An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain
Abstract
:1. Introduction
Contributions
- A blockchain-based secure energy trading system is proposed with minimum cost. The proposed system also achieves security and privacy by using blockchain.
- Transaction redundancy is tackled through a hashing algorithm. A hashing algorithm (SHA-256) is used as a tracer to remove redundancy.
- A machine learning algorithm, KNN, is used to calculate the shortest distance between a charging station and an EV.
- A reputation mechanism is proposed for the selection of EVs. This reputation mechanism also helps to avoid Sybil attacks.
- In the proposed system, energy aggregators are introduced as energy brokers that provide a significant way to find optimal charging stations for EVs with less energy consumption, calculate the time of charging and define the present state-of-charge.
- The proposed mechanism also calculates the energy required by an EV and presents the amount of energy available at the charging station. Moreover, DR is integrated with blockchain to manage EV demand and supply securely.
2. Related Work
Research Gaps
3. System Model
3.1. Electric Vehicle
3.2. Charging Station
3.3. Consortium Blockchain
3.4. Vehicle-to-Grid Energy Network
3.5. Role of Aggregators
3.6. Role of Tracer
3.7. Energy Transportation by Electric Vehicles
Algorithm 1: Energy Trading Request |
3.8. Demand Response
Algorithm 2: Demand Response |
3.9. Finding Minimum Distance
Algorithm 3: Selecting the Nearest Charging Station |
3.10. Selection of Charging Station Using KNN
3.11. Trust-Factor-Based Reputation
3.12. Registration and Authentication through Certificate Authority
3.13. Branching of Data
3.14. Payment to Charging Stations
4. Simulation Results
5. Security Analysis of the Proposed Smart Contract
- Re-entrancy vulnerability
- Timestamp dependency
- Callstack depth vulnerability
- Transaction ordering dependency
- Parity multisig bug
- Integer overflow
- Integer underflow
5.1. Security Features
5.1.1. Integrity
5.1.2. Availability
5.1.3. Confidentiality
6. Blockchain-Based Attacker Model
6.1. Double-Spending Attack
- : a catch-up function that shows the probability of the fake longer chain published by the attacker.
- T: a random variable that shows the time needed for mining.
- : a potential progress function. It shows the probability of mining by an attacker.
- m: in the double-spending attack, attackers mine the nth block and the honest nodes mine the mth block.
- z: z is the initial disadvantage of the attacker.
- x: the computation power available in the network.
- q: the probability that the attacker will mine the block before the honest miner when both miners start mining simultaneously. In other words, it can be said that q is the proportion of the attacker’s computation power. The value of q belongs to [0, 1], and .
- n: the number of mined blocks.
- t: the time advantage of the attacker.
- K: the number of confirmations needed to declare a block and the transaction as valid. This parameter depends upon the seller and not the network. The value of K belongs to the set of natural numbers .
- : the average time required by the honest and attacker nodes for block mining. The value of belongs to the set of real numbers .
6.2. Mathematical Formulation
6.3. Replay Attack
7. Conclusions
8. Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
Acronym | Description |
Battery capacity | |
Energy demand | |
Available energy at charging station | |
Threshold for charging | |
Present energy in EV | |
Location of charging station | |
Energy price | |
State-of-charge | |
Time required for charging | |
Catch-up function | |
K | Confirmation number to declare a block |
m | Block mined by the honest nodes |
n | Block mined by the attacker |
Potential progress function | |
q | Attack probability |
T | Time required for mining |
t | Time advantage for the attackers |
Average time to mine a block | |
x | Computational power available in the network |
z | Attacker’s initial disadvantage |
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---|---|---|---|---|
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Addressed Limitations | Proposed Solutions | Results and Validations |
---|---|---|
L1: Vehicles use high computational power and resources to find an optimal charging station. | S1: Finds the shortest distance by using a machine learning algorithm | V1: Figure 10 depicts the expenses used by an EV according to the travelling distance. |
L2: The energy sector faces new challenges such as imbalanced load supply, fluctuations in voltage level and load shedding. | S2: The integration of DR in VNs becomes necessary as it helps to manage the load supply and efficiently reduce the peak load. | V2: Figure 13 depicts the load consumption with and without using DR. |
L3: Multiple vehicles send requests to the aggregator simultaneously. Therefore, selecting the desired vehicle becomes difficult in the network/system. | S3: A reputation mechanism is proposed for the preferred selection of EVs. | V3: The validation of this reputation mechanism is shown in Figure 4 as the deployment of a smart contract that assigns reputations to EVs. |
L4: Malicious operators in energy markets are threats to network privacy and security through exploitation, e.g., privacy leakage and node impersonation. | S4: To resolve this problem, we use authentication. | V4: Figure 9 depicts the number of authentic and unauthentic messages generated by EVs. |
L5: Data redundancy issues exist. | S5: A SHA-256 hashing algorithm is used to remove/detect data redundancy. Hash values of newly uploaded data are compared with the hash values of existing data to find duplication. | V5: Figure 8 shows the encryption of character strings into bits. |
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Ashfaq, T.; Khalid, M.I.; Ali, G.; Affendi, M.E.; Iqbal, J.; Hussain, S.; Ullah, S.S.; Yahaya, A.S.; Khalid, R.; Mateen, A. An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain. Sensors 2022, 22, 7263. https://doi.org/10.3390/s22197263
Ashfaq T, Khalid MI, Ali G, Affendi ME, Iqbal J, Hussain S, Ullah SS, Yahaya AS, Khalid R, Mateen A. An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain. Sensors. 2022; 22(19):7263. https://doi.org/10.3390/s22197263
Chicago/Turabian StyleAshfaq, Tehreem, Muhammad Irfan Khalid, Gauhar Ali, Mohammad El Affendi, Jawaid Iqbal, Saddam Hussain, Syed Sajid Ullah, Adamu Sani Yahaya, Rabiya Khalid, and Abdul Mateen. 2022. "An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain" Sensors 22, no. 19: 7263. https://doi.org/10.3390/s22197263
APA StyleAshfaq, T., Khalid, M. I., Ali, G., Affendi, M. E., Iqbal, J., Hussain, S., Ullah, S. S., Yahaya, A. S., Khalid, R., & Mateen, A. (2022). An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain. Sensors, 22(19), 7263. https://doi.org/10.3390/s22197263