The intelligent cyber mapping methods based on Deep Reinforcement Learning （DRL） model the cyber mapping process as a Markov Decision Process （MDP） and train the attacking agents using error-driven learning to identify critical network paths and obtain network topology information. However， traditional cyber anti-mapping methods are usually based on fixed rules， making them difficult to face the dynamic behavioral strategies of DRL agents during the mapping process. Therefore， a cyber anti-mapping method based on adaptive perturbation， named AIP （Adaptive Interference Perturbation）， was proposed to defend against intelligent cyber mapping attacks. Firstly， the traffic conditions were predicted by using historical traffic sequence information， the gradient information was calculated according to the differences between the predicted conditions and real traffic data， and the gradient information was used to generate adversarial perturbations， which were injected back into the original traffic samples to produce adversarial examples. Then， a feature reconstruction method combining traffic posture and routing state was adopted to optimize the sparse dictionary dynamically through iteration， thereby realizing sparse transformation of traffic data. Finally， the sparse adversarial traffic was used as the observable traffic information of the network topology， and the defense performance of the AIP method was evaluated by analyzing the changes in the link-weight distribution assigned by the mapping agent and the variations in network latency. Experimental results show that compared to traditional perturbation defense methods such as Fast Gradient Sign Method （FGSM）and Random Attack （RA）， AIP increases the attacker’s susceptibility to perturbations significantly when the network traffic intensity exceeds 25%， resulting in greater changing amplitude in the link weights of the network topology and a noticeable impact on network delay. Furthermore， compared with Static Honeypot Deployment （SHD） and Dynamic Honeypot Deployment based on Q-Learning （DHD-Q） methods， according to the comparison of delay trends， AIP demonstrates continuous confusion of attackers， making it difficult to identify critical network paths， which ensures network delays remained within a controlled range and achieves better performance in defense efficiency and stability.

Group formation control technologies are ofen used for the film formation scenes of a large number of characters in film and television works, but a lot of group formation technologies tend to focus on the free-moving individual characters without considering the overall control of the formation, which causes the scene picture a lack of beauty, integrity and organization. In order to solve these problems, a group formation control method based on Voronoi diagram was proposed. Firstly, the group formation was divided into Voronoi diagram spaces to create a formation grid containing all the agents. Then, a new group formation deformation algorithm was proposed, in which artificial potential energy field and relative speed obstacle method were used to reasonably avoid obstacles, and a spring system was combined to keep the formation as stable as possible in the deformation process. Finally, Lloyd algorithm was used to quickly restore the target formation. The experimental results show that, the proposed method can simulate the group formation transformation motion well, is suitable for various complex scenes, and has an aesthetic, overall and organized formation transformation effect.

Delay/Disruption Tolerant Network (DTN) has characteristics of long delay, intermittent disruption, and limitation of buffer space and energy. To improve the delivery rate of messages, while reducing network overhead and the average latency, a new Routing Algorithm Based on Node Similarity (RABNS) in DTN was proposed. The algorithm used historical information to predict node encounter probability in future. The nodes which encountered historically were recorded as a collection, then the set intersection operation was applied to evaluate the similarity of a pair of nodes. And the similarity was used to control the number of copies in the network. Simulations were conducted on The ONE platform using RandomWaypoint motion model. In the simulation, RABNS performed better than PROPHET (Probabilistic ROuting Protocol using History of Encounters and Transitivity) in the message delivery rate. And the network overhead of RABNS was about half of PROPHET, which greatly improved the utilization of network resources. The average latency of RABNS was a little higher than Epidemic but lower than PPROPHET, the node cache size did not have a significant impact on average-hops, and its average-hops was about half of PROPHET. The simulation results show that RABNS can effectively limit the message flooding with higher message delivery rate, lower network overhead and average latency, therefore it is suitable for the DTN scenes with limited nodes' storage and also applicable in social DTN with gregarious characteristics.

An image positioning and scaling architecture for mobile video terminals was proposed for freely zooming and viewing video in detail. Then a gesture recognition processing approach was adopted in the architecture. Single-finger dragging and double-finger zooming detection were proposed for the gesture recognition. In addition, an approach to coordinates conversion calculation was proposed with boundary binding of coordinate transformation parameters using crossing boundary detection. Novel video display system was presented which consists of the video decoding, the image rendering and the interaction with synchronization. Finally these parts were concurrently implemented by three threads. The simulation results show that the proposed system obtains real-time image positioning and scaling while the traditional way of video playback is reserved. Interaction response time is controlled within 6ms to eliminate the screen flicker and skipping caused by interaction. Real-time image positioning and scaling of video playback for resources-limited mobile terminals will lead to a wide range of potential applications.

The wireless heterogeneous networks require efficient resource allocation between different communication systems with different modes. To gain effective wireless resource utilization between different systems, a resource allocation strategy based on XG system architecture was proposed. And then a multidimensional resource container was presented, which can meet the traffic demands of different communication systems. A two-level resource allocation strategy and its corresponding resource allocation algorithm were utilized to match the multidimensional resource container with diversified traffic demands between different communication systems. The simulation and performance analysis show that the proposed strategy can improve wireless frequency resource utilization and satisfy the traffic demands between different communication systems.

With the development of IPv6 networks， it is challenging to measure its topology for network management. By analyzing the difference between IPv6 and IPv4 protocols and researching the concepts of IPv6, the key problems that must be resolved were presented. Based on the above mentioned analysis， a topology discovery method for IPv6 network was proposed， the test experiment circumstance and result were given and a network topology automatic discovery system for IPv6 based on traceroute6 was accomplished too. Simulation results show that the system is correct and has better performance.