Classification and analysis of spectrum sensing mechanisms in Cognitive Vehicular Networks

Classification and analysis of spectrum sensing mechanisms in Cognitive Vehicular Networks

Vehicular Ad hoc Networks (VANETs) is an essential part of Intelligent Transportation System (ITS), which aims to improve the road safety. However, the main challenge in VANET is the spectrum scarcity which is more severe especially in the urban environment. In this view using Cognitive Radio (CR) t...

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Bibliographic Details
Published in:EAI endorsed transactions on smart cities Vol. 2; no. 7; p. 154105
Main Authors: Riyahi, A., Bah, S., Sebgui, M., Elgraini, B.
Format: Journal Article
Language:English
Published: Ghent European Alliance for Innovation (EAI) 12-02-2018
Subjects:
Classification
Cognitive radio
Crashworthiness
Data integration
Detection
Environmental effects
Environmental impact
Impact strength
Intelligent transportation systems
Mobile ad hoc networks
Multisensor fusion
Topology
Traffic safety
Traffic speed
Traffic volume
Urban environments
Vehicles
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Summary:Vehicular Ad hoc Networks (VANETs) is an essential part of Intelligent Transportation System (ITS), which aims to improve the road safety. However, the main challenge in VANET is the spectrum scarcity which is more severe especially in the urban environment. In this view using Cognitive Radio (CR) technology in VANET has emerged as a promising solution providing additional resources and allowing spectrum efficiency. But, vehicular networks are highly challenging for spectrum sensing due to speed and dynamic topology. Furthermore, these parameters depend on the CVNs’ environment such as highway, urban or suburban. Therefore, solutions targeting CVNs should take into consideration these characteristics. As a first step towards an appropriate spectrum sensing solution for CVNs, we first, provide a comprehensive classification of existing spectrum sensing techniques for CVNs. Second, we discuss, for each class, the impact of the vehicular environment effects such as traffic density, speed and fading on the spectrum sensing and data fusion techniques. Thirdly, we derive a set of requirements for CVN’s spectrum sensing that takes into consideration specific characteristics of CVN environments. Finally, we propose a new CVN scheme adopted in particular for urban environment where the spectrum sensing is more challenging due to dense traffic and correlated shadowing.
ISSN:2518-3893
2518-3893
DOI:10.4108/eai.12-2-2018.154105