Research ProposalPDF Available

Blockchain-based Data and Energy Trading in Internet of Electric Vehicles -MS Synopsis


Abstract and Figures

The radically increasing amount and enormous types of data generated by vehicles have brought in the innovated application of data trading in vehicular networks. Whereas the immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported distributed energy trading due to their bidirectional charging and discharging capabilities. The trustless environment of Internet of Electric Vehicles (IoEV), including fuel vehicles and EVs, faces conflicting interests and disputes among trading parties. We exploit consortium blockchain for secure data trading to achieve information transparency and build trust in IoEV. All trading actions are performed by using smart contracts to tackle disputes and illegal actions. Moreover, bloom filters are used for fast data lookup and data duplication verification through previously stored hash-list at roadside units (RSUs). Removing data duplication at an earlier stage helps in reducing storage cost. The reliability and integrity of traded data are ensured by using the digital signature scheme based on elliptic curve bilinear pairing. An external distributed storage, Inter-Planetary File System (IPFS), is used for long term availability of traded data, which provides reliable and high capacity storage resources. On the other hand, the energy trading transactions among EVs face some security and privacy protection challenges. An adversary can infer the energy trading records of EVs, and launch the data linkage attacks. To address this issue, an account generation technique is used to hide the energy trading trends that depends upon EVs' traded volume of energy.
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COMSATS University Islamabad, Islamabad Campus
Synopsis For the Degree of XM.S/MPhil. PhD.
Name of Student Ayesha Sadiq
Department Department of Computer Science
Registration No.
FA17-RIS-004 Date of Thesis Registration February 10, 2020
Name of
(i) Research Supervisor
(ii) Co-supervisor
(i) Dr. Nadeem Javaid
Research Area Intelligent System/Blockchain
Members of Supervisory Committee
1Dr. Nadeem Javaid
2 Dr. Mariam Akbar
3 Prof. Dr. Sohail Asghar
4 Dr. Muhammad Manzoor Ilahi Tamimy
Title of Research Proposal Blockchain-based Data and Energy Trading in Internet
of Electric Vehicles - MS Synopsis
Signature of Student:
Summary of the Research
The radically increasing amount and enormous types of data generated by vehicles have
brought in the innovated application of data trading in vehicular networks. Whereas
the immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported
distributed energy trading due to their bidirectional charging and discharging capabil-
ities. The trustless environment of Internet of Electric Vehicles (IoEV), including fuel
vehicles and EVs, faces conflicting interests and disputes among trading parties. We ex-
ploit consortium blockchain for secure data trading to achieve information transparency
and build trust in IoEV. All trading actions are performed by using smart contracts to
tackle disputes and illegal actions. Moreover, bloom filters are used for fast data lookup
and data duplication verification through previously stored hash-list at roadside units
(RSUs). Removing data duplication at an earlier stage helps in reducing storage cost.
The reliability and integrity of traded data are ensured by using the digital signature
scheme based on elliptic curve bilinear pairing. An external distributed storage, Inter-
Planetary File System (IPFS), is used for long term availability of traded data, which
provides reliable and high capacity storage resources. On the other hand, the energy
trading transactions among EVs face some security and privacy protection challenges.
An adversary can infer the energy trading records of EVs, and launch the data linkage
attacks. To address this issue, an account generation technique is used to hide the energy
trading trends that depends upon EVs’ traded volume of energy.
1 Introduction
In this section of thesis, the background and overview, research motivation,re-
search objectives,research scope,significance of proposed research,applications
of proposed research,contributions and organization of synopsis have been pre-
1.1 Background and Overview
With the growing technological advancements of vehicular telematics, vehicles are
generating enormous types of data. The modern vehicles are retrofitted with the
capabilities of communication and exchange of data with the surrounding environ-
ment. Due to long term data processing and sharing, Internet of Vehicles (IoV) is
developing gradually for an efficient and intelligent transportation system. How-
ever, the addition of Electric Vehicles (EVs) in IoV has given birth to the Internet
of Electric Vehicles (IoEV).
Vehicles are equipped with an On-Board Unit (OBU) that communicates with
Roadside Units (RSUs) and other OBUs, referred to as Vehicle to Infrastructure
(V2I) and Vehicle to Vehicle (V2V) communication, respectively [1]. For V2I
communication, a Dedicated Short-Range Communication (DSRC) protocol is used
[2]. The real-time data generated by vehicles can benefit multiple organizations in
the future. Data can be exchanged and traded among various entities in IoEV, in-
cluding data buyers, sellers and brokers.
However, IoEV is a trustless environment, where all trading parties cannot be
fully trusted. The parties involved in trading may have conflicts regarding pay-
ments and data exchange [3]. Thus, low trading information transparency, illegal
data modification and payments disputes are major challenges of Data trading (D-
trading) in IoEV.
IoEV nodes usually use third-party services and centralized storage systems
due to their limited storage capacities. The cloud servers are introduced to han-
dle large traffic and data storage related problems. Although these cloud servers
provide enough storage space and computing capabilities, the risk of data con-
tent exposure, and a single point of failure still prevail due to their centralized
approach [4]. Due to the growing number of vehicles, several IoEV nodes access
the data simultaneously that imposes a burden on the centralized data storage sys-
tem. There can be bottleneck and latency problems while accessing data. To ensure
reliable and long-term availability of data, a distributed storage system is required
to tackle the inefficiency and high cost problems in the centralized system. More-
over, traders with nefarious intentions can exchange the same or previously traded
data for financial gain. This leads to the second-hand sharing of data and data
duplication, which ultimately increases the storage cost.
Blockchain is considered a powerful solution for the trustless environment
of the vehicular network because of its decentralized, transparent, traceable and
tamper-resistant features. It was first introduced by Satoshi Nakamoto in 2008
as a digital cryptocurrency with Bitcoin [5]. State of the art vehicles share their
data with RSUs and store it on the blockchain. Information about the type of
data, ownership, collection, and even the data exchange process is recorded on the
blockchain. Thus, data can be securely traded among multiple entities in IoEV us-
ing blockchain. Secure data exchange mechanisms are introduced in the vehicular
networks together with blockchain technology [6,7,8,9,10,11]. Blockchain, us-
ing the smart contract, provides decentralization of transactions and transparency.
A smart contract is executed within the blockchain as an autonomous application
[12,13,14]. It provides a self-contained system with predefined rules to estab-
lish interactions among interested trading parties without any central trusted entity.
There are various other areas that accelerate the use of blockchain, such as the In-
ternet of Things [15,16,17,18,19], healthcare [21,22,23,24,25,26], agriculture
[27,28,29,30], and many more. Besides the immense benefit of blockchain in
a trustless environment and providing transparency in trading, privacy is still an
issue in Peer-to-Peer (P2P) Energy trading (E-trading) among EVs [31,32,33].
In the past decade, EVs as a subclass of smart vehicles, have received an im-
mense recognition due to their potential of providing viable and environmentally
sound transportation system. Frequent charging is required by an EV to operate
for certain miles. The rapid development in energy harvesting and communication
technology in smart grids has enabled EVs to communicate with the surrounding
environment. The energy generation cost is decreasing by integrating Renewable
Energy Sources (RES) into smart grids [34,35,36,37,38]. Apart from transporta-
tion service, EVs act as distributed moving energy carriers. They can play the role
of consumers as well as energy suppliers. The bidirectional communications of
EVs along with advanced energy supply systems alleviates the problem of energy
demand and supply imbalance, and assists in meeting the current needs of energy
in a sustainable and reliable manner [39,40]. EVs have ability to communicate
with service providers for charging their batteries and acquire information about
charging schedules [41]. The EV Charging Stations (EVCSs) are responsible for
energy supply and scheduling charging slots for EVs. There can be a scarcity in
energy supply during peak hours [42,43,44]. To handle this energy demand and
supply during peak hours, modern EVs can trade their surplus energy with other
EVs that need to charge their battery. Due to this social communication among
EVs, charging through another EV becomes possible [45].
However, during the E-trading and D-trading process, the privacy of vehicles
is at risk. While communicating with EVCSs or other EVs, the private information
of EVs can be revealed. Therefore, anonymous communications and secure pay-
ment mechanisms are required. In the previous centralized approaches, the central
authorities have the data of all registered nodes, which raises some serious privacy
issues. The trading transactions are stored publicly and validated by all participat-
ing nodes in the blockchain network. The transactions’ data can be misused by
linking it with other publicly available datasets to launch privacy-related linkage
attacks. There are multiple data mining algorithms available that work with the
raw data to launch attacks.
Thus, the major challenge in blockchain-based P2P E-trading and D-trading
among vehicles is to design a secure privacy-preserving scheme. The first concern
is to provide privacy while keeping EVs’ personal information anonymous. Second
issue is to hide data and energy trading trends of EVs using an appropriate method.
Although noise-based schemes are used to achieve differential privacy of records,
yet these schemes fail to provide accuracy in information [46]. Therefore, the
main objective is to hide the trading trends while restricting attackers to infer the
To address the aforementioned problems, a blockchain-based privacy-preserving
technique is proposed to hide trading trends in vehicles. During the payment mech-
anism in P2P trading, new accounts are generated by using account mapping tech-
nique to collect coins, which depends on a predefined criteria. Moreover, based
on the previous work [47], the data storage issue is solved using InterPlanetary
File system (IPFS), and blockchain is integrated in IoEV for trading transparency.
IPFS is previously used with blockchain for providing secure data sharing mecha-
nism and storage [48,49,50,51]. We aim to design a secure, trustful E-trading and
D-trading with reliable data storage system. Removal of data duplication at earli-
est, eventually saves the storage cost and bandwidth consumption. Additionally, it
alleviates the second-hand sharing problem by trading parties in IoEV. For making
data duplication process more efficient, bloom filters are employed as discussed in
Section 2.1.3.
1.2 Research Motivation
Over the last few decades, a lot of techniques are developed by researchers for data
collection in vehicular networks. The traditional approaches include road-mounted
devices like speed checking sensors, cameras and scanners. However, these tech-
niques are way too expensive to be installed. Modern vehicles are equipped with
OBUs that use can use vehicles specifically designed DSRC protocol for commu-
nication. With the ever-growing number of vehicles, a lot of data is generated that
can be very beneficial for several business entities [52,53]. This real-time vehi-
cle generated data and its purposeful usage is the real motivation behind this re-
search by encouraging vehicular users to participate in trading this data. Moreover,
addressing the problems that arise in trading data among vehicles and payment
mechanisms are major concerns in the proposed research.
1.3 Research Objectives
The primary objectives of the proposed research are as follows:
To maintain transparency in D-trading and E-trading operations
To encourage vehicular users to participate in D-trading and E-trading by
providing them dispute free and trustful trading environment
To ensure the long-term availability of data by providing a distributed storage
To remove data duplication at the earliest for reducing storage cost and band-
width consumption
To secure the E-trading trends of EVs by hiding trading transactions’ infor-
1.4 Research Scope
The scope of this research is only limited to trade data and energy in IoEV. Al-
though the traded data in IoEV may belong to several categories of vehicle gener-
ated data except for road safety messages. The main focus of research is encourag-
ing vehicular users to participate in trading without having any payment disputes.
1.5 Significance of Proposed Research
The D-trading in vehicular networks is in its early stage. In future, it might embrace
wider applications that could be studied and analyzed by researchers to enhance the
driving regulations and develop new system designs for traffic handling. The IoEV
enabled D-trading and its communication power can improve the quality of life by
providing new opportunities in today’s vehicular telematics. Whereas, E-trading
among EVs can facilitate in overcoming the energy imbalance energy demand-
supply and RES over generation problem
1.6 Applications of Proposed Research
The application of the proposed scheme may involve where there is a need to mo-
tivate users to trade their vehicle generated data or to trade their surplus energy
to other needy vehicle. The D-trading is applicable where new systems need to
be designed for handling vehicular traffic and defining new driving regulations.
The technical details of vehicles and data related to vehicle usage can help vehi-
cle manufacturers. Whereas, E-trading is applicable where EVs have capability of
performing energy suppliers to overcome energy-related problems.
1.7 Research Methodology
The first step in our research methodology involves literature studies. We have
studied literature and developed our background knowledge on blockchain and its
implementation in vehicular networks with the keyword searches like blockchain
in IoEV, blockchain-based data sharing in vehicles, EVs and energy trading, data
trading and storage in IoEV, and privacy challenges. On the basis of the litera-
ture review, we have figured out a few limitations in the literature and refined the
problem statement. Then, we have proposed a system model against the prob-
lem statement. After performing practical implementation steps as explained in
the proposed scheme, finally, the detailed simulation results are demonstrated and
1.8 Research Contributions
In our work, a privacy preserving blockchain-based solution is designed for secure
payments in P2P E-trading among EVs. A proper payment mechanism encourages
vehicles in IoEV to participate and increase the joining threshold in D-trading and
In our proposed work, we are going to make the following contributions:
A blockchain-based trading model is to be proposed in which blockchain is
implemented on RSUs for performing secure and fair trading with transpar-
ent legal actions.
We will exploit smart contracts that run autonomously among trading parties
to tackle trading disputes and facilitate in providing payments to the corre-
sponding data traders.
RSUs will be responsible for verifying data duplication at the initial phase of
D-trading by performing data hash computation and comparison with hash-
Bloom filters will be employed to reduce the response time of existing data
lookup mechanism.
To ensure reliable and long-term availability of traded data, IPFS will be
A consortium blockchain-based privacy-preserving model is to be proposed
for transparent E-trading among EVs.
An account generation method will be introduced for hiding E-trading trends
to prevent data linkage attacks.
A security analysis of smart contracts will be conducted to examine the code,
and making it free of bugs, vulnerabilities and secure against all known at-
1.9 Organization of Synopsis
The rest of the synopsis is organized as follows: The preliminaries for the pro-
posed system model are discussed briefly in Section 2. Section 3provides litera-
ture review along with its critical analysis. On the basis of limitations identified
in literature, the problem statement is defined in Section 4. The proposed system
model with its entities, design goals, attacker model and detailed scheme of D-
trading and E-trading is presented in Section 5followed by Section 7that explains
the algorithms used in the proposed scheme.
2 Preliminaries
In the following sections, preliminaries of the proposed model are discussed.
2.1 Bilinear Mapping
Bilinear Mapping [54] is constructed by Weil and Tate pairing. It is called pairing
because the elements of the two groups are associated in a third group. Let G1and
G2be the two additive cyclic groups that yield a third group GTof prime order q.
Let ê be the bilinear map, it is denoted as:
ê: G1×G2GT
There are three properties of Bilinear mapping: bilinearity, non-degeneracy and
Bilinearity: If two points X, Y G1for all a, b Z
q, we have:
ê(aX,bY )= ê(X,bY )a= ê(aX ,Y)b= ê(X,Y)ab
= ê (X,abY )= ê (abX,Y)
Non-degeneracy: X,YG1exit such that ê(aX,bY )6=1, where 1 is identity
element in group GT.
Computability: For all X, Y such that X, Y G1, there is an efficient algo-
rithm to compute ê(X,Y).
2.1.1 Blockchain
Blockchain is a time-stamped series of records that are stored in a distributed ledger
without any central controlling entity [5,55]. It provides a secure and reliable
method of online transactions. It removes reliance on a third-party server by pro-
viding reliable payment mechanism through smart contracts [56,57]. All nodes
in the network share the same copy of digital ledger. Therefore, no participating
node in the network can change the data, which makes it tamper-resistant. Informa-
tion stored on the blockchain is open to everyone, making the actions of all nodes
transparent. When a certain number of transactions are verified by nodes, a block
is formed and added chronologically in the chain. This process makes each trans-
action immutable, transparent, verifiable and auditable. Each block in the chain is
linked to the previous block through a cryptographic hash.
All nodes in network maintain a public ledger that is synchronized with other
nodes in the network via consensus algorithm. After the transaction is executed,
the transaction information is broadcasted to all nodes in the network. According
to the consensus algorithm, the transaction is verified and added to the ledger. The
consensus algorithm is a fault-tolerant mechanism that ensures the synchroniza-
tion of updated ledger among all nodes and their agreement on a uniform state. It
decides the roles of participating nodes in the blockchain network. There are vari-
ous consensus algorithms, including Proof of Work (PoW), Proof of Stake (PoS),
Proof of Authority (PoA) and Delegated Proof of Stake (DPoS). Among them PoW
is considered as the most secure consensus algorithm. These consensus algorithms
are categorized on the bases of public and private blockchain. A public blockchain
is a permissionless blockchain, which means there is no single controlling entity in
the network, and multiple nodes can join the network freely and perform read and
write operations. Whereas, the private blockchain is a permissioned blockchain
that works on access control by limiting the number of participating nodes in the
network. There can be one or more entities involved in controlling the network;
only the authenticated and legitimate nodes can perform read or write operation.
2.1.2 IPFS
IPFS is a P2P, content-addressable and distributed file storage system with a swarm
of computers connected together [58,59]. P2P file-sharing system overcomes the
problems of the client-server model without any single or central point of failure.
When a file is uploaded to IPFS, it is available to all peers in an IPFS network. The
uploaded file is divided into chunks that are assigned a unique cryptographic hash.
Thus, data added to IPFS can be accessed by using this unique cryptographic hash,
which makes it content addressable. IPFS uses Distributed Hash Tables (DHTs) to
find locations of files. The use of IPFS for storing data reduces huge cost of storage
space. In summary, IPFS provides high throughput with secure storage model that
supports concurrent access of data with high storage capacity [60].
2.1.3 Bloom Filter
Bloom filter is a probabilistic data structure with less space usage and time effi-
ciency. The multiple variants of bloom filters are being used for fast lookup and
detection of duplicate data to facilitate membership queries [61,62]. It consists
of a binary array with mbits of 0’s and 1’s. Initially, all bits are set to 0. There
are knumber of hash functions used to map data by setting kbits from 0 to 1 in
a bit array, where k<m. During data insertion operation, data is passed through
khash functions that return karray positions. All these karray positions are set
to 1. Meanwhile, data search operation is performed by checking whether kbits
are set to 1 or not against karray positions after passing data to hash functions.
Although fast insertion and searching for specific data make bloom filters so use-
ful, still they have a drawback of false positives. However, bloom filters can be
designed to minimize the rate of false-positives upto acceptable level [63,64].
2.2 D-trading
D-trading in vehicular networks is in its early stage. In future, it may embrace
wider applications that can be studied and analyzed for further enhancement in the
driving regulations and developing new system designs for traffic handling. The
IoEV enabled D-trading can improve the quality of life by providing new oppor-
tunities in today’s vehicular telematics. The real-time vehicle generated data can
benefit several business entities and trading parties. Although the trading data in
IoEV belong to several categories of vehicle generated data except for road safety
messages. The trading data may contain road conditions, congestion statuses and
environmental conditions. It may include data related to vehicles’ usage, and their
technical details like temperature oil, airbags and malfunction reports. Moreover,
the proper use of this data can help in betterment of remote services booking,
proactive safety measures, navigation services with virtual assistance, live road
and environmental conditions, and reducing engineering costs. This D-taring oc-
curs between RSUs and vehicles that are willing to participate in D-trading. Here,
in case of D-trading, RSUs are considered as data brokers. They handle all D-
trading requests from vehicles and process them. All D-trading validation checks
are carried out by RSUs.
2.3 E-trading
EVs’ collaboration with RES has facilitated in overcoming the various problems,
including energy demand-supply, energy requirement in peak hours and RES over
generation. EVs are provided incentives to take part in E-trading market. Since
EVs are capable of consuming energy as well as supplying energy to other EVs.
They can trade and communicate with the surrounding environment through V2V,
V2I or Vehicle to Grid (V2G) communication modes. In the proposed system
model, only V2V communication is considered for E-trading between EVs and
RSUs. The RSUs involved in E-trading are considered as energy brokers. They
handle E-trading requests and provide E-trading payment transfer decision for the
corresponding EV to preserve their privacy.
3 Literature Review
In this section, we categorize the literature review as blockchain-based D-trading
and storage, and blockchain-based E-trading.
3.1 Blockchain-based D-Trading and Storage
Storing and managing huge data intelligently is a great challenge in IoEV. With
the advancement in IoEV and increasing traffic, several nodes are accessing the
network at the same time. Data access authorization is a great challenge among
vehicles that require an efficient response with minimum delay. The increased
traffic load on a centralized network creates a bottleneck, and traditional networks
with centralized management and storage face severe issues. To support resource-
intensive tasks and data storage, edge computing is introduced [65,66,67]. How-
ever, due to open mobile nature of vehicles, edge nodes become less trustworthy
and cause security and privacy issues. Consequently, vehicles become reluctant to
upload their data to the edge nodes to preserve their privacy. To address the issues
of high costs, inefficient and insecure data storage, a decentralized and distributed
blockchain technology is integrated in IoV [9,68,69,70]. Authors in [9] have used
edge computing accompanied by smart contracts for efficient data sharing. Data
sharing rate increases with the increase in the number of vehicles, which inflicts a
high demand for data storage. A reputation-based data sharing scheme with Three-
Weight Subjective Logic (TWSL) is developed to specify a valid data source. The
delay problem in [9] is handled by using edge computing, whereas in [68], batch
signature verification is used to achieve efficiency in data sharing. However, there
is no mechanism specified for checking data duplication and data quality [9,68].
Information dissemination among vehicles is of utmost importance, and so
is the trustworthiness of information exchanged. Due to the high mobility and
variability of vehicles [71], it is difficult to trust neighbouring vehicles [71,72].
There are several types of communications in IoV, such as V2V, V2I and V2G.
The unique features of vehicular networks, such as high mobility and open nature
topology make them susceptible to various kinds of attacks. Thus, the security and
reliability of messages are essential aspects to be addressed in vehicular commu-
nications. Moreover, important information does not reach to the nearby vehicles
in real-time due to fake message dissemination. To prevent the distribution of fake
messages, a blockchain-based trust models are proposed in [73,74,75]. Authors
in [73] have used a reputation evaluation algorithm based on reputation scores to
calculate the trustworthiness of messages shared by vehicles. The reputation score
is generated by the number of evidences collected from peers. Moreover, an ef-
ficient privacy-preserving authentication mechanism is developed by utilizing the
features of the Lexicographic Merkle Tree (LMT). Whereas, authors in [74] have
proposed a framework for traffic event validation using the Proof of Event (PoE)
consensus mechanism. A threshold value is adjusted to reduce the dissemination
of fake messages, and events are validated by the consensus mechanism strategy.
However, the process of updating reputation values depends on the collected evi-
dences from peers that causes transmission delay in case of increase in the number
of vehicles.
Trust in vehicular networks encourages vehicles to communicate and share data
[76,77,78]. Existing centralized trust models use cloud for storing data while re-
stricting vehicles to rely only on the central server, which results in issues like
latency, bottleneck and a single point of failure. To solve these problem, authors in
[79] have used a local blockchain to store trust values and message trustworthiness
based on geographical location with PoW consensus mechanism. Whereas, au-
thors in [80] have presented a blockchain-based decentralized trust model in which
RSUs have information about vehicles. Trust is built upon the percentage of rat-
ings collected from neighbouring vehicles. However, vehicles may behave selfishly
and generate unfair ratings about other vehicles. Also, there is no authentication
mechanism specified for vehicles before joining the network. Real identities of ve-
hicles are not preserved, as ratings shared by vehicles contain sensitive data about
their identity and location. While providing location proof of vehicles in a certain
vicinity, location privacy of vehicles is not preserved [79]. Moreover, the PoW
consensus mechanism used in [79] is difficult to perform in mobile nodes, whereas
joint PoS and PoW algorithm is used in [80] that requires greater computational
Data exchange in vehicles is becoming popular with the growing vehicular
traffic [81]. Data exchange takes place simultaneously involving multiple parties
that causes low information transparency and data modification problems [82].
Blockchain technology is used for data exchange due to its decentralized, im-
mutable and traceable characteristics [83,84,85]. Due to the trustless environment
in vehicular networks, a secure and truthful data sharing model is essential. Au-
thors in [86,87,88,89] have proposed blockchain-based trust models with condi-
tional privacy to ensure the privacy of vehicles along with the credibility of shared
information. A protocol is designed in [86] that allows vehicles to communicate
anonymously using group signature. The reliability and effectiveness of protocol
depend upon the length of the ring. Moreover, logistic regression is used to iden-
tify malicious vehicles based on their reputation values. However, this protocol is
not efficient as the number of vehicles increases in the ring for generating a group
In vehicular networks, fog computing has been used to overcome latency prob-
lems [90,91,92]. In case of vehicular networks, continuous monitoring of users
can breach their location and identity privacy [93]. Authors in [94] have used fog
nodes to overcome the response delay and communication overhead problems in
carpooling. Blockchain-based fog nodes are introduced to store carpooling data to
ensure auditability. A proximity test is used along with the Public Key Infrastruc-
ture (PKI) approach for managing users’ drop-off locations. Performance analysis
is done on the blockchain construction, and time cost is compared for tracking users
by RSUs that are considered as fog nodes. However, fog nodes are semi-trusted,
and they can be compromised by malicious users.
A number of research efforts have been done in preserving privacy of vehi-
cles [95,96,97]. To preserve the privacy of users and to achieve social welfare
maximization, authors in [52] have proposed a framework based on consortium
blockchain in which pre-selected aggregators are responsible for auditing and ver-
ifying transactions. To improve D-trading efficiency, an iterative double auction
mechanism is used to maximize social welfare by optimizing data pricing and pro-
tecting traders’ privacy. Optimizing data pricing benefits both buyer and seller,
which encourages users to participate in D-trading. However, a detailed incentive
mechanism is required for the process of data transmission and a trusted interme-
diary is needed to tackle disputes among users.
Table 1shows the summary of Section 3.1. Most of the problems addressed
include transparency in D-trading [52], secure and efficient sharing of data [9,
68,73], data storage [9,68] and trust management problems [74,79,80,86].
Blockchain is exploited to solve several problems involving transparency, trust
management and secure data sharing among vehicles.
Table 1 Blockchain-based D-trading and Storage
Technique(s) Feature(s) Objective(s) Limitation(s)
Double auc-
tion mecha-
nism [52]
Transparency in
data trading
Social welfare max-
imization of buyers
and sellers
Auditing and verifi-
cation of transactions
Payment dis-
TWSL [9] Data sharing,
storage and ac-
cess authorization
Integration of edge
Reliable data storage
Reputation evalua-
High storage
Data dupli-
Batch verifi-
cation [68]
Efficient sharing
and storage of
Reliable and scalable
data storage
hand sharing
of data
LMT [73] Prevention of
fake message
Trust reputation
PoE [74] Reducing fake
message distribu-
Trust manage-
Identification of ma-
licious vehicles
Prevention of fake
messages distribu-
PoW [79] Trust manage-
Use of Edge
Scalable scheme
based on geographi-
cal locations
Reducing latency by
using edge nodes
Privacy leak-
PoW and
PoS [80]
Trust manage-
Reduced latency us-
ing edge computing
Logistic Re-
PoW and
PBFT [86]
Trust manage-
Conditional privacy
Auditable and scal-
able scheme
3.2 Blockchain-based E-Trading between EVs
text The installation of RES is increasing gradually. To reduce the problem of RES
over generation and divergence between the demand and supply, EV users need
motivation to use RES. There are number of research efforts devoted in designing
incentive schemes to provide motivation [98,99,100,101,102]. The EVs are pro-
vided incentives to consume more and more RES. The proposed incentive scheme
incorporates monetary and non-monetary incentives while reducing energy costs
and prioritizing the vehicles. The results show a significant increase in the solar
consumption ratio due to the incentive scheme. However, the proposed scheme
does not protect the privacy of the users.
The combination of RES and EVs has played an essential role in alleviating
the problem of energy demand and supply. With the integration of RES [103,104],
EVs are considered as the energy carriers for energy distribution to areas where
energy demand is high. The energy delivery process by EVs has encountered a lot
of problems, including EVs’ selfishness and uncooperative behavior. EVs may not
be willing to take part in the E-trading market. On the other hand, malicious EVs
can launch various attacks to downgrade the energy delivery scale. To address these
problems, authors in [105,106] have proposed reputation-based schemes for secure
E-trading. A reputation-based consensus protocol is used for efficient transaction
verification. Moreover, an incentive mechanism is used for proper scheduling of
charging and discharging of EVs. Finally, a comparison is made with conventional
schemes to demonstrate the efficiency of the proposed system. However, the PKI
is used for puzzle solving in reputation-based consensus that is computationally
expensive. The studies [107,108,109] are based on the E-trading of hybrid EVs
with surplus energy. The consortium blockchain is used for E-trading without de-
pendence on any third party. A double auction mechanism is used to achieve the
social welfare maximization of EVs and local aggregators [107,110,111]. EVs
place their electricity price bids without sharing any private information. Although
the scheme provides anonymity, however, the privacy attacks are not considered
that are possible by data mining methods. In [112,113], the authors have used
blockchain to enhance the security of E-trading transactions of EVs by using data
coins. The delivery of data coins is performed using pseudonyms to avoid privacy
disclosure. However, the detection of malicious attackers becomes complicated
when a different pseudonym is used for every new transaction.
The solar energy generation has facilitated individuals to produce their own
considerable amount of energy efficiently. However, due to varying usage of en-
ergy, the amount of solar energy produced by individuals may exceed their re-
quirements [114]. This surplus energy can help in an individual’s E-trading market
growth along with the reduction in cost of energy consumption [115,116,117].
For neighbouring E-trading, a trustable environment is required as individuals may
have their privacy concerns. In [118,119], the E-trading model is proposed in
the grid neighbouring system based on consortium blockchain. Authors in [118]
have proposed a solution to preserve the privacy of users by screening their trading
records to avoid privacy related attacks. Dummy accounts are used to add noise,
which hides the data about active as well as inactive users. New accounts are cre-
ated that depend on users’ energy usage to provide privacy protection.
A considerable amount of payment records are generated during the energy
transfer between EVs and smart grids. The payment records of energy usage can be
shared for load and price forecasting, valuable services of energy and scheduling of
energy consumption [120,121,122]. The sharing of payment records comes with
several privacy concerns. In [120], authors have proposed a privacy-preserving
payment mechanism for V2G network. The proposed work supports anonymous
payment and effective audit of energy transactions by leveraging a proper registra-
tion process. The proposed scheme surpasses Bitcoin and Ethereum in terms of
transaction confirmation time and throughput. However, there is no motivation for
EVs to participate in E-trading to meet the demand and supply of energy.
EVs are supposed to pay the charge amount after charging. EVs can deny the
payment amount, or the billing information can be modified. Therefore, a secure
and payment mechanism is required to satisfy the increasing requirements in the
energy trading market. Authors in [123] have provided a credit-based solution
for reducing transaction verification delay with secure payment mechanism. This
scheme involves central bank authority, which helps in managing payments and
reducing the transaction confirmation delay time. All nodes in the network have
the same ledger information, so there are no chances of manipulating payment in-
formation. However, the proposed blockchain-based scheme needs to be integrated
into each client module for payment process that may cause privacy issues.
EVs’ charging may require a longer time duration, which in turn demands for
in-advance charging schedules on charging slots. In [124,125], a blockchain-based
charging scheme is proposed in the smart grid system. The main objective is to
minimize the charging energy cost for EVs while minimizing the energy fluctua-
tion levels in the smart grid [126]. The frequent charging of EVs and their charging
schedules may leak their private information, including their locations, driving pat-
terns and charging schedules [127,128]. The location information of EVs obtained
by adversaries can cause serious harm. A number of research efforts are done in
preserving location privacy of vehicles [129,130,131]. Authors in [129] have
worked on charge supplier matching and charge scheduling. They have provided
a homomorphic scheme-based solution to enable the location privacy of EVs. In
[132,133,134], authors have proposed schemes for the selection of charging sta-
tions and dynamic pricing plans for EVs’ charging. A protocol is designed in
Table 2 Blockchain-based E-trading in EVs
Technique(s) Feature(s) Objective(s) Limitation(s)
consensus protocol
Incentive mecha-
nism for charging
and discharging of
Scheduling of EVs’
charging and dis-
To prevent inter-
nal and external
attacks by adver-
due to PKI
Social welfare
maximization us-
ing double auction
blockchain to
audit and verify
transaction records
Secure and trustful
E-trading model
Balancing demand
and response
Improving transac-
tion security with-
out any trusted in-
attacks are
not consid-
ered that
are possible
data mining
Proof of
Privacy Preser-
vation of shared
Auditing anony-
mous transactions
A reliable payment
A secure payment
Privacy preserva-
tion of payment
No moti-
vation for
EVs to par-
ticipate in
No focus
on Energy
demand and
Reduction in trans-
action verification
Transaction delay Requires in-
tegration in
each client
module that
is expensive
Preserving location
privacy of vehicles
Optimal charging
station selection
Location privacy
Not scalable
[132], for the selection of optimum charging stations by bidding dynamic tariffs,
and users can select charging stations based on dynamic tariff decisions. The ge-
ographic location and the vehicles’ identity are preserved at the charging stations,
and their bids are publicly available in the blockchain.
3.3 Critical Analysis
We have performed a critical analysis of the literature review presented in section
3. The table 1shows the summary of section 3.1. There is a lot of work done on
the sharing of data in vehicular networks using blockchain. The most of problems
addressed include transparency in trading [52], secure and efficient sharing of data
[9,68,73] , data storage [9,68] and trust management problems [74,79,80,86].
Blockchain is exploited to solve several problems like trading transparency and
trust management in data sharing among vehicles. Due to the growing number of
vehicles, demand for storing the shared has increased. Also, vehicular users need
to be encouraged to participate in trading vehicle generated data. For this purpose,
a secure and reliable payment mechanism is necessary that can provide trustful
and dispute free environment. While sharing and trading data, some other prob-
lems require attention as well. Authors in [74,79] have worked on data storage
problem by using edge nodes. However, edge nodes are susceptible to privacy at-
tacks. There must be a distributed storage mechanism for shared or traded data that
can ensure the long-term availability of shared data. On the other hand, vehicular
users may behave selfishly by trading the previously traded data for financial gains.
This causes second-hand sharing and data duplication problem, which ultimately
increases storage cost.
Table 2summarizes the literature presented in Section 3.2. The problems ad-
dressed in E-trading mainly include EVs’ charging and scheduling [105], energy
supply and demand [107,108], payment mechanisms [120] and optimal charg-
ing pricing [132,133,134]. During scheduling of EV charging and discharging,
there are chances of internal and external attacks by vehicular users with malicious
intent. Blockchain-based reputation schemes are developed that are capable of de-
tecting malicious vehicles. Furthermore, the combination of RES and EVs has
facilitated positive growth in energy trading market. EVs are motivated to partici-
pate in energy trading by providing them incentives and taking care of utilities of
energy buyers and sellers to acheive overall social welfare [105,107]. To enhance
the efficiency of E-trading, blockchain is used with the objective of minimum trans-
action delay [123]. However, most of the work in energy trading domain is based
on making energy trading process efficient with secure payment mechanisms and
incentive based. There is no work done in preserving privacy of energy trading
records and privacy of EVs is not majorly concerned.
4 Problem Statement
Efficient and secure trading mechanisms are required to trade data and energy in
IoEV. In [52,107], double auction mechanisms are proposed for trading among
buyers and sellers in IoV. However, a trusted mediator is required to tackle the
possible trading disputes in the market. Moreover, the privacy of sellers is not
preserved. In [120], a privacy preserving payment mechanism is proposed in V2G
network. The proposed scheme provides privacy preservation and reliable payment
mechanism with secure data sharing. However, tracking of vehicles’ real identity
and auditing their behavior involves a registration authority, which makes the pro-
posed scheme partially decentralized. Moreover, some vehicles act maliciously
to get benefits, e.g., sharing old or fake data. Therefore, a lookup mechanism is
required to prevent repetition in data storage and trading. In [9], a reputation-
based data sharing scheme with TWSL is developed to choose a more reliable data
source. However, the data duplication and second-hand sharing is not prevented,
which can cause high storage cost and unfair data trading. In [79,80], PoW con-
sensus is performed by vehicles, whereas joint PoS and PoW algorithm is used
in [80] to build trust among vehicles. However, PoW is difficult to implement on
mobile nodes because length of stable connection or meetup time is very short and
PoW requires greater computational efforts. Moreover, transaction verification de-
lay is another issue. Authors in [123] provided a credit-based solution for reducing
transaction verification delay. This scheme involves central bank authority, which
helps in managing payments and reducing the transaction confirmation delay time.
However, the proposed blockchain-based scheme needs to be integrated into each
client module for a payment transaction that may cause privacy issues, e.g., data
linkage attacks. To address the privacy related issues of EVs, authors in [132] pro-
posed a blockchain-based charging scheme for limited number of EVs. However,
the proposed scheme is not scalable when the number of EVs is increased.
5 Proposed System Model
A consortium blockchain-based trading model is proposed to guarantee secure
trading, storage and privacy of vehicles in IoEV. The proposed system model con-
sists of three layers as shown in Fig. 1. The first layer is composed of IoEV nodes
or vehicles that are communicating with RSUs. The second layer is the infras-
tructure layer that is comprised of RSUs. All RSUs are connected to one another
through a wired connection. The consortium blockchain is implemented on RSUs.
The third layer is composed of IPFS, which is used to store traded data.
In the proposed system model, we have addressed a number of limitations rang-
ing from L1 to L6. Fig. 1shows the mapping of identified limitations and their
Energy buying
request Energy response
Energy demand
L4 S3
L2 S2
L3 S2
L5 S4
L6 S5
L7 S6
L1 S1
Roadside Unit (RSU)
IPFS Storage
Blockchain Data
Data Uploading
Data Trading
Energy Trading
S1: Registrations of vehicles by TA
S2: Use of hash-lists
S3: Use of IPFS
S4: Use of smart contract
S5: Bloom filters
S6: Account mapping scheme
L1: Vehicle authentication
L2: Data duplication
L3: Second-hand sharing of data
L4: Data storage
L5: Dispute handling
L6: Inefficient data lookup
L7: Data linkage attacks
Proposed Solutions
Identified Limitations Interplanetary File System Fuel Vehicle
Electric Vehicle (EV)
Trusted Authority (TA)
Fig. 1. Proposed System Model
respective proposed solutions. The limitation L1 refers to the authentication of ve-
hicles in IoEV network. It is mapped with proposed solution S1 to authenticate
the vehicles. The limitations L2 and L3 refer to data duplication and second-hand
sharing of data, respectively, which occur during D-trading. These limitations are
addressed by solution S2 by performing data hash computation and its comparison
with hash-list. Vehicles are not capable of storing data for a longer time that causes
data storage problem, which is labeled as limitation L4. To solve this limitation,
an IPFS is used as a solution S4 to handle data storage problem. The trading par-
ties face payment disputes, indicated as limitation L5, which is handled by using
smart contracts mapped as S4. The limitation L6 is about inefficient data lookup
mechanism that is addressed by employing bloom filters for fast lookup referred to
as solution S5. The limitation L7 is about data linkage attacks, which is addressed
by using account mapping scheme referred to as solution S6.
5.1 Entities
System model entities are briefly explained as follows:
Trusted Authority (TA)
TA handles the registration of RSUs as data brokers and energy brokers, and
vehicles as IoEV nodes. It is responsible for maintaining the entire IoEV
network and avoids adding malicious vehicles in the network to ensure the
strength and security of the system. Before joining the IoEV network, all
vehicles are required to register themselves by providing their details to TA.
The registration process is done in an offline mode, i.e., vehicles submit
the necessary details, including name, mobile number, address, etc. After
that, each vehicle is provided by initial security parameters. Generally, TA
is assumed to be infeasible to get compromised by any adversary because
of its higher computational power, storage and communication capabilities.
Geographically, each state has its own TA that is responsible for verifying
the credentials of each vehicle from its corresponding registered state.
Vehicles in IoEV network include fuel vehicles or EVs. The simple fuel vehi-
cle can perform D-trading whereas, EVs are capable of performing D-trading
as well as E-trading. However, in our system model, EVs are restricted to
perform only E-trading. Vehicles can play any role in D-trading process,
namely data buyer and data seller. Whereas in E-trading, EVs act as either
energy sellers or energy buyers based on their need for energy. The OBU in
a vehicle has sufficient computational power and communication function-
alities for enabling the vehicle to communicate with RSUs for D-trading or
Smart Meters
The charging poles are integrated with smart meters that calculate and record
the energy volume being traded by seller EVs. The recorded volume of
energy helps buyer EVs to pay the price for traded energy.
In the proposed system model, RSUs are acting as both data and energy
brokers to handle the D-trading and E-trading requests, respectively. A con-
sortium blockchain is implemented on RSUs; thus, all RSUs share the same
distributed ledger. RSUs are responsible for:
checking authenticity of vehicles,
handling D-trading and E-trading requests,
checking data duplication,
uploading data to IPFS,
account mapping of vehicles.
In the proposed model, consortium blockchain is applied on RSUs and is
used to achieve trading transparency and security. Smart contracts are ex-
ecuted within the blockchain network and provide robustness in the sys-
tem. Blockchain ensures security by providing immutability, tamper-proof
records and transparency in trading. It consists of following three compo-
Transaction data: It is the information about trading transactions be-
tween vehicles and RSUs. It includes metadata, type of data, tags,
timestamps, pseudo-id of vehicles.
Blockchain network: The information about trading transactions is
stored and uploaded on the blockchain. It is comprised of data blocks
consisting of hash values and transactional data. Hash value refers to a
link that provides connectivity of data blocks to one another.
Consensus mechanism: In our scheme, PoW consensus is performed
by RSUs, and transactional data is added to the data block. The trans-
actional data is audited by all RSUs.
An IPFS is used for storing traded data to ensure long term availability of
5.2 Design Goals
The proposed system aims to guarantee the privacy of vehicles and secure trading
in IoEV.
Privacy Preservation of Trading Trends
A considerable amount of work is done to preserve the privacy of vehicles.
Besides the privacy preservation of vehicles, the primary objective of this
work is to prevent data linkage attacks on transactional data stored in the
Data Duplication
Data duplication is removed through hash computation and comparison. Hash
of traded data is compared with the previously stored hash-list at RSUs to
check whether data is already been traded or not.
Reliable and Efficient Payments
An efficient and reliable payment mechanism is used in the proposed system
model. All payment transactions are handled through smart contract. So,
there are no chances of any disputes among vehicles.
Effective Audit of Anonymous Transaction
Vehicles in IoEV are assigned pseudo-ids that are used for communication
and financial transactions in trading. The E-trading trends are hidden by
using an account mapping scheme in which new accounts are created for
seller EVs. Whenever a new account is created, the mapping between the
original id, pseudo-id and real account are maintained for an effective audit
of transactions in future.
All trading actions are performed through a trading smart contract. The trad-
ing information is recorded on the blockchain. The seller and buyer infor-
mation, related to either D-trading or E-trading, is publicly verifiable, which
reduces the chances of disputes. Apart from avoiding trading disputes, trans-
parency means that the vehicles must have knowledge about the shared in-
formation during trading.
It refers to the information flow and traceable vehicles interactions. All ve-
hicles, including malicious and non-malicious, are provided equal privileges
in a network. Information shared among vehicles can be tampered by adver-
5.3 Attacker Models
In this section, possible attacks on the proposed system are discussed.
Linkage Attacks
In the threat model, it is considered that the adversary may have prior knowl-
edge about datasets, including multiple resources, e.g., trading information
from charging station, and publicly available trading records in the blockchain.
The linkage attacks involve several methods that are launched by adversaries
using various techniques like data mining algorithms. The common linkage
attacks are semantic attacks [135], set theory-based attacks [136] and device
id-based attacks [137,138].
Privacy Disclosure
Vehicles are connected to RSUs through wireless communication channels.
The adversaries may eavesdrop payment-related information. This informa-
tion is publicly available to all participating nodes in the network. A poten-
tial adversary may infer sensitive information of vehicles that may include
vehicles’ locations, charging patterns, buying and selling trends.
Unreliable Payments
An adversary may attempt to cheat vehicles or RSUs by making fake and
unreliable payments. Double-spending attack can be caused by invalid use
of energy coins, i.e., an adversary may use the same energy coin with two
different transactions.
Denial of Payment
In trading, a seller vehicle may pretend about not receiving any payment. On
the other hand, a buyer EV may also deny buying energy. This ultimately
affects the transaction cost if any vehicle, either buyer or seller, causes the
dispute and refuses to pay the cost [139].
6 Proposed Scheme
In this section, detailed trading scenarios in IoEV are presented using consortium
blockchain. The first scenario is about D-trading and storage, and the second sce-
nario is about screening of E-trading trends to preserve the privacy of EVs. Initially,
all vehicles, either fuel vehicles or EVs, are registered by providing their personal
details to TA. After registration process, vehicles in IoEV network can perform
D-trading or E-trading.
6.0.1 System Initialization
By using bilinear pairing (as discussed in Section 2.1), TA generates G1and G2
along with two generators g1and g2, respectively, that yields to GTof same prime
order q. Two random numbers aand bare selected by TA as its private master
keys. By using these keys, TA computes its public key and a cryptographic hash
function Hsuch that H:{0,1} → Z
A vehicle Viprovides its details to TA for generating pseudo-id PIdiand system
parameters. knumber of hash functions are selected for bloom filter. The set of
system parameters published by TA include {q,G1,G2,GT,g1,g2,H,k}.
6.0.2 Registration
When a vehicle Vijoins the IoEV network, TA selects a random number ri, which
belongs to Z
qsuch that riZ
qwith ri+a6=0 mod qand generates a private key
Si=g1Vi+a. At the time of registration, the original identity Id Viis assigned
to the vehicle, and a pseudo-id PIdiis generated for anonymous communication
in the network. The mapping between Id Viand PIdiis maintained by TA. The
pseudo-ids are required to be generated for each vehicle to ensure the validity of
data source. Even if these pseudo-ids are inferred by adversary, the privacy of
vehicles is not revealed because of zero knowledge about the vehicle.
6.1 D-trading and Storage
In this section, detailed working steps for D-trading and storage are presented,
including system initialization, registration, D-trading request, RSU response and
data duplication verification, transaction verification and consensus and uploading
data to IPFS. Algorithm 1shows the simplified functional steps of D-trading. Fig. 2
shows the workflow of D-trading.
6.1.1 D-trading Request
A vehicle using its PIdirequests a nearby RSU for D-trading and generates a data
selling request Reqsiwith time stamp ti. Thus, the request generated by Viis as
Reqsi= {PIDi,ti,di}
Here, direpresents the data to be traded. Next, Vicomputes a certificate Certiand
signs the diwith its digital signature αthat is generated by using Si. For data coins
transfer, Visends its wallet address Wad dito RSU. Finally, Visends the data selling
request as {Reqsi,Certi,α,Waddi} to RSU.
6.1.2 RSU Response and Data Duplication Verification
RSU validates the signature αon direceived from Viwith PIDi. The PI Diis used
to authenticate the validity of data source. If a request is received from a vehicle
that is not part of IoEV network, then it is simply discarded by RSU. On the other
hand, if the D-trading request is initiated by a legitimate vehicle then RSU takes
further action. All D-trading actions are performed using a trading smart contract.
Algorithm 1 D-trading Algorithm
1: if (IsSourceValid == TRUE) AND
2: (IsDataValid == TRUE) then
3: Generate h1d1
4: if (Hlist contains h1)then
5: return FALSE
6: else
7: add h1to Hlist
8: funds transfer to Waddi
9: sendDataToIPFS di
10: Set dataSeller = {ViPIdi}
11: Store tradeData diagainst hi
12: else
13: Discard Request
The contract is triggered upon meeting some pre-defined conditions. For D-trading,
there are certain conditions that must be satisfied in order to trade data.
Authenticity of vehicles
Credibility of data
Data integrity verification
Data duplication verification
The initial checks, including the authenticity of source, data credibility and data
integrity, are verified by RSUs. For data duplication verification, if initial checks
return true, then the hash of received data is computed as h1. The h1is compared
against the previously stored hash-list. For efficient data lookup, we are using
bloom filters to verify data duplication. The diis passed through knumber of hash
functions in bloom filters; here, the kis 2, and the hash functions used are murmur
hash and fnv series hash. Bloom filter returns a quick response after checking
data duplication. It returns true if data is possibly present and returns false, if no
previous mapping of traded data is present in the list.
6.1.3 Transaction Verification and Consensus
The payment is transferred from RSU to data seller vehicles by using a smart con-
tract. The payment mechanism that we followed is quite similar to proof of delivery
Start Data Trading
Request Data
Generation Consensus
Cancel Reques t
Discard Request Yes
Storing Dat a
Fig. 2. Work Flow for D-trading Operations
scheme in [140]. By using this method, payment actions are made transparent, and
no trader can deny the payment transfer, which prevents payment disputes. Addi-
tionally, the information about D-trading is also recorded in the blockchain, which
is implemented on RSUs. Transactions are validated by all RSUs, whereas PoW
consensus is performed by RSUs for block validation.
A consensus is performed before updating the distributed shared ledger. Ini-
tially, a certain number of trading transactions are stored in a transaction pool, and
these transactions are then packed into a block, which is to be added in blockchain
after performing consensus. The block generation requires the consensus nodes to
participate in joint verification to generate a new block. In the proposed scheme,
consortium blockchain is used with the PoW consensus mechanism. A consensus
is reached when RSUs compete with each other to solve a hash difficulty. A con-
sensus node that solves the problem first is considered as the winner and has right
to add a block. The data block is broadcasted to other RSUs for its validation. The
winner RSU is provided some incentives by blockchain.
6.1.4 Uploading Data to IPFS
For reliable data storage that ensures availability of data for a long term, the traded
data is uploaded to IPFS, which is a P2P and distributed network, and it provides
reliable data storage. RSUs send payment to data seller vehicles and store transac-
tion information in the blockchain. The copy of ledger is updated among all RSUs
and the traded data is uploaded to the IPFS using storage smart contract.
6.2 E-trading and Privacy Preservation
The steps involved in E-trading, including energy demand request, request match-
ing and E-trading, are described as follows.
6.2.1 E-trading Demand Request
Whenever an EV requires energy, it sends energy buying or energy demand request
to RSU. This request includes the current location of EV, energy buying price,
timestamp and amount of required energy. RSU after receiving the demand request
from an EV, it authenticates the requesting EV. After authentication, EV is assigned
a token against its pseudo-id.
6.2.2 E-trading Response Request
Upon receiving an energy demand request from a legitimate EV, the RSU forwards
this request to a pool of EVs in the proximity of requesting EV without revealing
its location information to other EVs. The forwarding request includes energy
demand and price. In response to the RSU’s energy demand request, the EVs
that are willing to sell energy send their energy selling request to the RSU. This
response request includes energy volume and its price.
6.2.3 Request Matching and Token Assignment
Both demand and response requests are matched by RSUs. The response request
that better matches the demand request in terms of energy price and volume as per
demand is finalized by the RSU. After this request and response matching process,
the seller and buyer EVs are assigned a token that validates the authentication of
both seller and buyer EVs. The content of token assigned to both EVs includes
time slot, charging pole location, energy volume to be traded and energy price.
New account
Calculate energy
trading Estimate
Transfer Coins
to new Account
Transfer Coins to
Current Account
Yes Generate
New Account
Account Mapping
Fig. 3. Account Mapping of EVs
6.3 Account Mapping
In the account mapping phase, the account mapping module (depicted in Fig. 3) is
implemented with the objective to hide the private information of EVs about buying
and selling of energy. The payment mechanism is handled through smart contract.
It facilitates in providing payment in the form of coins to corresponding seller EV.
Initially, buyer and seller EVs are assigned tokens that contain the information
about traded energy volume and its price. After E-trading, coins are transferred to
either the current account of seller EV or a new account is created. The account
selection criteria depends on the energy volume and its price by seller EV. In the
account mapping scheme, a threshold value is used to determine whether coins
need to be transferred in a new or current account. A value setter function is used
to determine the threshold value. We use τto denote the threshold value.
Each time the account selection criteria is determined using a dynamic thresh-
old value instead of a fixed τ. The reason for using dynamic τis to prevent data-
mining attacks that are possible due to fixed threshold value. Let F(.)be the τ-
value setter function, now there are following two cases.
Case 1: If the coins transfer amount is lesser than the τ, then the transfer is
made to the current account of seller EV.
Case 2: If the coin transfer amount is greater than the τ, then a new account is
created for seller EV.
Account Generation: For the account generation, assume a function A(.),
which determines the condition for account generation. A(EVs|C)refers to the
function of seller EVswith Cbe the amount of traded energy. It is important to fig-
ure out the proper configuration of the τ-value for the account generation process.
There must be a proper method to discover its τ-value because simply relying on
switching accounts is not enough to defend against attacks. Thus, a proper method
for parallel trading with EV account is necessary instead of switching and block-
ing of the accounts. This value can be achieved through finding τ-value within a
certain period of time Tc. The maximum value for previously traded energy by a
seller EV is calculated within Tc. We have called this parameter a Trading Esti-
mate (TE). While new accounts are created for an EV, a value λis required to add
trading records in the older account by setting up a percentage value of TE. λis a
pre-configured value. In the following Eq. 1,τ|EVsshows the τ-value of seller EV
and E(.)denotes the function of EST.
A(EVs|C) = τ|EVs=T E ×λ=E(.)×λ(1)
Here, we are using the time-series method to predict the TE value from previous
E-trading records. An Exponential Smoothing Technique (EST) is used, which is
often used for the prediction of time-dependent data. The previous values are as-
signed an exponentially decreasing weight over time to predict future values using
exponential function [141]. Among single, double and triple EST, we have used
single smoothing technique [142]. The function of EST, i.e., E(.)is shown in Eq 2.
E(.) = E(i) = f(E(i1)) (2)
E=γEi+ (1γ)Ei1(3)
Here, in Eq 3,Erefers to the forecasted value of energy whereas, Eiand Ei1rep-
resent the values of energy at current iteration and traded energy till last iteration,
respectively. The γis a smoothing factor that shows the amount of weight added
to previous values within the scope of (0, 1) [143]. For the month factor of time
period Tc, the previous E-trading records are considered. Thus, Equation 3can be
written as follows:
E(.) = (γEi+ (1γ)Ei1)×Tc(4)
Using Equation 4, the TE can be calculated to figure out the τ-value for seller
7 Algorithms
In this section, the algorithms used in the proposed scheme are explained briefly.
The Algorithm 2is about account mapping, and the Algorithm 3is about τ-value
detection. The Algorithm 3is executed within the Algorithm 2.
Algorithm 2 Account Mapping Algorithm
Input: Ei,Ei1
Output: Coin Transfer Decision
1: Input Variables
2: Jump to Algorithm 3
3: Get τvalue
4: if Eiτ|EVithen
5: EVsi|CCoin transfer
6: Execute Case 1
7: return Decision: Add Coins to Current Account
8: else
9: Execute Case 2
10: Construct Account Mapping
11: return Decision: Add Coins to New Account
12: Add Records in Blockhain on returns
7.1 Account Mapping Algorithm
The Algorithm 2is for EV account mapping, which is implemented in the payment
transfer step. It depends on the two sets of chores to be completed first. The first
one is about decision making whether new account generation is required or not. If
it returns a positive response, then a new account is created. On the other hand, if
it returns a negative response, the execution of algorithm is terminated. The input
of algorithm includes the E-trading history of seller EV, i.e., history about energy
traded till last transaction and current trading energy volume of EV. The output
generates a decision label, whether payment need to be transferred in the current
or new account. The detailed description of Algorithm 2is given below.
1. The first phase of algorithm involves τ-value detection of seller EV. After
variables initialization, the execution is transferred to Algorithm 3to get τ-
value. This phase is the base for new account generation step. By using a
smart contract, the automatic sum for the previously traded energy volume
of seller EVs is calculated. As an energy broker, RSU is considered to be
trustworthy, so there is no chance of privacy leakage
2. After τ-value detection, the calculated sum of traded energy volume by EV
is compared. If the traded volume of an EV is less than the τ-value, then the
coins are transferred to its current account. Otherwise, coins are transferred
to newly mapped account of EV.
Algorithm 3 Threshold value Detection Algorithm
Input: Ei,Ei1,γ
Output: τ-value, τ|EVi
1: Input Variables
2: /Calculation for a single EV /
3: for trading records of an EV do
4: Calculate average traded energy volume by an EV
5: /Calculation for all EVs /
6: for trading records of sellers EVs do
7: Calculate average traded volume by all EVs
8: Initialize Time factor Tc
9: for each Coin Transfer request do
10: Read total accumulative traded Ei1,τ
11: Calculate τvalue using 1
12: return τ-value
3. The final phase outputs a decision label that determines the decision of coin
transfer, which is recorded in the blockchain.
7.2 Threshold Detection Algorithm
The Algorithm 3is about τ-value detection that runs during phase 1 of Algorithm 2.
This algorithm is formulated to achieve the upper limit of τ-value. The input pa-
rameters include the previous E-trading records of EVs. The previously traded
energy volume of EV is required to calculate the time period. The detailed descrip-
tion of Algorithm 3is given below.
1. In the first phase, the average traded energy of a single EV is calculated from
its previous E-trading records within Tc.
2. In the second phase, the average traded energy for all EVs is calculated using
the time period parameter.
3. In the final phase, input values, including λand γare used to calculate A(.),
i.e., A(EVb|C).
The time complexity for the τ-value detection algorithm is O(n). For each seller
EV, it is required to run this algorithm for calculating τ-value.
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Tentative Time Table
Activity Date
1 Background study and detailed literature review 18-11-2019
2 Formulation of problem and proposing solution 09-01-2020
3 Analysis and dissemination of results 15-02-2020
4 Thesis Writing 15-04-2020
Recommendation by the Research Supervisor
Name: Dr. Nadeem Javaid Signature_____________________Date: ____________________
Recommendation by the Research Co-Supervisor
Name: ____________________ Signature_____________________Date:
Signed by Supervisory Committee
S.# Name of Committee
Designation Signature & Date
1 Dr. Nadeem Javaid Associate Professor
2 Dr. Mariam Akbar Assistant Professor
3 Prof. Dr. Sohail Asghar Professor
4 Dr. Muhammad Manzoor
Ilahi Tamimy
Associate Professor
Approved by Departmental Advisory Committee
Certified that the synopsis has been seen by members of DAC and considered it suitable for
putting up to BASAR.
Departmental Advisory Committee
Name: _____________________________
Signature: _____________________________
Date: _____________________________
Chairman/HoD: ____________________________
Signature: _____________________________
Date: _____________________________
Dean, Faculty of Information Sciences & Technology
_____________________Approved for placement before BASAR.
_____________________Not Approved on the basis of following reasons
Secretary BASAR
_____________________Approved for placement before BASAR.
_____________________Not Approved on the basis of following reasons
Dean, Faculty of Information Sciences & Technology
Please provide the list of courses studied
1. Cryptography Security Essentials
2. Computer Forensics
3. Information Security Essentials
4. Advanced Network Security
5. Number Theory
6. Data Privacy
7. Elliptic Curve Cryptography
8. Advanced Computer Networks
ResearchGate has not been able to resolve any citations for this publication.
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