Dynamic Reliability-Aware Virtual Network Embedding for Airborne Tactical Networks

Miao, Jingcheng; Lv, Na; Chen, Kefan; Gao, Qi; Wang, Xiang

doi:10.1155/2022/7164854

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…We consider communication in an airborne tactical network (ATN) [9] where there are hostile aircraft. Previous studies on ATNs have, however, not considered the presence of hostile aircraft and the implications and restrictions that the presence of hostile aircraft have on routing [10][11][12].…”

Section: Related Workmentioning

confidence: 99%

Avoiding Detection by Hostile Nodes in Airborne Tactical Networks

et al. 2023

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Contemporary airborne radio networks are usually implemented using omnidirectional antennas. Unfortunately, such networks suffer from disadvantages such as easy detection by hostile aircraft and potential information leakage. In this paper, we present a novel mobile ad hoc network (MANET) routing protocol based on directional antennas and situation awareness data that utilizes adaptive multihop routing to avoid sending information in directions where hostile nodes are present. Our protocol is implemented in the OMNEST simulator and evaluated using two realistic flight scenarios involving 8 and 24 aircraft, respectively. The results show that our protocol has significantly fewer leaked packets than comparative protocols, but at a slightly higher cost in terms of longer packet lifetime.

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Section: Related Workmentioning

confidence: 99%

Avoiding Detection by Hostile Nodes in Airborne Tactical Networks

et al. 2023

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“…Additionally, load balancing metrics are incorporated into the embedding process to prevent some fixed links from becoming embedding bottlenecks. [8] describes the topology of requests and the dynamic changes of resources within a time window. The resource change relationship is represented by matching matrices, after which a dynamic embedding algorithm is proposed based on the matrix and the dynamic changes within the time window where the requests are located to enhance the adaptability of the embedding results.…”

Section: Introductionmentioning

confidence: 99%

Virtual network embedding based on space-time resource tree for space-ground integrated network

Qu,

Ren,

Zhao

et al. 2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

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Space-Ground Integrated Network (SGIN) is a multi-domain integrated network with a large coverage area, which can better meet the ubiquitous communication needs in the network and provide users with better services. It has become the main development direction of 6G network. The different networks integrated in SGIN are heterogeneous, which is specifically reflected in the fact that the periodic movement of satellite networks brings time-varying nature to resources, and the expansion of network scale also brings spatial attributes to resources. It can be concluded that resources in SGIN have multi-dimensional. When performing virtual network embedding(VNE) in SGIN, the existing embedding algorithms often do not consider the multi-dimensionality of resources, which may lead to problems such as spatially dispersed embedding results and inability to adapt to network dynamic changes. To address this problem, this paper uses the space-time resource tree(S-TRT) model to represent the multi-dimensional resources in SGIN, reflecting the performance of resources in four dimensions: time, space, type, and quantity. On the basis of this model, combined with multiple dimensions of resources and the spatial distribution of embedded nodes, the ranking vector of virtual nodes is established, and the dimensionality reduction sorting of each virtual node is carried out by multi-dimensional scaling method. Afterwards, the node embedding is completed by dynamically sorting the physical nodes to improve the spatial concentration of the embedding results and better adapt to the dynamic changes of the underlying network. Finally, we conducted a simulation experiment on the algorithm, and the results show that the algorithm has good performance in the request acceptance rate and long-term revenue-to-cost ratio.

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