ThesisPDF Available

Making Electric Vehicles Energy Efficient in Smart Grids using Blockchain

Authors:

Abstract

This thesis examines the use of blockchain technology with the Electric Vehicles (EVs) to tackle different issues related to the existing systems like privacy, security, lack of trust, etc., and to promote transparency, data immutability and tamper proof nature. Moreover, in this study, a new and improved charging strategy, termed as Mobile vehicle-to-Vehicle (M2V) charging strategy, is used to charge the EVs. It is further compared with conventional Vehicle-to-Vehicle (V2V) and Grid-to-Vehicle (G2V) charging strategies to prove its efficacy. In the proposed work, the charging of vehicles is done in a Peer-to-Peer (P2P) manner to remove the intermediary parties and deal with the issues related to them. Moreover, to store the data related to traffic, roads and weather conditions, a Transport System Information Unit (TSIU) is used, which helps in reducing road congestion and minimizing road side accidents. In TSIU, InterPlanetary File System (IPFS) is utilized to store the data in a secured manner. Furthermore, mathematical formulation of the total charging cost, the shortest distance between EVs and charging entities, and the time taken to traverse the shortest distance and to charge the vehicles is done using real time data of EVs. The phenomena of range anxiety and coordination at the crossroads are also dealt with in the study. Moving ahead, edge service providers are introduced to ensure efficient service provisioning. These nodes ensure smooth communication with EVs for successful service provisioning. A caching system is also introduced at the edge nodes to store frequently used services. The power flow and the related energy losses for G2V, V2V and M2V charging strategies are also discussed in this work. In addition, an incentive provisioning mechanism is proposed on the basis of timely delivery of credible messages, which further promotes users’ participation. Furthermore, a hybrid blockchain based vehicular announcement scheme is proposed through which secure and reliable announcement dissemination is realized. In addition, IOTA Tangle is used, which ensures decentralization of the system. The real identities of the vehicles are hidden using the pseudo identities generated through an Elliptic Curve Cryptography (ECC) based pseudonym update mechanism. Moreover, the lightweight trustworthiness verification of vehicles is performed using a Cuckoo Filter (CF). It also prevents revealing the reputation values given to the vehicles upon information dissemination. To reduce the delays caused due to inefficient digital signature verification, transactions are verified in the form of batches. Furthermore, a blockchain based revocation transparency enabled data-oriented trust model is proposed. Password Authenticated Key Exchange by Juggling (J-PAKE) scheme is used in the proposed model to enable mutual authentication. To prevent collusion attacks, message credibility check is performed using Real-time Message Content Validation (RMCV) scheme. Furthermore, K-anonymity algorithm is used to anonymize the reputation data and prevent privacy leakage by restricting the identification of the predictable patterns present in the reputation data. To enable revocation transparency, a Proof of Revocation (PoR) is designed for the revoked vehicles. The vehicle records are stored in IPFS. To enhance the chances of correct information dissemination, incentives are provided to the vehicles using a reputation based incentive mechanism. To check the robustness of the proposed model, attacker models are designed and tested against different attacks including selfish mining attack, double spending attack, etc. To prove the efficiency of the proposed work, extensive simulations are performed. The simulation results prove that the proposed study achieves high success in making EVs energy efficient, secure and robust. Furthermore, the security analysis of the smart contracts used in the proposed work is performed using Oyente, which exhibits the secure nature of the proposed work.
Making Electric Vehicles Energy Efficient in Smart
Grids using Blockchain
(PhD Thesis without Source Codes)
By
Muhammad Umar Javed
CIIT/FA18-PCS-004/ISB
PhD Thesis
In
Computer Science
COMSATS University Islamabad
Islamabad - Pakistan
Fall, 2021
COMSATS University Islamabad
Making Electric Vehicles Energy Efficient in Smart
Grids using Blockchain
A Thesis Presented to
COMSATS University Islamabad, Islamabad
In partial fulfillment
of the requirement for the degree of
PhD (Computer Science)
By
Muhammad Umar Javed
CIIT/FA18-PCS-004/ISB
Fall, 2021
ii
Making Electric Vehicles Energy Efficient in Smart
Grids using Blockchain
A Post Graduate Thesis submitted to the Department of Computer Science as partial
fulfillment of the requirements for the award of Degree of PhD (Computer Science).
Name Registration Number
Muhammad Umar Javed CIIT/FA18-PCS-004/ISB
Supervisor
Dr. Nadeem Javaid
Associate Professor, Department of Computer Science
COMSATS University Islamabad (CUI), Islamabad, Pakistan
Co-Supervisor
Dr. Mariam Akbar
Assistant Professor, Department of Computer Science
COMSATS University Islamabad (CUI), Islamabad, Pakistan
iii
Certificate of Approval
This is to certify that the research work presented in this thesis, entitled “Making Electric Ve-
hicles Energy Efficient in Smart Grids using Blockchain” was conducted by Mr. Muhammad
Umar Javed, CIIT/FA18-PCS-004/ISB, under the supervision of Dr. Nadeem Javaid. No part
of this thesis has been submitted anywhere else for any other degree. This thesis is submitted
to the Department of Computer Science, COMSATS University Islamabad, Islamabad, in the
partial fulfillment of the requirement for the degree of Doctor of Philosophy in the field of
Computer Science.
Muhammad Umar Javed Signature:
Examinations Committee:
..................................................... .....................................................
External Examiner 1: External Examiner 2:
(Designation and Office Address) (Designation and Office Address)
..................................................... .....................................................
Dr. Nadeem Javaid Dr. Majid Iqbal Khan
Supervisor, Head of Department,
Department of Computer Science Department of Computer Science
CUI, Islamabad CUI, Islamabad
..................................................... .....................................................
Dr. Ehsan Ullah Munir Prof. Dr. Zulfiqar Habib
Chairperson, Dean,
Department of Computer Science Faculty of Information Science
CUI and Technology, CUI
iv
Author’s Declaration
I, Muhammad Umar Javed, CIIT/FA18-PCS-004/ISB hereby state that my PhD thesis titled
“Making Electric Vehicles Energy Efficient in Smart Grids using Blockchain” is my own work
and has not been submitted previously by me for taking any degree from this University i.e.,
COMSATS University Islamabad or anywhere else in the country/world.
At any time if my statement is found to be incorrect even after I graduate the University has
the right to withdraw my PhD degree.
Date: September 21, 2021 Signature:
Muhammad Umar Javed
CIIT/FA18-PCS-004/ISB
v
Plagiarism Undertaking
I solemnly declare that research work presented in this thesis titled, “Making Electric Vehicles
Energy Efficient in Smart Grids using Blockchain” is solely my research work with no
significant contribution from any other person. Small contribution/help wherever taken has
been duly acknowledged and that complete thesis has been written by me.
I understand the zero tolernace policy of HEC and COMSATS University Islamabad towards
plagiarism. Therefore, I as an author of the above titled thesis declare that no portion of my
thesis has been plagiarized and any material used as reference is properly referred/cited.
I undertake if I am found guilty of any formal plagiarism in the above titled thesis even after
award of PhD degree, the University reserves the right to withdraw/revoke my PhD degree
and that HEC and the university has the right to publish my name on the HEC/university
website on which names of students are placed who submitted plagiarized thesis.
Date: September 21, 2021 Signature:
Muhammad Umar Javed
CIIT/FA18-PCS-004/ISB
vi
Certificate
It is certified that Muhammad Umar Javed, CIIT/FA18-PCS-004/ISB has carried out all the
work related to this thesis under my supervision at the Department of Computer Science,
COMSATS University Islamabad, Islamabad and the work fulfills the requirement for award
of PhD degree.
Date: September 21, 2021 Supervisor:
Dr. Nadeem Javaid
Associate Professor
Head of Department:
Dr. Majid Iqbal Khan
Associate Professor
Department of Computer Science
vii
DEDICATION
This research work is dedicated
to
my Parents and Siblings for their love and support
and
my Supervisor “Dr. Nadeem Javaid” for his keen guidance.
viii
ACKNOWLEDGMENTS
I pay special thanks to
Allah Almighty
, the most Benevolent and the most Merciful, the
creator of the Universe, who blessed me with the courage and vision to accomplish this work
successfully. I also pay my regards to the
Holy Prophet (PBUH)
without whose blessings I
wouldn’t have been able to accomplish this task.
I also pay my deep, heartfelt thankfulness to my family, especially my
Parents
, who made it
possible for me to pursue my studies despite all the difficulties. I express my sincere gratitude
for their prayers, affection, moral advice, support and encouragement.
I am also highly indebted to my worthy supervisor,
Dr Nadeem Javaid
, for providing me
his positive and professional advice, motivation, dedication and all the efforts he has put to
enhance my capabilities to such an extent that this thesis turned into a reality against all odds.
My gratitude goes out to ComSens family, all my colleagues and friends who directly or
indirectly helped in successful completion of the thesis, and for their constant guidance and
moral support.
It would be unfair if I do not mention all those who contributed to the success of my program.
I am also grateful to
Dr. Majid Iqbal Khan
, Head of Department, for his help in all the
matters, which required his due attention during the time of my research. I also thanks my
worthy co-supervisor Dr. Mariam Akbar and all other faculty members.
Last but not the least, my sincere appreciation goes to the
Higher Education Commission,
Pakistan
for providing me with the full time scholarship under the title
HEC Indigenous
Scholarship
, and helping me financially. Also, special thanks to COMSATS University
Islamabad (CUI), Islamabad campus for providing me with an exceptional environment,
which was essential to complete my thesis and PhD program.
Muhammad Umar Javed
CIIT/FA18-PCS-004/ISB
ix
ABSTRACT
Making Electric Vehicles Energy Efficient in Smart Grids using
Blockchain
This thesis examines the use of blockchain technology with the Electric Vehicles (EVs) to
tackle different issues related to the existing systems like privacy, security, lack of trust,
etc., and to promote transparency, data immutability and tamper proof nature. Moreover,
in this study, a new and improved charging strategy, termed as Mobile vehicle-to-Vehicle
(M2V) charging strategy, is used to charge the EVs. It is further compared with conventional
Vehicle-to-Vehicle (V2V) and Grid-to-Vehicle (G2V) charging strategies to prove its efficacy.
In the proposed work, the charging of vehicles is done in a Peer-to-Peer (P2P) manner to
remove the intermediary parties and deal with the issues related to them. Moreover, to store
the data related to traffic, roads and weather conditions, a Transport System Information Unit
(TSIU) is used, which helps in reducing road congestion and minimizing road side accidents.
In TSIU, InterPlanetary File System (IPFS) is utilized to store the data in a secured manner.
Furthermore, mathematical formulation of the total charging cost, the shortest distance
between EVs and charging entities, and the time taken to traverse the shortest distance and to
charge the vehicles is done using real time data of EVs. The phenomena of range anxiety
and coordination at the crossroads are also dealt with in the study. Moving ahead, edge
service providers are introduced to ensure efficient service provisioning. These nodes ensure
smooth communication with EVs for successful service provisioning. A caching system is
also introduced at the edge nodes to store frequently used services. The power flow and the
related energy losses for G2V, V2V and M2V charging strategies are also discussed in this
work. In addition, an incentive provisioning mechanism is proposed on the basis of timely
delivery of credible messages, which further promotes users’ participation. Furthermore, a
hybrid blockchain based vehicular announcement scheme is proposed through which secure
and reliable announcement dissemination is realized. In addition, IOTA Tangle is used,
which ensures decentralization of the system. The real identities of the vehicles are hidden
using the pseudo identities generated through an Elliptic Curve Cryptography (ECC) based
pseudonym update mechanism. Moreover, the lightweight trustworthiness verification of
vehicles is performed using a Cuckoo Filter (CF). It also prevents revealing the reputation
values given to the vehicles upon information dissemination. To reduce the delays caused
x
due to inefficient digital signature verification, transactions are verified in the form of batches.
Furthermore, a blockchain based revocation transparency enabled data-oriented trust model
is proposed. Password Authenticated Key Exchange by Juggling (J-PAKE) scheme is used in
the proposed model to enable mutual authentication. To prevent collusion attacks, message
credibility check is performed using Real-time Message Content Validation (RMCV) scheme.
Furthermore, K-anonymity algorithm is used to anonymize the reputation data and prevent
privacy leakage by restricting the identification of the predictable patterns present in the
reputation data. To enable revocation transparency, a Proof of Revocation (PoR) is designed
for the revoked vehicles. The vehicle records are stored in IPFS. To enhance the chances of
correct information dissemination, incentives are provided to the vehicles using a reputation
based incentive mechanism. To check the robustness of the proposed model, attacker models
are designed and tested against different attacks including selfish mining attack, double
spending attack, etc. To prove the efficiency of the proposed work, extensive simulations are
performed. The simulation results prove that the proposed study achieves high success in
making EVs energy efficient, secure and robust. Furthermore, the security analysis of the
smart contracts used in the proposed work is performed using Oyente, which exhibits the
secure nature of the proposed work.
xi
Journal Publications
10.
Javed, Muhammad Umar, Nadeem Javaid, Muhammad Waseem Malik, Omaji Samuel,
Adamu Sani Yahaya, and Jalel Ben Othman. “Blockchain based secure, efficient
and coordinated energy trading and data sharing between electric vehicles. Cluster
Computing, Volume NN, Pages NN, Accepted: August 2021, ISSN: 1573-7543. [IF=
1.809] [Download].
9.
Javed, Muhammad Umar, Nadeem Javaid, Abdulaziz Aldegheishem, Nabil Alrajeh,
Muhammad Tahir, and Muhammad Ramzan. “Scheduling charging of electric vehicles
in a secured manner by emphasizing cost minimization using blockchain technology
and IPFS.” Sustainability 12, no. 12 (2020): 5151. DOI: 10.3390/su12125151. [IF=
2.576] [Download].
8.
Javed, Muhammad Umar, Mubariz Rehman, Nadeem Javaid, Abdulaziz Aldegheishem,
Nabil Alrajeh, and Muhammad Tahir. “Blockchain-based secure data storage for
distributed vehicular networks. Applied Sciences 10, no. 6 (2020): 2011. DOI:
10.3390/app10062011. [IF= 2.474] [Download].
7.
Naz, Aqdas, Nadeem Javaid, Muhammad Asif, Muhammad Umar Javed, Abrar Ahmed,
Sardar Muhammad Gulfam, Muhammad Shafiq, and Jin-Ghoo Choi. “Electricity
consumption forecasting using Gated-FCN with ensemble strategy. IEEE Access,
Volume NN, Pages NN, Accepted: August 2021, ISSN: 2169-3536. [IF= 3.367]
[Download].
6.
Javaid, Nadeem, Naeem Jan, and Muhammad Umar Javed. “An adaptive synthesis to
handle imbalanced big data with deep siamese network for electricity theft detection in
smart grids. Journal of Parallel and Distributed Computing 153 (2021): 44-52. [IF=
3.734] [Download].
5.
Sadiq, Ayesha, Muhammad Umar Javed, Rabiya Khalid, Ahmad Almogren, Muham-
mad Shafiq, and Nadeem Javaid.“Blockchain Based Data and Energy Trading in
Internet of Electric Vehicles.” IEEE Access 9 (2020): 7000-7020. [IF= 3.367] [Down-
load].
4.
Yahaya, Adamu Sani, Nadeem Javaid, Muhammad Umar Javed, Muhammad Shafiq,
Wazir Zada Khan, and Mohammed Y. Aalsalem. “Blockchain-based energy trading
xii
and load balancing using contract theory and reputation in a smart community. IEEE
Access 8 (2020): 222168-222186. [IF= 3.004] [Download].
3.
Bukhsh, Rasool, Muhammad Umar Javed, Aisha Fatima, Nadeem Javaid, Muhammad
Shafiq, and Jin-Ghoo Choi. “Cost Efficient Real Time Electricity Management Services
for Green Community Using Fog. Energies 13, no. 12 (2020): 3164. [IF= 3.004]
[Download].
2.
Khalid, Rabiya, Nadeem Javaid, Ahmad Almogren, Muhammad Umar Javed, Sakeena
Javaid, and Mansour Zuair. “A blockchain-based load balancing in decentralized hybrid
P2P energy trading market in smart grid.” IEEE Access 8 (2020): 47047-47062. [IF=
3.367] [Download].
1.
Naz, Aqdas, Muhammad Umar Javed, Nadeem Javaid, Tanzila Saba, Musaed Alhussein,
and Khursheed Aurangzeb. “Short-term electric load and price forecasting using
enhanced extreme learning machine optimization in smart grids.” Energies 12, no. 5
(2019): 866. [IF= 3.004] [Download].
xiii
Conference Proceedings/Book Chapters
6.
Javed, Muhammad Umar, Abid Jamal, Nadeem Javaid, Noman Haider, and Muhammad
Imran. “Conditional Anonymity enabled Blockchain-based Ad Dissemination in
Vehicular Ad-hoc Network. In 2020 International Wireless Communications and
Mobile Computing (IWCMC), pp. 2149-2153. IEEE, 2020. [Download].
5.
Javed, Muhammad Umar, and Nadeem Javaid. “Scheduling charging of electric
vehicles in a secured manner using blockchain technology. In 2019 International
Conference on Frontiers of Information Technology (FIT), pp. 351-3515. IEEE, 2019.
[Download].
4.
Hameed, Javaria, Rabiya Khalid, Muhammad Umar Javed, Sakeena Javaid, Sheeraz
Ahmed, and Nadeem Javaid. “Enhanced Classification with Logistic Regression for
Short Term Price and Load Forecasting in Smart Homes. In 2020 3rd International
Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp.
1-6. IEEE, 2020. [Download].
3.
Khan, Beenish, Rabiya Khalid, Muhammad Umar Javed, Sakeena Javaid, Sheeraz
Ahmed, and Nadeem Javaid. “Short-Term Load and Price Forecasting based on
Improved Convolutional Neural Network.” In 2020 3rd International Conference on
Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1-6. IEEE,
2020. [Download].
2.
Ashfaq, Tehreem, Nadeem Javaid, Muhammad Umar Javed, Muhammad Imran, No-
man Haider, and Nidal Nasser. “Secure Energy Trading for Electric Vehicles using
Consortium Blockchain and k-Nearest Neighbor. In 2020 International Wireless
Communications and Mobile Computing (IWCMC), pp. 2235-2239. IEEE, 2020.
[Download].
1.
Rehman, Mubariz, Zahoor Ali Khan, Muhammad Umar Javed, Muhammad Zohaib
Iftikhar, Usman Majeed, Imam Bux, and Nadeem Javaid. A Blockchain Based
Distributed Vehicular Network Architecture for Smart Cities.” In AINA Workshops,
pp. 320-331. 2020. [Download].
xiv
TABLE OF CONTENTS
1 Introduction ..................................................................... 1
1.1 Problem statement ................................................................ 5
1.2 Contributions ...................................................................... 9
1.3 Thesis organization............................................................... 12
1.4 Conclusion of the chapter ....................................................... 13
2 Related Work .................................................................... 14
2.1 Blockchain......................................................................... 15
2.1.1 Blockchain technology .................................................. 15
2.1.2 Blockchain features ...................................................... 17
2.1.3 Architecture of blockchain ............................................. 17
2.1.4 Consensus mechanisms ................................................. 19
2.1.5 Properties of consensus mechanisms .................................. 22
2.1.6 Incentives ................................................................. 22
2.1.6.1 Types of incentives ........................................... 22
2.1.6.2 Incentive strategies ........................................... 23
2.1.7 Energy trading and data sharing and trading systems ............... 24
2.2 Related work of technical papers............................................... 25
2.2.1 Blockchain in VENs ..................................................... 25
2.2.1.1 Charging scheduling of EVs ................................ 27
2.2.1.2 Authorization of EVs......................................... 28
2.2.1.3 Coordination between EVs .................................. 28
2.2.2 Blockchain in AI and IoT ............................................... 29
2.2.3 Blockchain in WSNs .................................................... 30
2.2.4 Blockchain in healthcare ................................................ 30
2.3 Related work with respect to some important characteristics .............. 31
2.3.1 Authentication............................................................ 31
2.3.2 Trust management ....................................................... 32
2.3.3 Privacy..................................................................... 34
xv
2.3.4 Efficiency ................................................................. 34
2.4 Related work of survey paper ................................................... 35
2.4.1 Critical analysis of the survey papers ................................. 41
2.4.2 Recency calculation ..................................................... 45
2.5 Blockchain technology for energy trading, data trading and sharing,
and incentive mechanisms in industrial sector: review and open re-
search challenges ................................................................. 46
2.5.1 Energy trading, and data trading and sharing in industrial sector .46
2.5.1.1 Financial industry ............................................. 54
2.5.1.2 Data trading industries ....................................... 54
2.5.1.3 Crowdsensed data trading ................................... 55
2.5.1.4 Healthcare industry ........................................... 56
2.5.1.5 Logistic industry .............................................. 57
2.5.1.6 Manufacturing industry ...................................... 57
2.5.2 Incentives in industrial sector .......................................... 59
2.5.3 Comparative analysis .................................................... 65
2.5.3.1 Blockchain based energy trading, and data trading and
sharing methods............................................... 65
2.5.3.2 Blockchain based incentive methods....................... 71
2.5.4 Critical analysis .......................................................... 72
2.5.4.1 Critical analysis of papers having energy trading, data
trading and sharing and incentive mechanisms ........... 72
2.6 Conclusion of the chapter ....................................................... 74
3 Proposed System Models and Solutions ...................................... 78
3.1 Combined system model ...................................................... 79
3.2 Charging scheduling of EVs.................................................. 83
3.2.1 Workflow of the proposed system model ............................. 84
3.2.2 Authentication of vehicles .............................................. 86
3.2.3 Transport System Information Unit (TSIU) .......................... 86
3.2.4 InterPlanetary File System (IPFS) ..................................... 87
3.2.5 Location privacy ......................................................... 88
xvi
3.2.6 Charging schedule ....................................................... 88
3.2.7 Scheduling of vehicles’ charging ...................................... 90
3.2.8 Great-Circle Distance ................................................... 90
3.2.9 Calculation of time taken for covering the distance ................. 92
3.2.10 Calculation of time taken for charging the vehicles ................. 93
3.2.10.1 Charging scheduling algorithm ............................. 93
3.2.11 Mathematical formulation .............................................. 93
3.2.11.1 Charging cost calculation .................................... 93
3.2.11.2 Objective function ............................................ 95
3.2.11.3 Comparison of equations .................................... 96
3.2.12 Power flow and associated losses ...................................... 96
3.2.12.1 Formulating the associated energy losses ................. 98
3.2.13 Incentive provisioning...................................................101
3.3 Secure data storage of EVs ...................................................101
3.3.1 Architecture overview ...................................................101
3.3.2 Edge node model overview .............................................103
3.3.3 Service provisioning in VEN ...........................................103
3.3.4 RSU overview ............................................................105
3.3.5 Authentication of VEN..................................................105
3.3.6 Caching and IPFS ........................................................106
3.3.7 Workflow of the proposed system model .............................106
3.3.8 Propositions...............................................................107
3.4 Secure and coordinated energy trading for VEN ........................109
3.4.1 Algorithm for energy trading ...........................................116
3.4.2 Mathematical formulation ..............................................119
3.4.2.1 Delays in VEN ................................................119
3.4.2.2 Objective function ............................................120
3.4.3 Coordination at the crossroads .........................................120
3.4.4 Assumptions ..............................................................120
3.4.5 Coordination types .......................................................120
3.4.6 Coordination cases .......................................................122
xvii
3.4.6.1 Relation between coordination and EV charging.........123
3.4.7 Message credibility and range anxiety ................................124
3.4.8 Incentive provisioning...................................................125
3.5 Privacy preservation in VENs using a blockchain based announce-
ment scheme .....................................................................126
3.5.1 Entities ....................................................................127
3.5.2 Proposed methodology ..................................................128
3.5.3 IPFS........................................................................131
3.5.4 Algorithm complexity analysis ........................................132
3.6 A blockchain based revocation transparency enabled trust model
for VENs..........................................................................133
3.6.1 System initialization .....................................................134
3.6.2 Registration ...............................................................134
3.6.3 Mutual authentication ...................................................134
3.6.3.1 J-PAKE initialization .........................................134
3.6.4 RMCV based message trustworthiness calculation .................135
3.6.4.1 Message classification .......................................135
3.6.4.2 Data-oriented trustworthiness calculation .................136
3.6.5 Privacy preservation using K-anonymity .............................139
3.6.6 Revocation transparency ................................................140
3.6.7 CFs based revocation verification .....................................140
3.7 Conclusion of the chapter .......................................................140
4 Simulation Results and Discussion ............................................ 141
4.1 Simulation environment .........................................................142
4.1.1 Remix Integrated Development Environment (Remix IDE) .......142
4.1.2 Ganache ...................................................................142
4.1.3 Metamask .................................................................142
4.1.4 System specification .....................................................142
4.2 Charging scheduling of EVs..................................................143
4.3 Secure data storage of EVs ...................................................156
4.4 Secure and coordinated energy trading for VEN ........................162
xviii
4.4.1 Comparison ...............................................................171
4.5 Privacy preservation in VENs using a blockchain based announce-
ment scheme .....................................................................173
4.5.1 Results for Cuckoo Filter ...............................................173
4.5.2 Authentication scheme results .........................................175
4.5.3 Batch verification results................................................176
4.5.4 Ethereum blockchain results ...........................................177
4.5.5 IPFS results ...............................................................177
4.5.6 IOTA Tangle results .....................................................180
4.6 A blockchain based revocation transparency enabled trust model
for VENs..........................................................................180
4.7 Conclusion of the chapter .......................................................186
5 Security Analyses of Smart Contracts and Induction of Attacker Models 187
5.1 Security features of the proposed system .....................................188
5.2 Attacker model....................................................................189
5.2.1 Privacy leakage predictable patterns in vehicles’ data ..............197
5.2.2 Attacks on J-PAKE scheme ............................................199
5.3 Smart contract analysis ..........................................................201
5.3.1 Privacy leakage due to quasi-identifiers ..............................203
5.4 Conclusion of the chapter .......................................................204
6 Conclusion and Future Work ..................................................205
6.1 Conclusions and Future Recommendations...................................206
6.1.1 Conclusions...............................................................206
6.1.2 Significance of the research ............................................208
6.1.3 Future recommendations and suggested solutions ..................209
xix
LIST OF FIGURES
1.1 Flowchart of Problem Statement .................................................. 10
2.1 Block Structure ..................................................................... 17
2.2 Blockchain Features................................................................ 18
2.3 Blockchain Architecture ........................................................... 19
2.4 Incentives’ Strategies and Types................................................... 24
2.5 Blockchain Integration ............................................................. 60
2.6 Incentive Mechanisms in Different Domains ..................................... 66
3.1 Proposed System Model (Combined) ............................................. 83
3.2 Sub System Model 1 ............................................................... 85
3.3 Authentication of Vehicles......................................................... 87
3.4 TSIU Model ........................................................................ 88
3.5 Sequence Diagram of IPFS ........................................................ 89
3.6 Charging Process of an EV from Different Sources .............................. 91
3.7 Energy Losses in VEN ............................................................. 98
3.8 Flowchart of Rewards/Penalties Incurred by Vehicles ...........................102
3.9 Sub System Model 2 ...............................................................104
3.10 Sub System Model 3 ...............................................................110
3.11 Sequence Diagram for Buyer EV’s Energy Trading Scheme ....................112
3.12 Sequence Diagram for Seller EV’s Energy Trading Scheme ....................115
3.13 Coordination at the Crossroads ....................................................121
3.14 Sub System Model 4 ...............................................................127
3.15 Sub System Model 5 ...............................................................133
4.1 Gas Consumption for EV Registration and Charging Schedule .................144
4.2 Storage Time Comparison of Redundant and Filtered Data .....................144
4.3 Gas Consumption for IPFS ........................................................145
4.4 Average Payments..................................................................145
4.5 Cost Comparison of Different Scenarios..........................................146
xx
4.6 Charging Time Taken by EVs .....................................................147
4.7 Time Taken by EVs to Traverse the Distance to the Nearest Charging Entity .. 147
4.8 Cable Loss Incurred ................................................................148
4.9 Inverter Loss Incurred..............................................................148
4.10 Weather Loss Incurred .............................................................149
4.11 Combined Loss Incurred ...........................................................149
4.12 Users’ Participation against Reputation Values...................................150
4.13 Hashes Generated for Different Transactions..................................... 151
4.14 Mining Time for Different Transactions ..........................................151
4.15 Adding a Weather Information File in IPFS ......................................155
4.16 Assigning Malicious State to a Vehicle by IPFS..................................156
4.17 Get File Count and File ID from IPFS ............................................157
4.18 Smart Contracts Deployment Cost. ............................................... 157
4.19 Gas Consumption for IPFS Functions. ............................................158
4.20 Gas Consumption for IPFS Smart Contract. ......................................159
4.21 Gas Consumption for VN Functions. .............................................159
4.22 Gas Consumption for VN Smart Contract. .......................................160
4.23 Comparison of Hashing Algorithms. ..............................................160
4.24 Performance of System with and without Cache Server..........................161
4.25 Comparison of Encryption/Decryption Algorithms. .............................162
4.26 Trust Management between Ordinary and Edge Node Vehicles. ...............163
4.27 Real time Distribution of CSs .....................................................164
4.28 Gas Consumption of Main Smart Contract .......................................165
4.29 ESC’s Gas Consumption Cost .....................................................165
4.30 PSC’s Gas Consumption Cost .....................................................166
4.31 Authentication of Vehicles.........................................................166
4.32 Energy Required to Recharge EVs’ Battery ......................................167
4.33 Energy Units Available for Selling by EVs .......................................167
4.34 Energy Selling Price Announced by Multiple EVs ...............................168
4.35 Change in Energy Selling Price Announced by an EV with respect to Time . . . 168
4.36 SoCpr Values of Multiple EVs .....................................................169
xxi
4.37 Change in SoCpr of an EV with respect to Time .................................170
4.38 Filtration of Broadcasted Messages ............................................... 171
4.39 Range Anxiety of Vehicle Users...................................................171
4.40 Delays in the Vehicular Energy Network .........................................172
4.41 Reduction in Risk Factor due to Coordination ...................................172
4.42 False Positive Percentage Comparison of CF and BF ............................174
4.43 CF False Positive Rate vs Size ....................................................174
4.44 CF Time Consumption .............................................................175
4.45 Key Generation Time Comparison ................................................176
4.46 Key Generation Time Comparison ................................................176
4.47 Smart Contract Functions’ Execution Time ......................................177
4.48 Smart Contract Gas Consumption .................................................178
4.49 IPFS Execution Time ..............................................................178
4.50 IPFS Storage Results ...............................................................179
4.51 IOTA Data Upload and Retrieval Time ...........................................179
4.52 Time Taken by J-PAKE Scheme Operations......................................181
4.53 Different Levels of K-anonymity.................................................. 181
4.54 K-anonymity Effects on Size of Data .............................................182
4.55 Time Taken by IPFS Operations...................................................183
4.56 Time Taken by Certificate Revocation Operations ...............................183
4.57 False Positive Rate of CFs .........................................................184
4.58 Time Taken by CF Operations.....................................................184
4.59 Time taken for Trustworthiness Evaluation by RMCV...........................185
4.60 Effects of Route Path Similarity on the Trust Score ..............................185
4.61 Effects of Conflicting Values on the Trust Score .................................186
5.1 Probability of Double Spending Attack ...........................................195
5.2 Revenue Generated (%)............................................................197
5.3 Vehicles’ reputation data retrieved from Blockchain ............................198
5.4 Vehicles reputation data after sorting w.r.t reputation scores ....................198
5.5 PIDs that have predictable pattern in reputation scores ..........................199
5.6 Same RID for all the PIDs found in previous step................................199
xxii
5.7 Locations pattern exposed .........................................................199
5.8 Oyente Analysis of Secure Data Storage Smart Contract ........................202
5.9 Oyente Analysis of Energy Smart Contract.......................................202
5.10 Oyente Analysis of Payment Smart Contract .....................................202
5.11 Oyente Analysis of Encrypted Data Sharing Smart Contract ....................203
5.12 Oyente Analysis of CF Smart Contract ...........................................203
5.13 Oyente analysis of Revocation Smart Contract ...................................203
5.14 Probability of Privacy Leakage due to Quasi-identifiers .........................204
xxiii
LIST OF TABLES
2.1 Comparison between Consensus Mechanisms.................................... 21
2.2 Critical Analysis of the Survey Papers ............................................ 42
2.3 Ranking Criteria for Survey Papers ............................................... 45
2.4 Analysis of Energy Trading, and Data Trading and Sharing Mechanisms . .. .. . 50
2.5 Energy Trading, and Data Trading and Sharing Analysis in Other Domains . .. 58
2.6 Analysis of Incentive Mechanisms ................................................ 63
2.7 Critical Analysis of Platforms used for Incentive ................................ 74
2.8 Critical Analysis of Key Work Done on Trading, Data Sharing and Incentives 75
3.1 Mapping of Problems with the Proposed Solutions .............................. 80
3.2 Real Time Data of EVs ............................................................ 89
3.3 Real Time Charging Cost of EVs ................................................. 96
3.4 Comparison of Equations .......................................................... 97
3.5 Parameters and their Values ....................................................... 99
3.6
Time and Expenses Required to cover the Distance for Reaching the Nearest
CSs ..................................................................................118
4.1 Simulation Parameters ............................................................143
4.2 Values of Ether and its Multipliers ................................................143
4.3 Generated Keys for Digital Signatures ............................................152
4.4 Contract Creation ..................................................................152
4.5 EV Registration ....................................................................153
4.6 MV Registration....................................................................153
4.7 Get Energy Requirement...........................................................154
4.8 Energy Requirement for Next Schedule ..........................................154
4.9 Mining Time and Hashes Generated Against Different Difficulty Levels. . . . . .. 155
4.10 Simulation Parameters’ Values ....................................................163
xxiv
LIST OF ABBREVIATIONS
A
ABE Attribute Based Encryption
AES Advanced Encryption Standard
AI Artificial Intelligence
AU Authorization Unit
B
BF Bloom Filter
BGIRSS Blockchain based Government Information Resource Sharing System
BSIS Blockchain based Secure Incentive Scheme
B2ITS Blockchain based Intelligent Transport System
C
CA Certificate Authority
CF Cuckoo Filter
CP-ABE Ciphertext-Policy Attribute-Based Encryption
CRL Certificate Revocation List
CS Charging Station
CSI Channel State Information
CSP Cloud Service Provider
D
DAC Distributed Autonomous Corporation
DAG Directed Acyclic Graphs
DAO Decentralized Autonomous Organization
DApps Decentralized Applications
DCSM Data specific Centralized Sharing Model
DDoS Distributed Denial of Service
DHT Distributed Hash Table
DLT Decentralized Ledger Technology
xxv
DPoS Delegated Proof of Stake
DR Demand Response
DSRC Dedicated Short-Range Communication
D2D Device to Device
E
EC Energy Consumer
ECC Elliptic Curve Cryptography
ECDSA Elliptic Curve Digital Signature Algorithm
ECU Electronic Control Unit
E-health Electronic health
EMR Electronic Medical Record
ESC Energy Smart Contract
E-TX Express Transaction
EV Electric Vehicle
F
FBSS Fair Blind Signature Scheme
FTF Futile Transactions Filter
G
G2V Grid-to-Vehicle
I
ICS Indicator Centric Schema
ICT Information and Communication Technologies
IDE Integrated Development Environment
IoV Internet of Vehicles
IoT Internet of Things
IPFS InterPlanetary File System
IR Individual Rationality
xxvi
ITS Intelligent Transport System
IV Intelligent Vehicle
IVTP Intelligent Vehicle Trust Point
J
J-PAKE Password Authenticated Key Exchange by Juggling
L
LAG Local AGgregator
LCA Local Coordination Agent
M
MANET Mobile Ad-hoc Network
MAS Multi Agent System
MCSM Managerially Centralized Sharing Model
MCV Mobile Charging Vehicle
MDP Markov Decision Process
MG Micro Grid
MILP Mixed Integer Linear Programming
MNM Method of Node Matching
M-Pool Matching Pool
MPC Multi Party Computing
M2V Mobile vehicle-to-Vehicle
N
NSGA Non-dominated Sorting Genetic Algorithm
O
OBU On-Board Unit
OppNets Opportunistic Networks
P
xxvii
PBFT Practical Byzantine Fault Tolerance
PC Pseudonym Certificate
PCM Product Credit Mechanism
PDP Provable Data Possession
PEKS Public key Encryption with Keyword Search
PtMS Parallel transportation Management System
PHI Personal Health Information
PnL Peak and Loss ratio
PoA Proof of Authority
PoC Proof of Collaboration
PoD Proof of Delivery
PoI Point of Interest
PoR Proof of Revocation
PoS Proof of Stake
PoW Proof of Work
P-Pool Pairing Pool
PSC Payment Smart Contract
P2M Person to Machine
P2P Peer-to-Peer
P2PET P2P Energy Trading
Q
QGE Quality Grading Evaluation
QoI Quality of Information
R
RA Resource Agent
RA Registration Authority
RADT Reverse-Auction-and-blockchain based crowdsensed Data Trading
RES Renewable Energy Sources
RMCV Real-time Message Content Validation
xxviii
RSU Road Side Unit
R2R RSU to RSU
S
SCA Social Coordination Agent
SCM Supply Chain Management
SDN Software Defined Networking
SES Small Energy Supplier
SG Smart Grid
SH Smart Home
SMN Social Manufacturing Network
SMR Social Manufacturing Resource
SocialM Social Manufacturing
SPoR Sentinel Proof of Retrievability
SSE Searchable Symmetric Encryption
T
TSIU Transport System Information Unit
U
UKS Unknown Key Share
V
VANET Vehicular Ad-hoc Network
VEN Vehicular Energy Network
V2G Vehicle-to-Grid
V2I Vehicle-to-Infrastructure
V2R Vehicle to Road Side Unit
V2V Vehicle-to-Vehicle
W
xxix
WSN Wireless Sensor Network
xxx
NOMENCLATURE
αMining power of a selfish node
AESkey AES symmetric key
b0,b1,b2,b3Third order parameters
Bcap Battery Capacity
CCatch-up function
CCS CS charging cost
CEV EV charging cost
CMV MV charging cost
Cmax
EV Maximum EV charging cost
Cmax
MV Maximum MV charging cost
C1TCharging cost
C2TDistance cost
C3TWaiting cost
C4Tr Reward cost
C4T p Penalty cost
CSlat Latitude of CS
CSlong Longitude of CS
CTotal Total cost
disS2VDistance between vehicle and CS
disM2VDistance between vehicle and MV
disV2VDistance between vehicle and EV
DEuclidean Distance
DijDistance between EV and CS
ety pe Type of event
EElliptic curve
Eav Energy Available
Eav
kW h Energy Available in kWh
Enc Encryption
Epr Energy Present
Epr
kW h Energy Present in kWh
xxxi
Erq Energy Required
Erq
kW h Energy Required in kWh
Eth Energy Threshold
end to enddelay End-to-end-delay
EV bBuyer EV
EV sSeller EV
Exrq Required Energy Expenses
EVlat Latitude of EV
EVlong Longitude of EV
FB Feedback message
FBList
t1t2FB list from t1 to t2
gGenerator of elliptic group
HHash function
ID Identity
IPF SHasht1t2Hash of Tangle record stored in IPFS between t1 and t2
ItCurrent at time t
KThe number of minimum confirmations required
Lcable Length of charging cable
Link() Mapping
Loc Location
Loccur Current location
locint Location of initiator
locqLocation of query issuer
MaxDcMaximum distance
mpath Message propagation path
MVlat Latitude of MV
MVlong Longitude of MV
nNumber of blocks generated
NcNumber of messages in a cluster
Ndi f Number of distinct vehicles in the messages’ routing path
NeNumber of messages related to event
xxxii
Nsrc Number of distinct message sources
PAttacker’s progress function
PCn
ViCurrent PC of Vi
PCn+1
ViNew PC of Vi
P
CS CS generation price
P
EV EV generation price
PID Pseudo identity
P
MV MV generation price
Ptotal
loss Total power loss
P
leakage Probability of privacy leakage
procdelay Processing delay
propd elay Propagation delay
PK Public Key
PKCA CA public key
PKECC ECC public key
PKViPublic key of Vi
PsPrice for Traded Energy
PUReq Pseudonym update request
QPrice per unit
qAttacker’s probability
Qev
nNumber of EVs present in a queue
quedelay Queuing delay
RRole of EV
RID Real identity
RL Reputation list
RLenc Encrypted RL
Rpool Total revenue of the block pool
rpool Block mined within the pool
rothers Block mined outside the pool
Sig Digital signature
SK Private Key
xxxiii
SKCA CA private key
SKECC ECC private key
SKViPrivate key of Vi
SoCpr Present State of Charge
SoCth Threshold State of Charge
te Time when results are received
TpTime needed by honest nodes to mine blocks
TqTime needed by attacker nodes to mine blocks
Trq Time required to reach the CS
ts Timestamp
TsSaved units
TwWasted units
totaldelay Total delay
transdelay Transmission delay
T Recordt1t2Tangle record from t1 to t2
T xID Transaction identity
T xViTransaction by Vi
VTotal number of vehicles in queue
vNumber of incoming vehicle
ViAnnouncement initiator vehicle
V f Vehicle giving FB
xNumber of quasi-identifiers
ZMultiplicative group
zInitial disadvantage of an attacker
ηinverter Inverter inefficiency
τThreshold distance
δThreshold difference between vehicles in charging queue
Gap between lengths of public chain and private chain
ωWeather effect coefficient
γProbability of a selfish node taking over the network
xxxiv
Chapter 1
Introduction
1
Over the past few years, the world has witnessed an immense increase in the population and
the migration of people from rural to urban areas. This migration has provided people with
many benefits. On the other side, it has posed some serious threats as well. The scarcity
of various necessities like health facilities, employment opportunities, and physiological
and social needs are some of them. The outburst of population escalated the number of
houses, vehicles and industries. All these entities require a massive amount of energy to
power themselves. Moreover, electricity is needed by the people to do their daily chores like
washing and ironing clothes, using cellular and multimedia devices, running industrial plants
and motors, and traveling from one place to another place [
1
], [
2
]. This vast energy demand
has led to an imbalance between energy supply and demand [
3
]. It further directs the society
towards the problems of irregular load shedding, increased electricity bills and substantial
financial loss due to voltage fluctuation [4], [5].
Consequently, a green and sustainable smart city has become the need of the current times
to tackle the above mentioned issues [
6
]. Though, the concept of smart city is not new, and
many contributions have been made in making the cities smarter, cleaner and greener over the
past few years. In a smart city, everything is transformed from an old conventional mode to an
intelligent and updated form. These things include power grids, homes, hospitals, educational
institutes, industries and transportation. The scarcity of fossil fuels is tackled by exploring
Renewable Energy Sources (RES) and utilizing them to their full potential. Examples of
RES include solar photovoltaic, wind and hydro. Despite of RES adding up to the energy
generation, creating a balance between energy demand and supply, and forecasting electricity
load and price [
7
] - [
10
], some issues like intermittent nature and unavailability of RES also
exist [
11
]. The emergence of smart cities has changed the global transportation sector and
introduced a smart transportation system [
12
]. With the aid of communication and information
technologies, the smart transportation system provides various automated services, such as
traffic control, information sharing, navigation, provisioning of parking lot information to
public transport like taxis, etc., [
13
], [
14
]. It also ensures secure communication between
Electric Vehicles (EVs) and Road Side Units (RSUs), and the safety and privacy of vehicles’
owners [
15
]. Moreover, the idea of EVs in the smart transportation sector emerges in order to
develop an eco-friendly transportation system. Besides efficient transportation [
16
], EVs also
provide numerous benefits, such as less carbon emission, and less operation and maintenance
cost as compared to conventional vehicles [17].
The immense surge in the vehicles running on the highways has provided many benefits to
2
the people and have made traveling easy. The IHS Markit estimated that the sale of EVs
could exceed 12 million by 2025 [
18
]. With the introduction of state-of-the-art vehicles,
large distances are now covered in less time. However, the increase in vehicles exhibits
various issues like road congestion, road accidents and increased amounts of pollution. To
tackle these issues, intelligent vehicles are proposed, which are able to communicate with
each other, share important information, and avoid both road congestion and road accidents.
Furthermore, these vehicles are powered using RES, which helps in reducing the pollution
and the gas emissions [
19
]. Despite of these benefits, there still exist some issues like
data storage issue, lack of security and privacy, absence of trust between vehicle users and
unavailability of data transparency. In Vehicular Energy Networks (VENs), the vehicles share
important information with each other like road conditions and weather conditions. This type
of communication is known as Vehicle-to-Vehicle (V2V) communication. Moreover, the
communication between vehicles and the Charging Stations (CSs) takes place for fulfilling the
energy requirements and this kind of communication is termed as Vehicle-to-Grid (V2G) and
Grid-to-Vehicle (G2V) communication [
20
]. For the storage of important information, which
can be used for later use, the vehicles communicate with RSUs and other infrastructure in
Vehicle-to-Road Side Unit (V2R) and Vehicle-to-Infrastructure (V2I) modes, respectively. No
doubt, EVs have benefited the human race in numerous ways. However, they still exhibit some
issues like lack of trust, security and privacy, data redundancy [
11
], inefficient coordination
between EVs [
21
], delays in message propagation and range anxiety [
22
]. In VENs, several
other issues also arise, such as selfish behavior of EVs and CSs, large distance between nodes,
cost of traveling incurred to reach the CSs, etc. Therefore, many strategies and systems are
presented in literature to overcome those issues.
Intelligent Transportation System (ITS) plays a significant role in developing the smart
cities’ infrastructure by decreasing both the traffic congestion and the roadblocks. Mobile
Ad-hoc Network (MANET) based VENs are one of the most important uses of ITS. The
network enables both V2V and V2I communication between vehicles and RSUs. The
communication is performed to distribute important information regarding road and weather
conditions, road blockages, alternate routes, etc. Vehicles in VENs consist of On-Board Units
(OBUs), which assist in communicating with the nearby vehicles and other infrastructure
via a Dedicated Short-Range Communication (DSRC) protocol. VENs provide numerous
advantages to vehicle users. However, they are also subjected to many threats. Some of
them are disseminating fraudulent information, single point of failure and absence of privacy
3
preservation.
To overcome the issues, energy trading mechanisms are presented. However, the conventional
centralized energy trading mechanisms depend on intermediary bodies to audit, verify and
manage energy transactions [
23
]. The inclusion of an intermediary body in the network is
a security threat, and causes issues like Denial-of-Service (DoS) attack, a single point of
failure and privacy leakage [
24
]. Therefore, energy trading environment requires a secure,
reliable and distributed mechanism [
25
]. Moreover, an incentive mechanism, in terms of
cryptocurrency or reputation, is introduced to motivate nodes to perform efficient energy
trading. It also helps to avoid the selfish behavior of nodes [
26
]. Furthermore, different
solutions present efficient distance measurement techniques. These solutions also reduce the
amount of traveling required to reach the destination [27], [28].
In order to solve the aforementioned challenges in VENs, blockchain technology is considered
as an effective solution that provides distributed, secure and efficient energy trading [
29
] - [
33
].
The concept of blockchain technology was initially brought forward by Satoshi Nakamoto in
2008 [
34
]. Blockchain is a decentralized Peer-to-Peer (P2P) network technology in which
blocks are used to store the transactions. The blocks are joined together in a chronological
manner using hashes. Hence, it is given the name blockchain, i.e., blocks linked together
in a chain. The blocks comprise of hashes, transaction data, timestamp, Merkle root and
nonce [
32
]. Blockchain was proposed to tackle the issues related to the centralized networks
like a single point of failure, increased transaction cost and vulnerability to security breach.
Furthermore, it also ensures security, transparency, reliability, auditability and integrity of
the transactions. It also ensures privacy of the network nodes [
35
], [
36
]. Due to its benefits,
blockchain is widely used in many fields like energy [
37
] - [
42
], cloud computing [
43
],
Wireless Sensor Network (WSN) [
44
], [
45
], Vehicular Ad-hoc Network (VANET) [
46
]-[
50
],
Internet of Things (IoT) [51]-[53], etc.
In this study, several survey papers are initially selected for literature review. After scanning
the papers, only those papers are chosen, which are very closely related to the proposed
work. Similarly, a good number of technical papers are selected, which discuss about
blockchain based energy trading, data trading and sharing, EV charging, coordination between
EVs, incentive mechanisms, etc., in different fields mainly energy, finance, data trading,
crowdsensed data trading, healthcare, logistics and manufacturing. After identifying the
limitations from the intensive literature review, the respective solutions are proposed in this
work. The authentication of vehicles is performed at the first step. After the vehicles are
4
authorized, they start communicating with each other, generating huge amount of data. This
data is stored in InterPlanetary File System (IPFS) after data filtration. The transaction data
of EVs is stored in the blockchain, which makes the proposed work secure. For optimal
charging of EVs, a charging schedule is designed. Moreover, the total charging cost and