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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. XX, NO. XX, XXX 2016 1

A Reinforcement Learning-based Data Storage

Scheme for Vehicular Ad Hoc NetworksCelimuge Wu, Member, IEEE, Tsutomu Yoshinaga, Member, IEEE, Yusheng Ji, Member, IEEE,

Tutomu Murase, Member, IEEE, and Yan Zhang, Senior Member, IEEE

Abstract—Vehicular ad hoc networks (VANETs) have beenattracting interest for their potential roles in intelligent transportsystems (ITS). In order to enable distributed ITS, there is a needto maintain some information in the vehicular networks withoutthe support of any infrastructure such as road side units. Inthis paper, we propose a protocol which can store the data inVANETs by transferring data to a new carrier (vehicle) beforethe current data carrier is moving out of a specified region. Forthe next data carrier node selection, the protocol employs fuzzylogic to evaluate instant reward by taking into account multiplemetrics specifically throughput, vehicle velocity, and bandwidthefficiency. In addition, a reinforcement learning-based algorithmis used to consider the future reward of a decision. For the datacollection, the protocol uses a cluster-based forwarding approachto improve the efficiency of wireless resource utilization. We usetheoretical analysis and computer simulations to evaluate theproposed protocol.

Index Terms—Vehicular ad hoc networks, data storage scheme,reinforcement learning, fuzzy logic.

I. INTRODUCTION

For a large event like Olympic game, it is particularly

important to design an efficient navigation system to guide

visitors to/from the stadium. Since existing navigation systems

like VICS (Vehicle Information and Communication System)

in Japan are dependent on pre-installed infrastructure and

centralized control, they cannot attain expected real-time and

accurate information dissemination. It is very likely that all

people will be guided to the same route (resulting in traffic

congestion on that route) because the existing systems do

not take into account the load balancing and user-behavior

based adjustment. In order to solve this problem, we propose

a protocol which can store the data in distributed vehicular

networks, specifically vehicular ad hoc networks (VANETs),

without any support from infrastructure.

As shown in Fig. 1, some data, such as the change of

vehicle density with time domain, road status, camera sensor

Copyright (c) 2016 IEEE. Personal use of this material is permitted.However, permission to use this material for any other purposes must beobtained from the IEEE by sending a request to [email protected].

C. Wu and T. Yoshinaga are with the Graduate School ofInformatics and Engineering, The University of Electro-Communications,1-5-1, Chofugaoka, Chofu-shi, Tokyo, 182-8585 Japan (e-mail:{clmg,yosinaga,kato}@is.uec.ac.jp).

Y. Ji is with the Information Systems Architecture Research Division,National Institute of Informatics, 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo 101-8430 Japan (e-mail: {kei}@nii.ac.jp).

T. Murase is with the Information Technology Center, Nagoya University,Chikusa-ku, Nagoya (e-mail: [email protected]).

Y. Zhang is with University of Oslo, Norway (e-mail: [email protected]).Manuscript received 2016.

Fig. 1. Storing data in a distributed vehicular network.

information etc. [1], [2], can be stored locally in their interest

region (the rectangular area in the figure shows the interest

region; at least a vehicle in the region should have the data).

This can be usable when there is a request to know the

current traffic information and the future traffic estimation

of the corresponding area. By storing this information in a

distributed network, it is possible to provide more accurate

local information to intelligent transport systems without the

support of road side units or Internet connection. While some

recent works [3]–[9] discuss about the cloud-based resource

management and vehicular cloud computing, this paper de-

scribes the problem of how to store information in a vehicular

network, and propose a protocol which can efficiently maintain

information in VANETs.

There have been many studies discussing about data transfer

in VANETs [10]–[25]. The existing approaches can be clas-

sified into two main categories: broadcast protocols [11]–[17]

and unicast protocols [18]–[27]. Unicast protocols conduct

data transfer between one sender node and one receiver node.

In contrast, broadcast protocols are used to disseminate data

to multiple intended receivers. A unicast protocol is more

suitable for storing data in VANETs because it is low-cost (the

number of data carrier nodes is fixed to one) and bandwidth

efficient (efficient modulation and coding scheme can be used

to transmit data as compared with the broadcast scheme).

In this paper, we consider using unicast transmissions to

handoff data between vehicles in order to keep the data always

in the interest region. Only one data carrier node is required

for storing the data from the same source node with the

same interest region. However, a data carrier node has to

handover the data to the next data carrier node before moving




out of the interest region. We propose a protocol which can

select the next data carrier node efficiently. The proposed

protocol takes into account throughput, vehicle mobility, and

bandwidth efficiency by employing a fuzzy logic algorithm,

and considers long-term outcome by using a reinforcement

learning algorithm. Considering the possibility of multiple data

sources, we also propose an efficient route selection algorithm

to transfer data from each data source to the data carrier

node. We use theoretical analysis and computer simulations

to evaluate the proposed protocol.

The paper is an extension of our previous conference paper

[28]. While [28] only discussed the handoff of data between

data carrier nodes, the paper describes a solution which con-

ducts efficient data transmission from the data source nodes to

the data carrier node. We also present new theoretical analysis

and simulation results that have not been reported previously.

The remainder of the paper is organized as follows. Section II

gives a brief outline of related work. In section III, we give a

detailed description of the proposed data carrier node selection

algorithm. Next, we propose an efficient data transmission

scheme from the data source nodes to the data carrier node

in section IV. Theoretical analysis and simulation results are

presented in section V and section VI respectively. Finally, we

present our conclusions and future work in section VII.

II. RELATED WORK

There have been many studies related to the cloud comput-

ing (including data storage management) and routing protocols

for VANETs. However, the data storage issue in VANETs is

an under-explored research problem.

A. Storage management in VANETs

There have been some studies discussing about the man-

agement of data storage resources in vehicular environment.

Yu et al. [3] have studied the bandwidth and computing

resource (CPU, memory, and storage) sharing issues in cloud-

enabled vehicular networks, and proposed a coalition game

model to solve the idle resource sharing problem among

cloud service providers. In [4], the authors discussed the

opportunities and challenges in exploiting cloud computing

in vehicular networks, and proposed an integrated cloud

computing architecture which facilitates sharing of computa-

tional resources, storage resources, and bandwidth resources

among vehicles. Mershad and Artail [5] have presented a

cloud service discovery protocol for VANETs which can be

used to discover mobile cloud services provided by nearby

vehicles. The system is not a totally distributed approach

because the system depends on RSUs (roadside units) which

are used to register mobile cloud services. Lee et al. [6]

have reviewed emerging VANET applications and state-of-

the-art computing and networking models for vehicular cloud

networking systems. Bitam et al. [7] have proposed VANET-

Cloud, a cloud computing model for VANETs. VANET-Cloud

extends the conventional cloud infrastructure by introducing

vehicles as edge computing resources in order to allow drivers

and other users to access computing resources of vehicles.

Liu et al. [8] have proposed a cloud-assisted downlink safety

message dissemination framework where wireless networking

and cloud computing technologies are integrated to minimize

packet loss and redundancy. Kim et al. [9] have discussed

the data dissemination problem of providing reliable data

delivery services from a cloud data center to vehicles through

roadside wireless access points (APs) with local data storage,

and proposed two algorithms to prefetch a set of data from a

data center to roadside wireless APs. However, none of these

approaches discusses the problem of storing the vehicle data

in a totally distributed VANET.

B. Routing protocols for VANETs

The routing protocols for VANETs can be classified into

two categories specifically broadcast protocols and unicast

protocols. The aim of broadcast protocols is to provide an

efficient data dissemination for one-to-many communications.

Most broadcast protocols focus on reducing the broadcast

redundancy to improve the efficiency and packet dissemination

ratio. Yoo and Kim [11] have proposed a multi-hop broadcast

protocol called RObust and Fast Forwarding (ROFF) to miti-

gate the unnecessary contention delay and redundant packets.

Chuang and Chen [12] have resolved the broadcast storm

problem by finding the appropriate parameters for different

car densities via a mathematical model. Suthaputchakun et

al. [13] have proposed a trinary partitioned black-burst-based

broadcast protocol which takes into account packet priorities,

and uses the farthest possible vehicle to forward the data

packets in order to reduce the number of hops required for

data dissemination. However, the farthest vehicle could have

poor link quality. In [14], the inter-vehicle distance, signal

quality, and route length are jointly considered to improve the

efficiency without sacrificing the reliability. Bi et al. [15] have

proposed a multi-hop broadcast protocol which combines bi-

directional broadcast and directional broadcast. Ucar et al. [16]

have proposed a hybrid architecture which integrates LTE

with IEEE 802.11p-based multi-hop communication. Liu et

al. [17] have explored vehicle-to-vehicle data dissemination in

VANETs based on network coding.

The unicast routing protocols handle the packet forwarding

problem of one-to-one communications. Since the acquisition

and utilization of location information [29]–[31] are possi-

ble in VANETs, geographic protocols have attracted a great

interest. Shafiee and Leung [18] have proposed CMGR, a

connectivity-aware minimum-delay geographic routing proto-

col which changes route selection policy according to the

network connectivity. In a sparse network, CMGR gives a

higher weight to the connectivity of routes. In contrast, the

protocol chooses a route which has the minimal delay and

adequate connectivity in a high-density network. Yang et

al. [19] have proposed a protocol where the next forwarder

node is selected based on a metric which minimizes the packet

error rate of route. Eiza and Ni [20] have proposed an evolving

graph-based reliable routing protocol which can find a reliable

route without broadcasting the route request messages for each

route change. Al-Rabayah and Malaney [21] have proposed

a protocol which integrates the geographic routing approach

and reactive routing approach. Rak [22] has proposed an




opportunistic routing protocol which takes into account the

packet delivery ratio and link stability information for the route

selection. He et al. [23] have investigated the problem of delay

minimization for data dissemination in a large-scale vehicular

ad network, and proposed a routing strategy to minimize the

expected path delay with fixed-scheduled buses and random-

scheduled taxis. Zhang et al. [24] have proposed a street-

centric opportunistic routing protocol in which packets can

be dynamically forwarded at the intersections based on the

information of adjacent streets. Togou et al. [25] have proposed

SCRP, a distributed routing protocol that computes end-to-

end delay for the entire routing path before sending data

messages. SCRP is a distributed geographic source routing

scheme which takes advantage of the global network topology

to select routing paths. Zhu et al. [26] have proposed a

protocol which integrates both contact-level and social-level

mobility for fast routing. In [27], a cooperative protocol for

Roadside-Vehicle communication was proposed. The protocol

minimizes an average delivery delay of each user request while

maximizing the amount of data packets downloaded from the

RSU. However, all these protocols [11]–[25] do not discuss

the problem of collecting and maintaining vehicle data in

VANETs.

III. DATA CARRIER NODE SELECTION

A. Assumption

Each node (vehicle) is equipped with a positioning device.

Each node knows the road map information, and sends own

location information and velocity information using beacon

messages with a predefined interval (1 second by default).

We assume a connected network topology where at least one

multi-hop path exists between any two nodes.

B. System model and problem definition

For each block of data, there is an interest region for the

data. The data should be maintained in this region (some nodes

in this region have to maintain this information). We say that

the data are lost if the vehicles that storing the data go outside

of the interest region. The source node of the data sends the

interest region information with the data.

The data carrier node selection problem can be defined as

maximizeV1,V2,...,Vj

∑i=j−1

i=1TH(Vi,Vi+1)

j·(j−1)

subject to TH(Vi, Vi+1) ≥Sdata

CT (Vi,Vi+1), i = 1, ..., j − 1

d(Vi, Loc) ≤ RINT , i = 1, ..., j − 1. (1)

where Vi is the ith data carrier node, j is the number of

data carrier nodes used for the whole time domain, and

TH(Vi, Vi+1) is the throughput of data transmission between

Vi and Vi+1. The size of data that should be maintained in

the network is expressed as Sdata, and CT (Vi, Vi+1) is the

connection time between Vi and Vi+1. Here, d(Vi, Loc) is the

distance between Vi and the center location of interest region

(Loc is the center of interest region, and RINT is the radius of

interest region). The objective is to reduce the time required

[equivalent to increase the throughput and reduce the number

of data handoffs; this is why the objective function of (1)

considers the average throughput (∑i=j−1

i=1TH(Vi,Vi+1)

(j−1) ) and the

number of data carrier nodes (j)] while keeping the data in the

interest region (each data carrier node should handover the data

to the next carrier node before going out of the interest region).

In order to solve the problem, we first have to take into account

the throughput between the current data carrier node and the

next data carrier node because low throughput connection

could increase the channel time required for transmitting the

data. It is also important to consider vehicle velocity as a

metric because the position and velocity of the next carrier

node affects the future outcome. Considering there could be

multiple traffic source nodes in the network, the MAC layer

contention efficiency is another important metric should be

addressed. Therefore, in the paper, we use a heuristic approach

to solve the problem. We take into account throughput, vehicle

velocity, and bandwidth efficiency of the whole network by

using a fuzzy logic algorithm, and employ a reinforcement

learning approach to evaluate the long-term outcome of a

decision. The fuzzy logic is used to evaluate the next data

carrier node, and the reinforcement learning is used to evaluate

the possible future reward after selecting the next data carrier

node.

C. Fuzzy logic-based instant decision evaluation and rein-

forcement learning-based future reward evaluation

The source node selects its next data carrier node. We take

into account multiple metrics specifically throughput factor

(dependent on the inter-vehicle distance), vehicle stability

factor, and the bandwidth efficiency factor. Throughput factor

considers the achievable throughput between the sender and

the next data carrier node. Vehicle stability factor is used to

select a slowly moving vehicle in order to reduce the frequency

of data exchange. The consideration of bandwidth utilization

efficiency is also important especially when the number of

data blocks is large. Fuzzy logic is used to combine these

three factors to conduct an evaluation on the instant reward of

the selection (basically the efficiency of transmission from the

current sender node to the next data carrier node). In addition

to this, we have to consider long-term reward of the decision

as well. More specifically, the goodness of a next carrier node

selection is also dependent on the actions of the following data

carrier nodes. Here, we take into account this by considering

how much does the next carrier node close from the center of

interest region (the best action).

D. Fuzzy logic-based instant decision evaluation

1) Procedure: The sender node calculates the evaluation

value for each neighbor as follows.

• Step1: Fuzzification Use predefined linguistic variables

and membership functions to convert throughput factor

(TPF), vehicle stability factor (VSF), and bandwidth

efficiency factor (BEF) to fuzzy values (see Fig. 2, Fig. 3,

and Fig. 4).

• Step2: Mapping and combination of IF/THEN rules

Map the fuzzy values to predefined IF/THEN rules (see




Table II) and combine the rules to get the rank of the

neighbor as a fuzzy value.

• Step3: Defuzzification Use a predefined output member-

ship function (see Fig. 5) and defuzzification method (we

use the Center of Gravity method) to convert the fuzzy

output value to a numerical value (evaluation value).

2) Throughput factor and membership function: We take

into account the achievable throughput between the sender

node and the next carrier node. Since an accurate estimation

of throughput is difficult if not impossible in dynamic environ-

ment, for simplicity, we use inter-vehicle distance to estimate

throughput. We define a distance metric as

DMc(x) =

{

d(x,c)R

, d(x, c) <= R

1, d(x, c) > R(2)

where d(x, c) denotes the distance between the current node

(c) and node x. The throughput factor membership function is

defined based on the distance metric as shown in Fig. 2.

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Deg

ree

DM

High Medium Low

Fig. 2. Throughput factor membership function.

3) Vehicle stability factor and membership function: The

vehicle stability factor is calculated as

VSFc(x) =

{

ζ + (1− ζ)× (1 − |V (x)|maxy∈Nc |V (y)| ), C1

(1− ζ)× (1 − |V (x)|maxy∈Nc |V (y)| ), otherwise

(3)

where C1 denotes the case when the node is moving toward

the center of interest region. Nc is the set of one-hop neighbors

of c. ζ is set to 13 . The vehicle stability factor intends to give

a lower speed vehicle a higher evaluation. The factor also

takes into account the relative moving direction of vehicles

in relation to the center of interest region by applying the

condition C1. Therefore, by using this factor, the absolute

vehicle velocity and relative moving direction of vehicle can

be evaluated jointly. The value of ζ determines the weight

of the moving direction. A higher ζ intends to give a higher

evaluation for the vehicles moving toward the center of interest

region as compared with those leaving the center. The corre-

sponding fuzzy membership function for the vehicle stability

factor is defined as shown in Fig. 3.

4) Bandwidth efficiency factor and membership function:

Since IEEE 802.11p uses contention-based channel access

scheme, the channel utilization efficiency (bandwidth effi-

ciency) decreases as the number of sender nodes increases.

Therefore, it is important to reduce the number of sender

nodes in the network as far as possible. Here, we take into

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Deg

ree

VSF

Bad Medium Good

Fig. 3. Vehicle stability factor membership function.

account bandwidth efficiency factor by considering how much

the carrier node selection algorithm can reduce the number

of sender nodes in the network. Bandwidth efficiency factor

(BBF) is calculated as

BEFc(x) =

{

1, maxy∈NcCnt(y) = 0

Cnt(x)maxy∈NcCnt(y) , otherwise

(4)

where Cnt(x) denotes the number of data blocks that are

using node x as the data carrier. The information about

Cnt(x) is exchanged through hello messages among one-

hop neighbors. The bandwidth efficiency factor membership

function is defined as shown in Fig. 4.

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Deg

ree

BEF

Bad Medium Good

Fig. 4. Bandwidth efficiency factor membership function.

5) Fuzzy rules: The fuzzy rules are defined as shown in

Table II.6) Defuzzification: The output function is defined as Fig. 5.

We use Center of Gravity (COG) method to defuzzify the

fuzzy result.

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

VeryBad Bad Unpreferable Acceptable Good Perfect

Fig. 5. Output membership function for the next data carrier node selection.

E. Reinforcement learning-based future reward evaluation

We use a fuzzy logic-based approach to evaluate each

neighbor as explained in §III-D, and then use a reinforce-

ment learning approach, specifically Q-Learning, to evaluate




TABLE IFUZZY RULES

Throughput Stability Bandwidth efficiency Rank

Rule1 High Good Good PerfectRule2 High Good Medium GoodRule3 High Good Bad UnpreferableRule4 High Medium Good GoodRule5 High Medium Medium AcceptableRule6 High Medium Bad BadRule7 High Bad Good UnpreferableRule8 High Bad Medium BadRule9 High Bad Bad VeryBadRule10 Medium Good Good GoodRule11 Medium Good Medium AcceptableRule12 Medium Good Bad BadRule13 Medium Medium Good AcceptableRule14 Medium Medium Medium UnpreferableRule15 Medium Medium Bad BadRule16 Medium Bad Good BadRule17 Medium Bad Medium BadRule18 Medium Bad Bad VeryBadRule19 Low Good Good UnpreferableRule20 Low Good Medium BadRule21 Low Good Bad VeryBadRule22 Low Medium Good BadRule23 Low Medium Medium BadRule24 Low Medium Bad VeryBadRule25 Low Bad Good BadRule26 Low Bad Medium VeryBadRule27 Low Bad Bad VeryBad

whether the decision is a long-term good decision or not. The

Q-learning model is defined as follows. Each node (vehicle)

is an agent. Each possible selection of the next carrier node

is considered a state of the agent. The set of all possible

candidate nodes for the next data carrier is the state space.

The learning task is to find the best data carrier node for

the corresponding network environment in relation to the

feedback. An action is to select the next data carrier node

that would be used for storing the data.

Each agent updates its Q-table after specifying the next data

carrier node, or reception of a hello message from a neighbor

node. Q-table is updated as

Qc(Loc, x)

← α× ls(c, x)× {Rwd+ γ ×maxy∈NxQx(Loc, y)}

+ (1− α)×Qc(Loc, x). (5)

where Loc denotes the center of interest region, and x is a

possible action (a neighbor node which can be used as the

next data carrier node). c is the current node, and ls(c, x) is

the instant evaluation value for the decision (this is basically

the evaluation of the link between the current node and the

next data carrier node as calculated in §III-D). Nx denotes

the one-hop neighbor set of x. The learning rate α is set to

0.7. Since the hello messages are exchanged periodically with

interval of 1 second by default, the value of 0.7 is enough to

reflect the network topology changes. The discount factor γ is

set to 0.9. The reward is calculated as

Rwd =

{

1, d(Loc, x) < RINT

2

0, otherwise(6)

where d(Loc, x) is the distance between the next data carrier

node and the center of interest region. The reward is 1 only

if d(Loc, x) is smaller than RINT

2 where RINT is the radius

of interest region. This is to guide the agent to select a node

which is closer to the center of interest region.

Each Q-value [Qc(Loc, x)] shows the evaluation value

for a selection of the next data carrier node. Each node

attaches its position information, and maximal Q-values

[maxy∈NxQx(Loc, y)] for active interest regions to the hello

messages. Here, we use “active interest regions” to denote

the interest regions which cover the position of the current

vehicle). Each node only needs to maintain the information

about the active interest regions. After reception of a hello

message, a node can update its knowledge about the distance

to the center of interest region by using the corresponding

maximal Q-value extracted from the hello message. As shown

in (12), the reward Rwd is discounted with the increase of

the distance from the center of interest region. The rationale

behind this is that we want to maintain the data at the center

of interest region in order to reduce the probability of data loss

due to vehicle mobility (in contrast, if the data are maintained

at the border of the interest region, there is a high probability

of failing to find a vehicle to perform the data handoff,

resulting in data loss).

F. Some considerations

The handoff timing is decided as follows. Based on the

selected data carrier node, the current data carrier node will

decide when to handover the data to the next data carrier

node. As explained before, we assume a connected network

topology. This means that there will be always at least one

node can be a candidate for maintaining the data in the

interest region. Each node maintains a table which shows the

relationship between the distance and the throughput, and then

makes a decision based on this table. Since each data carrier

node knows the link quality between itself and the next data

carrier node, the data carrier node can adjust the handoff

timing in order to ensure that the data would be always in

the interest region. The parameters used for this paper are set

based on our simulation results. Note that different application

requirements and network environment could require different

sets of parameters. However, the problem of how to efficiently

tune these parameters automatically with the environment

change is considered as our future work.

IV. ROUTE SELECTION TO THE DATA CARRIER NODE:

COLLECTION OF DATA USING DYNAMIC CLUSTERING

There could be multiple data sources sharing the same

interest region. This happens often especially in high-density

networks because the information collected from multiple

vehicles could be required to be maintained in the same area.

Typically, the size of interest region would be much larger than

the transmission range. Therefore, a multi-hop communication

could be required to transmit data from multiple data source

nodes to the data carrier node. In order to overcome the

problem of MAC layer performance degradation due to the

increase of the number of sender nodes, the proposed protocol

conducts efficient data transmissions from the data source

nodes to the data carrier node by employing a routing approach

based on dynamic clustering.




A. Virtual clustering of vehicles and packet forwarding with

cluster heads

In IEEE 802.11p standard, with the increase of the number

of sender nodes, the MAC contention efficiency decreases

significantly. Therefore, it is important to reduce the number

of data sender nodes as far as possible. In the proposed

protocol, we use a cluster-head based forwarding where data

are forwarded by cluster heads which are generated using

a distributed approach (this will be explained in the next

Subsection). As shown in Fig. 6, different source nodes (S1

and S2) could use the same forwarder nodes (CH1 and CH2)

to deliver packets, which are efficient in the utilization of

wireless resources by reducing the number of forwarder nodes

especially when the number of traffic flows is large.

Fig. 6. Packet forwarding to the data carrier node (S1 and S2 are the datasource nodes; D is the data carrier node).

B. Criteria for selecting cluster heads

The proposed protocol selects the cluster heads using a

distributed approach. In order to generate a stable cluster, the

protocol takes into account the vehicle velocity, the number

of neighbors driving to the same direction, and the channel

condition between the cluster head and cluster members for

the cluster head selection. The vehicle velocity is considered

in order to select slow vehicles as cluster heads, which is

efficient in terms of avoiding the frequent change of cluster

heads. The number of neighbors moving to the same direction

can reflect the long-term vehicle velocity (in a two-way road,

the cluster heads should be selected from the vehicles which

have more vehicles moving toward the same direction). The

channel condition is also an important metric because a cluster

head which has better link with cluster members (for example,

higher antenna height) is preferred. We use a fuzzy logic-based

approach to jointly consider these three metrics for evaluating

the fitness for a cluster head.

In the proposed protocol, each node sends the required

information (vehicle velocity and the number of neighbor

vehicles driving to the same direction) using hello messages.

For each hello interval, each node calculates a competency

value (as being a cluster head) for itself and each neighbor

vehicle. If the node has the largest competency value in its

vicinity (R2 where R is the average transmission range), the

node announces itself as a cluster head node using the next

hello message. Cluster head selection is conducted on road

basis. This ensures that the selected cluster heads can generate

a connected network.

C. Calculation of competency value based on fuzzy logic

Each node evaluates its one-hop neighbors to determine

which node should be the cluster head. The evaluation is

conducted by using a fuzzy logic-based algorithm.

1) Procedure: For each neighbor vehicle, each node calcu-

lates a competency value as follows.

• Acquisition of the three factors: Get the vehicle veloc-

ity, the number of vehicles moving to the same direction,

and the channel condition information mentioned before.

• Fuzzification: Use predefined linguistic variables and

membership functions to convert these factors to fuzzy

values.

• Mapping and combination of IF/THEN rules: Map the

fuzzy values to predefined IF/THEN rules and combine

the rules to get the rank of the neighbor as a fuzzy value.

• Defuzzification: Use a predefined output membership

function and defuzzification method to convert the fuzzy

output value to a numerical value.

2) Calculation of multiple factors: Upon reception of a

hello message from a neighbor x, node s calculates the

following factors.

Velocity Factor (V F ): Node s extracts the velocity of

node x, υ(x), and calculates V F (s, x) (the velocity factor for

node x calculated at node s) as

V F (s, x) =|υ(x)| −miny∈Ns

|υ(y)|

maxy∈Ns|υ(y)|

(7)

where Ns denotes the neighbor set of node s. A lower V F

indicates a lower velocity. V F is updated every hello interval

using a weighted exponential moving average as

V F (s, x) ← (1−α)×V Fi−1(s, x)+α×V Fi(s, x), (8)

where V Fi−1(s, x) and V Fi(s, x) denote the previous V F

value and current V F value respectively. V F is initialized to

1. The coefficient α is set to 0.7 which is the best value for

many cases according to our simulation results.

Follower Density Factor (FDF ): Node x announces the

number of neighbor vehicles [c(x)] driving to the same direc-

tion by using hello messages. FDF of node x is calculated

as

FDF (s, x) =c(x)

maxy∈Nsc(y)

. (9)

FDF indicates the vehicle density for the same direction. A

higher FDF means that the node is more suitable for being

a cluster head. FDF is updated every hello interval using a

weighted exponential moving average (FDF is initialized to

0) as

FDF (s, x) ← (1−α)×FDFi−1(s, x)+α×FDFi(s, x).




(10)

Channel Condition Factor (CCF ): We use the hello

packet reception ratio to infer the channel Condition Factor

(CCF ). We calculate the number of hello messages received

from the nodes located in R where R is the average transmis-

sion range. The hello messages are sent for each predefined

time interval (1 second by default). If a vehicle has better

channel quality than other vehicles (for example, a truck with

higher antenna), the CCF is larger. The CCF is initialized

to 0, and updated as

CCF (s)← (1− α)CCFi−1(s) + α× CCFi(s). (11)

3) Fuzzification: Figure 7 shows the fuzzy membership

functions for the velocity factor, follower density factor and

channel condition factor. The velocity membership function

defines what degree the velocity factor belongs to {Slow,

Medium, Fast}. Similarly, the follower density membership

function defines what degree belongs to {Heavy, Medium,

Light} and what degree the channel condition factor belongs

to {Good, Medium, Bad}.

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

VF

Slow Medium Fast

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

FDF

Light Medium Heavy

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

CCF

Bad Medium Good

Fig. 7. Fuzzy membership functions (left: V F , middle: FDF , right: CCF ).

TABLE IIRULE BASE

Velocity Follower density Channel Condition Rank

Rule1 Slow Heavy Good Perfect

Rule2 Slow Heavy Medium Good

Rule3 Slow Heavy Bad Unpreferable

Rule4 Slow Medium Good Good

Rule5 Slow Medium Medium Acceptable

Rule6 Slow Medium Bad Bad

Rule7 Slow Light Good Unpreferable

Rule8 Slow Light Medium Bad

Rule9 Slow Light Bad VeryBad

Rule10 Medium Heavy Good Good

Rule11 Medium Heavy Medium Acceptable

Rule12 Medium Heavy Bad Bad

Rule13 Medium Medium Good Acceptable

Rule14 Medium Medium Medium Unpreferable

Rule15 Medium Medium Bad Bad

Rule16 Medium Light Good Bad

Rule17 Medium Light Medium Bad

Rule18 Medium Light Bad VeryBad

Rule19 Fast Heavy Good Unpreferable

Rule20 Fast Heavy Medium Bad

Rule21 Fast Heavy Bad VeryBad

Rule22 Fast Medium Good Bad

Rule23 Fast Medium Medium Bad

Rule24 Fast Medium Bad VeryBad

Rule25 Fast Light Good Bad

Rule26 Fast Light Medium VeryBad

Rule27 Fast Light Bad VeryBad

4) Mapping and combination of IF/THEN rules: Each node

uses the IF/THEN rules (see Table II) to calculate the rank of

the vehicle as being a cluster head. Since there can be multiple

rules applying at the same time, we use the Min-Max method

to combine their evaluation results (the same method as used

in [14]).

5) Defuzzification: For the defuzzification, we use the out-

put membership function as shown in Fig. 8, and the Center of

Gravity (COG) method where the x coordinate of the centroid

is the defuzzified value which shows the competency value of

the node.

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

VeryBad Bad Unpreferable Acceptable Good Perfect

Fig. 8. Output membership function for the calculation of cluster-headcompetency value.

D. Reinforcement learning-based last two-hop optimization

Fig. 9. Last two-hop optimization.

The proposed route selection algorithm uses cluster heads

to forward packets. As a result, different traffic flows could

use the same cluster heads to forward packet. This is efficient

for utilizing wireless resources by reducing the number of

sender nodes. However, this could increase the number of hops

when the source (destination) node is very close from the next

(previous) cluster head. In order to make the routing more

efficient, we utilize a reinforcement learning-based algorithm

to optimize the last 2-hops from/to the source/destination node.

As shown in Fig. 9, since CH1, CH2, CH3, and CH4 are the

cluster heads, the default route from the source node (S) to the

destination node (D) is “S→CH1→CH2→CH3→CH4→D”.

By using the last two-hop optimization, the route can be

optimized to “S→F1→CH2→CH3→F2→D” which is more

efficient than the default route. The cluster-head based for-

warding can improve the MAC layer contention efficiency

while the last two-hop optimization can improve the effi-

ciency of a multi-hop route. Therefore, considering the trade-

off between cluster-head based forwarding and last two-hop




optimization, we only conduct the last two-hop optimization

when the distance between the source (destination) node and

the next (previous) cluster head is smaller than 14R where R

is the average transmission range.

1) Q-Learning model: We define the following Q-learning

model for the last two-hop optimization.

TABLE IIIQ-LEARNING MODEL

Environment The entire vehicular ad hoc network

Agent Each packet P (o, r)State of the agent Each node in the network

State space The set of all nodes in the network

Actions Selecting an one-hop neighbor as the next hop

We define the Q-Learning algorithm as follows. The entire

network is the environment. Each packet P (o, r), indexed by

its originator node o and the reference node r (the destination

node or a cluster head node) is an agent. A node selects

the next hop that it should forward a packet to. Hence the

possible set of actions allowed at the node is the set of one-

hop neighbors. Every node maintains a Q-Table which consists

of Q-value (Q(r, x)) whose value ranges from 0 to 1, where

x is the next hop to the reference node.

2) Update of Q-values: For each one-hop neighbor, a node

maintains a value lq(c, x) which shows the link status between

node c and x. For simplicity, we use hello reception ratio

to estimate the link status. However, the estimation can be

improved by taking into account the vehicle velocity in the link

status evaluation. The Q-Table is updated upon the reception

of hello messages. Each node needs to maintain a Q-value

for each one-hop neighbor, the destination (the traffic source

node for the TCP ACK messages) node, and the cluster head

nodes located in two-hop distance. Q-values are broadcasted

by each node using hello messages. Each Q-value is initialized

to 0. Upon reception of a hello message from node x, node c

updates the corresponding Q-value to the node r as

Qc(r, x)

← α× lq(c, x)×{

ˆRwd+ γ ×maxy∈NxQx(r, y)

}

+ (1− α)× Qc(r, x). (12)

The learning rate (α) is 0.7, and the discount factor (γ) is

0.9. maxy∈NxQx(r, y) is the maximal Q-value of x to node

r. The reward ˆRwd is calculated as

ˆRwd =

{

1, if c ∈ Nr

0, otherwise(13)

where Nr denotes the one-hop neighbor set of node r. When

node c is a neighbor of the node r, the reward is 1 and

otherwise 0. Note that there is only one Q-value for each

pair of state and action. Upon reception of hello messages,

the corresponding Q-value is updated as shown in (12). As

shown in (12), the algorithm discounts the reward when the

number of hops increases. This means that a larger number

of hops results in a smaller reward and smaller Q-value. The

reward is also discounted depending on the quality of each

link which constitutes the communication path. As a result, a

Q-value represents the quality of a next packet forwarder node

considering the multi-hop performance. This ensures that the

proposed protocol can optimize the route to the reference node.

V. THEORETICAL ANALYSIS

A. Impact of velocity

If the length of the interest region is L (L = 2RINT ), the

maximum data transfer (handoff) interval is Lυ

where υ is the

vehicle velocity (the data transfer should be conducted at least

once for each Lυ

interval). Fig. 10 shows the maximum data

transfer intervals for different lengths of interest regions and

velocities. We can observe that selecting a vehicle with low

velocity is particularly important for reducing the data transfer

frequencies.

0

20

40

60

80

100

120

30 40 50 60 70 80

Max

imum

dat

a tr

ansf

er i

nte

rval

(se

cond)

Velocity (km/h)

L=200mL=400m

L=600mL=800m

L=1000m

Fig. 10. Maximum data transfer interval for different lengths of interestregions and velocities.

B. Impact of the inter-vehicle link quality and the number of

sender nodes

In the IEEE 802.11p standard, the backoff time is a random

number which is drawn from a uniform distribution over the

interval [0,CW] where CW is the current contention window. If

multiple sender nodes are located closer than the sensing range

and they choose the same contention window size, there will

be collisions at some receiver nodes. Since a transmission is

successful only when all sender nodes choose different backoff

values, we can calculate the probability of collisions as

Pc(N) =

{

1, if CW+ 1 ≤ N

1−∏N−1

k=0 (CW+1−k

1 )(CW+1)N

, otherwise(14)

where N is the number of sender nodes. Since a packet canbe lost due to either collisions or weak signal strength, thepacket loss probability can be calculated as

P (N) = Pc(N) + Pl(N) − Pc(N) × Pl(N) (15)

where Pl(N) is the packet loss probability due to weak

signal strength. Based on (15), we show the packet loss

probability for different numbers of sender nodes and link loss

probabilities in Fig. 11.




0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Pac

ket

loss

pro

bab

ilit

y

Link loss probability

N=1N=2

N=3N=4

N=5N=6

N=7N=8

Fig. 11. Packet loss probability for different numbers of sender nodes andlink loss probabilities.

Since TCP is the most commonly used transport layer pro-

tocol, here we analyze the TCP congestion window size which

basically determines the throughput of a TCP connection. In

the TCP slow start phase, congestion windows is increased

[by 1 MSS (maximum segment size)] upon reception of an

ACK. When the end-to-end loss probability is Pl, the average

congestion window in the slow start phase for the first 5 RTTs

(round trip time) is

Pl +

∑5RTTn=1

(2RTTn−1)∑

i=1

(1− Pl)i× (i + 1) (16)

where RTTn is the number for RTT. Figure 12 shows the

average TCP congestion window for different numbers of

sender nodes and link loss probabilities. This shows that the

consideration of link quality and the number of sender nodes

in the data carrier selection is very important.

0

10

20

30

40

50

60

70

80

90

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Conges

tion w

indow

(M

SS

)

Link loss probability

N=1N=2

N=3N=4

N=5N=6

N=7N=8

Fig. 12. Average TCP congestion window size for different numbers ofsender nodes and link loss probabilities (with window scaling).

C. Impact of cluster-head based forwarding

When the number of source nodes is N , the end-to-end

packet delivery probability for H hop transmission is

Pe2e(N) =

H∏

i=1

(1− iP (N)) (17)

where iP (N) is the packet loss probability for ith hop.

Based on (17), Fig. 13 shows the end-to-end packet delivery

probability. Since the proposed protocol uses cluster head

nodes to deliver data to the data carrier node, the number

of contending nodes can be reduced significantly. As a result,

the proposed protocol can improve the end-to-end delivery

probability especially when the number of hops is large.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2 3 4 5 6

End-t

o-e

nd p

acket

del

iver

y p

robab

ilit

y

Number of hops

Conventional(N=1)Proposed(N=2)

Conventional(N=3)

Proposed(N=3)Conventional(N=4)

Proposed(N=4)

Fig. 13. End-to-end packet delivery probability (without retransmission; thelink loss probability was 0.1).

VI. SIMULATION RESULTS

We used ns-2.34 [32] to conduct simulations in freeway

scenarios (see Table IV). We used a freeway which had two

lanes in each direction [33]. The distance between any two

adjacent lanes was 5m. Nakagami propagation model was used

to simulate channel fading (see Table V) [34]. The proposed

protocol was compared with “Velocity” and “Velocity + Link

quality” where “Velocity” chooses the vehicle which has the

slowest moving speed as the next data carrier node, and

“Velocity + Link quality” chooses the slowest vehicle from the

vehicles that are strongly connected (hello message reception

ratio is larger than 70%). The maximum transmission range

was 250m. We evaluated the protocol performance in terms of

data transfer throughput (§VI-A,§VI-B, and §VI-C) and data

collection efficiency (§VI-D) for various vehicle velocities and

various numbers of source nodes. In the following simulation

results, the error bars indicate the 95% confidence intervals.

A. Number of data handoffs for various vehicle velocities

Fig. 14 shows the number of data handoffs (transfers) for

various vehicle velocities. There was one data source (see

§VI-C for the performance with multiple data sources). The

volume of the data was 1 MB. The length of the interest

region was 600m. Since “Velocity” (velocity only approach)




TABLE IVSIMULATION ENVIRONMENT

Topology 2000m, 4lanes

Number of nodes 200

Maximum velocity 100 km/h

Mobility generation Ref. [33]

MAC IEEE 802.11p MAC (27 Mbps)

Propagation model Nakagami Model

Simulation time 1500 s

TABLE VPARAMETERS OF NAKAGAMI MODEL

gamma0 gamma1 gamma2 d0 gamma d1 gamma

1.9 3.8 3.8 200 500

m0 m1 m2 d0 m d1 m

1.5 0.75 0.75 80 200

takes into account the velocity information for the data carrier

node selection, the protocol could select a node either located

far away from or located very close to the sender node. A

decision of selecting a too close node can result in a high

number of data handoffs. In contrast, as will be shown later, the

selection of a far node deteriorates the data transfer throughput.

“Velocity + Link quality” is likely to select a vehicle in

close proximity, resulting in a large number of data handoffs.

The proposed protocol shows the best performance by taking

into account the vehicle velocity and the distance to the best

action (how much the next carrier node is closer to the center

of interest region) by using the reinforcement learning-based

carrier node selection approach.

0

10

20

30

40

50

60

70

80

60 65 70 75 80 85 90 95 100

Num

ber

of

dat

a han

doff

s

Maximum velocity (km/h)

VelocityVelocity + Link quality

Proposed

Fig. 14. Number of data handoffs for various vehicle velocities.

B. Average data transfer throughput for various vehicle ve-

locities

Fig. 15 shows the average data transfer throughput (the

throughput between a sender node and the next data carrier

node) for various vehicle velocities. “Velocity” also could

select a vehicle which is weakly connected to the current node,

which is inefficient in terms of bandwidth utilization (this

will be explained later). By considering inter-vehicle distance,

“Velocity + Link quality” performs better than “Velocity”.

Since the fuzzy logic algorithm takes into account throughput

factor, the average transmission time of the proposed protocol

is lower than other approaches.

0

500

1000

1500

2000

2500

60 65 70 75 80 85 90 95 100

Thro

ughput

(kbps)

Maximum velocity (km/h)


Proposed

Fig. 15. Average data transfer throughput for various vehicle velocities.

C. Average data transfer throughput for various numbers of

data sources

Fig. 16 shows the average data transfer throughput for vari-

ous numbers of data sources. The data volume for each source

node was 500 KB. Since the fuzzy logic takes into account

throughput factor, the average required time for handoff is

lower than other approaches. As shown in the figure, with

the increase of the number of data sources, the advantage

of the proposed protocol over other approaches becomes

more notable. This is due to the consideration of bandwidth

efficiency factor which can significantly improve the MAC

layer contention efficiency resulting in a lower collision ratio

and higher TCP throughput.

0

500

1000

1500

2000

2500

0 2 4 6 8 10

Thro

ughput

(kbps)

Number of data sources


Proposed

Fig. 16. Average data transfer throughput for various numbers of datasources.

D. Transmission throughput from the data source nodes to the

data carrier node

Fig. 17 shows the average data collection throughput from

the data source nodes to the data carrier node for various




source-carrier node distances (the distance between the data

source node and the data carrier node). The number of

source nodes was 6. The proposed protocol shows a notable

improvement in the throughput over other approaches due

to the cluster-based forwarding. The improvement is more

significant when the source-carrier distance is larger because

the cluster-based forwarding approach can improve the MAC

layer contention efficiency.

0

100

200

300

400

500

600

700

800

900

1000

400 600 800 1000 1200 1400 1600

Thro

ughput

(kbps)

Distance (m)


Proposed

Fig. 17. Average data collection throughput for various source-carrierdistances.

Fig. 18 shows the required time for collecting 1MB data

from each source node to the data carrier node for various

numbers of source nodes. The conventional approaches (“Ve-

locity + Link quality” and “Velocity”) selects routes based on

each traffic flow. This is inefficient when the number of source

nodes (number of traffic flows) is large. Since the proposed

protocol utilizes a cluster-based forwarding approach which

conducts packet forwarding based on cluster-heads, the MAC

layer contention efficiency and the transmission throughput

are improved. As a result, the data collection delay is reduced

significantly.

0

50

100

150

200

250

300

350

2 4 6 8 10 12

Req

uir

ed t

ime

(s)

Number of data sources


Proposed

Fig. 18. Required time for collecting 1MB data from each source node tothe data carrier node for various numbers of source nodes.

VII. CONCLUSIONS

We proposed a protocol which can store data in vehicular

ad hoc networks. The protocol takes into account throughput,

vehicle velocity, and bandwidth efficiency by using fuzzy

logic to conduct short-term evaluation and using a Q-learning

algorithm to consider long-term efficiency. The protocol also

employs a cluster-based forwarding approach to collect vehicle

data to the data carrier node. Through computer simulations,

we confirmed the advantages of the proposed protocol over

possible alternatives.

ACKNOWLEDGMENT

This research is partially supported by the projects

240079/F20 funded by the Research Council of Norway,

the project IoTSec – Security in IoT for Smart Grids, with

number 248113/O70 part of the IKTPLUSS program funded

by the Norwegian Research Council, and JSPS KAKENHI

Grant Number 16H02817.

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Celimuge Wu received his ME degree from theBeijing Institute of Technology, China in 2006, andhis PhD degree from The University of Electro-Communications, Japan in 2010. He has been anassistant professor with the Graduate School ofInformation Systems, The University of Electro-Communications since 2010, where he is currentlyan associate professor. His current research interestsinclude vehicular ad hoc networks, sensor networks,intelligent transport systems, IoT, 5G, and mobilecloud computing. He has been a track co-chair

or workshops co-chair of several international conferences including IEEEPIMRC 2016, IEEE CCNC 2017, ISNCC 2017 and WICON 2016.

Tsutomu Yoshinaga received the BE, ME, andDE degrees from Utsunomiya University in 1986,1988, and 1997, respectively. From 1988 to July2000, he was a research associate of the Faculty ofEngineering, Utsunomiya University. He was also avisiting researcher at Electro-Technical Laboratoryfrom 1997 to 1998. Since August 2000, he hasbeen with the Graduate School of Information Sys-tems, The University of Electro-Communications,where he is now a professor. His research interestsinclude computer architecture, interconnection net-

works, and network computing. He is a member of ACM, IEEE, IEICE andIPSJ.

Yusheng Ji received the BE, ME, and DE degreesin electrical engineering from the University ofTokyo, Japan. She joined the National Center forScience Information Systems, Japan in 1990. Sheis currently a professor with the National Instituteof Informatics, Japan, and Graduate University forAdvanced Studies. Her research interests includenetwork architecture, resource management, and per-formance analysis for quality of service provisioningin wired and wireless communication networks. Sheis a member of IEICE and IPSJ.

Tutomu Murase was born in Kyoto, Japan in1961. He received his M.E. degree from GraduateSchool of Engineering Science, Osaka University,Japan in 1986. He also received his Ph.D. degreefrom Graduate School of Information Science andTechnology, Osaka University, Japan in 2004. Heworked at NEC Corporation for 1986–2014. He wasa visiting professor of Tokyo Institute of Technol-ogy for 2012–2014. He is currently a professor inNagoya University. He has been engaged in researchon QoS control and traffic management for high-

quality and high-speed Internet. His current interests include wireless networkQoS control, MAC, transport and session layer traffic control, and networksecurity. He received Best Tutorial Paper Award on his invited paper in IEICEtransaction on communication in 2006. He is an IEEE member and an IEICEfellow.




Yan Zhang is a Full Professor at the Depart-ment of Informatics, University of Oslo, Norway.He received a PhD degree in School of Electrical& Electronics Engineering, Nanyang TechnologicalUniversity, Singapore. He is an Associate TechnicalEditor of IEEE Communications Magazine, an Edi-tor of IEEE Transactions on Green Communicationsand Networking, an Editor of IEEE CommunicationsSurveys & Tutorials, an Editor of IEEE Internet ofThings Journal, and an Associate Editor of IEEEAccess. He serves as chair positions in a number of

conferences, including IEEE GLOBECOM 2017, IEEE VTC-Spring 2017,IEEE PIMRC 2016, IEEE CloudCom 2016, IEEE ICCC 2016, IEEE CCNC2016, IEEE SmartGridComm 2015, and IEEE CloudCom 2015. He servesas TPC member for numerous international conference including IEEEINFOCOM, IEEE ICC, IEEE GLOBECOM, and IEEE WCNC. His currentresearch interests include: next-generation wireless networks leading to 5G,green and secure cyber-physical systems (e.g., smart grid, healthcare, andtransport). He is IEEE VTS (Vehicular Technology Society) DistinguishedLecturer. He is also a senior member of IEEE, IEEE ComSoc, IEEE CS,IEEE PES, and IEEE VT society. He is a Fellow of IET.

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IEEE TRANSACTIONS ON VEHICULAR …folk.uio.no/yanzhang/IEEETVTVANET2017.pdfmanagement and vehicular cloud computing, this paper de-scribes the problem of how to store information in