In unmanned aerial vehicle (UAV) path planning, evolutionary algorithms are commonly used due to their ability to handle high-dimensional spaces and wide generality. However, traditional evolutionary algorithms have difficulty with population initialization and may fall into local optima. This paper proposes an improved genetic algorithm (GA) based on expert strategies, including a novel rapidly exploring random tree (RRT) initialization algorithm and a cross-variation process based on expert guidance and the wolf pack search algorithm. Experimental results on baseline functions in different scenarios show that the proposed RRT initialization algorithm improves convergence speed and computing time for most evolutionary algorithms. The expert guidance strategy helps algorithms jump out of local optima and achieve suboptimal solutions that should have converged. The ERRT-GA is tested for task assignment, path planning, and multi-UAV conflict detection, and it shows faster convergence, better scalability to high-dimensional spaces, and a significant reduction in task computing time compared to other evolutionary algorithms. The proposed algorithm outperforms most other methods and shows great potential for UAV path planning problems.

Reducing carbon emission to improving the economy of fuel vehicle is one of the effective ways to achieve Carbon Neutrality. The Internet of Vehicles (IoV) is a developing technology for deep integration of the Internet of Things (IoT) and transportation. The Industrial Internet of Things (IIoT) can incorporate vehicle information to pinpoint vehicle carbon emissions and provide a foundation for the subsequent carbon-neutral decision-making process. To achieve the precision needs of IoT, however, more than conventional Global Navigation Satellite Systems (GNSS) are required. To achieve carbon emission detection, provide high-precision positioning, and provide a foundation for subsequent carbon-neutral decision-making, it is essential to design a carbon emission detection and positioning system with the capability of vehicle networking. The geographic proximity of edge Ultra Wide Band (UWB) nodes and the merging of various data sources are two methods we suggest employing in this study to increase location accuracy in IIoT situations. After carefully examining the positioning error of the single-edge node and the range error achieved in the UWB communication system, we choose a suitable filtering strategy to enhance single-node accuracy. Following the improvement of single-node accuracy, we fuse the location information of multiple edge nodes using a Weighted Least Squares algorithm in the spatial dimension; in the temporal dimension, we use Extended Kalman filtering to fuse the data over a period of time due to the temporal correlation of inter-node communication. Experimental results demonstrate that our co-localization method, which combines temporal and spatial information, achieves higher localization accuracy in comparison with previous work.

This paper studies the design of dual-mode resilient event-triggered control strategy for Markov jump cyber–physical systems (MJCPSs) under denial-of-service (DoS) attacks. Firstly, a novel resilient event-triggering scheme dependent on the DoS signal is developed to select the corresponding control protocol based on the current network quality of services. Particularly, the potential relationship between the triggering signal and system mode under DoS attacks is discussed, aiming to eliminate both Zeno behavior and singular triggering behavior by calculating the minimum and maximum data update rates. Then, we design an event-based dual-mode security controller to ensure that the closed-loop system has stochastic stability and good robust $H_{\infty }$ performance under DoS attacks. By constructing a Lyapunov–Krasovskii functional which depends on the lower and upper bounds of time delay, sufficient conditions for the existence of dual-mode security controller gains and resilient triggering parameters are presented with the LMI form. Finally, simulation results show that the proposed security control strategy has good robustness against DoS attacks.