A framework to compare topology algorithms in multi-channel multi-radio wireless mesh networks

Avonts, Jeroen; Blondia, Chris

doi:10.1016/j.comnet.2016.02.002

Cited by 6 publications

(13 citation statements)

References 47 publications

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“…Therefore, finding the joint configuration of both is considered as main challenges in multi-radio WMN (MR-WMN) and several joint solutions for channel assignment and routing were proposed and evaluated in the literature [6][7][8][9][10][11][12][13][14][15][16]. Some other issues is also been studied in the literature to improve the performance of multi-hop wireless networks such as developing routing metrics [17][18][19] , multi-channel MAC protocols [20], power and topology control [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

State-Aware Re-configuration Model for Multi-Radio Wireless Mesh Networks

Omar

Hashim

Hassan³

et al. 2016

KSII TIIS

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Joint channel assignment and routing is a well-known problem in multi-radio wireless mesh networks for which optimal configurations is required to optimize the overall throughput and fairness. However, other objectives need to be considered in order to provide a high quality service to network users when it deployed with high traffic dynamic. In this paper, we propose a re-configuration optimization model that optimizes the network throughput in addition to reducing the disruption to the mesh clients' traffic due to the re-configuration process. In this multi-objective optimization model, four objective functions are proposed to be minimized namely maximum link-channel utilization, network average contention, channel re-assignment cost, and rerouting cost. The latter two objectives focus on reducing the re-configuration overhead. This is to reduce the amount of disrupted traffic due to the channel switching and path rerouting resulted from applying the new configuration. In order to adapt to traffic dynamics in the network which might be caused by many factors i.e. users' mobility, a centralized heuristic re-configuration algorithm called State-Aware Joint Routing and Channel Assignment (SA-JRCA) is proposed in this research based on our re-configuration model. The proposed algorithm reassigns channels to radios and re-configures flows' routes with aim of achieving a tradeoff between maximizing the network throughput and minimizing the re-configuration overhead. The ns-2 simulator is used as simulation tool and various metrics are evaluated. These metrics include channel-link utilization, channel re-assignment cost, rerouting cost, throughput, and delay. Simulation results show the good performance of SA-JRCA in term of packet delivery ratio, aggregated throughput and re-configuration overhead. It also shows higher stability to the

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Section: Introductionmentioning

confidence: 99%

State-Aware Re-configuration Model for Multi-Radio Wireless Mesh Networks

Omar

Hashim

Hassan³

et al. 2016

KSII TIIS

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“…The grid topology naturally occurs in built environments such as roads, railway tracks, and buildings. Some studies concluded that this natural occurrence of grid placement outperforms arbitrary placement in terms of coverage, connectivity, and fair capacity allocation [16,129].…”

Section: Architecture and Application Scenarios In Ieee 80211mentioning

confidence: 99%

“…Network topology is the arrangement of the various elements such as nodes and links of a network, which forms the layout of IEEE 802.11 WBNs. Several studies demonstrated that the performance of an IEEE 802.11 WBN is largely determined by its topology [16,129]. Such a topology in the IEEE 802.11 WBN is determined in the planning stage.…”

Section: Wbnsmentioning

confidence: 99%

“…The performance of an IEEE 802.11 WBN depends on its topology [16,129]. Moreover, node placement is an important task determined in the planning stage such as the number of nodes and the location of these nodes in an IEEE 802.11 WBN.…”

Section: Goodput Models Considering Structured Topologymentioning

confidence: 99%

“…All averages of simulation results shown are reported with confidence interval of 95% with the range from [3.6, 16.962] kbps under the assumption that the averages are normally distributed. First, the predefined QoS requirement is set as SR Thre = 0.0, FI Thre = 0.7, and AG Thre = 0.2.…”

Section: The Design Of Traffic-unaware Multi-objective Channel Assignment Algorithmmentioning

confidence: 99%

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Goodput Modelling and Optimisation of Channel Assignment For Planning IEEE 802.11Wireless Backhaul Networks

Qu¹

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<p>IEEE 802.11Wireless backhaul networks (WBNs) provide scalable and cost-effective solutions for interconnecting small-cell networks and backbone networks or Internet. With newer and farther reaching applications being developed in IEEE 802.11 WBNs, such as smart grids and intelligent transportation systems, users expect high goodput and better fairness. However, some performance issues in IEEE 802.11 protocols such as border effect, exposed nodes and hidden nodes are exacerbated as network densification occurs, leading to goodput degradation and severe unfairness such as flow starvation (extreme low goodput). These issues may cause an IEEE 802.11 WBN to form a bottleneck and impact the overall network performance. Therefore, in-depth study is required in order to improve the IEEE 802.11 WBN planning to achieve better goodput and fairness. This research aims to improve IEEE 802.11 WBN planning through goodput modelling and optimising channel assignment. A novel simple goodput distribution model is proposed to predict goodput and fairness in IEEE 802.11 WBNs. Simulation results show that the proposed goodput model accurately predicts goodput with consideration of carrier sensing effect and traffic demands. Based on this goodput model, a new interference model is proposed to more realistically reflect both local and global interference in IEEE 802.11 WBNs. With the proposed interference model, two anti-starvation channel assignments have been developed to prevent flow starvation. Simulation validations show that the new anti-starvation channel assignments effectively prevent flow starvation and improve network fairness in IEEE 802.11 WBNs. This research also optimises channel assignment to achieve desired fairness and goodput. A multi-objective optimisation problem is formulated and a new fitness function is designed to evaluate a channel allocation with accurate prediction of goodput and fairness. Utilising the new fitness function, two multi-objective channel assignments have been developed to achieve both fairness and goodput. Compared with existing channel assignments through simulation, the proposed multi-objective channel assignments provide a set of feasible solutions that meet desired fairness and goodput. This research helps network planners or service providers to improve the IEEE 802.11 WBN planning from predicting network performance to optimising goodput and fairness. The proposed goodput model, interference model, and fitness function are also useful for node placement, and optimising routing and scheduling in IEEE 802.11 WBNs.</p>

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Development of a self-organized network to optimize the data transmission in BECMP based on minimum spanning tree algorithm

Zhang

Liang

2019

Build. Simul.

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A framework to compare topology algorithms in multi-channel multi-radio wireless mesh networks

Cited by 6 publications

References 47 publications

State-Aware Re-configuration Model for Multi-Radio Wireless Mesh Networks

State-Aware Re-configuration Model for Multi-Radio Wireless Mesh Networks

Goodput Modelling and Optimisation of Channel Assignment For Planning IEEE 802.11Wireless Backhaul Networks

Development of a self-organized network to optimize the data transmission in BECMP based on minimum spanning tree algorithm

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