IEEE Consumer Electronics Magazine - November/December 2022 - 44

Security, Trust, and Privacy Solutions for Intelligent Internet of Vehicular Things
Vehicular Things network's RSU and T.A. are
shown in Figure 2. Both used different OBU sensors
to create data sent to the PPDM algorithm
shared via RSU and T.A. mentioned in Figure 2.
Once the sanitization process is complete and
the data are stored, and group anonymization is
done to hide any group information. This article
uses a Markov decision process for the sanitation
process, as given in Figure 3.
We can see the method as proposed in
Figure 2. VANETs communication with trusted units
and others components.
model. The method first selects the key utility
transaction and then clusters them for hiding
the sensitive information. The rule-based
approach is also used for data privacy preservation.9
The model used the k-anonymous imprecise
rules to compose the data tables. The
composed data are then used to protect privacy
ability. Vehicular sensors' data are also used for
the motor torque based on the model prediction.10
The model is used to close the loop
between system engineering. The model output
is used in E-powertrain mounted vehicles. The
privacy-preserved call data record analysis is
also performed for the COVID-19 patients to control
the pandemic.11
Problem Statement
PPDM for VANETs can be seen in Figures 2
and 3 that represent the RSU V2V, V2I, I2I, and T.
A., respectively. The Intelligent Internet of
Figure 3 and described in Algorithm 1. In step 1,
the model takes all its arguments as input. Next,
the algorithm extracts all of the F.I.s, or frequent
itemsets Fitemsets ¼ f1;f2; ... ;fkfg. The F.I.s need
to have a support count value that cannot be
less than the min support count value as given
in Line 1 of Algorithm 1. Based on F.I.s, we select
20% of the F.I.s for utility and data sanitization.
We project each itemset to get instances InsI:D:
from the raw original dataset, as shown in Line 1
of Algorithm 1. Next, we can represent the states
as a set of instances InsI:D: as shown in Line 3 of
Algorithm 1. We initialize the Q table and simultaneously
set the exploration rate. Randomness
is used in the model based on the epsilon greedy
policy given in Liu et al.'s work,12 from this each
episode is updated as given in Lines 4-6 of Algorithm
1. Action reward R represents the change
based on the action, state and next state (see
Algorithm 1, Line 10) and is then calculated
using a fitness function (see Algorithm 1, Lines
7-13)
fitness sðÞ ¼ w1 a þw2 b þ w3 d:
(1)
Figure 3. DSRL framework.
44
IEEE Consumer Electronics Magazine

IEEE Consumer Electronics Magazine - November/December 2022

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