B-ETS: A Trusted Blockchain-based Emissions Trading System for
Vehicle-to-Vehicle Networks
Lam Duc Nguyen
1 a
, Amari N. Lewis
2 b
, Israel Leyva-Mayorga
1 c
,
Amelia Regan
2 d
and Petar Popovski
1 e
1
Department of Electronic System , Aalborg University, Denmark
2
Department of Computer Science, University of California, Irvine, U.S.A.
Keywords:
V2V, Distributed Ledger Technologies, Blockchain, Emission Allowances Trading.
Abstract:
Urban areas are negatively impacted by Carbon Dioxide (CO
2
) and Nitrogen Oxide (NO
x
) emissions. In
order to achieve a cost-effective reduction of greenhouse gas emissions and to combat climate change, the
European Union (EU) introduced an Emissions Trading System (ETS) where organizations can buy or receive
emission allowances as needed. The current ETS is a centralized one, consisting of a set of complex rules.
It is currently administered at the organizational level and is used for fixed-point sources of pollution such as
factories, power plants, and refineries. However, the current ETS cannot efficiently cope with vehicle mobility,
even though vehicles are one of the primary sources of CO
2
and NO
x
emissions. In this study, we propose
a new distributed Blockchain-based emissions allowance trading system called B-ETS. This system enables
transparent and trustworthy data exchange as well as trading of allowances among vehicles, relying on vehicle-
to-vehicle communication. In addition, we introduce an economic incentive-based mechanism that appeals to
individual drivers and leads them to modify their driving behavior in order to reduce emissions. The efficiency
of the proposed system is studied through extensive simulations, showing how increased vehicle connectivity
can lead to reduction of the emissions generated from those vehicles. We demonstrate that our method can be
used for full life-cycle monitoring and fuel economy reporting. This leads us to conjecture that the proposed
system could lead to important behavioural changes among the drivers.
1 INTRODUCTION
Typical passenger vehicles emit about 4.6 metric
tons of carbon dioxide CO
2
per year. The Euro-
pean Union’s Emission Trading System (EU-ETS) is
the world’s first major carbon trading market with
the main goal to combat climate change and reduce
Greenhouse Gas (GHG) emissions in a cost effec-
tive way. The EU-ETS works on a Cap-and-Trade
(CAP) principle which allows companies that gener-
ate point source emissions to receive or buy emission
allowances, which can be traded as needed (Com-
mision, 2015). The process of our B-ETS CAP pro-
gram is described in Figure 1, where it is seen that
it is based on a complex centralized method of trad-
a
https://orcid.org/0000-0003-0161-3055
b
https://orcid.org/0000-0001-9685-4403
c
https://orcid.org/0000-0002-7116-397X
d
https://orcid.org/0000-0003-4220-2148
e
https://orcid.org/0000-0001-6195-4797
ing among the organizations involved. The first step
in CAP is to make a centralized decision (by a regu-
latory agency or some other collective entity) on the
aggregate quantity of emissions allowed. Allowances
are then written in accordance with this quantity, after
which they are distributed among the sources respon-
sible for the emissions.
Since 2018, the EU-ETS began penalizing vehi-
cle manufacturers for exceeding the targets for fleet-
wide emissions for new vehicles sold in any given
year. The manufacturers are required to pay an excess
emissions premium for each newly registered car. A
penalty of e95 must be paid for each gram per km
above the target (Commision, 2015) and the target
of CO
2
for the 2020-2021 period is set to 95 grams
per km. In this work, we address the need for a new
trusted and distributed system which can audit emis-
sions at the vehicle-level.
The emerging Distributed Ledger Technologies
(DLTs) brought a new era of distributed peer-to-peer
applications and guarantees trust among involved par-
Nguyen, L., Lewis, A., Leyva-Mayorga, I., Regan, A. and Popovski, P.
B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks.
DOI: 10.5220/0010460501710179
In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 171-179
ISBN: 978-989-758-513-5
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
171
CAP CAP
Surplus of
Emission
Allowances
Shortage of
Emission
Allowances
Emission Allowances
Credit
Emission Trading
Sale Purchase
EMITTER A
EMITTER B
Carbon Market
EAB(A) EAB(B)
Excess
Available
A
B
Distributed Ledger & Smart Contract
Transactions
Transactions
B-ETS
Standard CAP
Figure 1: B-ETS general architecture.
ties. The terms DLT and Blockchain will be used in-
terchangeably throughout this paper, Blockchains are
a type of DLTs, where chains of blocks are made up of
digital pieces of information called transactions and
every node maintains a copy of the ledger. In DLTs,
the authentication process relies on consensus among
multiple nodes in the network (Nguyen et al., 2020).
Each record has a timestamp and cryptographic signa-
ture; the system is secure and maintains a transaction
ledger that is immutable and traceable. Ultimately,
the goal of applying Blockchain technology to the
transportation industry is to provide a fully distributed
ETS system that can encourage direct communication
between producers and consumers. A primary reason
to embrace new DLTs is to bypass the administrative
pitfalls that have plagued current emissions monitor-
ing systems. Security is another aspect that motivates
this approach. For instance, data pollution attacks are
incredibly dangerous, these attacks typically occur in
centralized systems and involve an adversary trying
to modify the content of the packets and then forward
the corrupted messages to neighboring nodes. The in-
tegration of Blockchain in individual carbon trading
will accelerate the involvement of the public in car-
bon trading and sensitize society to individual level
carbon footprints.
Current V2V approaches have limitations such
as: the need for trusted third-party entities, security
hardware, higher communication and storage over-
head, high implementation costs, and issues related
to the confidentiality of data. Studies (Zheng et al.,
2017), (Alsabaan et al., 2013), (Li et al., 2018) have
strictly considered Vehicle-to-Infrastructure (V2I) ap-
proaches incorporating additional resources such as
On-board Units (OBUs) and Roadside Units (RSUs).
Eckert et al. develop a carbon Blockchain framework
for Smart Mobility Data-Market as a trading system
for CO
2
in the form of carbon tokens in (Eckert et al.,
2019). The evaluation is done on the user and vehicu-
lar levels. Pan et al. outlined some advantages of the
use of Blockchain in ETS namely safety and reliabil-
ity, efficiency, convenience, openness and inclusive-
ness (Pan et al., 2019). That work was not concerned
with V2V networks or mobile carbon emissions trad-
ing, but it did introduce the concept of personal car-
bon emissions trading which could be applied in ve-
hicular networks.
In this study, we first tackle the challenges of the
current EU-ETS system by proposing a distributed
emissions allowance trading system called B-ETS.
The system creates an account for the emissions gen-
erated from each vehicle and allows exchanges among
vehicles in a trusted manner based on Blockchain
and Smart Contracts. In B-ETS, each vehicle acts as
a light client in the global Blockchain network and
manages its own Emission Allowance Balance (EAB)
which is reset at the beginning of each day. The EAB
data is recorded transparently and immutably in the
distributed ledger. It should be noted that we use one
day as our unit of time without loss of generality. Any
other unit (a week, a month) could be used if that
seemed more suitable.
Then, we introduce an economic incentive-based
mechanism which attracts drivers to change their driv-
ing behavior in order to reduce emissions. Each vehi-
cle’s generated emissions are calculated and the data
are recorded immutably in the distributed ledger. If
the emission level is higher than the defined thresh-
old, the EAB will be reduced. If the EAB goes to
zero, the driver needs to buy credits in the form of
EAB from others.
The proposed V2V-based allowance trading sys-
tem would not replace the in-service fleet-wide moni-
toring required by the EU-ETS plan. Rather, it would
complement that plan by making it the responsibility
of drivers to meet personal emissions targets. That is,
without individualized feedback, drivers cannot mea-
sure the environmental impacts of their actions. Fur-
thermore, without incentives, they might not be will-
ing to contribute to environmental sustainability.
Given the proposed B-ETS system, vehicles par-
ticipating in the program will be influenced by the
economic incentive. Drivers are more prone to be-
have better when their EAB and driving privileges
are at stake. If drivers contribute to lower emissions
(i.e., demonstrate healthy driving habits), their EAB
will increase or remain positive. Essentially, drivers
VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems
172
want to avoid having to purchase credits from others
or having a negative EAB balance as this could lead
to driving restrictions.
Our mechanism can be compared to the traffic
point penalty system in the U.S., Canada and other
countries. As punishment for committing traffic vio-
lations, the drivers risk the suspension or revocation
of their license based on a point-record mechanism
in place. As a result, the Department of Motor Vehi-
cles (DMV) can revoke the driver’s license of that per-
son and they are not allowed to drive any motor vehi-
cle. In order to mitigate the social cost of license sus-
pensions, point-removal systems exist for most point-
record drivers licenses (Dionne et al., 2011). In con-
trast, our system proposes a daily (or weekly or other
period as appropriate) record of associated driving be-
haviors with vehicle emissions data and individual ac-
counts.
The execution of the smart contract guarantees
trust among vehicles and driving habits, (e.g, avoid
idling, speeding, etc) and CO
2
levels. Vehicles in the
system are alerted via rules defined in the smart con-
tract to reduce emissions (EEA, 2019) (Zheng et al.,
2017).
Our solution to reducing vehicle CO
2
emissions
involves the use of DLT-enabled emissions monitor-
ing, which could be applicable to any market world-
wide. In this work, we focus on the EU, but, our
method can comply with regulations in China and
could be implemented in the US to measure life-
cycle Corporate Average Fuel Efficiency (CAFE)
standards.
The contributions of this study are described as
follows:
First, we propose a distributed Blockchain-based
emission trading system named B-ETS that will
meet the requirements of the EU-ETS plan for
reducing vehicular emissions. B-ETS overcomes
the disadvantages of current centralized ETS sys-
tems and provides a trustworthy approach for ex-
changing data in vehicle-to-vehicle networks.
Second, we introduce an economic incentive-
based system which motivates drivers to reduce
fuel consumption and pollution. Based on the au-
tonomous execution of smart contracts, the incen-
tive mechanism is guaranteed to work in a trusted
and distributed manner.
Third, realizing the lack of communication and
computation analysis in Blockchain-enabled ve-
hicle networks, we present a theoretical model to
derive the communication efficiency of the pro-
posed system B-ETS.
Distributed Ledger
NOx
CO2
Road
V2V
EAB Trading
Transactions
Transactions
Figure 2: Blockchain-enabled vehicular emission trading
system.
The remainder of this paper is organized as follows.
In the next section, we present the system model and
analysis. In section III, the performance evaluation is
outlined including our results. Finally, in section IV,
we provide our conclusion and plan future work.
2 SYSTEM MODEL AND
ANALYSIS
2.1 Blockchain as a Ledger for VANET
The system operates within periods of duration T . In
this section, we describe the two major system com-
ponents: the vehicles and the distributed ledger, fol-
lowed by the selected model for CO
2
emissions. Ta-
ble 1 presents the nomenclature used throughout the
paper.
2.1.1 Vehicles
Let V be the set of vehicles in the system. An On-
Board-Unit (OBU) is installed in each vehicle i V
in the Blockchain-based VANET. The OBU performs
light tasks, including collection and transmission data
to other vehicles according to the IEEE 802.11p com-
munication standard, and provides support to passen-
gers and drivers.
Within each system period of duration T , the CO
2
emission monitoring system takes samples of the av-
erage CO
2
emissions per km in each vehicle i and up-
dates the ledger. The CO
2
is sampled at fixed intervals
of duration T
s
< T hours. The sample taken by vehi-
cle i at time t
{
0,T
s
,2T
s
,... ,T
}
hours is denoted as
ε
i
(t) and consists of the taken measurement, the vehi-
cle ID i, and a timestamp, generated as a function of t.
B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks
173
Table 1: Nomenclature.
Symbols Descriptions
T Considered system period [hours]
V Set of vehicles
i Vehicle i V
T
s
CO
2
sampling period
ε
i
(t) Average CO
2
emissions per km for
vehicle i at time t
B
i
(t) Emission allowance balance of
vehicle i at time t [0, T )
p
i
(t) Penalty/tax for vehicle i at time t
s
i
(t) Incentive (subsidy) for vehicle i at time t
L
total
Total allowed latency
L
trans
Communication latency
L
comp
Blockchain verification latency
R Communication data rate [packets/s]
v
i
(t) Speed of vehicle i at time t [km/h]
S
B
Blockchain block size in bits
v
i j
(t) Relative speed between i and j at time t
r
i j
Communication Range between i and j
e
i, j
(t) Allowances sold by j to i at time t
T Maximum allowed CO
2
emissions gener-
ated by vehicles per km.
The amount of CO
2
generated at the vehicles is reset
to zero at the beginning of each period of duration T ,
hence, ε
i
(0) = 0.
2.1.2 Distributed Ledger
The distributed ledger records the data exchange his-
tory grouped into blocks and linked together chrono-
logically. To minimize the cost of storage, the sens-
ing data could be hashed and stored at more power-
ful nodes, and only the hash of data is recorded to
the blockchain. Next, a confirmation message is sent
back to confirm that the data has been added to the
ledger as presented in Figure 4. We assume that the
data services (e.g., data storage, trading and task dis-
patching) are implemented on top of a permissionless
Blockchain (Nguyen et al., 2021).
In a permissionless blockchain, any peer can join
and leave the network at any time as a reader or writer.
Permissionless Blockchains are open and decentral-
ized with no central authority. Bitcoin and Ethereum
are instances of permissionless Blockchains. In con-
trast, in permissioned Blockchains a central author-
ity decides and attributes the right to individual peers
to participate in the write or read operations of the
blockchain. Examples of these include Hyperledger
Fabric and R3 Corda (Wust and Gervais, 2018).
The sensing data are formatted into transactions
of fixed size. To enhance efficiency, only the digest of
each transaction is stored on the chain, and the content
of the transactions are stored by each consensus node
off-chain or at the IPFS storage.
2.1.3 Emissions Model
The amount of CO
2
generated from vehicles depends
on various factors such as: national average age dis-
tributions, vehicle activity speeds, operating modes,
vehicle-miles traveled, starts and idling, temperatures,
maintenance, anti-tampering programs, and average
gasoline fuel properties in that calendar year (EPA,
2018). The calculation of emissions in our simula-
tions are based on the Handbook Emission Factors for
Road Transport V3.1 (HBEFA), the model was im-
plemented by extracting the data from HBEFA and
fitting them to a continuous function obtained by sim-
plifying the function of the power the vehicle engine
must produce to overcome the driving resistance force
(Krajzewicz et al., 2015).
2.2 Emission Allowances Trading
2.2.1 Traditional Cap-and-Trade
Traditionally, cap-and-trade commonly refers to gov-
ernmental regulations and programs in place to limit
the levels of CO
2
emissions as a result of industry ac-
tivity. As briefly mentioned, the EU-ETS works on a
cap and trade principle, where the cap is a dynamic
limitation, set on the total amount of GHG emitted by
installations covered by the system. Within the sys-
tem, companies receive or buy emission allowances
which can be traded. Although, vehicular emissions
were not initially considered, in 2006, researchers at
MIT joint program on the science and policy of global
change introduced the implementation of a cap-and-
trade policy for vehicles. Their central conclusion in-
dicated that there are important efficiency gains to be
realized by including transport emissions under the
CAP and by integrating pre-existing programs, such
as CAFE, and cap-and-trade systems (Ellerman et al.,
2006).
2.2.2 B-ETS Framework
Our B-ETS framework considers an economy where
vehicles produce goods over a system period
[0,T ] hours. Therefore, each vehicle i acts as a wal-
let in the Blockchain network and its EAB at time t
is denoted as B
i
(t) R. In the system, the updates
to the EAB are triggered by the sampling of the CO
2
emissions of the vehicles, hence, the system operates
at specific times t {0, T
s
,2T
s
,. .. }. At the beginning
of each period of duration T , the EAB of each vehicle
i is reset to a pre-defined value B
i
(0). So, the EAB
VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems
174
0 20 40 60 80 100
Average Speed (mph)
0
500
1000
1500
2000
CO2 (g/mi)
Congestion
Mitigation
Traffic Smotthing
Speed
Management
Figure 3: CO
2
emissions (grams/mile) as a function of av-
erage speed (mph) (Cappiello et al., 2002).
cannot be accumulated between subsequent periods.
However, if i were to hold on to this initial allowance
endowment until the end of the period, it would be
able to offset the system’s cap by up to B
i
(0) units of
emissions credits. This is the cap aspect in our B-ETS
scheme.
The EAB pertains to an individual account
in which the allowances are used and exchanged
amongst vehicles for environmental sustainability. In
order to offset penalties, the vehicles with low bal-
ances may engage in buying allowances from vehi-
cles that expect to meet demand with fewer emissions
than their own cap. This is our trade aspect of B-ETS
framework.
Remark 1: A CAP program is only feasible in sce-
narios where the vehicles have a positive allowance
balance at the beginning of the periods. Hence, the
following inequality must hold:
B
i
(0) > 0 for all i V . (1)
The maximum allowed CO
2
emissions generated
by vehicles per km is denoted as T (which is defined
as a rule in smart contract). If ε
i
(t) > T , then our ini-
tial smart contract is executed to generate an alert to i
to reduce vehicle speed as a direct solution to reduce
amount of generated CO
2
, and the fine p
i
(t) will be
deducted from its EAB. In contrast, the subsidy s
i
(t)
will be endowed to i for maintaining the CO
2
emis-
sions below T .
The values of p
i
(t) and s
i
(t) are considered as
taxes and subsidies for vehicle i that depend on their
behavior. The incentive may help encourage the
driver to control their driving behavior to avoid gen-
erating CO
2
higher than the allowed standard. The
driver needs to choose between receiving an incen-
tive by reducing amount of emissions or being fined
due to overloaded generated emissions. The penalties
and subsidies are computed based on the theoretical
model presented in (Fullerton and West, 2000) which
depends on various vehicular factors.
In order to increase the subsidies and reduce the
penalties, the drivers can follow strategies defined in
smart contracts. For example, Figure 3 shows that
CO
2
is a function of average speed. First, we observe
that very low average speeds generally represent stop
and start driving periods, and vehicles traveling in
short distances, in these cases, the emission rates are
quite high. In this period, the smart contract defines
rules to increase traffic speeds and reduce congestion
by, for instance avoiding high traffic roads to reduce
emissions. Second, when the speed of the vehicle is
too high, it demands high engine loads which require
more fuel, leading to higher CO
2
emission rates. The
techniques to manage high speeds are implemented in
the contracts which recommends the drivers to simply
reduce their speeds. Consequently, moderate speeds
of around 40 to 60 mph are ideal speeds which re-
duce emissions and will give the drivers incentive to
improve their balances.
In addition, the EAB can be traded among vehi-
cles based on predefined smart contracts. Whenever
B
i
(t) < 0, there will be a red alert issued to i for hav-
ing a negative-balance. This alert is in the form of
penalties, or restricted road access to zero-balance ve-
hicles. In this cases, the vehicles can either wait until
the next period for their EAB of to be reset or buy
the EAB from other vehicles. We consider the case
of vehicles exchanging EAB on-road via execution
of smart contract and distributed ledger. For this, let
e
i, j
(t) be the amount of allowances sold by vehicle j
from vehicle i at time t. These operations are recorded
in the distributed ledger.
Remark 2: The vehicle j cannot sell more al-
lowances e
i j
(t) than it actually owns. In other words,
i cannot buy more than is actually available. Hence,
e
i, j
(t) B
j
(t),for all j V , t [0,T ) (2)
2.2.3 Operation
The operation of the vehicle’s emission allowance
trading is performed in the following steps:
Step 1. Publishing Data. Each vehicle i
V computes its own average generated CO
2
emis-
sions, namely, ε
i
(t) as shown in Figure 2 for t
{0,T
s
,2T
s
,. .. ,T }. These values are published to light
ledger version of each vehicle and synchronized with
the full ledger stored in DLT full nodes.
Step 2. Emission Control. The generated CO
2
emis-
sions data is recorded in the ledger, and the smart con-
tract with the predefined rules is executed. These rules
are characterized by two categories namely maximum
CO
2
emissions and actions: warnings, alerts and re-
minders. The published CO
2
data is formatted and
arranged into blocks to be verified through a consen-
sus process. If ε
i
(t) > T , the smart contract issues
an alert message to i to control its driving behavior
B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks
175
i
j
Confirmation
Confirmation
Ask for EAB, e
i,j
(t)
Agreement
Buying EAB e
i,j
(t) from j
Selling EAB to i
Settlement
Settlement
B
i
(t) B
i
(t)
+ e
i,j
(t)
Mining
DLT Miners
B
j
(t) B
j
(t)
- e
i,j
(t)
B
i
(t) B
i
(t-T
s
)
- p
i
(t)
B
j
(t) B
j
(t-T
s
)
+
s
j
(t)
Figure 4: Communication System.
and p
i
(t) is deducted from B
i
(t) via smart contract.
Hence, the ledger is updated with the value B
i
(t)
B
i
(t T
s
) p
i
(t). In contrast, if j has maintained a
safe speed and emitted reasonable amounts of CO
2
, it
receives an incentive s
j
(t) to its balance. Hence, the
ledger is updated with B
j
(t) B
j
(t T
s
) + s
j
(t).
Step 3. Emission Allowance Trading. After receiv-
ing a confirmation with the required action from the
smart contract, if B
i
(t) < 0, then i needs to re-charge
its EAB by buying emission allowances from other
vehicles. For example, i makes an agreement with j to
buy an amount of emission allowances e
i, j
(t). Then,
i sends the buying request for the amount e
i, j
(t) to
execute a smart contract. Next, j updates the smart
contract with a selling request and e
i, j
(t).
Step 4. Settlement. Finally, the EAB of each vehicle
is updated and settled as B
i
(t) B
i
(t) + e
i, j
(t) and
B
j
(t) B
j
(t) e
i, j
(t).
In this paper, we focus on the efficiency of V2V
communication between vehicles for exchanging data
and trading EAB. We study these in terms of end-
to-end latency which includes the transmission la-
tency among vehicles and computation latency of
Blockchain validation processes.
2.3 Joint Communication and
Computation Model
In this section, we define the total available time for
communication between two vehicles and the impact
of the Blockchain computation latency.
0 25 50 75 100 125 150
10
1
10
0
10
1
10
2
10
3
10
4
r
ij
= 10 m
r
ij
= 50 m
r
ij
= 100 m
r
ij
= 500 m
r
ij
= 1000 m
Relative speed kv
ij
(t)k (km/h)
Upper bound for L
total
with
constant relative speed (s)
Figure 5: Upper bound fr the total latency L
total
for com-
munication between vehicles with constant speed.
Let (x
i
(t),y
i
(t)) denote the position of vehicle i at
time t. If communication is initiated at time t, the
time in which two vehicles, namely i and j, are avail-
able for communication is defined by 1) their commu-
nication range r
i j
2) their positions (x
i
(t),y
i
(t)) and
(x
j
(t),y
j
(t)), 3) their relative speed, given by vector
v
i j
(t) = v
i
(t) v
j
(t) km/h. Clearly, to initiate com-
munication at time t, the distance between the vehi-
cles must be
d
i, j
(t) =
q
(x
i
(t) x
j
(t))
2
+ (y
i
(t) y
j
(t))
2
r
i j
.
(3)
Then, the total time for V2V communication between
vehicles i and j at time t is given as
L
total
(t) = min
`R
`
` > t, d
i, j
(`) r
i j
t. (4)
It is immediate to see that L
total
when
kv
i j
(t
0
)k 0 for all t
0
[t,`]. This implies that when-
ever both vehicles move in the same direction and
with near equal speed, they will have a long time
L
total
to communicate and exchange messages. Fur-
thermore, it can be seen that, the upper bound for
L
total
seconds for the case where the relative speed
v
i j
(t
0
) km/h remains constant for all t
0
[t, `] is
L
total
7.2r
i j
kv
i j
(t)k
(5)
Figure 5 illustrates the upper bound for L
total
with
several values of kv
i j
(t)k.
The time needed to complete a trade between two
vehicles i and j in B-ETS can be divided into two
parts. First, the communication between vehicles,
simply denoted as L
trans
, and, second, the time needed
for the verification process in the distributed ledger,
denoted as L
comp
. Hence, a trade is completed suc-
cessfully if and only if
L
total
L
trans
+ L
comp
. (6)
VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems
176
From there, we define the probability of successful
data trading as
P
success
= Pr(L
comp
+ L
trans
L
total
) (7)
The latency for the communication between vehi-
cles i and j, denoted simply as L
trans
, is a function of
the amount of data that must be exchanged and the
effective data rate selected for communication R in
packets per second. The data that must be exchanges
is defined by the block size of the Blockchain, de-
noted as S
B
. On the other hand, the effective data
rate R is determined by the implemented protocol, the
wireless conditions (e.g., fading, noise, interference,
and number of active devices), and the modulation
and coding scheme; where the latter determines the
instantaneous data rate. The implemented protocol
for communication is the IEEE 802.11p standard and
the wireless environment are given in Section 3. Nev-
ertheless, we can approximate the latency for com-
munication by assuming that the effective data rate
remains constant throughout the trade as
L
trans
S
B
R
. (8)
The formulations to calculate L
comp
are presented
in the following.
2.3.1 Blockchain Computation Latency
We consider a Blockchain-based VANET network
that includes a subset of vehicles M V that work as
miners. These miners start their Proof-of-work (PoW)
mechanism computation at the same time and keep
executing the PoW process until one of the miners
completes the computational task by finding the de-
sired hash value(Nakamoto, 2019). When a miner i
executes the computational task for the POW of cur-
rent block, the time period required to complete this
PoW can be formulated as an exponential random
variable W
i
whose distribution is f
W
(w, i) = λ
c
e
λ
c
w
,
in which λ
c
= λ
0
P
c
presents for the computing speed
of a miner, P
c
is power consumption for computation
of a miner, and λ
0
is a constant scaling factor. Once a
miner completes its PoW, it will broadcast messages
to other miners, so that other miners can stop their
PoW and synchronize the new block.
For the PoW computation, we are interested in
finding the time in which the first miner i, among
all the M = |M | miners, finds out the desired hash
value. This is the time for the fastest PoW computa-
tion among miners and denoted by the random vari-
able W
i
. By assuming {W
i
} are i.i.d. random vari-
ables, we can calculate the complementary cumula-
Table 2: Smart contract execution costs.
Smart Contracts Gas Ether USD
UserAuthority 159430 15.9·10
5
0.0723
RecordData 152443 15.2·10
5
0.0692
AlertControl 213924 21.3·10
5
0.0971
Incentive 224934 22.4·10
5
0.1021
RecordData 276394 27.6·10
5
0.1254
EABTransfer 246374 24.6·10
5
0.1118
* 1 Ether = 10
9
Gwei; 1 USD = 4,182,471.9949 Gwei
tive probability distribution of W
i
as
Pr(W
i
> w) = Pr
min
iM
(W
i
) > w
=
iM
Pr(W
i
> w)
= (1 Pr(W
i
< w))
M
, s.t. i M . (9)
Hence, L
comp
is the average computational latency of
the fastest miner i, calculated as
L
comp
=
Z
0
(1 Pr(W
i
w))
M
dw =
Z
0
e
λ
c
Mw
dw
(10)
Now we can calculate the communication latency as
L
trans
+ L
comp
.
Note that it can occur that the communication de-
lay exceeds the available communication time L
total
.
In such a case, a proposed transactions with poten-
tially valid PoW solution must be abandoned. Hence,
finding a valid puzzle solution does not guarantee that
the proposed transactions will be finally accepted by
the network because of the propagation delay. In such
cases, a Blockchain fork can only be adopted as the
canonical Blockchain state when it is first dissemi-
nated across the network. In scope of this research,
to simplify, we do not address the problem of fork,
please refer to (Wang et al., 2019) for more detail.
3 PERFORMANCE EVALUATION
In this section, we analyze the performance of our
proposed B-ETS system.
In order to emulate a realistic vehicle network as
presented in Figure 2, a combination of micro sim-
ulators, network libraries and open-source vehicular
network simulators is employed. Specifically, SUMO
(Krajzewicz et al., 2015), OMNET++ which runs in
parallel via a proxy TCP connection, and Veins. The
IEEE 802.11p standard is used for communication be-
tween vehicles and a simple path loss model is se-
lected. In each simulation, 120 vehicles are generated
and located randomly. The CO
2
emissions are cal-
culated reading the Traffic Control Interface (TraCI)
commands from SUMO. Ethereum is deployed as a
B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks
177
0 20 40 60 80 100 120 140
Time (s)
1
2
3
4
5
6
CO2 (mg/s) * 10
3
CO2 standard
CO2 DLT-based
0 20 40 60 80 100 120 140
Time
0.5
1
1.5
2
2.5
3
3.5
4
NOx (mg/s)
NOx standard
NOx DLT-based
(a) CO2 Emission
0 20 40 60 80 100 120 140
Time
1
2
3
4
5
6
CO2 (mg/s) * 10
3
CO2 standard
CO2 DLT-based
0 20 40 60 80 100 120 140
Time (s)
0.5
1
1.5
2
2.5
3
3.5
4
NOx (mg/s)
NOx standard
NOx DLT-based
(b) NOx Emission
(c) V2V communication latency
Figure 6: Performance Evaluation. (a) and (b): The CO
2
and NOx emission generated in standard and DLT based
systems; (c) Communication latency between standard and
Blockchain-based system.
ledger in the experiments by using local Ganache plat-
form.
The computational efforts to execute smart con-
tracts in Blockchain are measured in units of gas. The
currency for Ethereum is Ether (ETH). In our simu-
lations, the transaction costs and execution costs are
converted to ETH and USD, see Table 2. The ETH
gas station was used to estimate the costs, the price
is generated using a static average of 20 Gwei, where
one Ether is equivalent to 10
9
Wei. The transaction
costs are the costs associated with sending the con-
tract codes to the Ethereum blockchain, dependent on
the size of the contract.
The amount of CO
2
generated from vehicles is
dependent upon various factors such as: speed, age
of vehicles, etc. We ran two separate experiments to
compare the amount of emissions generated between
a standard CAP system and a Blockchain-based sys-
tem when the driving behavior is controlled. Figure 6
illustrates the generated CO
2
and NO
x
, along with the
V2V communication latency for the standard and the
DLT-based trading. In the DLT-based trading, vehi-
cles follow defined rules such as dropping their speed
in the smart contract. In Figure 6 we observe that the
amount of CO
2
and NO
x
generated from DLT-based
system is lower than conventional system. These re-
sults prove that our system has the ability to reduce
the overall CO
2
emitted from vehicles on the network.
In B-ETS, the transactions exchanged between ve-
hicles are encrypted, and verified before attached in
the distributed ledger. Therefore, the trusted record-
ing and trading data is guaranteed in comparison with
standard system. However, because of extra verifica-
tion steps in Blockchain, the time to complete a trans-
action between vehicles is higher. This is a trade-off
between trust and latency in Blockchain-based sys-
tems.
4 CONCLUSION
In this paper, we first proposed a Blockchain-based
Emission Trading System, called B-ETS, to support
the accounting and monitoring of emissions in ve-
hicular networks. B-ETS provides a trustworthy and
transparency for accounting the emissions generated
from vehicles. The vehicles can exchange their emis-
sion allowances through autonomous smart contracts
in a trusted manner. We introduce an economic in-
centive scheme based on smart contracts to encourage
drivers to behave in environmentally friendly ways.
This work provides a mechanism for policy mak-
ers, vehicle manufacturers and the EU-ETS to enforce
the carbon emissions regulations in a more efficient,
secure manner as well as to perform full life-cycle
analysis of vehicles. Using the proposed method
could result in vehicle manufacturer savings, ensur-
ing that they are not subject to excess emissions fees
at the end of the year through the continuous monitor-
ing and reporting of CO
2
.
The next stage of this work involves further
analysis of the current system in two ways. First,
VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems
178
we will include the analysis of more pollutants such
as Particulate Matter (PM
x
), Carbon Monoxide (CO),
Sulfur Dioxide (SO
2
) into B-ETS. Then, we will ad-
dress the limitations of this work by diversifying the
vehicles on the network, thereby incorporating other
types of vehicles (other than passenger vehicles), such
as: buses, vans and trucks.
ACKNOWLEDGEMENTS
This work has been in part supported by the European
Union’s Horizon 2020 program under Grant 957218
IntellIoT, the Independent Research Fund Denmark
(DFF) under Grants Nr. 8022-00284B (SEMIOTIC)
and Nr. 9165-00001B (GROW), and the National Sci-
ence Foundation Graduate Research Fellowship un-
der Grant DGE-1839285.
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