Resilient communication model for satellite networks using clustering technique

Geng, Sunyue; Liu, Sifeng; Fang, Zhigeng

doi:10.1016/j.ress.2021.107850

Cited by 16 publications

(2 citation statements)

References 39 publications

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“…The deep reinforcement learning algorithm is seen as the most promising method. It has been used for multiple engineering applications such as inspection and maintenance planning [20,21], satellite communications [22], and production systems [23]. Although many studies have demonstrated its ability in tackling the optimization problems with high dimensional decision space and state space [24][25][26][27], few studies have used it for the emergency management, such as the decisions in the recovery process [28].…”

Section: Fig 1 Illustration Of the Concept Of Resilience And Maintena...mentioning

confidence: 99%

A Novel Deep Reinforcement Learning Model for Resilient Road Network Recovery from Multiple Hazards

Fan

Zhang

Wang

et al. 2022

Preprint

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As the backbone and the ‘blood vessel’ of modern cities, road networks provide critical support for community activities and economic growth, with their roles even more crucial due to the dramatic progress in urbanization. The service of road networks is subjected to the increasing frequency of high-consequence natural hazards such as earthquakes, floods, hurricanes, etc. Identifying resilient restoration sequences is essential to mitigate the disruption of such important infrastructure networks. This paper investigates a novel decision-support model to optimize post-disaster road network repair sequence. The model, named as GCN-DRL model, integrates the advantages of deep reinforced learning (DRL) with graph convolutional neural network (GCN), two emerging artificial intelligence (AI) techniques to achieve efficient recovery of road network service. The model is applied to analyze two cases of community road networks in the US that are subjected to different types of hazards, i.e., earthquakes and flooding. The performance of repair sequence by the GCN-DRL model is compared with two commonly used methods, i.e., repair sequence by the genetic algorithm and by prioritization based on graph importance with betweenness centrality. The results showed the decision sequence by GCN-DRL model consistently achieved superior performance in road network restoration than the conventional methods. The AI-based decision model also features high computational efficiency since the GCN-DRL model can be trained before the hazard. With a pre-trained GCN-DRL model, a close to optimal decision-making process can be made available rapidly for different types of new hazards, which is advantageous in efficiently responding to hazards when they happen. This study demonstrates the promise of a new AI-based decision support model to improve the resilience of road networks by enabling efficient post-hazards recovery.

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Section: Fig 1 Illustration Of the Concept Of Resilience And Maintena...mentioning

confidence: 99%

A Novel Deep Reinforcement Learning Model for Resilient Road Network Recovery from Multiple Hazards

Fan

Zhang

Wang

et al. 2022

Preprint

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“…In addition to ensuring network QoS [89], the routing strategy also needs to evaluate routing nodes' reputation. Due to the openness and mobility of SIN, the routing mechanism is subject to various threats [90].…”

Section: Impact On Routing Mechanismmentioning

confidence: 99%

Threat analysis for space information network based on network security attributes: a review

Fan

et al. 2022

Complex Intell. Syst.

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Space Information Network (SIN) is a multi-purpose heterogeneous network. Due to the large-scale of SIN, its secure and stable operation is vulnerable to various threats. Much of current threat analysis for SIN is based on the network function or architecture. However, this approach cannot clearly divide the relation between threats and secure communication measures for a highly integrated network. Furthermore, it will lead to overlapping in segregation of secure duties. This paper presents a comprehensive review of threats and corresponding solutions in SIN from the perspective of network security attributes. In order to make the analysis applicable to more scenarios, the following three most essential attributes, confidentiality, integrity and availability, are selected as the threatened objectives. At the same time, for cross-reference with the analysis based on network function or architecture, this paper relates network layers to network security attributes through secure communication mechanisms. Specifically, the confidentiality includes confidential information-exchange and Authentication and Key Agreement (AKA), the integrity includes information identification and information restoration, and the availability includes link establishment, routing mechanism, and mobility management. According to above framework, this paper provides a cross-layer perspective for analyzing threat and enhancing the security and stability of SIN. Finally, this paper concludes with a summary of challenges and future work in SIN.

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Improving the reliability of nanosatellite swarms by adopting blockchain technology

Ibrahim,

Shouman,

El-Fishawy

et al. 2024

Complex Intell. Syst.

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Satellite swarm networks have occupied a prominent position in many modern applications due to their low cost, simplicity of design, and flexibility. Reliability is an influential factor in the design of satellite networks with different structures. Usually, small satellites are based on COST components, which may reduce continues operability due to the lack of using backup system on board the sagecraft. Any failure in one subsystem means a complete loss of the function and data stored in this subsystem; hence the need for a reliable and applicable solution for this matter is a crucial topic. Using the redundancy strategy in satellite swarm networks increases reliability and availability. Blockchain is characterized by using a distributed ledger which enables the database to be replicated across nodes in the network and results in increasing transparency, security, and trust. This paper suggests adoption of blockchain technology in distributed multi-satellite mission swarm networks to provide a high level of reliability and availability of the entire system; the blockchain is usually used to secure system transactions in multilayer approach by storage of the key parameters in more than one node; here we suggest the adoption of this approach not only to secure satellite network transaction, but also to increase system reliability so that failure of one node can be recovered by other nodes. We compared this approach with similar traditional networks that do not use blockchain. The results show a higher reliability efficiency of 95.3% for applying blockchain technology compared to 64.3% without the use of blockchain, as well as a higher availability of 99% compared to 91%.

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Resilient communication model for satellite networks using clustering technique

Cited by 16 publications

References 39 publications

A Novel Deep Reinforcement Learning Model for Resilient Road Network Recovery from Multiple Hazards

A Novel Deep Reinforcement Learning Model for Resilient Road Network Recovery from Multiple Hazards

Threat analysis for space information network based on network security attributes: a review

Improving the reliability of nanosatellite swarms by adopting blockchain technology

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