Predictability of Aggregated Traffic of Gateways in Wireless Mesh Network with AODV and DSDV Routing Protocols and RWP Mobility Model

Xiang, Zhengtao; Chen, Yufeng; Li, Yujin

doi:10.1007/s11277-014-1893-x

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Typical table-driven routing protocols include Destination-Sequenced Distance-Vector (DSDV) and Optimized Link State Routing (OLSR) [7]. DSDV enables each node to advertise the routing table to its neighbors and maintain and update the routing table regularly, as well as when certain events occur, such as a disconnection [8]. Imani A et al proposed a new heterogeneous routing protocol based on DSDV [9].…”

Section: Related Researchmentioning

confidence: 99%

Traffic-Predictive Routing Strategy for Satellite Networks

Liu,

Wang

et al. 2023

Electronics

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To address the issues of uneven satellite network load and unstable link connections, a globally adaptive satellite network routing strategy based on traffic prediction (G-AODV) is proposed by enhancing the existing Ad hoc On-Demand Distance Vector Routing (AODV) protocol. To prevent heavily loaded nodes from becoming intermediate nodes, G-AODV introduces a traffic prediction mechanism in the route discovery phase. The routing request packet adopts the corresponding forwarding control policy based on the comparison between the predicted traffic load value at the next moment and the dynamic threshold. At the same time, a path replacement strategy is adopted to replace paths before node congestion occurs to achieve load balancing. Considering the unstable characteristics of the satellite chain that easily breaks, the route maintenance phase selects a path repair method by judging the node stability to avoid secondary breaks in the route. The simulation results show that in scenarios with different packet delivery rates, compared with the three comparative routing strategies, the packet delivery rate of G-AODV is increased by up to 20%, the packet loss rate is reduced by 22%, and the end-to-end delay is significantly reduced. In scenarios with different communication connection pairs, G-AODV’s packet delivery rate increased by up to 20%, the packet loss rate decreased by 18%, and the end-to-end delay was still reduced.

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Section: Related Researchmentioning

confidence: 99%

Traffic-Predictive Routing Strategy for Satellite Networks

Liu,

Wang

et al. 2023

Electronics

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“…Therefore, in this paper Granger Causality is used to enhance the accuracy and computation simpleness for multivariate time serie mode.Granger Causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another. Compared with prediction based on entropy theory [23][24][25][26] in the latest researches, more direct correlation of different traffic series are considered in the prediction based on Granger causality.…”

Section: Related Workmentioning

confidence: 99%

Integrating Granger Causality and Vector Auto-Regression for Traffic Prediction of Large-Scale WLANs

Zheng¹,

Zhou²,

Wu³

et al. 2016

KSII TIIS

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Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method-Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

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Forecasting Research on the Wireless Mesh Network Throughput Based on the Support Vector Machine

Yan

2017

Wireless Pers Commun

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Predictability of Aggregated Traffic of Gateways in Wireless Mesh Network with AODV and DSDV Routing Protocols and RWP Mobility Model

Cited by 3 publications

References 25 publications

Traffic-Predictive Routing Strategy for Satellite Networks

Traffic-Predictive Routing Strategy for Satellite Networks

Integrating Granger Causality and Vector Auto-Regression for Traffic Prediction of Large-Scale WLANs

Forecasting Research on the Wireless Mesh Network Throughput Based on the Support Vector Machine

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