Network coding‐based multi‐path routing algorithm in two‐layered satellite networks

Xu, Wenchao; Jiang, Meng; Tang, Feilong; Yang, Yanqin

doi:10.1049/iet-com.2017.0325

Cited by 16 publications

(6 citation statements)

References 20 publications

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“…The algorithm adjusts the cost between links in combination with the regional traffic and uses the shortest path priority idea to find the path. The following will introduce the algorithm from several aspects such as traffic estimation, congestion judgment, and cost transformation [27].…”

Section: Low-earth Satellite Network Load Balancing Algorithmmentioning

confidence: 99%

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

Liu

Sun

Zhang

2021

Wireless Communications and Mobile Computing

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Satellite communication has become an important research trend in the field of communication technology. Low-orbit satellites have always been the focus of extensive attention by scholars due to their wide coverage, strong flexibility, and freedom from geographical constraints. This article introduces some technologies about low-orbit satellites and introduces a routing algorithm DDPG based on machine learning for simulation experiments. The performance of this algorithm is compared with the performance of three commonly used low-orbit satellite routing algorithms, and a conclusion is drawn. The routing algorithm based on machine learning has the smallest average delay, and the average value is 126 ms under different weights. Its packet loss rate is the smallest, with an average of 2.9%. Its throughput is the largest, with an average of 201.7 Mbps; its load distribution index is the smallest, with an average of 0.54. In summary, the performance of routing algorithms based on machine learning is better than general algorithms.

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Section: Low-earth Satellite Network Load Balancing Algorithmmentioning

confidence: 99%

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

Liu

Sun

Zhang

2021

Wireless Communications and Mobile Computing

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“…The link quality of the channel is one of the key factors in the selection of the reliable route, which is estimated based on the Expected Number of Transmission (ETX) and Expected Any-path Count (EAX) parameters [59]. The ETX is the expected number of transmissions required before a packet is successfully delivered to the next-hop which also determines link quality and packet loss on both directions of a link.…”

Section: Link Quality Estimation Of Mobile Nodesmentioning

confidence: 99%

Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm

et al. 2019

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To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MANETs). However, the potentials of MANETs are yet to be fully realized as existing MANETs routing protocols still suffer some major technical drawback in the areas of mobility, link quality, and battery constraint of mobile nodes between the overlay connections. To address these problems, a routing scheme named Mobility, Residual energy and Link quality Aware Multipath (MRLAM) is proposed for routing in MANETs. The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. The proposed routing scheme reduces energy cost by 33% and 23%, end to end delay by 15% and 10%, packet loss ratio by 30.76% and 24.59%, and convergence time by 16.49% and 11.34% approximately, compared with other well-known routing schemes such as Multipath Optimized Link State Routing protocol (MP-OLSR) and MP-OLSRv2, respectively. Overall, the acquired results indicate that the proposed MRLAM routing scheme significantly improves the overall performance of the network.

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“…Because of the increasing demand of global communications, as an extension of the terrestrial network and a component of the global information grid, the spatial information network urgently needs to be interconnected with the ground network . Therefore, researchers have carried out many aspects of research on the new network design ideas such as SDN and ICN, such as architecture fusion and routing algorithm . Among them, the SDN‐based satellite network architecture integrates functions such as data routing, resource allocation, network failure, and security monitoring on the controller in the satellite network under the concept of software‐defined networks, by using the global nature of the controller; the management functions of the satellite network and centralized control data routing are more simplified .…”

Section: Introductionmentioning

confidence: 99%

Routing algorithm design of satellite network architecture based on SDN and ICN

Liu

Zhu

Zhang

et al. 2019

Satell Commun Network

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Summary In view of the problems of low routing efficiency, complex control process, and difficult network management in big data environment in the traditional integrated space‐terrestrial network, in the paper, we propose a satellite network architecture called software‐defined information centric satellite networking (SDICSN) based on software‐defined networking (SDN) and information‐centric networking (ICN), and we design a virtual node matrix routing algorithm (VNMR) under the SDICSN architecture. The SDICSN architecture realizes the flexibility of network management and business deployment through the features of the separation of forwarding and controlling by the SDN architecture and improves the response speed of requests in the network by the centric of “content” as the ICN idea. According to the periodicity and predictability of the satellite network, the VNMR algorithm obtains the routing matrix through the relative orientation of the source and destination nodes, thus reducing the spatial complexity of the input matrix of the Dijkstra algorithm and then reducing the time complexity of the routing algorithm. For forwarding information base (FIB), the mechanism of combination of event driven and polling can be quickly updated in real time. Finally, the advantages of the SDICSN architecture in routing efficiency, request delay, and request aggregation are verified by simulation.

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Network coding‐based multi‐path routing algorithm in two‐layered satellite networks

Cited by 16 publications

References 20 publications

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm

Routing algorithm design of satellite network architecture based on SDN and ICN

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