Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

An Adaptive Anomaly Detection Algorithm Based on CFSFDP

CFSFDP(Clustering by fast search and find of density peak)is a simple and crisp density clustering algorithm.It does not only have the advantages of density clustering algorithm,but also can find the peak of cluster automatically.However,the lack of adaptability makes it difficult to apply in intrusion detection.The new input cannot be updated in time to the existing profiles,and rebuilding profiles would waste a lot of time and computation.Therefore,an adaptive anomaly detection algorithm based on CFSFDP is proposed in this paper.By analyzing the influence of new input on center,edge and discrete points,the adaptive problem mainly focuses on processing with the generation of new cluster by new input.The improved algorithm can integrate new input into the existing clustering without changing the original profiles.Meanwhile,the improved algorithm takes the advantage of multi-core parallel computing to deal with redundant computing.A large number of experiments on intrusion detection on Android platform and KDDCUP 1999 show that the improved algorithm can update the profiles adaptively without affecting the original detection performance.Compared with the other classical algorithms,the improved algorithm based on CFSFDP has the good basic performance and more room of improvement.

Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission

Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands.Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content.In this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing images.Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion.We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction.To address these challenges,we leverage federated reinforcement learning techniques.Additionally,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images.Through software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery delay.This paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.

A multipath routing algorithm for satellite networksbased on service demand and traffic awareness

With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates,satellites have become an important part of data transmission in air-ground networks.However,due to the factors such as geographical location and people’s living habits,the differences in user’demand for multimedia data will result in unbalanced network traffic,which may lead to network congestion and affect data transmission.In addition,in traditional satellite network transmission,the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner,which is not conducive to calculating optimal routes.The service quality requirements cannot be satisfied when multiple service requests are made.Based on the above,in this paper artificial intelligence technology is applied to the satellite network,and a software-defined network is used to obtain the global network information,perceive network traffic,develop comprehensive decisions online through reinforcement learning,and update the optimal routing strategy in real time.Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability.Compared with traditional routing,the throughput is 8%higher,and the proposed method has load balancing characteristics.