SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

Ganesan, Elaiyasuriyan; Hwang, I‐Shyan; Liem, Andrew Tanny; Ab-Rahman, Mohammad Syuhaimi

doi:10.3390/photonics8060201

Cited by 18 publications

(11 citation statements)

References 36 publications

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“…In order to solve this problem, a reconfigurable network supporting a software-defined network (SDN) has recently attracted much attention and can adapt to the service demand flow as much as possible [16], [17]. The general reconfiguration framework based on SDN technology consists of two parts, including modeling and forecasting traffic demand flow, and using prediction for active (offline) network optimization between predefined (reconfiguration) time points [18]. The overall goal is to find a resource allocation strategy that is most suitable for the future traffic demand of network [19].…”

Section: Related Workmentioning

confidence: 99%

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Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

et al. 2023

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By integrating communications in different domains, integrated radio and optical networks can serve a wider range of applications and services. Integrated radio and optical network scenarios will involve more weak-computation-ability network nodes, such as small-cell base stations. To pursue efficient integrated radio and optical networks, more efficient ways to conduct transmission under the demand of edge and cloud collaboration are required. The lack of forward-looking resource allocation may easily lead to a waste of network resources without an expected return. Therefore, an efficient resource allocation scheme needs to consider certain issues: 1) a comprehensive perspective of traffic prediction; 2) a release of pressure on the transmission pipeline during the prediction process; and 3) a reduction of loss of edge nodes due to the computation. In this paper, benefiting from machine learning, we propose a resource allocation with edgecloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. We maximize the utilization of limited resources to improve the awareness of network status. We present three evaluation indicators and build a network architecture to evaluate our resource allocation scheme. Through edge-cloud collaboration, our proposal can improve traffic prediction accuracy by 9.5% compared with single-point traffic prediction, and resource utilization is also improved by edge-cloud collaborative traffic prediction.INDEX TERMS Integrated radio and optical networks, resource allocation, edge-cloud collaboration, traffic prediction.

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Section: Related Workmentioning

confidence: 99%

“…Generally, the previous works mainly have focused on optimizing the efficiency of the resource allocation scheme, without considering the uniformity and sustainability of occupied resources [17], [18],…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

et al. 2023

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“…The cell selection decision can be coordinated by using SDN solution [27], and the combination of SDN and ML creates a new network management approach [28], [29].…”

Section: Introductionmentioning

confidence: 99%

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Alablani

Arafah²

2023

IEEE Access

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Heterogeneous ultra-dense networks (HUDNs) are one of the key enabling technologies for the fifth-generation (5G) networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection is a challenging issue in HUDNs, due to the different characteristics of base stations (BSs) and the existence of a large number of them. Thus, the traditional cell selection scheme is not applicable in such a network. In this paper, a novel adaptive cell selection strategy is proposed, called adaptive two-tier based on adaptive boosting (A2T-Boost). It can adapt to the various characteristics of base stations, as well as the different movement features of mobile stations such as vehicles and pedestrian. It is a software-defined networking (SDN)/machine learning (ML)-based scheme. A real-world case is considered in the downtown of Los Angeles city. Simulation results demonstrate that A2T-Boost achieves high prediction performance and it outperforms other related schemes in terms of average number of handovers (HOs) by up to 50%. Moreover, it enhances the average achievable downlink sum-rates and network energy efficiency achieved by vehicles by up to 33.76%. Furthermore, the average packet delay is decreased using the proposed scheme by up to 12.87%.

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“…There have been several issues that essentially need to be investigated before the implementation of the FiWi network. 4 Optical network unit (ONU) placement, survivability, and energy consumption/saving are the prominent concerns in FiWi. Survivability is the capability of the network to provide continuous services in case of fiber or optical device failures.…”

Section: Introductionmentioning

confidence: 99%

CSAT‐FRMET: An energy‐efficient hybrid FiWi network‐based 5G model

Kali Raman,

Saini

2023

Trans Emerging Tel Tech

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Hybrid fiber wireless (FiWi) access networks seek to combine the vast amount of bandwidth that is available for optical networks and wireless network mobility; hence, it favors larger bandwidth request but cause the highly nonlinear lagrangian dual problem that decrease energy‐efficient network resilience. Hence, a novel Ceplato‐SAT3 model has been proposed in which the SAT3 algorithm locates the optical network unit (ONU) in an optimal position with high‐speed cost‐efficient placement and weight factor introduced into the position update to improve the exploitation and exploration capability of the algorithm. To prioritize the large bandwidth request during resource allocation, gradient priority justifier requests (GPJR) has been utilized and a novel messaging technique termed flow‐random‐message‐exchanger‐topology (FRMET) provides the index for these prioritized requests depending on QoS parameters such as notification control, reflective QoS attribute (RQA), flow bit rates and maximum packet loss rate thereby decreasing the overhead and eliminate nonlinear langrage dualand perform O2U scheduling to reduce network resilience however synchronization between different ONU clocks may cause upstream data fatal crash at the optical line terminal (OLT). Hence, curl‐trio‐opt control mechanism has been proposed in which the minimization problem is transformed into a joint optimization problem of energy consumption, traffic demand, network requests, and service flow requests by the Lyapunov technique thereby minimizing the upstream data fatal lash. The proposed model has been implemented in Python and the output obtained showed better performance in terms of ONU arrangement, throughput, energy consumption, computation, latency, and error.

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SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

Cited by 18 publications

References 36 publications

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

CSAT‐FRMET: An energy‐efficient hybrid FiWi network‐based 5G model

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