Effective Wide Spectrum Sharing Techniques Relying on CR Technology toward 5G: A Survey

Mousa, Saif Hikmat; Ismail, Mahamod; Nordin, Rosdiadee; Abdullah, Nor Fadzilah

doi:10.12720/jcm.15.2.122-147

Cited by 21 publications

(9 citation statements)

References 117 publications

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“…In SDN, the centralized controller can view the complete network and manage network traffic efficiently compared to traditional networks. Similarly, in [ 10 ], the author proposed the Roadside Unit (RSU)-based SDN model, in which they used CR technology to transmit the vehicle information by using the 5G networking model. In [ 11 ], the author proposed the Software-Defined Network Edge (SDNE) model.…”

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

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“…In this model centralized SDN controller control and view the whole network traffic for effective control compared to traditional network. In [17]proposed a roadside unit based SDN unit model. In this model the authors used CVR technology to transmit the vehicles information through 5G network.…”

Section: Related Workmentioning

confidence: 99%

A Novel Blockchain Based Secured and QoS Aware IoT Vehicular Network in Edge Cloud Computing

et al. 2022

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A software-defined vehicular network is made up of an IoT (Internet of Things) based vehicular ad-hoc network and a software-defined network. For better communication in IoT based vehicle networks, researchers are now working on the VANET (Vehicular Ad-hoc Network) to increase the overall system performance. To maximize the VANET ad-hoc network's information application performance and reliability, edge computing has gained the attention of researchers. In current research, cloud computing is used for message related task execution, which increases the response time. We propose a Software-defined Fault Tolerance and QoS-Aware (Quality of Service) IoT-Based Vehicular Networks Using Edge Computing Secured by Blockchain to reduce overall communication delay, message failure fault tolerance, and secure service provisioning for VANET ad-hoc networks in this article. We proposed heuristic algorithms to solve the above mentioned problems of response delay, message failure, fault tolerance, and security provided by the Blockchain. The proposed model gets vehicle messages through SDN (Software defined network) nodes, which are placed on nearby edge servers, and the edge servers are validated by the blockchain to provide secure services to vehicles. The SDN controller, which exists on an edge server, which is placed on the road side to overcome communication delays, receives different messages from the vehicles and divides these messages in to two different categories. The message division is performed by the edge server by judging the time line, size, and emergency situation. SDN controller organized these messages and forwarded them to their destination. After the message is delivered to its destination, a fault tolerance mechanism checks their acknowledgements. If the message delivery fails, the fault tolerance algorithm will resend the failure message. The proposed model is implemented using a custom simulator and compared with the latest VANET based QoS and fault tolerance models. The result shows the performance of the proposed model, which decreased the overall message communication delay by 55% of the normal and emergency messages by using the edge server SDN controller. Furthermore, the proposed model reduces the execution time, security risk, and message failure ratio by using the edge server, cloud server and blockchain infrastructure.

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“…CR is a network lesson that enables the effective utilization and flexibility of the radio spectrum. It is intended on wireless devices for opportunistically access sectors of all radio spectrum without an adverse conflict for licensed users 14 . And then, simulation is performed at MATLAB software by applying a cognitive radio schedule for detecting the slots in spectrums; i.e., empty is used for a performance matrix for plotting the detection possibility of empty slots for every combination of customers.…”

Section: Related Workmentioning

confidence: 99%

A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

Perumal¹,

Nagarajan²

2022

Int J Communication

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Summary In recent times, evolution of communication technology and standard has grown in leaps and bounds from a conventional 1G communication technology towards the recent 5G and 6G technologies in a very short span of time. However, due to increasing scarcity of spectrum for these devices, cognitive radio networks (CRNs) have emerged to be promising solutions to allocate the required spectrum to the users in an intelligent manner. The method of compressive sensing‐based cyclo‐stationary feaure detection method is implemented based on a powerful CNN classifier to detect the presence or absence of PU activity. Detection probability and MSE have been improved, and the optimal detection has been reformulated for minimizing the possibility of error. Performance metrics have been compared against benchmark methods and superior performance reported. The sensing conduction and the accuracy of the proposed design are increased as 98.5%.

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Effective Wide Spectrum Sharing Techniques Relying on CR Technology toward 5G: A Survey

Cited by 21 publications

References 117 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

A Novel Blockchain Based Secured and QoS Aware IoT Vehicular Network in Edge Cloud Computing

A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

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