This paper studies an underwater inhalation-type cleaning robot designed to remove floating garbage. The dynamic model of the underwater cleaning robot is established to show the relationship between the speed of the propeller and the motion state, where the added mass and the viscous resistance coefficient during translational motion are obtained by the empirical formulas, and the added moment of inertia and the resistance moment coefficient are determined using Fluent software. Then a cascade PID motion control strategy with a position outer loop and a speed inner loop is proposed for this inhalation-type garbage cleaning robot to improve control performance. Finally, the effectiveness of the obtained results is demonstrated by simulations in MATLAB/Simulink.

Edge intelligence can significantly enhance the real-time performance and robustness of multimedia retrieval. However, its privacy and intellectual property security face various challenges. In this paper, we propose a model training framework based on the gradient optimization concept, synchronously optimizing model parameters and model license. This approach ensures that the trained model only correctly retrieves information for inputs containing the correct license, actively protecting its intellectual property by restricting its usability. Additionally, we devise a data irreversibility standardization method based on random perturbation to safeguard the privacy of both data and license. We conduct extensive experiments in edge intelligence scenarios, and the results demonstrate that, compared to the state-of-the-art approaches in this field, our method achieves accuracy closer to the baseline model. The injected license are more covertly secured, and the anti-fine-tuning capability is improved by an average of more than 25.1%.

IoTs generally rely on resource-constrained devices to sense, relay, and collect data, which are highly vulnerable to Distributed-Denial-of-Services (DDOS) attacks on network throughput. In this paper, we propose a trust-based method to optimize the network throughput of IoTs under DDOS attacks. Specifically, with the assistance of a small number of dedicatedly deployed defense nodes as defenders, a network controller can first measure the behavior of other IoT nodes and categorize them into three types (i.e., innocent, selfish, and attack) through a well-designed trust evaluation model. Then, a Stackelberg game model is constructed accordingly, where defenders are leaders and other nodes are followers. We carefully define the utilities of the leaders and the followers in the game, and transform the optimization problem of the network throughput into the maximization problem of the defenders' utilities considering the utilities of the followers. We adopt the Dinkelbach Programming (DP)-based algorithm to solve the maximization problem such that a Stackelberg equilibrium can be reached with optimized network throughput. Extensive simulations are performed to demonstrate that the proposed defense method can significantly increase the IoT network throughput under different DDOS attack intensities.

To solve the problem of security deployment in a hybrid wireless sensor network, a novel privacy‐preserving mobile coverage scheme based on trustworthiness is proposed. The novel scheme can efficiently mitigate some malicious attacks such as eavesdropping and pollution and optimize the coverage of hybrid wireless sensor networks (HWSNs) at the same time. Compared with the traditional mobile coverage scheme, the security of data transmission and mobility are considered in the deployment of HWSNs. Firstly, our scheme can mitigate the eavesdropping attacks efficiently utilizing privacy‐preserving signature. Then, the trust mobile protocol based on the trustworthiness is used to defend the pollution attacks and improve the security of mobility. In privacy‐preserving signature, the hardness of discrete logarithm determines the degree of security of the privacy‐preserving signature. The correctness and effectiveness of signature algorithm are proven by the probabilities of the native messages which can be recovered and forged which is negligible. Furthermore, a mobile scheme based on the trustworthiness (MSTW) is proposed to optimize the network coverage and improve the security of mobility. Finally, the simulation compared with a previous algorithm is carried out, in which the communication overhead, computational complexity, and the coverage are given. The result of the simulation shows that our scheme has roughly the same network coverage as the previous schemes on the basis of ensuring the security of the data transmission and mobility.

Network coding is a promising technique to improve the throughput and robustness of wireless ad hoc networks. However, the packet‐mixing nature of network coding also renders it more prone to pollution attacks. Most existing schemes to combat pollution attacks did not consider the defender's resource limit, nor the trade‐off between defensive performance and other metrics such as delay and resource consumption. The authors investigate how to achieve such a trade‐off optimally by proposing a two‐player strategic game model between the attack and the defender. In this model, the utilities of both players are well defined, and thus the defender can obtain its best strategy by maximising its utility. To do so, a graph‐based simulated annealing algorithm is proposed to derive the utility‐maximising strategy. Finally, they conduct extensive experiments to evaluate their scheme from different aspects. The results show that their scheme can achieve better utility than existing schemes, and is more computationally efficient in the meanwhile. Moreover, their scheme can obtain a sub‐optimal solution within a small number of iterations, which implies that it can be implemented in the short‐session communication scenario where it is required to find a sufficiently good solution within a short time.