The multi-agent task allocation problem has attracted substantial research interest due to the increasing demand for effective solutions in large-scale and dynamic environments. Despite advancements, traditional algorithms often fall short in optimizing efficiency and adaptability within complex scenarios. To address this shortcoming, we propose a genetic algorithm-enhanced PPO (GAPPO) algorithm, specifically developed to enhance decision-making in challenging task allocation contexts. GAPPO employs a deep reinforcement learning framework, enabling each agent to independently evaluate its surroundings, manage energy resources, and adaptively adjust task allocations in response to evolving conditions. Through iterative refinement, GAPPO achieves balanced task distribution and minimizes energy consumption across diverse configurations. Comprehensive simulations demonstrate that GAPPO consistently outperforms traditional algorithms, resulting in reduced task completion time and heightened energy efficiency. Our findings underscore GAPPO’s potential as a robust solution for real-time multi-agent task allocation.

The accurate determination of vehicle location is of great research significance, considering challenges such as the multipath environment and the absence of Global Navigation Satellite System (GNSS) signals. In this particular environment, vehicles equipped with 5G wireless communication devices can enhance their positioning accuracy by exchanging information with infrastructure (vehicle-to-infrastructure, V2I). Therefore, in this paper, we propose a multifrequency ranging method and positioning algorithm specifically designed for 5G orthogonal frequency division multiplexing (OFDM) communication systems. Our approach involves selecting specific subcarriers within the OFDM communication system for transmitting ranging frames and capturing delay observations. Importantly, this selection does not affect the functionality of other subcarriers used for regular communication. By utilizing dedicated subcarriers for ranging and positioning, we achieve accurate vehicle location without significantly impacting communication capacity. We outline the method for selecting ranging subcarriers and describe the format of the ranging frame carried by these subcarriers. To evaluate the effectiveness of our system, we prove the Cramér-Rao lower bound of this ranging positioning system. The obtained ranging positioning accuracy meets the requirements for vehicle location applications. In our experimental simulations, we compare the performance of our system with other positioning methods, demonstrating its superiority. Additionally, we provide theoretical proofs and simulations that establish the relationship between ranging accuracy and channel parameters in a multipath environment. The simulation results indicate that, under the conditions of a 5 GHz frequency and a high signal-to-noise ratio, our system achieves a positioning accuracy of approximately 5 cm.

Faced with challenges posed by sophisticated cyber attacks and dynamic characteristics of cyberspace, the autonomous cyber defense (ACD) technology has shown its effectiveness. However, traditional decision-making methods for ACD are unable to effectively characterize the network topology and internode dependencies, which makes it difficult for defenders to identify key nodes and critical attack paths. Therefore, this paper proposes an enhanced decision-making method combining graph embedding with reinforcement learning algorithms. By constructing a game model for cyber confrontations, this paper models important elements of the network topology for decision-making, which guide the defender to dynamically optimize its strategy based on topology awareness. We improve the reinforcement learning with the Node2vec algorithm to characterize information for the defender from the network. And, node attributes and network structural features are embedded into low-dimensional vectors instead of using traditional one-hot encoding, which can address the perceptual bottleneck in high-dimensional sparse environments. Meanwhile, the algorithm training environment Cyberwheel is extended by adding new fine-grained defense mechanisms to enhance the utility and portability of ACD. In experiments, our decision-making method based on graph embedding is compared and analyzed with traditional perception methods. The results show and verify the superior performance of our approach in the strategy selection of defensive decision-making. Also, diverse parameters of the graph representation model Node2vec are analyzed and compared to find the impact on the enhancement of the embedding effectiveness for the decision-making of ACD.

In order to solve the problem that the IoT wireless positioning technology is easily affected by the environment, which leads to the unsatisfactory positioning effect, this paper proposes the Improving Particle Filter-Map Matching Algorithm (IPF-MM). Firstly, we propose to use the particle filter algorithm to filter the positioning trajectory from the wireless positioning base station. In this method, we combine the particle filter algorithm and outdoor map information and we use the centerline of the road to update the weight of the particles to improve positioning accuracy. Secondly, when the amount of data of positioning track points is too large, we set the threshold for the number of anchor tracks and reduce the number of positioning points by multiples to improve positioning efficiency. In the simulation experiment, the average relative error of the UWB positioning track is reduced by 47.7% after using the IPF-MM. Besides, the IPF-MM reduces the running time by 42.9% compared with PF-MM, which indicates that the IPF-MM can improve both the accuracy and efficiency of positioning.

The multi-agent task allocation problem has attracted substantial research interest due to the increasing demand for effective solutions in large-scale and dynamic environments. Despite advancements, traditional algorithms often fall short in optimizing efficiency and adaptability within complex scenarios. To address this shortcoming, we propose a genetic algorithm-enhanced PPO (GAPPO) algorithm, specifically developed to enhance decision-making in challenging task allocation contexts. GAPPO employs a deep reinforcement learning framework, enabling each agent to independently evaluate its surroundings, manage energy resources, and adaptively adjust task allocations in response to evolving conditions. Through iterative refinement, GAPPO achieves balanced task distribution and minimizes energy consumption across diverse configurations. Comprehensive simulations demonstrate that GAPPO consistently outperforms traditional algorithms, resulting in reduced task completion time and heightened energy efficiency. Those findings underscore GAPPO’s potential as a robust solution for real-time multi-agent task allocation.

The sensor coverage problem is a well-known combinatorial optimization problem that continues to attract the attention of many researchers. The existing game-based algorithms mainly pursue a feasible solution when solving this problem. This problem is described as a potential game, and a memory-based greedy learning (MGL) algorithm is proposed, which can ensure convergence to Nash equilibrium. Compared with existing representative algorithms, our proposed algorithm performs the best in terms of average coverage, best value, and standard deviation within within a suitable time. In addition, increasing memory length helps to generate a better Nash equilibrium.