On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Adnan, Muhammad; Iqbal, Jawaid; Waheed, Abdul; Amin, Noor Ul; Zareei, Mahdi; Goudarzi, Shidrokh; Umer, Asif

doi:10.3390/s21041400

Cited by 18 publications

(16 citation statements)

References 20 publications

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“…Another scheduling algorithm is provided in which the authors proposed a scheduling model and gave high priority to the packets whose deadline is near to expire. In [ 27 , 28 ], the authors recently proposed efficient architecture for SDVN by using a priority basis scheduling algorithm for time-critical and non-critical messages. Its main limitation is that they did not use fog instead of cloud to decrease response time, and they also did not use a fault tolerance mechanism to make the overall process of SDVN communication efficient.…”

Section: Literature Reviewmentioning

confidence: 99%

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QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing

Syed

Rashid

Amir

et al. 2022

Sensors

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Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and prioritize the messages in two different ways. One is the message nature way, while the other one is deadline and size way of messages prioritization. SDN controller categorized in safety and non-safety messages and forward to the destination. After sending messages to their destination, we check their acknowledgment; if the destination receives the messages, then no action is taken; otherwise, we use a fault tolerance mechanism. We send the messages again. The proposed model is implemented in CloudSIm and iFogSim, and compared with the latest models. The results show that our proposed model decreased response time by 50% of the safety and non-safety messages by using fog nodes for the SDN controller. Furthermore, we reduced the execution time of the safety and non-safety messages by up to 4%. Similarly, compared with the latest model, we reduced the task failure ratio by 20%, 15%, 23.3%, and 22.5%.

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Section: Literature Reviewmentioning

confidence: 99%

“…As reflected from the Literature Review [ 5 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 31 ], they used the Cloud for data execution and storage, which has the following limitations, as shown in Figure 1 :…”

Section: Problem Formulationmentioning

confidence: 99%

QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing

Syed

Rashid

Amir

et al. 2022

Sensors

Self Cite

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“…The infrastructure layer, also known as the data plane, consists of numerous network devices with network functionality. This layer allows for managing and forwarding IP packets based on SDN controller decisions, that is, the forwarding rules for actions initiated by the applications running on the SDN controller, and in addition, installed on network devices by the so-called southbound interface [69,[72][73][74]. Next is the the control plane, where the centralized and logical SDN controller is located.…”

Section: Rq2: What Are the Latest Studies Of Botnet Detection Based On Machine Learning?mentioning

confidence: 99%

“…It hides underlying hardware and software infrastructure complications. It also provides a centralized view of the entire network, up to the application layer [69,[73][74][75]. The management plane, also known as the Application layer, is situated at the top of the SDN architecture.…”

Section: Rq2: What Are the Latest Studies Of Botnet Detection Based On Machine Learning?mentioning

confidence: 99%

Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review

et al. 2021

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In recent decades, the internet has grown and changed the world tremendously, and this, in turn, has brought about many cyberattacks. Cybersecurity represents one of the most serious threats to society, and it costs millions of dollars each year. The most significant question remains: Where do these attacks come from? The answer is that botnets provide platforms for cyberattacks. For many organizations, a botnet-assisted attack is a terrifying threat that can cause financial losses and leave global victims in its wake. It is therefore imperative to defend organizations against botnet-assisted attacks. Software defined networking (SDN) has emerged as one of the most promising paradigms for this because it allows exponential increases in the complexity of network management and configuration. SDN has a substantial advantage over traditional approaches with regard to network management because it separates the control plane from network equipment. However, security challenges continue to arise, which raises the need for different types of implementation strategies to spread attack vectors, despite the significant benefits. The main objective of this survey is to assess botnet detection techniques by using systematic reviews and meta-analyses (PRISMA) guidelines. We evaluated various articles published since 2006 in the field of botnet detection, based on machine learning, and from 2015 in the field of SDN. Specifically, we used top-rated journals that featured the highest impact factors. In this paper, we aim to elaborate on several research areas regarding botnet attacks, detection techniques, machine learning, and SDN. We also address current research challenges and propose directions for future research.

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“…The proposed secure hierarchic architecture for SDVN is based on the work of [ 23 ]. This scheme proposes a secure hierarchic architecture for SDVN to secure traffic flow messages between V2V, communication of V2R, and between the SDN controllers as shown in Figure 4 .…”

Section: Proposed Schemementioning

confidence: 99%

Towards the Design of Efficient and Secure Architecture for Software-Defined Vehicular Networks

Adnan

Iqbal

Waheed

et al. 2021

Sensors

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Recently, by the rapid development of Vehicular Ad Hoc Networks (VANETs) and the advancement of Software Defined Networking (SDN) as an emerging technology, the Software-Defined Vehicular Network (SDVN) has a tremendous attraction in the academia and research community. SDN’s unique properties and features, such as its flexibility, programmability, and centralized control, make the network scalable and straightforward. In VANETs, traffic management and secure communication of vehicle information using the public network are the main research dimensions in the current era for the researchers to be considered while designing an efficient and secure VANETs architecture. This paper highlights the possible identified threat vectors and efficiently resolves the network vulnerabilities to design a novel and secure hierarchic architecture for SDVN. To solve the above problem, we proposed a Public Key Infrastructure-based digital signature model for efficient and secure communication from Vehicle to Vehicle. We also used the public key authority infrastructure for Vehicle to Infrastructure and the three-way handshake method for secure session creation and secure data communication in the SDN controller. The proposed security is validated through the well-known simulation tool AVISPA. Additionally, a formal security model is applied to validate the design hierarchic architecture’s fundamental security properties for SDVN in an efficient and desirable way. In a comparative analysis, we prove that our proposed scheme fulfills all the essential security properties compared to other states of the art schemes.

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On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Cited by 18 publications

References 20 publications

QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing

QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing

Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review

Towards the Design of Efficient and Secure Architecture for Software-Defined Vehicular Networks

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