An infrastructure-aided cooperative spectrum sensing scheme for vehicular ad hoc networks

Baraka, Kim; Safatly, Lise; Artail, Hassan; Ghandour, Ali; El‐Hajj, Ali

doi:10.1016/j.adhoc.2014.10.012

Cited by 23 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is done by spectrum sensing where each node (vehicle and ship) uses an energy detection scheme to detect the existence of the PU. PUs are incumbent users utilizing licensed bands, such as high-frequency and very high-frequency, and can be any automobile traveler or other consumers located in places such as houses, shops, or restaurants for the terrestrial environment [19] and things such as oil/gas platforms or large vessels for the marine environment [20]. The TV spectrum was considered as the cognitive radio spectrum in this paper.…”

Section: Belief Propagation-based Channel Selection Algorithmmentioning

confidence: 99%

An Integrated Cognitive Radio Network for Coastal Smart Cities

Ghafoor

Koo

2019

Applied Sciences

View full text Add to dashboard Cite

The integration of different networks has attracted significant attention in academia. Both terrestrial and maritime communications systems have been attracting keen interest for ways to deal with various applications. As the environment of cognitive vehicular and maritime networks is extremely dynamic, these networks suffer with a long delay because of intermittent links while providing services for different applications. To this end, here we introduce the integration of cognitive vehicular and maritime networks to design a coastal smart city by utilizing software-defined networking, network function virtualization, and fog computing under the same infrastructure. This novel integrated cognitive coastal city fulfills the demand of each application user in a hybrid environment with a quicker response time. The idea is to combine vehicular and maritime communications to meet different user demands. Different virtual networks are launched by network function virtualization, and are managed and controlled by a software-defined networking controller. From the integration of software-defined networking, network function virtualization, and fog computing, both vehicular and marine users are provided with stable paths to meet each application's demands. Appl. Sci. 2019, 9, 3557 2 of 16 spectrums for automobile and maritime communication systems have been found to be insufficient due to an inevitable increase in the number of both vehicular and marine users and the demands of each of these applications [3,4]. The cognitive radio seems to be a corrective approach to overcome spectrum shortage issues in different networks. Consequently, to maintain stable networking and meet the increasing demands of mobile data traffic, network engineers are required to provide an effective solution to improve users' experiences. This means that citizens need an all-in-one framework to meet their growing needs in a more effective and efficient way.To cope with the inevitable increase in data traffic, integration of SDN and NFV seems to be a viable solution to launching and managing virtual networks (VNs) on demand and at greater speed, respectively. NFV is basically a transition from proprietary hardware-based solutions to multi-vendor open solutions. NFV is like a soft appliance that can be installed on demand, whereas SDN is smart plumbing that can be changed on command. The integration reduces provisioning time from months to minutes, reduces costs, improves service-request response times, reacts faster to changing services (allowing right-sized deployments to customers), and offers competitive services. Due to this integration, the changing needs of virtual network functions (VNFs) are now automatically followed by network configurations, and an authorized person is able to track the owner of a service and the reasons why these certain configurations are required. A VNF is the virtualization of a certain NF that should operate independently. NFs are actually services that are deployed by NFV. SDN is a critical component in the maj...

show abstract

Section: Belief Propagation-based Channel Selection Algorithmmentioning

confidence: 99%

An Integrated Cognitive Radio Network for Coastal Smart Cities

Ghafoor

Koo

2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Authors in [16] recommended an infrastructure based spectrum sensing technique for the vehicular environment to identify ISM bands along the highway. A study by [17] explored challenges in using wideband sensing to detect PU activity and impact of vehicle mobility on time of spectrum sensing against the complexity of algorithms.…”

Section: Related Sensing Schemes In Cvnmentioning

confidence: 99%

Cooperative Spectrum Decision in Cognitive Vehicular Network Based on Support Vector Machine

Chembe

Ahmedy

Noor

et al. 2019

MJCS

View full text Add to dashboard Cite

Spectrum sensing is one of the core functionality of a cognitive radio system. Accurate identification of primary system signal can prevent interference and provide maximum protection to licensed users. However, many challenges associated with spectrum sensing still exist. Examples include shadowing, multipath fading of the primary user signal, hidden primary user problem and many more. To circumvent the challenges faced by sensing device, the cooperative decision from participating devices has been proposed in the literature. In this paper, we suggest a cooperative decision based on support vector machine (SVM) for spectrum sensing in vehicular communication environment to mitigate shadowing and multipath fading. Spectrum sensing can be formulated as a pattern recognition problem with sensing results grouped into two distinct classes corresponding to the presence or absence of a primary user signal. Therefore, we employ SVM at the fusion centre to classify the primary signal correctly. The roadside units along the highway are selected to act as the fusion centre where sensing decision is made. The performance of the proposed scheme is evaluated using receiver operation characteristic curves in MATLAB. We compare the probability of detection for the proposed cooperative scheme to hard fusion rule. The simulation result shows that SVM based cooperative decision performed better than hard fusion rule.With an increase in the number of emerging applications and services developed for wireless technologies, demand for bandwidth has been growing steadily. For example, it is envisioned that by 2020, there will be more than 50 billion devices connected to the Internet mostly through wireless communication [3]. This poses a major challenge on the already scarce radio frequency spectrum which is a finite resource. For instance, the Federal Communications Commission (FCC) in the USA has allocated almost all the channels in the communicable frequency bands to licensed users [4]. The unlicensed spectrum channels are reserved for Industrial, Scientific and Medical (ISM) usage and shared among different radio technologies such as Bluetooth, WiFi, Microwave ovens and emerging radio technologies. ITS applications have been allocated 75MHz at the 5.9GHz band for dedicated short range communication (DSRC) for V2V and V2I communications [5]. The 75MHz is divided into 7 channels with 10MHz each. 5MHz is reserved as guard band channel. Regardless, these channels can become congested, particularly during peak hours or accident scenarios, when the number of vehicles contending for the same channels increases [6]. In such situations, the delivery of delay sensitive safety and emergency messages becomes difficult.

show abstract

“…Also, the system performance was evaluated for throughput and transmission delay. A CSS scheme using symmetry property of cyclic autocorrelation function method for each vehicle was proposed to detect the available channels in industrial, scientific, and medical band in Baraka et al 18 The opportunistic spectrum access in the CR-VANET was formulated by using game theory, where the availability of the licensed band to the vehicles was based on the spatial and temporal behaviours of the primary transmitter. 19 Recently, green CR has been introduced as an effective approach to meet the demands of high data rate traffic using the limited energy resources.…”

Section: Introductionmentioning

confidence: 99%

An intelligent approach for resource allocation in the cognitive radio vehicular ad hoc network

Das

2017

Trans. Emerging Tel. Tech.

View full text Add to dashboard Cite

With the overwhelming demand for wireless devices, cognitive radios (CRs) have been envisioned to be applied in the vehicular communications in the near future. In the moving vehicular scenario, reliable detection of the primary user and the interference‐aware power allocation to the vehicular secondary users (VSUs) are amongst the main challenging issues in CR. So, this paper proposes techniques to evaluate the false alarm probability and detection probability on the basis of the spatial correlation among the vehicular users in a soft decision fusion‐based CR vehicular ad hoc network. Further, an attempt has been made to optimize the power allocation to the VSUs. The energy efficiency maximization problem is formulated under the constraints of maximum transmission power limit, interference to the primary receiver and the minimum achievable data rate. For the nonlinear and nonconvex optimization problem to be solved, the parametric transformation is used. The power allocation to the VSUs satisfying the nonlinear constraints is solved by our proposed adaptive scheme on the basis of normalized least mean square algorithm. The relevant simulation results provided show the efficacy of our proposed scheme, and the effect of different network parameters is also studied.

show abstract

An infrastructure-aided cooperative spectrum sensing scheme for vehicular ad hoc networks

Cited by 23 publications

References 22 publications

An Integrated Cognitive Radio Network for Coastal Smart Cities

An Integrated Cognitive Radio Network for Coastal Smart Cities

Cooperative Spectrum Decision in Cognitive Vehicular Network Based on Support Vector Machine

An intelligent approach for resource allocation in the cognitive radio vehicular ad hoc network

Contact Info

Product

Resources

About