Table 2.

Mapping of problems with proposed solutions and validation results.

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.