The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area. A highly efficient route planning approach is required for this complex high dimensional optimization problem. However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment. In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem. Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem. Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm. Finally, the Cauchy–Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation. The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration.

The Internet of Underwater Things (IoUT) relies on wireless communication devices that are arranged in an open underwater environment and can interact with other devices through acoustic communication technology. However, due to their limited resources and open underwater environment, IoUT has been suffering from a high risk of malware attacks. As the two main parts of IoUT, autonomous underwater vehicles (AUVs) and underwater wireless rechargeable sensor networks (UWRSNs) are more favored by attackers and can be directly attacked by malware, which can lead to the cross-propagation of malware when AUVs and UWRSNs exchange information. To mitigate that threat, there is an urgent need to study the propagation patterns of malware and control their spread. Therefore, we establish a mathematical model based on the fractional-order theoretical framework to investigate the malware propagation patterns in two coupled networks (UWRSNs and AUVs). Then, we combine immune, charging and quarantine delays in control and derive the optimal control strategies based on optimal control theory. Moreover, to improve the generality of control, we propose a machine learning (ML) controller that combines machine learning (e.g., deep neural network (DNN) and random forest (RF)) with control theory. Ultimately, our simulation experiments show that the proposed optimal control strategy is more effective in inhibiting the spread of malware while obtaining the minimum control cost under different fractional-order scenes. At the same time, the ML-based control results are close to the optimal control.

This study examines the intermittent control of a rumor propagation system with anti-rumor mechanism. The interaction with the anti-rumor mechanism is investigated, including the existence and stability of two boundary equilibriums, the condition of bistability behavior. Threshold parameters are identified which determine the global exponential stability of the rumor-free equilibrium. To combat rumor spreading, we design deterministic and stochastic control strategies with aperiodically intermittent control time. The expressions of the minimum control intensities are obtained, which are related to the control ratio and system parameters. Numerical examples are carried out to verify the validity of the theoretical results and evaluate the potential roles of the intermittent control strategies.

With the development of wireless rechargeable sensor networks (WRSNs ), security issues of WRSNs have attracted more attention from scholars around the world. In this paper, a novel epidemic model, SILS(Susceptible, Infected, Low-energy, Susceptible), considering the removal, charging and reinfection process of WRSNs is proposed. Subsequently, the local and global stabilities of disease-free and epidemic equilibrium points are analyzed and simulated after obtaining the basic reproductive number $R_0$. Detailedly, the simulations further reveal the unique characteristics of SILS when it tends to being stable, and the relationship between the charging rate and $R_0$. Furthermore, the attack-defense game between malware and WRSNs is constructed and the optimal strategies of both players are obtained. Consequently, in the case of $R_0<1$ and $R_0>1$, the validity of the optimal strategies is verified by comparing with the non-optimal control group in the evolution of sensor nodes and accumulated cost.

Unmanned aerial vehicle (UAV) and wireless sensor network (WSN) system widely used in emergency sites monitoring, disaster relief and data collection, is vulnerable to malware attack. In this paper, we establish a novel epidemic model to accurately describe the characteristics of malware cross-propagation and stochastic disturbances between UAV and WSN. To control their spread, the hybrid charging and patching strategies are optimized by the proposed stochastic hybrid optimal control method while minimizing control costs. An effective stacking ensemble learning control (SELC) approach is developed to obtain a suboptimal control strategy. Extensive comparison and simulations are shown to validate the effectiveness of the proposed stochastic hybrid optimal control method and SELC method.