Towards Artificial Intelligence Assisted Software Defined Networking for Internet of Vehicles

Sharma, Sachin

doi:10.1007/978-3-030-76493-7_6

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…To achieve automatic configuration of SDN/OpenFlow in the wireless ad hoc networks in this paper. This will be achieved by implementing an automatic configuration method on testbeds, as proposed in [6]. The efficiency of the method will be calculated by measuring the automatic discovery time.…”

Section: Discussionmentioning

confidence: 99%

“…As part of our EU NGIAtlantic project [3,4], wireless Internet of Things (IoT) devices send data to an IoT application in the cloud via a gateway that runs network functions to ensure security and latency. In this project, software-defined networking ( [5,6]) and machine learning are used to make intelligent IoT decisions. In addition, several EU and US testbeds-W-iLab1.t, W-iLab2.t, CityLab, COSMOS, and POWDER-are considered for experimentation.…”

Section: Introductionmentioning

confidence: 99%

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Future Wireless Networking Experiments Escaping Simulations

et al. 2022

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In computer networking, simulations are widely used to test and analyse new protocols and ideas. Currently, there are a number of open real testbeds available to test the new protocols. In the EU, for example, there are Fed4Fire testbeds, while in the US, there are POWDER and COSMOS testbeds. Several other countries, including Japan, Brazil, India, and China, have also developed next-generation testbeds. Compared to simulations, these testbeds offer a more realistic way to test protocols and prototypes. In this paper, we examine some available wireless testbeds from the EU and the US, which are part of an open-call EU project under the NGIAtlantic H2020 initiative to conduct Software-Defined Networking (SDN) experiments on intelligent Internet of Things (IoT) networks. Furthermore, the paper presents benchmarking results and failure recovery results from each of the considered testbeds using a variety of wireless network topologies. The paper compares the testbeds based on throughput, latency, jitter, resources available, and failure recovery time, by sending different types of traffic. The results demonstrate the feasibility of performing wireless experiments on different testbeds in the US and the EU. Further, issues faced during experimentation on EU and US testbeds are also reported.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Future Wireless Networking Experiments Escaping Simulations

et al. 2022

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“…For instance, statistical learning, neural networks, or deep learning could be utilized to create the routing metric, routing policy, or even per-flow QoS parameters based on historical and real-time data. Despite the potential benefits of AI in SDVNs, new challenges may arise, such as computational complexity, learning and manned strategies, and pre-processing real-time data [175]. 7) The need for a recovery strategy of failed routing: In real-world scenarios, computed routes may become unusable if a link breaks due to node dynamicity.…”

Section: Open Issues and Future Challengesmentioning

confidence: 99%

“…This can help improve performance and ensure the system can handle a higher density of vehicles [179]. Besides, the complex routing mechanisms and intelligent decision-making processes can also contribute to controller bottlenecks, particularly when utilizing historical and real-time data [175]. So, the controller bottleneck should be considered in upcoming SDN-based data routing work.…”

Section: Open Issues and Future Challengesmentioning

confidence: 99%

SDN-Based VANET Routing: A Comprehensive Survey on Architectures, Protocols, Analysis, and Future Challenges

Hussein,

Koh,

Yaw

et al. 2024

IEEE Access

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As the automotive and telecommunication industries advance, more vehicles are becoming connected, leading to the realization of intelligent transportation systems (ITS). Vehicular ad-hoc network (VANET) supports various ITS services, including safety, convenience, and infotainment services for drivers and passengers. Generally, such services are realized through data sharing among vehicles and nearby infrastructures or vehicles over multi-hop data routing mechanisms. Vehicular data routing faces many challenges caused by vehicle dynamicity, intermittent connectivity, and diverse application requirements. Consequently, the software-defined networking (SDN) paradigm offers unique features such as programmability and flexibility to enhance vehicular network performance and management and meet the quality of services (QoS) requirements of various VANET services. Recently, VANET routing protocols have been improved using the multilevel knowledge and an up-to-date global view of traffic conditions offered by SDN technology. The primary objective of this study is to furnish comprehensive information regarding the current SDN-based VANET routing protocols, encompassing intricate details of their underlying mechanisms, forwarding algorithms, and architectural considerations. Each protocol will be thoroughly examined individually, elucidating its strengths, weaknesses, and proposed enhancements. Also, the software-defined vehicular network (SDVN) architectures are presented according to their operation modes and controlling degree. Then, the potential of SDN-based VANET is explored from the aspect of routing and the design requirements of routing protocols in SDVNs. SDVN routing algorithms are uniquely classified according to various criteria. In addition, a complete comparative analysis will be achieved to analyze the protocols regarding performance, optimization, and simulation results. Finally, the challenges and upcoming research directions for developing such protocols are widely stated here. By presenting such insights, this paper provides a comprehensive overview and inspires researchers to enhance existing protocols and explore novel solutions, thereby paving the way for innovation in this field.

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“…Further, anomalies are detected using ensemble methods applied to software-defined networking (SDN) in IoT at [32]. In SDN, IoT networks could be controlled from a central server called a controller [33,34]. Further, in [35], DDoS attacks are detected by using an ensemble method that uses traffic flow metrics to classify attacks.…”

Section: Ensemble Machine Learning-based Attack Detectionmentioning

confidence: 99%

Detecting IoT Attacks Using an Ensemble Machine Learning Model

Tomer

Sharma

2022

Future Internet

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Malicious attacks are becoming more prevalent due to the growing use of Internet of Things (IoT) devices in homes, offices, transportation, healthcare, and other locations. By incorporating fog computing into IoT, attacks can be detected in a short amount of time, as the distance between IoT devices and fog devices is smaller than the distance between IoT devices and the cloud. Machine learning is frequently used for the detection of attacks due to the huge amount of data available from IoT devices. However, the problem is that fog devices may not have enough resources, such as processing power and memory, to detect attacks in a timely manner. This paper proposes an approach to offload the machine learning model selection task to the cloud and the real-time prediction task to the fog nodes. Using the proposed method, based on historical data, an ensemble machine learning model is built in the cloud, followed by the real-time detection of attacks on fog nodes. The proposed approach is tested using the NSL-KDD dataset. The results show the effectiveness of the proposed approach in terms of several performance measures, such as execution time, precision, recall, accuracy, and ROC (receiver operating characteristic) curve.

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Towards Artificial Intelligence Assisted Software Defined Networking for Internet of Vehicles

Cited by 5 publications

References 34 publications

Future Wireless Networking Experiments Escaping Simulations

Future Wireless Networking Experiments Escaping Simulations

SDN-Based VANET Routing: A Comprehensive Survey on Architectures, Protocols, Analysis, and Future Challenges

Detecting IoT Attacks Using an Ensemble Machine Learning Model

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