A Comparative Analysis of Hybrid Routing Schemes for SDN Based Wireless Mesh Networks

Bano, Mukhtiar; Gilani, Syed Sherjeel A.; Qayyum, Amir

doi:10.1109/hpcc/smartcity/dss.2018.00200

Cited by 6 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where a xy is the constant given in Table 2. The charts in Figure 4 show the PBP of the training data and that of the calculation according to the regression function (3). We can observe that the regression model delivers high accurate prediction outcomes with SSE = 0.03098, R-square = 0.9978, adjusted R-square = 0.9977, and RMSE = 0.005717.…”

Section: Pbp On a Routementioning

confidence: 98%

See 1 more Smart Citation

IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking

Duong

Bình

2022

ETRI Journal

View full text Add to dashboard Cite

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics.These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other wellknown routing algorithms.

show abstract

Section: Pbp On a Routementioning

confidence: 98%

“…For the architecture of the SDWN, the network system is divided into three layers, namely, infrastructure layer, control layer, and application layer [1][2][3][4]. The infrastructure layer includes the devices of the wireless network, such as WRs and MHs.…”

Section: Introductionmentioning

confidence: 99%

IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking

Duong

Bình

2022

ETRI Journal

View full text Add to dashboard Cite

show abstract

“…Because of certain economic and operational problems, the full implementation of SDN, such as SDN support for the entire network nodes and managed by a logically centralized controller, does not work. Hence, incremental and hybrid deployment of SDN in WMN is necessary [23]. Furthermore, networks are becoming increasingly sensitive to the data handling, speed, and processing capabilities of the wireless nodes.…”

Section: Figure 2 Working Of Sdnmentioning

confidence: 99%

Soft-Mesh: A Robust Routing Architecture for Hybrid SDN and Wireless Mesh Networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

Wireless Mesh Networks (WMNs) are considered self-organizing, self-healing, and self-configuring networks.Despite these exciting features, WMNs face several routing challenges including scalability, reliability and link failures, mobility, flexibility, and other network management issues. To address these challenges, WMNs need to make programmable to allow modifications of standard techniques to be configured and implemented through software programs that can be resolved by integrating Software Defined Networking (SDN) architecture. SDN, being a cutting-edge technology promises the facilitation of network management as well as routing issues of wireless mesh networks. However, the evolution of the legacy IP-based network model in its entirety leads to technical, operational, and economic problems that can be mitigated by full interoperability between SDN and existing IP devices. This study introduces a Robust Routing Architecture for Hybrid Software-Defined and Wireless Mesh Networks (Soft-Mesh), by systematic and gradual transitioning of WMNs to SDNs in an efficient manner. The main objective of this paper is to suggest improvements to the architecture of the SDN node that allow the implementation of various network functions such as routing, load balancing, network control, and traffic engineering for the hybrid SDN and IP networks. Mininet-WiFi Simulator is used to perform various experiments to evaluate the performance of proposed architecture by creating a hybrid network topology with a varying number of nodes that is 50, 100, 150, 200, and 250 including SDN hybrid and legacy nodes with varying proportion of SDN hybrid and legacy nodes. Results are taken for the average UDP throughput, end-to-end delay, packet drop ratio, and routing overhead while comparing with traditional routing protocols including Optimized Link State Routing (OLSR) and Better Approach to Mobile Adhoc Networking (BATMAN) and with existing hybrid SDN/IP routing architectures including Hakiri and wmSDN. The analysis of simulation results shows that the proposed architecture Soft-Mesh outperforms in terms of the aforementioned performance metrics than the traditional and exiting hybrid routing protocols. Soft-Mesh gives 50% to 70% improved results concerning the incremental proportion of SDN hybrid nodes.

show abstract

“…The programmability is improved and it will be easy to manage the whole network [94] . SDN can be applied in existing WMN to combine the advantages of centralized control in SDN and low cost in WMN [95][96][97] . The centralized network controller can provide high computing ability and storage capability.…”

Section: Software-defined Networking For Effective Wmnmentioning

confidence: 99%

The development of green wireless mesh network: A survey

Chai¹,

Zeng²

2021

JSEGC

View full text Add to dashboard Cite

Wireless mesh network (WMN) is a type of self-healing, self-configuration, and peer-to-peer wireless network. Without expensive and fixed base stations, WMN can be established fast, easily, and flexibly at a low cost. The convenience and flexibility of establishing WMN brings a wide range of applications. The request of high bandwidth, high coverage and high transmission rate can be satisfied. Current research on WMN involves routing, resource allocation, mobility control, security and so on. With the increasing concern of green communication, improving energy efficiency is more and more important nowadays. As energy harvesting can overcome energy constraints and extend the network lifetime, it has attracted the great attention from many researchers when designing network. Further, WMN is an important networking mode in next-generation communication to guarantee the quality of service and reduce the networking complexity. Due to such importance, a survey of the research and development in WMN including the future research direction and opportunities is given in this paper.

show abstract

A Comparative Analysis of Hybrid Routing Schemes for SDN Based Wireless Mesh Networks

Cited by 6 publications

References 2 publications

IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking

IRSML: An intelligent routing algorithm based on machine learning in software defined wireless networking

Soft-Mesh: A Robust Routing Architecture for Hybrid SDN and Wireless Mesh Networks

The development of green wireless mesh network: A survey

Contact Info

Product

Resources

About