B-ETS: A Trusted Blockchain-based Emissions Trading System for Vehicle-to-Vehicle Networks
Lam Duc Nguyen, Amari N. Lewis, Israel Leyva-Mayorga, Amelia Regan, Petar Popovski
11 B-ETS: A Trusted Blockchain-based EmissionsTrading System for Vehicle-to-VehicleNetworks
Lam Duc Nguyen , Amari N. Lewis , Israel Leyva-Mayorga ,Amelia Regan , and Petar Popovski Department of Electronic Systems, Aalborg University, Denmark Department of Computer Science, University of California, Irvine, United StatesE-mail: { ndl, ilm, petarp } @es.aau.dk, { amaril, aregan } @uci.edu Abstract
Urban areas are negatively impacted by Carbon Dioxide (CO ) 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 buyor receive emission allowances as needed. The current ETS is a centralized one, consisting of a setof complex rules. It is currently administered at the organizational level and is used for fixed-pointsources of pollution such as factories, power plants, and refineries. However, the current ETS cannotefficiently cope with vehicle mobility, even though vehicles are one of the primary sources of CO and NO x emissions. In this study, we propose a new distributed Blockchain-based emissions allowancetrading system called B-ETS. This system enables transparent and trustworthy data exchange as wellas trading of allowances among vehicles, relying on vehicle-to-vehicle communication. In addition, weintroduce an economic incentive-based mechanism that appeals to individual drivers and leads them tomodify their driving behavior in order to reduce emissions. The efficiency of the proposed system isstudied through extensive simulations, showing how increased vehicle connectivity can lead to reductionof the emissions generated from those vehicles. We demonstrate that our method can be used for fulllife-cycle monitoring and fuel economy reporting. This leads us to conjecture that the proposed systemcould lead to important behavioural changes among the drivers. Index Terms
Distributed Ledger Technology, Blockchain, Data Trading, Emission Trading, EU-ETS, V2V. a r X i v : . [ c s . M A ] M a r . INTRODUCTIONTypical passenger vehicles emit about 4.6 metric tons of carbon dioxide CO per year. TheEuropean Union’s Emission Trading System (EU-ETS) is the world’s first major carbon tradingmarket with the main goal to combat climate change and reduce Greenhouse Gas (GHG)emissions in a cost effective way. The EU-ETS works on a Cap-and-Trade (CAP) principle whichallows companies that generate point source emissions to receive or buy emission allowances,which can be traded as needed [1]. The process of our B-ETS CAP program is describedin Figure 1, where it is seen that it is based on a complex centralized method of tradingamong the organizations involved. The first step in CAP is to make a centralized decision(by a regulatory agency or some other collective entity) on the aggregate quantity of emissionsallowed. Allowances are then written in accordance with this quantity, after which they aredistributed among the sources responsible for the emissions.Since 2018, the EU-ETS began penalizing vehicle manufacturers for exceeding the targets forfleet-wide emissions for new vehicles sold in any given year. The manufacturers are required topay an excess emissions premium for each newly registered car. A penalty of e
95 must be paidfor each gram per km above the target [1] and the target of CO for the 2020-2021 period is setto 95 grams per km. In this work, we address the need for a new trusted and distributed systemwhich can audit emissions 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 parties. The terms DLT and Blockchainwill be used interchangeably throughout this paper, Blockchains are a type of DLTs, wherechains of blocks are made up of digital pieces of information called transactions and everynode maintains a copy of the ledger. In DLTs, the authentication process relies on consensusamong multiple nodes in the network [2]. Each record has a timestamp and cryptographicsignature; 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 providea fully distributed ETS system that can encourage direct communication between producers andconsumers. A primary reason to embrace new DLTs is to bypass the administrative pitfalls thathave plagued current emissions monitoring systems. Security is another aspect that motivates thisapproach. For instance, data pollution attacks are incredibly dangerous, these attacks typicallyoccur in centralized systems and involve an adversary trying to modify the content of the packets2 AP CAPSurplus ofEmissionAllowances Shortage ofEmissionAllowancesEmission AllowancesCreditEmission TradingSale PurchaseEMITTER A EMITTER BCarbon Market
EAB(A) EAB(B)
Excess
Available
A B
Distributed Ledger & Smart Contract T r an s a c t i on s T r an s a c t i on s B-ETSStandard CAP
Fig. 1: B-ETS general architecture.and then forward the corrupted messages to neighboring nodes. The integration of Blockchainin individual carbon trading will accelerate the involvement of the public in carbon trading andsensitize 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 overhead, high implementation costs, andissues related to the confidentiality of data. Studies [3], [4], [5] have strictly considered Vehicle-to-Infrastructure (V2I) approaches incorporating additional resources such as On-board Units(OBUs) and Roadside Units (RSUs). Eckert et al. develop a carbon Blockchain framework forSmart Mobility Data-Market as a trading system for CO in the form of carbon tokens in [6]. Theevaluation is done on the user and vehicular levels. Pan et al. outlined some advantages of theuse of Blockchain in ETS namely safety and reliability, efficiency, convenience, openness andinclusiveness [7]. That work was not concerned with V2V networks or mobile carbon emissions3rading, but it did introduce the concept of personal carbon emissions trading which could beapplied in vehicular networks.In this study, we first tackle the challenges of the current EU-ETS system by proposing adistributed emissions allowance trading system called B-ETS. The system creates an account forthe emissions generated from each vehicle and allows exchanges among vehicles in a trustedmanner based on Blockchain and Smart Contracts. In B-ETS, each vehicle acts as a light client inthe global Blockchain network and manages its own Emission Allowance Balance (EAB) whichis reset at the beginning of each day. The EAB data is recorded transparently and immutably inthe distributed ledger. It should be noted that we use one day as our unit of time without lossof 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 changetheir driving behavior in order to reduce emissions. Each vehicle’s generated emissions arecalculated and the data are recorded immutably in the distributed ledger. If the emission level ishigher than the defined threshold, the EAB will be reduced. If the EAB goes to zero, the driverneeds to buy credits in the form of EAB from others.The proposed V2V-based allowance trading system would not replace the in-service fleet-widemonitoring required by the EU-ETS plan. Rather, it would complement that plan by making it theresponsibility of drivers to meet personal emissions targets. That is, without individualized feed-back, drivers cannot measure the environmental impacts of their actions. Furthermore, withoutincentives, they might not be willing to contribute to environmental sustainability.Given the proposed B-ETS system, vehicles participating in the program will be influencedby the economic incentive. Drivers are more prone to behave better when their EAB and drivingprivileges are at stake. If drivers contribute to lower emissions (i.e., demonstrate healthy drivinghabits), their EAB will increase or remain positive. Essentially, drivers want to avoid having topurchase credits from others or having a negative EAB balance as this could lead to drivingrestrictions.Our mechanism can be compared to the traffic point penalty system in the U.S., Canada andother countries. As punishment for committing traffic violations, the drivers risk the suspensionor revocation of their license based on a point-record mechanism in place. As a result, theDepartment of Motor Vehicles (DMV) can revoke the driver’s license of that person and they arenot allowed to drive any motor vehicle. In order to mitigate the social cost of license suspensions,point-removal systems exist for most point-record drivers licenses [8]. In contrast, our system4roposes a daily (or weekly or other period as appropriate) record of associated driving behaviorswith vehicle emissions data and individual accounts.The execution of the smart contract guarantees trust among vehicles and driving habits, (e.g,avoid idling, speeding, etc) and CO levels. Vehicles in the system are alerted via rules definedin the smart contract to reduce emissions [9] [3].Our solution to reducing vehicle CO emissions involves the use of DLT-enabled emissionsmonitoring, which could be applicable to any market worldwide. In this work, we focus on theEU, but, our method can comply with regulations in China and could be implemented in the USto 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 thatwill meet the requirements of the EU-ETS plan for reducing vehicular emissions. B-ETSovercomes the disadvantages of current centralized ETS systems and provides a trustworthyapproach for exchanging data in vehicle-to-vehicle networks. • Second, we introduce an economic incentive-based system which motivates drivers to reducefuel consumption and pollution. Based on the autonomous execution of smart contracts, theincentive mechanism is guaranteed to work in a trusted and distributed manner. • Third, realizing the lack of communication and computation analysis in Blockchain-enabledvehicle networks, we present a theoretical model to derive the communication efficiency ofthe proposed system B-ETS.The remainder of this paper is organized as follows. In the next section, we present the systemmodel 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.II. SYSTEM MODEL AND ANALYSIS
A. Blockchain as a Ledger for VANET
The system operates within periods of duration T . In this section, we describe the two majorsystem components: the vehicles and the distributed ledger, followed by the selected model forCO emissions. Table I presents the nomenclature used throughout the paper.
1) Vehicles:
Let V be the set of vehicles in the system. An On-Board-Unit (OBU) is installedin each vehicle i ∈ V in the Blockchain-based VANET. The OBU performs light tasks, including5ABLE I: Nomenclature Symbols Descriptions T Considered system period [hours] V Set of vehicles i Vehicle i ∈ V T s CO sampling period (cid:15) i ( t ) Average CO emissions per km forvehicle i at time tB i ( t ) Emission allowance balance ofvehicle i at time t ∈ [0 , T ) p i ( t ) Penalty/tax for vehicle i at time ts i ( t ) Incentive (subsidy) for vehicle i at time tL 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 ij ( t ) Relative speed between i and j at time tr ij Communication Range between i and je i,j ( t ) Allowances sold by j to i at time t T Maximum allowed CO emissions generatedby vehicles per km. collection and transmission data to other vehicles according to the IEEE 802.11p communicationstandard, and provides support to passengers and drivers.Within each system period of duration T , the CO emission monitoring system takes samplesof the average CO emissions per km in each vehicle i and updates the ledger. The CO issampled at fixed intervals of duration T s < T hours. The sample taken by vehicle i at time t ∈ { , T s , T s , . . . , T } hours is denoted as (cid:15) i ( t ) and consists of the taken measurement, thevehicle ID i , and a timestamp, generated as a function of t . The amount of CO generated atthe vehicles is reset to zero at the beginning of each period of duration T , hence, (cid:15) i (0) = 0 .
2) Distributed Ledger:
The distributed ledger records the data exchange history grouped intoblocks and linked together chronologically. To minimize the cost of storage, the sensing datacould be hashed and stored at more powerful nodes, and only the hash of data is recorded to the6 istributed Ledger
NOx
CO2
Road V2VEAB Trading T r a n sac t i on s T r a n sac t i on s Fig. 2: Blockchain-enabled vehicular emission trading system.blockchain. Next, a confirmation message is sent back to confirm that the data has been added tothe ledger as presented in Figure 4. We assume that the data services (e.g., data storage, tradingand task dispatching) are implemented on top of a permissionless Blockchain [10].In a permissionless blockchain, any peer can join and leave the network at any time as areader or writer. Permissionless Blockchains are open and decentralized with no central authority.Bitcoin and Ethereum are instances of permissionless Blockchains. In contrast, in permissionedBlockchains a central authority decides and attributes the right to individual peers to participatein the write or read operations of the blockchain. Examples of these include Hyperledger Fabricand R3 Corda [11].The sensing data are formatted into transactions of fixed size. To enhance efficiency, only thedigest of each transaction is stored on the chain, and the content of the transactions are storedby each consensus node off-chain or at the IPFS storage.
3) Emissions Model:
The amount of CO generated from vehicles depends on various factorssuch as: national average age distributions, vehicle activity speeds, operating modes, vehicle-miles traveled, starts and idling, temperatures, maintenance, anti-tampering programs, and av-7rage gasoline fuel properties in that calendar year [12]. The calculation of emissions in oursimulations are based on the Handbook Emission Factors for Road Transport V3.1 (HBEFA),the model was implemented by extracting the data from HBEFA and fitting them to a continuousfunction obtained by simplifying the function of the power the vehicle engine must produce toovercome the driving resistance force [13]. B. Emission Allowances Trading1) Traditional Cap-and-Trade:
Traditionally, cap-and-trade commonly refers to governmentalregulations and programs in place to limit the levels of CO emissions as a result of industryactivity. As briefly mentioned, the EU-ETS works on a cap and trade principle, where the capis a dynamic limitation, set on the total amount of GHG emitted by installations covered bythe system. Within the system, companies receive or buy emission allowances which can betraded. Although, vehicular emissions were not initially considered, in 2006, researchers at MITjoint program on the science and policy of global change introduced the implementation ofa cap-and-trade policy for vehicles. Their central conclusion indicated that there are importantefficiency gains to be realized by including transport emissions under the CAP and by integratingpre-existing programs, such as CAFE, and cap-and-trade systems [14].
2) B-ETS Framework:
Our B-ETS framework considers an economy where vehicles producegoods over a system period [0 , T ] hours. Therefore, each vehicle i acts as a wallet in theBlockchain network and its EAB at time t is denoted as B i ( t ) ∈ R . In the system, the updates tothe EAB are triggered by the sampling of the CO emissions of the vehicles, hence, the systemoperates at specific times t ∈ { , T s , T s , . . . } . At the beginning of each period of duration T , theEAB of each vehicle i is reset to a pre-defined value B i (0) . So, the EAB cannot be accumulatedbetween subsequent periods. However, if i were to hold on to this initial allowance endowmentuntil the end of the period, it would be able to offset the system’s cap by up to B i (0) units ofemissions 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 exchangedamongst vehicles for environmental sustainability. In order to offset penalties, the vehicles withlow balances may engage in buying allowances from vehicles that expect to meet demand withfewer emissions than their own cap. This is our trade aspect of B-ETS framework.8
20 40 60 80 100
Average Speed (mph) C O ( g / m i ) CongestionMitigation Traffic Smotthing SpeedManagement
Fig. 3: CO emissions (grams/mile) as a function of average speed (mph) [15] Remark 1 : A CAP program is only feasible in scenarios where the vehicles have a positiveallowance balance at the beginning of the periods. Hence, the following inequality must hold: B i (0) > for all i ∈ V . (1)The maximum allowed CO emissions generated by vehicles per km is denoted as T (whichis defined as a rule in smart contract). If (cid:15) i ( t ) > T , then our initial smart contract is executed togenerate an alert to i to reduce vehicle speed as a direct solution to reduce amount of generatedCO , and the fine p i ( t ) will be deducted from its EAB. In contrast, the subsidy s i ( t ) will beendowed to i for maintaining the CO emissions below T .The values of p i ( t ) and s i ( t ) are considered as taxes and subsidies for vehicle i that depend ontheir behavior. The incentive may help encourage the driver to control their driving behavior toavoid generating CO higher than the allowed standard. The driver needs to choose betweenreceiving an incentive by reducing amount of emissions or being fined due to overloadedgenerated emissions. The penalties and subsidies are computed based on the theoretical modelpresented in [16] which depends on various vehicular factors.In order to increase the subsidies and reduce the penalties, the drivers can follow strategiesdefined in smart contracts. For example, Figure 3 shows that CO 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. Inthis period, the smart contract defines rules to increase traffic speeds and reduce congestion9y, for instance avoiding high traffic roads to reduce emissions. Second, when the speed of thevehicle is too high, it demands high engine loads which require more fuel, leading to higherCO emission rates. The techniques to manage high speeds are implemented in the contractswhich recommends the drivers to simply reduce their speeds. Consequently, moderate speeds ofaround 40 to 60 mph are ideal speeds which reduce emissions and will give the drivers incentiveto improve their balances.In addition, the EAB can be traded among vehicles based on predefined smart contracts.Whenever B i ( t ) < , there will be a red alert issued to i for having a negative-balance. Thisalert is in the form of penalties, or restricted road access to zero-balance vehicles. In this cases,the vehicles can either wait until the next period for their EAB of to be reset or buy the EABfrom other vehicles. We consider the case of vehicles exchanging EAB on-road via executionof smart contract and distributed ledger. For this, let e i,j ( t ) be the amount of allowances soldby vehicle j from vehicle i at time t . These operations are recorded in the distributed ledger. Remark 2 : The vehicle j cannot sell more allowances e ij ( t ) than it actually owns. In otherwords, i cannot buy more than is actually available. Hence, e i,j ( t ) ≤ B j ( t ) , for all j ∈ V , t ∈ [0 , T ) (2)
3) Operation:
The operation of the vehicle’s emission allowance trading is performed in thefollowing steps:
Step 1. Publishing Data . Each vehicle i ∈ V computes its own average generated CO emissions,namely, (cid:15) i ( t ) as shown in Figure 2 for t ∈ { , T s , T s , . . . , T } . These values are published tolight ledger version of each vehicle and synchronized with the full ledger stored in DLT fullnodes. Step 2. Emission Control . The generated CO emissions data is recorded in the ledger, andthe smart contract with the predefined rules is executed. These rules are characterized by twocategories namely maximum CO emissions and actions: warnings, alerts and reminders. Thepublished CO data is formatted and arranged into blocks to be verified through a consensusprocess. If (cid:15) i ( t ) > T , the smart contract issues an alert message to i to control its drivingbehavior and p i ( t ) is deducted from B i ( t ) via smart contract. Hence, the ledger is updated withthe value B i ( t ) ← B i ( t − T s ) − p i ( t ) . In contrast, if j has maintained a safe speed and emittedreasonable amounts of CO , it received an incentive s j ( t ) to its balance. Hence, the ledger isupdated with B j ( t ) ← B j ( t − T s ) + s j ( t ) . 10 j Confirmation ConfirmationAsk for EAB, e i,j (t) AgreementBuying EAB e i,j (t) from j Selling EAB to i Settlement Settlement B i (t) B i (t) + e i,j (t) M i n i ng 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) Fig. 4: Communication System
Step 3. Emission Allowance Trading . After receiving a confirmation with the required actionfrom the smart contract, if B i ( t ) < , then i needs to re-charge its EAB by buying emissionallowances from other vehicles. For example, i makes an agreement with j to buy an amount ofemission allowances e i,j ( t ) . Then, i sends the buying request for the amount e i,j ( t ) to execute asmart 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 exchang-ing data and trading EAB. We study these in terms of end-to-end latency which includes thetransmission latency among vehicles and computation latency of Blockchain validation processes.11 . Joint Communication and Computation Model In this section, we define the total available time for communication between two vehiclesand the impact of the Blockchain computation latency.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 available for communication is definedby 1) their communication range r ij
2) their positions ( x i ( t ) , y i ( t )) and ( x j ( t ) , y j ( t )) , 3) theirrelative speed, given by vector v ij ( t ) = v i ( t ) − v j ( t ) km/h. Clearly, to initiate communicationat time t , the distance between the vehicles must be d i,j ( t ) = (cid:113) ( x i ( t ) − x j ( t )) + ( y i ( t ) − y j ( t )) ≤ r ij . (3)Then, the total time for V2V communication between vehicles i and j at time t is given as L total ( t ) = max (cid:96) ∈ R { (cid:96) | d i,j ( (cid:96) ) ≤ r ij } − t. (4)It is immediate to see that L total → ∞ when (cid:107) v ij ( t (cid:48) ) (cid:107) → for all t (cid:48) ∈ [ t, (cid:96) ] . This implies thatwhenever both vehicles move in the same direction and with near equal speed, they will havea long time L total to communicate and exchange messages. Furthermore, it can be seen that,the upper bound for L total seconds for the case where the relative speed v ij ( t (cid:48) ) km/h remainsconstant for all t (cid:48) ∈ [ t, (cid:96) ] is L (cid:48) total ≤ r ij . (cid:107) v ij ( t ) (cid:107) (5)Figure 5 illustrates the upper bound for L total with several values of (cid:107) v ij ( t ) (cid:107) .The time needed to complete a trade between two vehicles i and j in B-ETS can be dividedinto 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 successfully if and only if L total ≥ L trans + L comp . (6)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 vehicles i and j , denoted simply as L trans , isa function of the amount of data that must be exchanged and the effective data rate selected12
25 50 75 100 125 15010 − r ij = 10 m r ij = 50 m r ij = 100 m r ij = 500 m r ij = 1000 m Relative speed k v ij ( t ) k ( km / h ) U pp e r bound f o r L t o t a l w it h c on s t a n t r e l a ti v e s p ee d ( s ) Fig. 5: Upper bound of total latency L total for communication between vehicles.for communication R in packets per second. The data that must be exchanges is defined bythe block size of the Blockchain, denoted 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 thelatter determines the instantaneous data rate. The implemented protocol for communication is theIEEE 802.11p standard and the wireless environment are given in Section III. Nevertheless, wecan approximate the latency for communication by assuming that the effective data rate remainsconstant throughout the trade as L trans ≈ S B R . (8)The formulations to calculate L comp are presented in the following.
1) Blockchain computation latency:
We consider a Blockchain-based VANET network thatincludes 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 processuntil one of the miners completes the computational task by finding the desired hash value [17].When a miner i executes the computational task for the POW of current block, the time periodrequired to complete this PoW can be formulated as an exponential random variable W i whose13istribution is f W ( w, i ) = λ c e − λ c w , in which λ c = λ P c presents for the computing speed of aminer, P c is power consumption for computation of a miner, and λ is a constant scaling factor.Once a miner completes its PoW, it will broadcast messages to other miners, so that other minerscan 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 thefastest PoW computation among miners and denoted by the random variable W i ∗ . By assuming { W i } are i.i.d. random variables, we can calculate the complementary cumulative probabilitydistribution of W i ∗ as Pr( W i ∗ > w ) = Pr (cid:18) min i ∈M ( W i ) > w (cid:19) = (cid:89) i ∈M 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 = (cid:90) ∞ (1 − Pr( W i ≤ w )) M d w = (cid:90) ∞ e − λ c Mw d w (10)Now we can calculate the communication latency as L trans + L comp .Note that it can occur that the communication delay exceeds the available communicationtime L total . In such a case, a proposed transactions with potentially valid PoW solution mustbe abandoned. Hence, finding a valid puzzle solution does not guarantee that the proposedtransactions will be finally accepted by the network because of the propagation delay. In suchcases, a Blockchain fork can only be adopted as the canonical Blockchain state when it is firstdisseminated across the network. In scope of this research, to simplify, we do not address theproblem of fork, please refer to [18] for more detail.III. PERFORMANCE EVALUATIONIn 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 ofmicro simulators, network libraries and open-source vehicular network simulators is employed.Specifically, SUMO [13], OMNET++ which runs in parallel via a proxy TCP connection, andVeins. The IEEE 802.11p standard is used for communication between vehicles and a simplepath loss model is selected. In each simulation, 120 vehicles are generated and located randomly.14ABLE II: SMART CONTRACT EXECUTION COSTS Smart Contracts Gas Ether USD
UserAuthority 159430 15.9 · − · − · − · − · − · − * 1 Ether = Gwei; 1 USD = 4,182,471.9949 Gwei
The CO emissions are calculated reading the Traffic Control Interface (TraCI) commands fromSUMO. Ethereum is deployed as a ledger in the experiments by using local Ganache platform.The computational efforts to execute smart contracts in Blockchain are measured in units ofgas. The currency for Ethereum is Ether (ETH). In our simulations, the transaction costs andexecution costs are converted to ETH and USD, see Table II. The ETH gas station was used toestimate the costs, the price is generated using a static average of 20 Gwei, where one Ether isequivalent to Wei. The transaction costs are the costs associated with sending the contractcodes to the Ethereum blockchain, dependent on the size of the contract.The amount of CO 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 emissionsgenerated between a standard CAP system and a Blockchain-based system when the drivingbehavior is controlled. Figure 6 illustrates the generated CO and NO x , along with the V2Vcommunication latency for the standard and the DLT-based trading. In the DLT-based trading,vehicles follow defined rules such as dropping their speed in the smart contract. In Figure 6we observe that the amount of CO and NO x generated from DLT-based system is lower thanconventional system. These results prove that our system has the ability to reduce the overallCO emitted from vehicles on the network.In B-ETS, the transactions exchanged between vehicles are encrypted, and verified beforeattached in the distributed ledger. Therefore, the trusted recording and trading data is guaranteedin comparison with standard system. However, because of extra verification steps in Blockchain,the time to complete a transaction between vehicles is higher. This is a trade-off between trustand latency in Blockchain-based systems. 15
20 40 60 80 100 120 140
Time (s) C O ( m g / s ) * CO2 standardCO2 DLT-based
Time N O x ( m g / s ) NOx standardNOx DLT-based (a) CO2 Emission
Time C O ( m g / s ) * CO2 standardCO2 DLT-based
Time (s) N O x ( m g / s ) NOx standardNOx DLT-based (b) NOx Emission
Latency (s) E m pe r i c a l CD F Conventional SystemDLT-based System (c) V2V communication latency
Fig. 6: Performance Evaluation. (a) and (b): The CO and NOx emission generated in standardand DLT based systems; (c) Communication latency between standard and Blockchain-basedsystem. IV. CONCLUSIONIn this paper, we first proposed a Blockchain-based Emission Trading System, called B-ETS, tosupport the accounting and monitoring of emissions in vehicular networks. B-ETS provides atrustworthy and transparency for accounting the emissions generated from vehicles. The vehiclescan exchange their emission allowances through autonomous smart contracts in a trusted manner.We introduce an economic incentive scheme based on smart contracts to encourage drivers tobehave in environmentally friendly ways.This work provides a mechanism for policy makers, vehicle manufacturers and the EU-ETSto enforce the carbon emissions regulations in a more efficient, secure manner as well as toperform full life-cycle analysis of vehicles. Using the proposed method could result in vehicle16anufacturer savings, ensuring that they are not subject to excess emissions fees at the end ofthe year through the continuous monitoring and reporting of CO .The next stage of this work involves further analysis of the current system in two ways.First, we will include the analysis of more pollutants such as Particulate Matter (PM x ), CarbonMonoxide (CO), Sulfur Dioxide (SO ) into B-ETS. Then, we will address the limitations of thiswork by diversifying the vehicles on the network, thereby incorporating other types of vehicles(other than passenger vehicles), such as: buses, vans and trucks.ACKNOWLEDGEMENTSThis work has been in part supported by the European Union’s Horizon 2020 program underGrant 957218 IntellIoT, the Independent Research Fund Denmark (DFF) under Grants Nr. 8022-00284B (SEMIOTIC) and Nr. 9165-00001B (GROW), and the National Science FoundationGraduate Research Fellowship under Grant DGE-1839285.R EFERENCES [1] E. Commision, “Road transport: reducing CO2 emissions from vehicles,” 2015, (Accessed on 10/28/2020).[2] L. D. Nguyen, A. E. Kalor, I. Leyva-Mayorga, and P. Popovski, “Trusted wireless monitoring based on distributed ledgersover NB-IoT connectivity,”
IEEE Communications Magazine , vol. 58, no. 6, pp. 77–83, 2020.[3] F. Zheng, J. Li, H. van Zuylen, and C. Lu, “Influence of driver characteristics on emissions and fuel consumption,”
Transportation Research Procedia , vol. 27, pp. 624–631, 2017.[4] M. Alsabaan, K. Naik, and T. Khalifa, “Optimization of fuel cost and emissions using V2V communications,”
IEEETransactions on Intelligent Transportation Systems , vol. 14, no. 3, pp. 1449–1461, 2013.[5] L. Li, J. Liu, L. Cheng, S. Qiu, W. Wang, X. Zhang, and Z. Zhang, “Creditcoin: A privacy-preserving blockchain-basedincentive announcement network for communications of smart vehicles,”
IEEE Transactions on Intelligent TransportationSystems , vol. 19, no. 7, pp. 2204–2220, 2018.[6] J. Eckert, D. L´opez, C. L. Azevedo, and B. Farooq, “A blockchain-based user-centric emission monitoring and tradingsystem for multi-modal mobility,” arXiv preprint arXiv:1908.05629 , 2019.[7] Y. Pan, X. Zhang, Y. Wang, J. Yan, S. Zhou, G. Li, and J. Bao, “Application of blockchain in carbon trading,”
EnergyProcedia , vol. 158, pp. 4286–4291, 2019.[8] G. Dionne, J. Pinquet, M. Maurice, and C. Vanasse, “Incentive mechanisms for safe driving: a comparative analysis withdynamic data,”
The review of Economics and Statistics
IEEE Internet of Things Journal , pp. 1–1, 2021.[11] K. Wust and A. Gervais, “Do you need a blockchain?” in
Proceedings IEEE Crypto Valley Conference on BlockchainTechnology (CVCBT) , 2018, pp. 45–54. Modeling mobility with open data . Springer, 2015, pp. 203–221.[14] A. D. Ellerman, H. D. Jacoby, and M. B. Zimmerman, “Bringing transportation into a cap-and-trade regime,” 2006.[15] A. Cappiello, I. Chabini, E. K. Nam, A. Lue, and M. Abou Zeid, “A statistical model of vehicle emissions and fuelconsumption,” in
Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems . IEEE,2002, pp. 801–809.[16] D. Fullerton and S. West, “Tax and subsidy combinations for the control of car pollution,” National Bureau of EconomicResearch, Tech. Rep., 2000.[17] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” Manubot, Tech. Rep., 2019.[18] W. Wang, D. T. Hoang, P. Hu, Z. Xiong, D. Niyato, P. Wang, Y. Wen, and D. I. Kim, “A survey on consensus mechanismsand mining strategy management in blockchain networks,”
IEEE Access , vol. 7, pp. 22 328–22 370, 2019., vol. 7, pp. 22 328–22 370, 2019.