUVs, which is inspired from the speed cell and boundary cell of biological brain. Firstly, this method establishes a brain-inspired navigation scene of multi-AUV, where the stereo camera of AUVs is used to collect environmental information. Next, this method establishes speed cells based on Spiking Neural Network (SNN) to obtain semantic information, the location, score, and descriptor of feature points, which helps to estimate the motion information of AUVs such as displacement changes and heading angle changes) accurately. In addition, based on the distance information and angle information from stereo camera of AUVs to static obstacles, the proposed method calculates the activity value of boundary cells to assist in the loop-closure detection of local view cells. Finally, this method uses pose cells to represent the motion posture of AUVs, and applies experience map to record the movement trajectory of AUVs. To measure the performance of proposed method, this paper establishes an underwater SLAM dataset and a land SLAM dataset respectively, which are affected by dynamic entities. Extensive experimental results show that this method has good adaptability in different SLAM datasets, and is better than other methods in terms of trajectory error.

In Internet of Underwater Things (IoUT), multiautonomous underwater vehicles (AUVs) can hunt specific targets by performing collaborative hunting tasks. Collaborative hunting task refers to multi-AUV hunts underwater target through collaborative hunting algorithm, which includes target searching, tracking path planning, and encirclement keeping. However, in actual scenarios, this task requires hunter AUVs to search targets independently, and encircle targets continuously, which poses a huge challenge to the collaborative hunting algorithm. To solve this challenge, this article proposes a collaborative hunting algorithm for dynamic ocean targets to ensure collaborative hunting effects. First, this algorithm models the collaborative hunting of hunter AUVs, and designs the kinematic model and detection model of hunter AUVs. According to the 3-D encirclement of hunter AUVs, this algorithm establishes a tracking encirclement metric to evaluate the encirclement keeping of hunter AUVs. In addition, this algorithm adjusts divide areas based on robots initial position (DARP) and bio-inspired neural network (BINN) to achieve collaborative searching in 3-D environment. Then, this algorithm designs an improve crayfish optimization algorithm (ICOA) to obtain tracking paths and hunting actions of hunter AUVs. Specifically, ICOA uses planning space and cubic map to initialize populations, and performs the optimization operation through Levy flight. Extensive simulation results show that the proposed algorithm can complete the collaborative hunting task, and is better than other algorithms in terms of encirclement keeping and tracking path planning.

As one of the important tools for exploring the ocean, multiple autonomous underwater vehicles (multi-AUVs) system can complete complex tasks in complex Internet of Underwater Things. Collaborative target search, as a typical application of multiple autonomous underwater vehicle (AUV) systems, has been applied in the fields of territorial sea security and marine biology research. Among them, hunting task allocation is a key issue determining the effective application of multiple AUV systems. Therefore, this article proposes a hunting task assignment framework based on contract network (CN) to assign hunting tasks. In the investigated framework, the tenderee AUV (TAUV) is responsible for setting the task reward and assigning hunting tasks, while bidder AUVs (BAUVs) set the working time as bidding information. Combining the mobile energy consumption and communication energy consumption of hunter AUVs, we establish the revenue optimization model of BAUVs and the TAUV. Based on the above model, we model the interaction process of hunting task allocation process between BAUVs and the TAUV as a Stackelberg game, and use the backward induction method to prove that there is a unique Stackelberg equilibrium (SE) in the game. In addition, this article proposes a strategy search algorithm based on the steepest descent method (SSA_SDM) to obtain the optimal strategy of BAUVs and the TAUV, which can achieve SE. Finally, experimental results show that SSA_SDM can reach the SE and outperform other algorithms.

Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology, blockchain has been broadly used in fintech, smart homes, public health, and intelligent transportation due to its properties of decentralization, collective maintenance, and immutability. Although the dynamism of blockchain abounds in various fields, concerns in terms of network communication interference and privacy leakage are gradually increasing. Because of the lack of reliable attack analysis systems, fully understanding some attacks on the blockchain, such as mining, network communication, smart contract, and privacy theft attacks, has remained challenging. Therefore, in this study, we examine the security and privacy of the blockchain and analyze possible solutions. We systematical classify the blockchain attack techniques into three categories, then discuss the corresponding attack and defense methods based on these categories. We focus on (1) the attack and defense methods of mining pool attacks for blockchain security issues, such as block withholding, 51%, pool hopping, selfish mining, and fork after withholding attacks, in the attack type of consensus excitation; (2) the attack and defense methods of network communication and smart contracts for blockchain security issues, such as distributed denial-of-service, Sybil, eclipse, and reentrancy attacks, in the attack type of middle protocol; and (3) the attack and defense methods of privacy thefts for blockchain privacy issues, such as identity privacy and transaction information attacks, in the attack type of application service. Finally, we discuss future research directions for blockchain security.