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Integrating Licensed and Unlicensed Spectrumin Internet-of-Vehicles with Mobile Edge
ComputingCelimuge Wu, Senior Member, IEEE, Xianfu Chen, Member, IEEE, Tsutomu Yoshinaga, Member, IEEE,
Yusheng Ji, Senior Member, IEEE, Yan Zhang, Senior Member, IEEE

Abstract—In order to satisfy the application requirements
in Internet-of-Vehicles scenarios, it is important to efficiently
utilize different wireless spectrums while considering the
dynamic features of network environments. In this article, we propose a context-aware communication approachto efficiently integrate different licensed and unlicensed
spectrums leveraging the edge computing technologies. In
addition, a joint Aggregation, Caching & Decentralization
scheme is proposed to efficiently combine route aggregation,
data caching, and decentralized computing approaches tocompensate for the limited wireless resources. Asynchronous
multi-hop broadcast and Asynchronous multi-hop unicast
scheme are introduced to improve the routing performance inmulti-hop broadcast and multi-hop unicast communications,
respectively. We conduct computer simulations to evaluate
the effects of the proposed scheme by comparing with otherbaseline approaches.
Index Terms—Licensed and unlicensed spectrum, Internetof-Vehicles, dynamic edge, asynchronous multi-hop broadcast, asynchronous multi-hop unicast.

I. Introduction
Vehicle-to-everything (V2X) communications are important for realizing enhanced intelligent transport systems, autonomous driving, etc. Vehicle mobility, highly
dense distribution of vehicles, complex road conditions
make the V2X communications particularly challenging.
Current Internet-of-Vehicles (IoV) applications mainly
use Sub-6 GHz cellular communications in providing
connections to vehicles on the road. However, cellularcommunications are not sufficient to provide a highquality-of-service (QoS) due to the limited resources andradio interferences [1]. As the vehicle density increases,
it becomes more difficult to provide all the services
through infrastructure-based wireless communications(including cellular networks, and wireless access pointbased communications) due to the cost of deploying andCelimuge Wu and Tsutomu Yoshinaga are with the
University of Electro-Communications, Tokyo, Japan. Emails:{celimuge, yoshinaga}@uec.ac.jp
Xianfu Chen is with the VTT Technical Research Centre of Finland,
Finland. Email: xianfu.chen@vtt.fi
Yusheng Ji is with the National Institute of Informatics and
SOKENDAI (Graduate University for Advanced Studies), Tokyo,
Japan. Email: kei@nii.ac.jp
Yan Zhang is with University of Oslo, Norway. Email:yanzhang@ieee.org.

maintaining the infrastructures. Thus, 3GPP intends tofully leverage unlicensed bands for cellular V2X services,such as by placing cellular V2X and 802.11p in different channels of the ITS band. Multi-hop decentralized
communications in unlicensed bands can provide a low
cost and efficient solution for emerging IoV due tothe flexibility and expandability. However, the limited
wireless resources and the changes of environment posenew challenges on the design of an efficient end-to-end
multi-hop transmission scheme [2, 3].
IEEE 802.11p is designed as a standard for decentralized vehicle-to-vehicle (V2V) communications. There
have been lots of work discussing an efficient use ofIEEE 802.11p in vehicular networks [4–6]. These studies employ different types of technologies in the design of packet forwarding algorithms including positionbased approach, cooperative forwarding, opportunistic
forwarding, and so on. Operating in ITS band of 5.9 GHz,
although IEEE 802.11p can provide bit rate of 27 Mbps,
it is still far from being sufficient, especially in a highly
dense vehicular environment. By using unlicensed 60
GHz spectrum, millimeter wave (mmWave) communication [7] is able to provide up to 2.5 Gbps, which could
be a possible solution to achieve larger throughput.
However, some critical challenges exist in deploying
mmWave in vehicular networks. First, a line-of-sightcommunication path is required between the transmitterand receiver. Second, a directional antenna or beamforming technology is needed to overcome pathloss, which
determines that the mmWave communications only support unicast applications. Therefore, it is important toefficiently integrate these three communication technologies. Some existing studies [8, 9] discussed about an efficient integration of these technologies. However, the vehicular network environment is extraordinarily complex,
and the best solution is dependent on the context information including available resources (communication,
computing and storage resources), communication types,
and application requirements. When the communication
resource is scarce, computing and storage resources canbe used to compensate for the communication limitation. For example, by caching contents at mobile-edge
nodes, the communication overhead can be significantly
reduced.

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Fig. 1.

Aggregation, Caching & Decentralization scheme (left: Aggregation, middle: Caching, right: Decentralization).

According to the communication types and application requirements, the networking problem in vehicular
networks can be classified into two categories, namely,
broadcast data delivery and unicast data delivery. Existing approaches fail to fully address the context information in the route selection and packet forwarding.
Mobile edge computing (MEC) is an approach that
conducts computing tasks and data caching near theend users. Although there have been many studies [10,11] explaining the advantages of MEC in performance
improvement, most studies mainly consider conductingedge computing on the base stations, and the edge nodeformation problem in a decentralized vehicular network
is not seriously discussed. In order to deal with the
dynamic feature of vehicular networks, the edge node
selection algorithm should be able to address the context
information.
In this paper, we propose a context-aware vehicle
edge-based communication approach, where different
licensed and unlicensed spectrums are integrated efficiently through edge nodes, and different edge nodes
are used for different contexts in order to optimize
the whole network performance. We propose a joint
Aggregation, Caching & Decentralization scheme to handle
the resource limitations (Fig.1). Aggregation employs aroute aggregation technology to improve the wirelessresource utilization efficiency, which is a promising way
to solve the resource limitations incurred from highly
dense distribution of vehicles. Caching is used to shorten
the delay and reduce the wireless resource usage by
caching contents at edge nodes. Decentralization denotes
a distributed way of storing data and executing computation tasks, which improves the performance by fully
utilizing the local resources. In order to take into accountdifferent types of networking requirements, we also propose Asynchronous multi-hop broadcast and Asynchronous
multi-hop unicast schemes for broadcast applications andunicast applications, respectively. These two schemes
use different types of communications in data delivery,
resulting in more efficient use of different spectrums.
Realistic computer simulations are conducted to show
the advantage of the proposed approaches over the baselines, and several open research problems are discussed.
The remainder of this article is organized as follows.

We describe our solutions for the efficient use of different
spectrums in Section II. Simulation results are presented
in Section III. Finally, we point out future research directions and then draw our conclusions in sections IV andV, respectively1 .
II. Dynamic Edge-based Context-aware Integration ofDifferent Spectrums
Major challenges in vehicular networking come fromdynamic vehicular environment due to the vehicle mobility, varying vehicle density, and resource limitations.
Therefore, it is important to consider these characteristics
in the design of a communication protocol. Here, wefirst propose a context-aware dynamic edge conceptthat selects different edge nodes based on the context
information. Two different classes of context information, specifically resource limitations and networking
requirements are mainly addressed in the paper. We thenpropose a joint Aggregation, Caching & Decentralization
approach to handle the resource limitations, and obtain
Asynchronous multi-hop broadcast and Asynchronous multihop unicast schemes to support broadcast communications and unicast communications, respectively.
A. Context-aware hierarchical dynamic edge
Here, Dynamic means that the edges should be different for different contexts, and the edges should be
updated with the environmental change. We proposea hierarchical dynamic edge architecture that creates
multi-tier edge clusters by integrating licensed Sub6 GHz band, large-throughput short-range unlicensed
commutations (such as mmWave), and low-throughput
long-range unlicensed communications (IEEE 802.11p).
As shown in Fig. 2, short-range communications areused to provide a high-throughput connection between
end users and Tier-2 edges. Long range unlicensed
communications are used to guarantee the connectivity
between different edges. Finally, licensed Sub-6 GHz
communications are used to connect Tier-1 edges directly
to the cellular base stations. Based on the exchange of
1 We use the words “vehicle” and “node” interchangeably in the restof this paper.

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hello messages, these edges are created dynamically according to the context information, namely, node densityand propagation condition [12]. The vehicle mobility, thelink quality between vehicles, and vehicle distributions
are considered in the edge node selection. Edge nodes
are responsible for forwarding data packets, and our aim
is to facilitate the use of edge nodes in the route selectionin order to improve the packet forwarding performance.
The hierarchical feature of this edge architecture ensuresthat the proposed approach could deal with a highdensity condition by utilizing different types of wirelessspectrums.

Fig. 2.

Hierarchical dynamic edge.

B. The context information for route selection
Two classes of contexts are addressed in the paper.
The first class of context is the resource limitations
due to the high mobility, high network density, etc.
The communication protocols should optimize packet
forwarding process according to the available resources.
The overall quality of a computer system is determinedby the joint effect of communications, computing, andstorage resources. In many scenarios, the communication
resource is the main bottleneck. Therefore, a full utilization of computing and storage resources would be a way
to solve the communication resource limitation problem.
For example, it is possible to improve the communication performance by conducting efficient caching andcomputing near the end users, which is also the mainfunctionality of mobile edge computing.
The second class of context is the application requirements on the networking. Specifically, some applications
such as vehicular safety applications require broadcast
type data delivery while some applications require unicast type data delivery. Broadcast communications andunicast communications require totally different typesof networking protocols. Therefore, these requirements
should be considered in the design of communication
protocols.
C. Joint Aggregation, Caching & Decentralization scheme
As show in Fig.1, a joint Aggregation, Caching &Decentralization scheme is designed to provide a better communication performance by efficiently utilizing
the available computing and storage resources. Here,
Aggregation denotes route aggregation instead of data

aggregation. The data aggregation mainly denotes theaggregation of data from different sources that have similar information. The difference between data aggregation
and route aggregation is that the latter one does notchange the data and therefore is independent of specificapplications. In the route aggregation, multiple routes
are aggregated at edge nodes in order to reduce thenumber of transmission nodes in the network withoutchanging the data delivered. This scheme is specifically designed for data delivery scenarios in vehicular
networks where the movement of vehicles is restrictedby roads resulting in a higher chance of conducting
route aggregation. Caching is the approach of conducting
caching at edge nodes, which is particularly efficientfor one-to-many data delivery scenarios such as video
data downloading. Decentralization means decentralized
storage and computing. In particular, the decentralized
storage can store the data in a distributed network, andthe decentralized computing is useful for executing some
jobs that require high computational capabilities andshort response delay.
The main purpose of Aggregation is to improve thewireless resource utilization efficiency by reducing the
MAC layer contention time. Aggregation does not reducethe volume of data transmitted in the network, but itis efficient when multiple traffic flows coexist in the
network. Caching is used to reduce the volume of datatransmitted in the network by caching some data at somevehicles. Decentralization is an approach to redistributethe data and computing tasks in the network, which
can improve the communication efficiency by integratingwith other two approaches. All these three schemes or
any two of these can be combined together to achievea better utilization of wireless resources. Aggregation
and Caching scheme can be integrated in to the routing
protocol, and Decentralization scheme can be integrated
with resource scheduling protocols.
D. Asynchronous multi-hop broadcast
In broadcast communications, it is particularly difficult
to check the reception status of intended receivers as
there is no acknowledgment for a broadcast frame at the
MAC layer. Therefore, an upper layer acknowledgment
approach is required to confirm a successful delivery.
However, requesting acknowledgments from all the receivers could possibly cause a traffic congestion due tolimited wireless resources. We propose a Asynchronous
multi-hop broadcast approach that uses two different typesof communications for the broadcasting procedure. Morespecifically, as shown in Fig.3, the broadcast capability of IEEE 802.11p signal is used to send broadcast
packets and the large bandwidth of mmWave is used
to send acknowledgement packets. By using explicit
acknowledgments, each sender node is able to ensure
the successful delivery of packets. As data packets andacknowledgement packets can be delivered concurrently
using different spectrums, this approach is beneficial for4vehicular safety applications that require a short delay
with high reliability.

combined with the route aggregation approach proposed
in Subsection II-C as the routes from different sources areaggregated at edge nodes except the last hops.

Fig. 4.

Asynchronous multi-hop unicast.

III. Performance analysis
Fig. 3.

Asynchronous multi-hop broadcast.

E. Asynchronous multi-hop unicast
As shown in Fig.4, Asynchronous multi-hop unicast involves different types of communications in different
hops. In order to achieve a smooth end-to-end datadelivery, a virtual data delivery approach based on a
hop-by-hop custody transfer approach is derived. Different from the conventional custody transfer used indelay tolerant networks, in the Asynchronous multi-hop
unicast, all the data delivery procedure is controlled andobserved by edge nodes based on session informationthat is maintained by the communications between edge
nodes using IEEE 802.11p. Each session is described by
a tuple of {Session ID, Source IP, Destination IP} where
the session ID field is generated based on the traffic
flow identifier. Each edge node is responsible for finding
the best next edge node for the destination node ofeach session, and forwards this session information to
the next edge node using IEEE 802.11p. By exchanging
the session information between edge nodes, end-to-end
data delivery could be possible without synchronous
multi-hop communications.
The data forwarding is achieved by whether mmWave
or IEEE 802.11p depending on the network environment.
In the route selection, we use a fuzzy constraint Qlearning approach as in [13]. In the evaluation of eachdirect wireless link, the available bandwidth, vehicle mobility, and link quality between vehicles are accounted
for by using a fuzzy logic. The number of hops is considered by discounting the reward with the increase in
the number of hops. In addition to this, the overhead ofbeam alignment in mmWave should be used to discountthe reward when a mmWave link is used. Therefore,
we propose a specific discount factor. The value of
this factor should be a system specific parameter thatvaries with the system hardware. In order to simply this,we use a simplified version of Asynchronous multi-hop
unicast where only the last one hop uses the mmWave
communications. This simplified version is easy to be

The network simulator ns-2.34 was used for conducting simulations. We evaluated the proposed approachin V2V data delivery for two different types of communications, namely broadcast transmissions and unicast
transmissions. The performance of vehicle-to-cloud communications depends on the available cellular resources,
which can be found in [14]. The same freeway topology
and street topology as in [13] were used. The Nakagami
propagation model was integrated to simulate a realistic
vehicular communication channel [13]. There were twodifferent types of communications available for vehicles,
namely, IEEE 802.11p and mmWave.
A. Broadcast applications
A freeway with two lanes in each direction was used
to evaluate the performance in broadcast applications.
The number of source nodes was set to 2 (the two
nodes were neighbors) in order to simulate a conditionof two collided vehicles sending data messages at thesame time. The proposed protocol was compared with
“EMPR” [15], “Weighted p-persistence” [15], and “Edgewith aggregation”. Here, “Edge with aggregation” denotes the edge-based forwarding with route aggregation,
which is actually the proposed protocol without using
mmWave.
Fig. 5 is the performance comparison for various
numbers of vehicles. We can observe that the conventional sender-based approach (“EMPR”) and probabilistic approach (“Weighted p-persistence”) are not able toprovide a high data dissemination ratio. This is due tothe lossy wireless channel and the difficulty in checking
the reception status of the receivers. Although “Edgewith aggregation” can achieve a better performance byusing efficient edge nodes for the data forwarding, it
is still insufficient to achieve a high data dissemination ratio. With the asynchronous multi-hop broadcast
approach, the proposed protocol can efficiently utilizemmWave communications to check the reception statusof broadcast packets, resulting in a high dissemination
ratio. The low end-to-end delay shown in Fig. 5 (b) canbe explained by two reasons. First, the use of mmWave

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1

0.9

0.8

0.4

Delay (s)

1.2
0.6

0.4

1
0.8
0.6

0
100

0.4

Without edge
Edge with aggregation
Proposed
150

200

0.2

250

Number of Vehicles

0.5

Without edge
Edge with aggregation
Proposed

1.4

0.2

0.6
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1.8
1.6

0.7

Delay (s)Data dissemination ratio

0.8
0.8

EMPR
Weighted p-persistence
Edge with aggregation
Proposed

1

Packet delivery ratio

can efficiently reduce the MAC layer contention in IEEE
802.11p. Second, by using different spectrums, the proposed asynchronous multi-hop broadcast conducts thedata transmission and acknowledgment packet transmission concurrently, which is particulary effective in termsof fast data delivery.

300

0
100

150

200

250

300

Number of Vehicles

Fig. 6.
Performance for multi-hop unicast communications (left:
packet delivery ratio, right: end-to-end delay).

0.4
0.3
0.2

0.2

0
100

EMPR
Weighted p-persistence
Edge with aggregation
Proposed
200

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Number of Vehicles

600

0
100

200

300

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Number of Vehicles

Fig. 5. Performance for multi-hop broadcast communications (left:
data dissemination ratio, right: end-to-end delay).

B. Unicast applications
We used a 2500 m × 2500 m square area of New
York City as shown in [13] to evaluate the protocol
performance for unicast communications. The proposed
approach was compared with “Without edge” approach.
“Without edge” is a typical approach without usingedge computing, and AODV-L [13] is used in the routeselection of “Without edge”. There were 30 CBR flows
with 36kbps data rate for each. The maximum allowablevelocity of the vehicles was 12 m/s. Fig. 6 shows theperformance for unicast communications in terms ofpacket delivery ratio and end-to-end delay for various
numbers of vehicles. As shown in the figure, the proposed protocol can provide the highest packet delivery ratio by using the asynchronous multi-hop unicast
approach. Especially in terms of delay, while the otherprotocols show a drastic increase of end-to-end delay
as the node density increases, the proposed protocol
achieves a stable end-to-end delay due to the efficientutilization of mmWave communication resources.
IV. Future research directions
The dynamic features of vehicular networks, the limited wireless resources, and the variety of wireless spectrums open up many interesting research topics including:• Data driven approaches for learning the best edge
placement policy– There is a need to disseminate
and collect information from/to the large number ofvehicles. Due to the limited wireless resources andlarge node density, it is difficult to provide a satisfactory throughput with the traditional networking

technologies. Therefore, designing a dynamic edgemechanism that turns on/off some edges (accesspoints or the edge functionality of user devices)
according to the number of devices, traffic distribution, QoS requirement, and device capacities would
continually be an important topic. However, due tovarious traffic patterns, node distributions, device
types, and QoS requirements, the optimization ofdata forwarding is difficult to achieve using simple
mathematical models. In recent years, data driven
approaches are attracting increasing interests due toits capability of finding a good solution for a complex system. This would create new opportunities
for a study on data driven approaches in learning
the best edge placement policy.
• Deep reinforcement learning based approachfor handling complexity– Reinforcement learningbased approaches are able to find the best action
without labeled, classified or categorized data. However, well-used reinforcement learning approaches,such as Q-learning, require a clear structure to store
the states and actions, which is insufficient for representing the complex network environment anduser requirements. Since deep neural networks areable to store complex information, a combination ofreinforcement learning and deep neural networks
would be an interesting way to allocate the licensed
and unlicensed spectrums automatically based on
the past experiences and the feedbacks from environments.
• Adapting quickly with the environment change–
The use of different spectrums in vehicular networks
poses various challenges as the spectrum resourceschange over the road, time and other factors. Therefore, it is crucial to design an approach that is
capable of adapting to these changes. Reinforcement learning can be used to learn the best action.
However, due to the movement of vehicles, it ischallenging to converge to the best action before
the environment changes, especially for vehicles that
newly join the network. Transfer learning could

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be away to speed up the learning. How to transfer learned information between vehicles by using
different spectrums would be an interesting andimportant topic.
• Privacy
issues and edge-computing based
solutions– Efficient utilization of different
spectrums could significantly improve system
performance. However, privacy issues would be amajor concern as the owners of different networks
could be different. This is a sensitive issue that
could directly affect the deployment of a goodsystem. While some IoV applications require
collecting a large amount of vehicular data, it issimply unproductive to transmit in real-time all
sensed data, for processing and analysis in thecloud. Hence the concept of edge computing could
be a way to solve the privacy issues. Some data
could be pre-processed and potentially used in realtime within different edges (belonging to different
communities) whereas other data are stored or evenarchived (taking into consideration legal aspects)
for much later use in a more centralized (e.g.
regional) cloud infrastructure. An interesting topic
would be to investigate the privacy aspect involvedin managing such distributed data among different
communities and regions based on edge computing
technologies while utilizing both licensed andunlicensed spectrums.
• Intermediate layer for handling route changes–
An efficient use of different spectrums in a multihop data delivery could possibly incur frequentchanges of routes. This could affect the upper layer
protocols, resulting in performance degradation. For
example, the change of routes could directly reduce
the transmission rate of a TCP connection. Therefore, it is important to design an intermediate layer
protocol that exists between the network layer andthe transport layer to conceal the change of data
forwarding path from the transport layer.
V. Conclusions
In the paradigm of an efficient use of licensed andunlicensed spectrums in vehicular networks, this article discussed the importance of utilizing the context
information with edge computing in highly mobile andresource limited networks. We first introduced a contextaware vehicle edge-based communication approach tointegrate different licensed and unlicensed spectrums
where different edge nodes are selected for different
contexts in order to optimize the whole network performance. Then we proposed a joint Aggregation, Caching& Decentralization scheme to handle the communication
resource limitations by utilizing local computing andstorage resources. Asynchronous multi-hop broadcast andAsynchronous multi-hop unicast scheme were further put
forward to enhance multi-hop data delivery with different spectrums. Simulation results explain that the proposed approaches achieve much better performance thanother baselines. Finally, we pointed out some importantresearch topics for the next step research.
Acknowledgment
This research was supported in part by the open collaborative research program at National Institute of Informatics (NII) Japan (FY2018), the Telecommunications
Advanced Foundation, JSPS KAKENHI Grant Numbers
16H02817, 18KK0279 and 16K00121, and JST-Mirai Program Grant Number JPMJMI17B3, Japan.
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Celimuge Wu [M’10-SM’18] (celimuge@uec.ac.jp) received his ME
degree from the Beijing Institute of Technology, China, in 2006, and his
PhD degree from The University of Electro-Communications, Japan,
in 2010. He is currently an associate professor with the Graduate
School of Informatics and Engineering, The University of ElectroCommunications. His current research interests include vehicular networks, sensor networks, intelligent transport systems, IoT, 5G, andmobile cloud computing.

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Xianfu Chen [M’13] (xianfu.chen@vtt.fi) received his Ph.D. degreein Signal and Information Processing, from the Department of Information Science and Electronic Engineering at Zhejiang University,
Hangzhou, China, in March 2012. He is currently a Senior Scientist
with the VTT Technical Research Centre of Finland, Oulu, Finland. His
research interests cover various aspects of wireless communications
and networking, with emphasis on software-defined networking, green
communications, centralized and decentralized resource allocation,
dynamic spectrum access, and the application of artificial intelligenceto wireless communications.

Tsutomu Yoshinaga [M’99] (yoshinaga@uec.ac.jp) received BE, ME,
and DE degrees from Utsunomiya University in 1986, 1988, and 1997,
respectively. Since August 2000, he has been with the Graduate Schoolof Information Systems, The University of ElectroCommunications,where he is now a professor. His research interests include computerarchitecture, interconnection networks, and network computing. He isa fellow of IEICE, and a member of ACM, IEEE, and IPSJ.

Yusheng Ji [M’94-SM’18] (kei@nii.ac.jp) received B.E., M.E., and D.E.
degrees in electrical engineering from the University of Tokyo. She
joined the National Center for Science Information Systems, Japan, in
1990. Currently, she is a Professor at the National Institute of Informatics (NII) and SOKENDAI (the Graduate University for Advanced
Studies). Her research interests include network architecture, resource
management, and quality of service provisioning in wired and wirelesscommunication networks.

Yan Zhang [M’05-SM’10] (yanzhang@ifi.uio.no) is a full professor
at the University of Oslo. He is an Editor of several IEEE publications, including IEEE Communications Magazine, IEEE Network,
IEEE Transactions on Green Communications and Networking, IEEE
Communications Surveys & Tutorials, and IEEE Internet of Things. His
current research interests include next generation wireless networks
leading to 5G and cyber physical systems. He is an IEEE VTS Distinguished Lecturer and a Fellow of IET. He received the award “Highly
Cited Researcher” (Web of Science top 1% most cited worldwide)
according to Clarivate Analytics.