IHSF: An Intelligent Solution for Improved Performance of Reliable and Time-Sensitive Flows in Hybrid SDN-Based FC IoT Systems

Wang, Lei; Muntean, Gabriel‐Miro; Chen, Jenhui; Shah, Nadir; Akbar, Aamir

doi:10.1109/jiot.2020.3024560

Cited by 38 publications

(17 citation statements)

References 35 publications

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“…Some machine learning methods are used for traffic classification based on the applications in a network with SDN architecture. IHSF [32] has used three approaches, an algorithm to place SDN switches and legacy ones together, a regression-based algorithm for predicting the reliability of legacy links, and a deep deterministic policy gradient optimization algorithm that determines forwarding routes for time-sensitive IoT flows. This study uses deep learning techniques to predict link reliability, which in turn increases network response time.…”

Section: ) Network Componentmentioning

confidence: 99%

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

Sedaghat

Jahangir

2021

IEEE Access

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This paper proposes a novel and efficient real time network architecture, named RT-TelSurg, for one of the most appealing tactile Internet applications, i.e., Telesurgery. In telesurgery, the patient's vital signs and status and the required robotic commands during the surgery should be received on time. Otherwise, the life of the patient or the safety of the operation is endangered. Hence, transmitted packets should meet their respective relative deadlines. Software-defined networking is a relatively new architecture for computer and telecommunications networks in which the network control plane is separated from the data plane. One way to achieve real time telesurgery is to employ cloud and fog networks using SDN as infrastructure. By using a real time cloud controller in the SDN as the core and a fog controller on the edge of the network on the master (physician) side, one may satisfy the acceptable level of the timing constraints of the telesurgery. Accordingly, based on the presented architecture, we develop the statistical performance model of our proposed approach, RT-TelSurg. This statistical queuing theory model is designed for critical and non-critical states of the network. Based on these states, network resources are allocated to telesurgery data in both modes, and a computational bottleneck is detected. RT-TelSurg is evaluated according to real time and surgery efficiency parameters. The results show that the average deadline hit ratio is 98.2% in different conditions, which is quite acceptable for telesurgery applications.

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Section: ) Network Componentmentioning

confidence: 99%

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

Sedaghat

Jahangir

2021

IEEE Access

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“…A novel algorithm for the deployment of the SDN switches is proposed in Ref. [22], where the network observability is improved, and then a K-nearest neighbor regression algorithm is designed for the prediction of the real time reliability of legacy links at the SDN controller with historic data. A Fuzzy logic-based Q-Learning Routing Protocol is proposed in Ref.…”

Section: Related Workmentioning

confidence: 99%

QoTa-MPR: QoS-oriented and traffic-aware multi-path routing protocol for internet of remote things

Liu

Guo

et al. 2021

Telecommun Syst

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Internet of Remote Things (IoRT) is widely used in both military and civilian applications. However, due to the unique characteristics, which is characterized as long-distance and high-latency, the design of the routing protocol is a great challenge of IoRT. In this paper, a QoS-oriented and traffic-aware multi-path routing protocol (QoTa-MRP) is proposed for IoRT. QoTa-MRP is composed of two parts. The first part is the link traffic-aware based muti-paths source route discovery mechanism, which is used to establish multipaths with lower link disjoint degree. The second part is path similarity and traffic priority based multi-path selection mechanism, which is used to determine the transmission mode of the traffic flows to enhance the reliability or effectiveness of the transmission. Simultaneously, the protocol is theoretically analyzed in terms of the successful transmission rate of routing transmission data packet. Finally, the dynamic source routing, which is a very representative protocol and is usually used as the baseline comparison protocol, is revisited for the performance verification of the QoTa-MRP in IoRT. It is shown in the simulation results that there are significant superiorities of QoTa-MRP than that of DSR in terms of network throughput, packet loss rate and routing packet header overhead in IoRT.

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“…Similarly, Artificial Intelligence (AI) or Machine Learning (ML) [2] is another popular area that has seen significant attention recently. The integration of IoT and AI technologies dramatically improves the effectiveness of IoT devices in the production of social and economic applications that are useful [3]- [5]. For example, the Intelligent Transportation System (ITS) for the Internet of Vehicles (IoV) [6] is a typical application of IoT technology and is essentially a transport system for the coming decades.…”

Section: Introductionmentioning

confidence: 99%

ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

Akbar

Jan

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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The convergence of Software-Defined Networking (SDN) and Internet of Vehicular (IoV) integrated with Fog Computing (FC), known as Software Defined Vehicular based FC (SDV-F), has recently been established to take advantage of both paradigms and efficiently control the wireless networks. SDV-F tackles numerous problems, such as scalability, load-balancing, energy consumption, and security. It lags, however, in providing a promising approach to enable ultra-reliable and delay-sensitive applications with high vehicle mobility over SDV-F. We propose ARTNet, an AI-based Vehicle-to-Everything (V2X) framework for resource distribution and optimized communication using the SDV-F architecture. ARTNet offers ultra-reliable and low-latency communications, particularly in highly dynamic environments, which is still a challenge in IoV. ARTNet is composed of intelligent agents/controllers, to make decisions intelligently about (i) maximizing resource utilization at the fog layer, and (ii) minimizing the average end-to-end delay of time-critical IoV applications. Moreover, ARTNet is designed to assign a task to fog nodes based on their states. Our experimental results show that considering a dynamic IoV environment, ARTNet can efficiently distribute the fog layer tasks while minimizing the delay.

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IHSF: An Intelligent Solution for Improved Performance of Reliable and Time-Sensitive Flows in Hybrid SDN-Based FC IoT Systems

Cited by 38 publications

References 35 publications

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

QoTa-MPR: QoS-oriented and traffic-aware multi-path routing protocol for internet of remote things

ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

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