Content uploaded by Waslon Terllizzie A Lopes
Author content
All content in this area was uploaded by Waslon Terllizzie A Lopes on Aug 17, 2015
Content may be subject to copyright.
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science00 (2015) 000–000
www.elsevier.com/locate/procedia
1877-0509© 2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of Universal Society for Applied Research.
International Conference on Communication, Management and Information Technology (ICCMIT
2015)
Cognitive Vehicular Networks: An Overview
Fabrício B. S. de Carvalhoab*, Waslon T. A. Lopesac, Marcelo S. Alencarac, José V. S.
Filhod
aFederal University of Campina Grande, Department of Electrical Engineering, Campina Grande-PB, 58429-900, Brazil
bFederal University of Paraíba, Department of Electrical Engineering, João Pessoa-PB, 58051-970, PO BOX 5115, Brazil
cInstitute of Advanced Studies in Communications (Iecom), Campina Grande-PB, 58109-970, Brazil
dFederal University of Reconcavo Baiano, Cruz das Almas-BA, Brazil
Abstract
Cognitive Radio (CR) is extending the applications of wireless communications worldwide. Cognitive radio verifies the
electromagnetic spectrum availability and permits the modification of the transmission parameters using the interaction with the
environment. The goal is to opportunistically occupy spectral bands with minimum interference to other users or applications.
Cognitive radio for Vehicular Ad hoc Networks (CRVs or CR-VANETs) is a new trend in the automotive market. Recent and
future vehicles will offer functionalities for the transmission of intra-vehicular commands and dynamic access to wireless
services, while the car is in transit. This paper describes the cognitive radio technology and its signal processing perspectives for
the automotive market.
© 2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of Universal Society for Applied Research.
Keywords: Cognitive Radio; Vehicular Networks; CRVs; CR-VANETs; V2V; V2I.
* Corresponding author. E-mail address: fabricio@cear.ufpb.br
2 Author name / Procedia Computer Science00 (2015) 000–000
1. Introduction
The expansion of wireless applications and services is modifying daily lifes around the worlde. The advances of
microelectronics and computing added portable devices everywhere. Besides, the evolving of new wireless
communications as WiMAX, 4G and cognitive radio, the available applications for customers multiply1.
Vehicular market is facing a similar reality. With the merge of these technologies inside cars, new products and
features must be implemented to offer communication in the vehicles’interior and among vehicles in transit. Recent
applications on traffic monitoring and alerts, infotainment, collision avoidance, Internet access, WiFi or Bluetooth
availability among others transformed the automobiles in an extension of the portable devices of the drivers and
passengers.
Vehicle networks are a major research and development topic for automakers worldwide2. In this context, this
paper focus in the cognitive radio technology and how expansion of vehicular networks and new applications are
related to wireless communications. The paper is organized as follows: Section 2 presents the concepts of cognitive
radio and spectrum sensing; the definition and some main features of vehicular ad hoc networks (VANETs) and
cognitive vehicular networks (CRVs) are detailed in Sections 3 and 4, respectively. Finally, the conclusions of this
article are presented in Section 5.
2. Cognitive Radio
The electromagnetic spectrum is overloaded in some bands while it is sub-utilized in others. Government and
international agencies use to grant operation licenses in specific frequency bands. As most part of the spectrum is
already allocated, new licenses or the improvement of current services in operation face difficulties. On the
opposite, some frequency bands experiment minimum spectrum usage in specific regions in the world3.
Cognitive radio extends the spectral efficiency, with opportunistic access, for the available frequency bands in a
region. CR monitors the available spectrum and when a spectral opportunity is identified, adapts its transceivers to
operate in that frequency channel (when temporarily not occupied by primary or licensed users or even when the
interference levels do not harm other users) 4.
Spectrum users are classified as Primary (or Licensed) Users (PU) or Secondary (or Cognitive) Users (SU).
Licensed users are authorized to operate in a specific frequency band, while secondary users do not have a grant to
transmit and receive in that frequency bands. Cognitive user should monitor the frequency spectrum to find out if
there is any licensed user occupying the spectrum or if there is a spectral opportunity5.
Cognitive users verify the presence or absence of spectrum holes via spectrum sensing. Spectrum holes are
defined as temporarily non-used spectrum bands that can be opportunistically accessed by cognitive users6. If a
band is available, the cognitive user can opportunistically use that channel; although, when a priority user is present,
the CR will not be allowed to benefit from that frequency band.
Spectrum Sensing (SS) is fundamental in a cognitive radio network. Higher bandwidth and lower error rates in
the transmission can be observed with the monitoring of channel occupancy. Different spectrum sensing techniques
can be adopted by cognitive networks7: Energy Detection; Matched Filtering detection; Cyclostationary (or Feature)
Detection and other techniques help improve the spectral detection. Also, the combination of two or more spectrum
sensing techniques (hybrid sensing) is a recent approach in recent researches.
New applications in different scenarios are arising grace to cognitive radio. Some of the major applications of CR
are:
• TV White Spaces and regulation8;
• Smart Grids9;
• Wireless Sensor Networks (WSN) 10;
• Public safety and medical networks11;
• Power Line Communications12;
• Vehicular networks13.
Cognitive Radio Vehicular Ad Hoc Networks (CRVs or CR-VANETs) are one of the most promising
applications of cognitive radio14. Each vehicle in a geographic area could communicate with other vehicles directly
Author name / Procedia Computer Science00 (2015) 000–000 3
or via some communication infrastructure7. Before defining CRVs, the concept of multihop ad hoc networking and
vehicular ad hoc networking is described in the next section.
3. Vehicular Ad Hoc Networks (VANETs)
A MANET (Mobile Ad Hoc Network) refers to wireless network nodes that communicate with each other
without no central control station or any infrastructure network to coordinate the communication. Nodes (or users)
can receive data sent by others devices around the neighborhood via wireless transmission in ad hoc mode2.
A MANET uses multihop strategies to improve the range of the data to be transmitted. The lack of a coordinator
node or a specific infrastructure are desired features of specific networking strategies. Wireless Sensor Networks
(WSNs) and Vehicular Ad hoc NETworks (VANETs) are the most common applications of MANETs2, and can be
adopted in multiple ways (traffic control, environmental monitoring, smart building deployments, military
applications, between many others13).
VANETs are networks based on different sensors that can establish communication with the other nodes (or
vehicles) in transit in a specific region. Fig.1 presents how vehicle-to-vehicle links via radio can enable a high
capacity communication network7.
Fig. 1. Vehicular communication2,15.
VANETs are motivated by the development of Intelligent Transportation Systems (ITS) and Wireless Access in
Vehicle Environment (WAVE)14. ITS and WAVE aim to reduce traffic congestions and accidents by adding
transceivers in the cockpits. Also, non-safety applications (as entertainment, Internet access or electronic messages)
can be offered by these systems14. The wireless transceivers will enable any vehicle to change real-time information
(as road situations or weather conditions) and to warn others drivers to avoid collisions and accidents2.
ITS is an important design issue for automakers today. Although, ITS is not restricted to automotive market1; it
refers to the interchange of information in several transportation systems as rail, water and air transport15. WAVE is
an amendment to the IEEE 802.11 standard to support ITS applications in short-range communications14,16.
3.1. V2V, V2I and V2P
ITS and WAVE are relevant design metrics for the automakers today. In this way, ITS technology will require
two possibilities of communication possibilities to be implemented1: V2V (Vehicle-to-vehicle) communications, in
which automobiles will change information and data without a coordination infrastructure; and V2I (Vehicle-to-
infrastructure), in which wireless technologies infrastructures (cellular base stations or routers and repeaters) along
the roads will serve vehicles to gather specific traffic information from an access point or to establish a
communication with other vehicles15.
4 Author name / Procedia Computer Science00 (2015) 000–000
Additionally, V2P (Vehicle-to-person or vehicle-to-pedestrian) is being developed to improve ITS and WAVE
functionalities14. V2P intends to enhance the pedestrian’s safety in roads by enabling its communication with
vehicles. The goal is to warn in-movement vehicles that pedestrians or byciclists are too close and an accident can
happen17.
In V2I the information is exchanged between the RSU (Roadside Unit) or a cellular network and the OBU
(Onboard Unit). V2R (vehicle-to-roadside) is another variation of V2I: in V2R, information is transmitted between
the RSU and the OBU units15.
V2V and V2I evolution motivated an amendment to IEEE 802.11 standard. The IEEE 802.11p - also known as
Dedicated Short-Range Communications (DSRC) standard or IEEE 160916,18 - supports data exchange between
high-speed vehicles and between automobiles and the fixed infrastructure in the licensed band of 5,9 GHz (5,85 –
5,925 GHz) 2,15.
In USA, vehicular communication up to 200 km/h in the 5,9 GHz band are predicted15. DSRC spectrum is
divided in seven channels, each one with a 10 MHz bandwidth2. V2V and V2I communication are illustrated in Fig.
2.
Fig. 2. V2V and V2I communication2,15.
3.2. Challenges in VANETs
The improvement of safety and traffic efficiency when in-movement vehicles dynamically self-organize via the
available wireless communication interfaces is the main goal of a VANET. Driver assistance and infotainment are
important features that can also be offered to drivers and passengers2.
Although, specific design issues still challenge researchers and automakers. Security and privacy are fundamental
for the consolidation of the VANETs. Privacy of the messages interchanged between two vehicles or through the
roadside infrastructure must be guaranteed. Additionally, vehicular networks must provide liability for warning or
alert messages broadcasted; if this not happens, false informations would harm and confuse the drivers2.
Also, the high-speed characteristic of a VANET and the changeable topology (with vehicles moving to different
directions in distinct environments) provoke multipath fading, delays and other undesirable effects that penalyze the
communication14. In order to deal with these challenges, cognitive radio concept was introduced in vehicular
applications.
Author name / Procedia Computer Science00 (2015) 000–000 5
4. Cognitive Radio Vehicular Ad Hoc Networks (CRVs)
Recently, almost all automakers are investing to provide infotainment solutions and new alternatives to drivers
and passengers14. Features and services tend to overload the available spectrum in the automotive context. Internet
access or Bluetooth connections inside cars are already suffering interferences in heavy-traffic roads.
Cognitive radio can be inserted to vehicular communication. Opportunistic and dynamic access strategies derived
from CR are a new trend in automotive market. Cognitive radio for vehicular Ad hoc Networks (CRVs or CR-
VANETs) lead cars to monitor the available frequency bands and to opportunistically operate in these
frequencies2,15.
CRVs can improve the throughput; also, CR-VANETs enable more users to operate in high user density
scenarios14. Transceivers with reconfigurable software defined radio (SDR) devices are being added to vehicles. The
operating parameters can be dynamically modified via software, which optimizes flexibility and operation in
different bands15. Consequently, hardware limitations are reduced in the development of new devices.
Spectrum sensing is fundamental in cognitive networks – as well as in cognitive vehicular networks14. Spectrum
sensing intends to properly detect the presence or absence of PUs or SUs in a specific frequency band; this
optimizes the usage of spectrum holes opportunistically. Spectrum sensing schemes are classified as2:
• Per-vehicle sensing;
• Spectrum database techniques;
• Cooperation.
In per-vehicle sensing, each car performs the spectrum sensing independently and autonomously from the others.
The spectrum sensing is performed with traditional SS strategies as energy detection, matched filter, cyclostationary
detection and others14.
However, despite the fact that each car can perform it own sensing with no need of any infrastructure support, the
accuracy of the sensing decreases in scenarios with tunnels, mountains or other obstructions2. Fig.3 presents the
scenario of an opportunistic communication in a vehicular network.
Fig. 3. Opportunistic Communication in a Vehicular Network2,15.
Spectrum database techniques are employed to establish a centralized database with information collected from
all PUs operating in a geographic region. This centralized database can reduce the limitations observed in the per-
vehicle sensing, however the control of this database is complex and expensive2.
Based on the CR concept of cooperation, the data gathered from all vehicles are forwarded to a fusion center to
be processed and transmitted to all users in the range of the central node19.
6 Author name / Procedia Computer Science00 (2015) 000–000
4.1. CRV Network Architecture
The network architecture of a CRV is based on vehicles with onboard units (OBUs) and infrastructure facilities
(as RSUs and base stations of cellular) networks) 15. The following network architectures for a cognitive vehicular
radio are defined2,14:
• Without infrastructure support (non-centric architecture);
• Limited infrastructure support;
• Complete infrastructure support.
When vehicles operate to transmit data only as V2V or V2P, the architecture is defined as without infrastructure
support or non-centric architecture; communication is established only between the vehicles (which have CR
devices to perform the spectrum sensing). The sensing can be shared among them. Infrastructure support abscence
reduces the geographical coverage of the communication performed2,14. Fig.4 illustrates a non-centric architecture.
Fig. 4. Non-centric CRV architecture14.
With limited infrastructure support, low complexity base stations and other facilities as repeaters and routers are
installed through the highways. The range of the data is limited by this minimum infrastructure, consequently the
data is transmitted only to the vehicles in the range of the facilities installed (few kilometers) 2,14. Fig.5 represents
this architecture.
Fig. 5. Limited Infrastructure CRV architecture14
The best scenario would be based in a complete and centralized infrastructure support: a base station enables the
connection with (and between) vehicles14. Although, if there is not enough base stations alongside the highways and
streets, the communication will be penalyzed. Fig.6 summarizes a complete infrastructure support architecture.
Author name / Procedia Computer Science00 (2015) 000–000 7
Fig. 6.Complete Infrastructure Support CRV architecture14.
4.2. Open Issues and Challenges in CRVs
Cognitive radio was originally conceived for opportunistically access for TV bands, then this solution would not
be directly applied to CRVs. The elevated mobility of the vehicles and the demand for vehicular communication
would impact in the usual rigorous quality of service (QoS) requirements in automotive market2,15.
Despite these restrictions, the evolving of cognitive vehicular networks is motivated by bandwidth scarcity and
congestion (new technologies made available inside cars demand high bandwidth and reliable wireless connections
– as Internet access, traffic and weather announcements)1. Cognitive vehicular networks can benefit from the
spectrum sensing to temporarily and opportunistically use available spectrum, which combats bandwidth scarcity
and congestion in the main frequency bands14.
The flexibility of SDR devices makes cognitive radios very useful in safety and emergency ITS and WAVE
applications. Additionally, when compared to fixed cognitive radio applications, vehicles can benefit from the
mobility to detect spectrum holes in highways and roads. With the variation of the propagation characteristics and
spectrum occupancy alongside highways and streets, more transmission opportunities can be detected by SDR inside
cars15.
Another important requirements are space and power supply issues. Vehicles have enough space in the chassis to
insert new devices and components. Besides, battery and alternator supply the energetic consumption need by
wireless transceivers, eliminating extra batteries and minimizing dead battery restrictions1,15.
Although, some issues for CRVs applications still challenge automakers: interference; mobility of the vehicles
and security and privacy are the main pending issues in cognitive vehicular networks evolution.
Interference is a remarkable issue in cognitive radio, consequently it is also a challenge in CR-VANETs. Two
main interference issues are being investigated lately: the interference to primary users in a CRV; and the
interference from other networks in an operating CRV14.
Due to vehicles’ unpredictable tracks, SDR inside cars will face different environments and spectrum
occupancies as well as specific infrastructure conditions. Minimum coverage must be guaranteed for the transceivers
when in transit2,15.
In the same way, privacy and security policies must be adopted as a vehicle should not access unauthorized
messages and data from other drivers in the same region. An important investigation line focus on routing
algorithms to minimize the risks of capturing in-transit messages and data2,15.
4.3. Applications
Recent applications devoted to VANETs and CR-VANETs are under development to be deployed in next-
generation vehicles. Main objectives are public safety; V2V and V2P communication; and vehicle entertainment and
information systems2,14.
8 Author name / Procedia Computer Science00 (2015) 000–000
VANETs and CRVs primary concern is the improvement of safety for drivers and passengers by means of the
optimization of the communication between vehicles20. Driving Safety Support Systems (DSSS) are under
development to reduce traffic accidents and to increase drivers’ awareness. Vehicles communicate with each other
and with the infrastructure alongside the highways and roads. Global automakers as Toyota and Nissan are
developing solutions for new vehicles1.
Vehicle-to-Person applications aim to extend the security to pedestrians, bicycles and motorcycles. To eliminate
V2P collisions, the pedestrian DSRC-enabled smartphone sends an audible warning and a navigation screen alert to
the vehicle that is approaching21. On the other side, when the under development system detects a vehicle close to
the person (or bicyle or motorcycle), a high-volume warn and a visual alert are forwarded to the walker. Honda also
develops similar solutions for the named Vehicle-to-Motorcycle (V2M) safety22.
Volvo is in field tests of a vehicle prototype that moves in Sweden autonomously without human intervention23.
Google is also testing an autonomous car. Test vehicle already traveled more than 700.000 miles in the United States
until 201424. Other companies as Audi, Mercedes, General Motors and Nissan work and invest on similar projects23.
5. Conclusions
Cognitive radio is an evolving wireless communication technology that can extend the vehicular communication
networks features and applications. Cognitive radio motivates the expansion and development of vehicle-to-vehicle,
vehicle-to-infrastructure and vehicle-to-pedestrian communications However, there are still relevant design issues to
be solved and to consolidate the CRVs.
Acknowledgements
The authors would like to express their thanks to Iecom, Copele, CEAR/UFPB, Capes and CNPq for the financial
support of this work.
References
1. S. Haykin; D. J. Thomson and J. H. Reed, “Spectrum Sensing for Cognitive Radio”, Proceedings of the IEEE. Vol. 97, No 5, pp. 849-877,
2009.
2. F. B. S. Carvalho; W. T. A. Lopes and M. S. Alencar, “Cognitive Vehicular Networks”, Journal of Communication and Information
Technology (RTIC), ISSN 2237-5112, Vol. 4, pp. 47-53, 2014.
3. Di Felice, M., Chowdhury, K. R., and Bononi, L.,“Cognitive Radio Vehicular Ad Hoc Networks: Design, Implementation, and Future
Challenges”, available in “Mobile Ad Hoc Networking: The Cutting Edge Directions”, Second Edition, Wiley-IEEE Press, 2013.
4. H. Harada, “White Space Communication Systems: An Overview of Regulation, Standardization and Trial”, IEICE Transactions on
Communications, Vol. E97-B, No. 2, pp. 261 - 274, 2014.
5. Vijay, G., Ben Ali Bdira, E., and Ibnkahla, M., “Cognition in Wireless Sensor Networks: A Perspective”, IEEE Sensors Journal, Vol. 11, 2011,
doi: 10.1109/JSEN.2010.2052033.
6. Haykin, S., “Cognitive Radio: Brain-Empowered Wireless Communications”, IEEE Journal on Selected Area in Communications, Vol. 23,
No. 2, 2005, doi: 10.1109/JSAC.2004.839380.
7. R. Tandra; S. M. Mishra and A. Sahai, “What is a Spectrum Hole and What Does It Take to Recognize One?”, Proceedings of the IEEE. Vol.
97, No. 5, p. 824 - 848, 2009.
8. E. Biglieri et al, “Principles of Cognitive Radio”, Cambridge University Press, 2013.
9. Sum, C. E. et al, “Cognitive Communication in TV White Spaces: An Overview of Regulations, Standards, and Technology”, IEEE
Communications Magazine, Vol. 51, No. 7, 2013, doi: 10.1109/MCOM.2013.6553690.
10. S. Bu and F.R. Yu, “Green Cognitive Mobile Networks With Small Cells for Multimedia Communications in the Smart Grid Environment”,
IEEE Transactions on Vehicular Technology, Vol. 63, No. 5, pp. 2115-2126, 2014.
11. G. Vijay et al, “Cognition in Wireless Sensor Networks: A Perspective”, IEEE Sensors Journal. Vol.11, No. 3, p.582-592, 2011.
12. M. P. Sousa et al, “Cognitive LF-Ant: A Novel Protocol for Healthcare Wireless Sensor Networks”, Sensors (Basel), v. 12, p. 10463-1048},
2012.
13. Y. Lu et al, “Measurement and Cognitive Detection Method of Broadcast Radio Stations in Distribution Networks”, 18th IEEE International
Symposium on Power Line Communications and its Applications (ISPLC), pp 75-80, 2014.
Author name / Procedia Computer Science00 (2015) 000–000 9
14. F. B. S. de Carvalho, W. T. A. Lopes and M. S. Alencar, “Introduction to Cognitive Vehicular Networks”, Journal of Communication and
Information Technology (RTIC), ISSN 2237-5112, Vol. 4, pp. 47-53, 2014.
15. X. He; W. Shi and T. Luo, “Survey of Cognitive Radio VANET”, KSII Transactions on Internet and Information Systems, Vol. 8, No. 11, pp.
3837-3859, 2014.
16. Singh, K. D., Rawat, P., and Bonnin, J. M., “Cognitive Radio for Vehicular Ad Hoc Networks (CR-VANETs): Approaches and Challenges”,
Eurasip Journal on Wireless Communications and Networking, 2014. doi:10.1186/1687-1499-2014-49.
17. IEEE Standard, “1609.0-2013 - IEEE Guide for Wireless Access in Vehicular Environments (WAVE) – Architecture”, available at
http://standards.ieee.org/findstds/standard/1609.0-2013.html, Accessed on February 2015.
18. A. Lewandowski et al, “Design and Performance Analysis of an IEEE 802.15.4 V2P Pedestrian Protection System”, 2013 IEEE 5th
International Symposium on Wireless Vehicular Communications (WiVeC), pp. 1-6, 2013.
19. IEEE Standard, “1609 WG-Dedicated Short Range Communication Working Group”, available at
http://standards.ieee.org/develop/wg/1609_WG.html, Accessed on February 2015.
20. H. Li, and D.K. Irick, “Collaborative Spectrum Sensing in Cognitive Radio Vehicular Ad Hoc Networks: Belief Propagation on Highway”,
71st IEEE Vehicular Technology Conference (VTC 2010-Spring), 2010.
21. P. E. Ross, “Self-Driving Cars You Can Trust”, Available at: http://spectrum.ieee.org/cars-that-think/transportation/human-
factors/selfdriving-cars-need-to-inspire-trust/, Accessed on February 2015.
22. K. Buchholz, “Honda Works to Prevent Vehicle-to-Pedestrian Accidents”, Automotive Engineering Magazine, Available at:
http://articles.sae.org/12408/, Accessed on February 2015.
23. Honda Motor Co., “Honda Demonstrates Advanced Vehicle-to-Pedestrian and Vehicle-to-Motorcycle Safety Technologies”, News Releases,
Available at: http://www.honda.com/newsandviews/article.aspx?id=7352-en , Accessed on February 2015.
24. P. E. Ross, “Volvo Matches Google’s Test of City-roaming Cars”, Available at: http://spectrum.ieee.org/cars-that-
think/transportation/advanced-cars/volvo-matches-googles-test-of-cityroaming-cars-, Accessed on February 2015.
25. E. Ackerman, “Google’s Autonomous Cars are Smarter than Ever at 700,000 Miles”, Available at: http://spectrum.ieee.org/cars-that-
think/transportation/self-driving/google-autonomous-cars-are-smarter-than-ever/, Accessed on February 2015.
