Efficient data transmission on wireless communication through a privacy-enhanced blockchain process

Research introduction

Wireless communication has been vital in data transmission using the network-based Blockchain and security purposes. The application and the remote act as the appropriate rapid deployment of the data transmission proceeded. The 6G mobile system’s remote access also manages the advanced enhancement of the wiles communication (Alhalabi et al., 2023). This makes the advantage of the features and the application of IoT technologies. Then the data is monitored using the data devices (Manoharan et al., 2022).

Also, the gradient boosting method, the type of diseases, and the curing technique can be analyzed by the data stored in the big data using the gradient boosting process. Only the established person can access the patient’s details and treatments (Peng et al., 2021). The authentication process utilizing blockchain technology is discussed, especially in “Proposed authentication process using blockchain technology.” This is the actual function that blockchain technology plays in effective wireless data transmission. A decision regarding the growth of mobile and wearable devices is evaluated decision-based on wireless communication. This evaluation manages the recent trends in wireless communications. This communication contains the factor to access the process of security among the data. This application permanently stores the data as a copy of the database. All people can access the data for phone calls (Dubé & Wen, 2022).

LITERATURE SURVEY

Ometov et al. (2021) study discussed wearable technology and proposed challenges facing technology development. The process of managing the state-of-the-art has been proposed. Also, implementing this in the proposed system, the current and historical data have been analyzed for better decision-making. Cheng et al. (2021) manuscript study discussed the human-computer interaction and proposed the principle of monitoring the environmental factor. The transmission application manages the wireless technology for demanding the data, which is elaborated in the wireless communication for the diverse antennas and the beamforming of the data. By using this method in the currently proposed system, the system’s formation to make wireless communication and decision-making has been elaborated by Wu et al. (2022). Gao et al. (2021) study discussed the communication-based factor for accepting the transmitting control effects to manage the anomalies which affect the abnormal physical aspect to make the decision based on the intrusion detection method. An effective machine learning algorithm has been proposed to enable this in the currently proposed system (Tran et al., 2021). Juneja, Pratap & Sharma (2021) study discussed the 5G technologies that manage the design and the frequencies for the state of work in the semiconductor for analyzing the national communication network to analyze the system.

The cellular system tracking and the formation of the technology for the CMOS technology. Using this millimeter frequency, the frequency of mobile phones has been proposed to find the crime person. Dilli (2021) study the multi-user performance analysis using the MIMO hybrid beamforming system for analyzing the frequency band based on the procedures for the frequency analysis. Also, by using this in the currently proposed system, the generation between the wave frequencies and the multi-user propagation factor to produce the frequency of the tele calling can be presented. Makimoto & Yamashita (2001) study discussed the frequency of traveling the data transmission for the data transmission and the communication between the wireless and the cloud data. This analyses the frequency of the system to analyze the crime data. By implementing this in the currently proposed method, the data is analyzed using the millimeter wave is done. Hannachi & Slimani (2022) In this study, the microwave allocation and the RF for finding the frequency to detect the electronic devices have been unable to find the crime person. Also, the threat of phone frequency has been found. Also, by implementing this in the currently proposed system, the frequency has been found concerning the location for traction, the exact person, and the location for the analysis. Prabakaran & Ramachandran (2022) discussed that the blockchain-based, cloud-based safe method of conducting financial transactions had been made available here.

Sajja et al. (2021) discussed that blockchain-based agricultural and sector information allows clients to have faith in their data security while enabling global employment and heterogeneity. The data is related to the pharmacokinetics needed to collect the patient’s data and the worry about managing the privacy of the data, which is identifiable of the data that exposes the implementation of the blockchain-based data can be managed to the data that is accessed for the adoption of the system to produce the informatics solution and the stakeholders to produce the privacy worry and the storage movement that enables the genomic and the contracts which are proposed by Albalwy et al. (2022). Moreover, Alssaheli et al. (2022) research distributes the nonprogrammable settings and incoming load balancing, enabling the existing load balancing and cloud facilities, which control traffic congestion. Mobile device data can influence load balancing, and incoming data is examined depending on the internet and blockchain security protocols.

Iot founded health monitoring of patients

The Internet of Things (IoT) represents a rapidly growing network technology of connected objects which has the potential to collect and share real-time data using embedded sensors. This process is designed as a compact wearable device that helps collect real-time health data like blood pressure, heart rate, and so on to identify the disease. It collects, filters, and archives long-term activity data and physiological data using various sensors. In healthcare sector, a type of swarm intelligent algorithm is implemented in the recognition procedure with a modified fitness function and it is termed fruit fly optimization (FFO) (Shitharth et al., 2023). The disease can be identified by evaluating the extracted data and its simulations before it gets grievous (Selvarajan et al., 2022). By providing good medical care, patients heal faster as soon as possible in the beginning stage. As several devices generate a considerable amount of data, it is not easy to store and manage, so cloud technology is adopted. These collected data are analyzed to find records simulations to predict the patient’s conditions or find a particular disease based on symptoms. After analysis, a gradient boosting method is used to decide the disease (Lee et al., 2021). As the health pieces of information are highly confidential, it has been kept secret to prevent data theft by providing access only to an authorized person; a doctor or consultant patient can access the data for monitoring and decision-making.

In Fig. 1, physiological and activity data are collected through embedded medical sensors to form a wearable device. All these extracted data are transmitted through wireless transmission to store the information in the hospital’s cloud server; these signals communicate with the database. The controller processes signals from patients to find the location of particular patients and stores data in an exact location (Liu, Wang & Xiao, 2021). Doctors or consulters supervise these data to avoid risk; thus, in case of emergencies, the controller generates an indication message in the form of an alarm that helps doctors provide timely treatment.

Gradient boosting method for decision making

The gradient boosting (GB) is a machine-learning method. This method is used to predict disease or syndrome based on the acquired data through sensors with the help of IoT. This method collected the vulnerable predictive trees and combined them with a machine until a prediction became closer to a ground truth value (Cao, Zhang & Cao, 2021). This recursively splits the tree, and so it is also known as recursive portioning (Metwally et al., 2021).

Ensemble learning is a model used for predicting with more models, such as classifiers or experts, which are strategically generated and combined to solve a particular computational intelligence problem. In the boosting method, random data samples are considered, fitted with a decision tree model, and then trained each model sequentially (Ometov et al., 2021). It improves the mistakes made in the previous learning process through the next learner. A weak learner is used to perform slightly better than a random chance. The development of new weaker learners for handling the rest of the problematic comments is developed from stress (Kumar et al., 2021). Gradient boosting improvised upon some of the features of AdaBoost to create a more robust and efficient algorithm. Expanding is used to lower errors in medical data predictions, such as predicting disease risk using the collected data through wearable devices, as illustrated in Fig. 2.

Proposed enhanced gradient boosting decision

In the gradient boosting decision tree method, a combination of many weak learners extracts one stronger learner. The weak learners fit in such a way that each new learner does into the residuals of the previous step as the model improves (Padmaja et al., 2021). The final model or tree represents the result of each stage, and the most robust learner is obtained.

Gradient descent algorithm that assures the occurrence of gradient descent boosting tree. A gradient-boosting decision tree combines multiple weak classifiers to bring one strong learner. The vulnerable learners are referred to as individual decision trees. Due to this, the boosting algorithms give an accurate output and take more time to learn (Lv et al., 2021). The patient’s health is monitored using wearable devices acquired approximately every second. For each validation process, the inputs are divided into 80% training data and 20% testing data.

Algorithm for making decisions on the collected data

For each input

Initialize; ${\mathit{F}}_{\mathit{a}}^0$

${\mathit{F}}_{\mathit{a}}^0$= $\mathit{A}\mathit{r}\mathit{g}\mathit{m}\mathit{i}\mathit{n}{\sum }_{\mathit{k}=1}^{\mathit{K}}\mathit{L}\left({\mathit{b}}^{\mathit{k}},\propto \right)$

For n $\in$ [1−N] & k $\in$ [1−K]

Compute residuals

${\mathit{R}}_{\mathit{n}}^{\mathit{k}}$=− ${\left[{\displaystyle \frac{\mathrm{\partial }\mathit{L}\left({\mathit{b}}^{\mathit{k}},\mathit{F}\left({\mathit{a}}^{\mathit{k}}\right)\right)}{\mathrm{\partial }\left({\mathit{a}}^{\mathit{k}}\right)}}\right]}_{{\mathit{F}}_{\mathit{a}}={\mathit{F}}_{\mathit{a}}^{\mathit{n}-1}}$

End for

Create ${\mathit{r}}_{\mathit{i}}^{\mathit{n}}$ node

For i=1…In

Compute ${\beta }_{\mathit{i}}^{\mathit{n}}$= $\mathit{A}\mathit{r}\mathit{g}\mathit{m}\mathit{i}\mathit{n}\sum _{\mathit{k}=1}^{\mathit{K}}\left({\mathit{b}}^{\ast \mathit{k}}-\propto \right)$

Compute ${\propto }_{\mathit{i}}^{\mathit{n}}$= $\mathit{A}\mathit{r}\mathit{g}\mathit{m}\mathit{i}\mathit{n}\sum _{\mathit{k}=1}^{\mathit{K}}\mathit{L}\left({\mathit{b}}^{\mathit{k}},{\mathit{F}}_{{\mathit{a}}^{\mathit{k}}}^{\mathit{n}-1}+\propto \right)$

Update ${\mathit{F}}_{\mathit{a}}^0$

${\mathit{F}}_{\mathit{a}}^0$= ${\mathit{F}}_{{\mathit{a}}^{\mathit{k}}}^{\mathit{n}-1}+\mathit{V}\sum _{\mathit{i}=1}^{{\mathbf{I}}^{\mathit{n}}}{\propto }_{\mathit{i}}^{\mathit{n}}\mathit{l}\left(\mathit{a}\mathrm{ϵ}{\mathit{r}}_{\mathit{i}}^{\mathit{n}}\right)$

End for

End for

DOI: 10.7717/peerj-cs.1308/table-4

Let take { $x^k,y^k$} where k $\in$ [1−K] is an input dataset. Set the primary learner as h(a^k) $\in$ a^k = { a^1k, a^2k … a^pk} Where the P denotes the count of predicted variables. $y^k$ Represents the output as a predicted label. The procedure for gradient-boosting decision trees is as follows. Initially, address the constant value ( $\propto$) of the model as,

(1) $$F_a^0=Argmin\sum _{k=1}^{K}L\left(b^k,\propto \right)$$

The number of n iterations belongs to [1−N]. Then computes the gradient direction of residuals as follows,

(2) $$R_n^k=-{\left[{\displaystyle \frac{\mathrm{\partial }L\left(b^k,F\left(a^k\right)\right)}{\mathrm{\partial }\left(a^k\right)}}\right]}_{F_a=F_a^{n-1}}$$

The essential learner h is utilized to fit sample data and get the initial model. According to the least square approach, the parameter ${\beta }_i^n$ of the model is obtained as,

(3) $${\beta }_i^n=Argmin\sum _{k=1}^K\left(b^{\ast k}-\propto \right)$$

The loss function is minimized. According to Eq. (4), a new step size of the model, namely the current model weight, is calculated.

(4) $${\propto }_i^n=\sum _{k=1}^{K}L\left(b^k,F_{a^k}^{n-1}+\propto \right)$$

The updated model is as follows:

(5) $$F_a^0=F_{a^k}^{n-1}+V\sum _{i=1}^{{\mathrm{I}}^n}{\propto }_i^{n}l\left(a\in r_i^n\right)$$

When the raw data has limited dimension and size as input, the feature branch points information must be computed multiple times. Using an excellent learning rate will make optimization easier (Sprangers, Schelter & de Rijke, 2021). Still, it is usually supposed to be a smaller value, such as 0.1. This shows how the framework of gradient boost predicts complex targets, such as finding the disease or syndrome of the patient with the help of the sensors with IoT (Velthoen et al., 2021).

Blockchain for healthcare

As the digitalization process is disseminated speedaily, the attacks of cyber security and data breaches have increased; thus, information about patients becomes more complicated than before, so the highest standard of privacy for data is more critical in the healthcare system. In case security is breached, the individuals whose health information was inappropriately accessed face several potential harms (Fu et al., 2023). Thus, to increase the system’s safety, the authentication of data using blockchain technology is incorporated into this system. Some features for authentication using blockchain, namely the decryption keys and signing keys on the device, encryption and verification keys, are stored on a blockchain, and it protects data against cyber-attacks (Le et al., 2021).

For an authorised individual to participate in the review process, the user’s identity is necessary. Usually, the authentication is based on the user id and password. Still, blockchain uses an infrastructure of public keys and smart contracts as authentication processes based on certificates, as illustrated in Fig. 3. This removes human factors from the authentication process. These certificates were formulated using a key size higher than a size of a password, as 2,048 bits. These certificates contain the expiry date to reduce the risk of long-drawn-out exposure. Blockchain technology can leverage smart contracts to validate user certificates and effectively mitigate the risk of a single point of failure (Zou et al., 2021).

Thus, to restrict access to a particular function, a method of authorization is considered to mitigate the risk of information disclosure for unauthorized users. The restriction of help to users is based on the distributed list of points: action, object, contextual, and subject. The action attribute consists of activities that allow users to perform, like retrieving data and updating records. The object attribute is used to implement which type of object can be accessed by a user or doctor that is medical data related to a particular department (Diao, Wang & Gong, 2022).

At last, the subject attributes denote a user’s request for data access. These attribute types are encoded in a smart contract and shared across the blockchain network. For recording each action, a user identity is used on the network of the blockchain method. A transaction is used to add, update, or retrieve data from a database. This is also used for sharing information between authorized users. An administrator or doctor creates the flow of information in the authentication-based blockchain method and system-based health records and revokes digital certificates (Zhou et al., 2022). A CRL gets generated while revoking a user’s digital certificates. The peers in the blockchain network receive a CRL update command to update the CRL stored on the peers. The users who require access can authenticate with healthcare records by giving certificates to the system.

Figure 4 shows the centralized approval architecture with distributed blockchain method for revokes digital certificates from the distributed blockchain method. These certificates are then shared with users and received by peers in the network; they are established by running a smart contract that validates the credentials and returns a response. When the above-stated conditions are valid, the system allows access to the user; otherwise, it denies access to users (Aljuhani et al., 2022). The privacy preservation of hospital data uses blockchain technology that secures and maintains the hospital data as a ledger. This is referred to as a distributed database; a group of individual people updates that. In this technology, each user or device is named a node. The user is a patient, the wearable devices send the data to the peer network such as the hospital node, and these electronic health records of a patient data are immutable. When updating the record initially, it needs a user for the process.

Data structures created by blockchain technology include built-in security features. It eliminates duplication, streamlines procedures, eases the load of audits, boosts security, and guarantees data integrity. It keeps a secure, decentralised log of cryptocurrency transactions. It has a distinctive and secure digital identification reference that enables the distribution and recording of digital information without its editing.

From above figure a new distributed blockchain aspect diagram concerning the healthcare system and respective public keys smart contracts with 2,048 bits for revokes digital certificates. It will maintain the adequate security verification of the hospital data using privacy preservation of hospital data based on blockchain technology. This will be illustrated in the following and algorithms.

Pseudocode for the security verification of the hospital data using privacy preservation of hospital data based on blockchain

Initially, the sensor collects data from a patient

Data. Store→EHR

EHR. Store→blockchain ()

For each user

Create digital certificate

revokes. Certificate→(CA) certificate authority

generate URL

end for

for each user authentication

validate certificate

user→peer authenticates

if (CA sign present in the certificate) && (user present in CRL)

allow user access

else

refuse user access

end if

end for

Output: allow or refuse the user access

DOI: 10.7717/peerj-cs.1308/table-5

The permissioned blockchain technology needs authentication access to verify certificates and intelligent contracts. Systems can identify user access and restrict unauthorized access to system functions (Abyaneh, Zorba & Hamdaoui, 2022). Generally, this research system uses password authentication, but this research system is replacing this authentication with certificate-based authentication. This certificate-based authentication removes human interference during the process of authentication. These certificates are created with a critical size of 2,048-bit, which is larger than the size of an average password and includes the expiration date, reducing the risk of prolonged data exposure (Beniiche, Rostami & Maier, 2022). Blockchain technology has the advantage of smart contracts that validate the user certificates and can efficaciously mitigate the risk of a single point of failure.

The administration creates digital certificates to maintain the electronic health record (EHR) system, and the authority revokes issues in the certificates. The certificate revocation list (CRL) is then generated among all network peers (Park et al., 2023). Before that, the system needs to authenticate by digital certificates (Khan et al., 2022). The certificate is passed to the client, which is then sent to a peer in the network. The peer authenticates the user by running the smart contract, which contains information about the user/doctors, action attribute, object attributes, and contextual and subject attributes (Xu et al., 2021). That validates the certificate and returns a response. This response influences the authentication condition of the user’s access as allowing or refusing.

Algorithm for improving the data transmission method for wireless transmission

Input: Triangles (G), L-link, R-result

Output: Predict the path

Require: G = (V, E)

Load(G)

Triangles = Find Triangles (G)

return null

initialize: N = size (Triangles), k= 1

While (k ≤ N)

New_L= Predict (Triangles) [i]

For each_L ∈ New_L

If (R_contains L)

R [L]++

Else

R = R + L

End If

End For

increment k

End While

R = Sort Descending Result

Return R

DOI: 10.7717/peerj-cs.1308/table-6

Results examining resemblances of performance

The IoT devices continuously sense and collect data on a user’s health that helps the user’s health status. In this work, the personal health data of a user is collected by wearable devices. Human heart rate, blood pressure, temperature, blood oxygen rate, the glucose level in the blood, and other physiological data activities like motion, running, and exercise data are collected using sensors like heart rate sensors, blood pressure sensors, motion sensors, temperature sensor and similarly, can be used to collect the physiological data of a user. To assess our proposed approach, this research takes samples of 200 users containing each user’s sensor data. For evaluating the efficiency of the predicted health data, this research uses confusion metrics to classify the performance. Also, the following parameters are used to enhance the processing scheme concerning the black chain technology parameters (Table 1).

The confusion metrics have five factors, including four parameters: accuracy, f-score, precision, recall, and execution time of the sensor data. Whenever a researcher concludes something is real when it is actually wrong, this is known as a type 1 error (also called a false positive type I error). A “false alarm” is a false positive. Saying something is untrue when it is truly true, is known as a false negative (also called a type 1 error). four parameters are true positive, false negative, false positive, true negative, because uts useful detect following. Accuracy, Sensitivity and Specificity. The parameters are true positive (tp), which defines the samples data that are correctly classified as abnormal cases, and false negative (fn), which indicates the anomalous samples are classified wrongly as usual. False positive (FP) defines the standard examples that are classified incorrectly as abnormal cases, and true negative (TN) shows the traditional models that are correctly classified. The GB Gradient boosting is a machine learning method. The word boosting represents a method where weak predictions are combined for a more robust prediction method. In this method, the vulnerable predictive trees were collected and combined with a machine until a prediction became closer to a ground truth value. The decision trees are generated through iterative questions to the separation of data and to obtain a decision. This method is used to predict disease or syndrome based on the acquired data through sensors with the help of IoT.

(12) $$\mathbf{A}\mathbf{c}\mathbf{c}\mathbf{u}\mathbf{r}\mathbf{a}\mathbf{c}\mathbf{y}={\displaystyle \frac{\left(\mathrm{t}\mathrm{p}+\mathrm{t}\mathrm{n}\right)}{\left(\mathrm{t}\mathrm{p}+\mathrm{t}\mathrm{n}+\mathrm{f}\mathrm{p}+\mathrm{f}\mathrm{n}\right)}}$$

(13) $$\mathbf{P}\mathbf{r}\mathbf{e}\mathbf{c}\mathbf{i}\mathbf{s}\mathbf{i}\mathbf{o}\mathbf{n}={\displaystyle \frac{\left(\mathrm{t}\mathrm{p}\right)}{\left(\mathrm{t}\mathrm{p}+\mathrm{f}\mathrm{p}\right)}}$$

(14) $$\mathbf{R}\mathbf{e}\mathbf{c}\mathbf{a}\mathbf{l}\mathbf{l}={\displaystyle \frac{\left(\mathrm{t}\mathrm{p}\right)}{\left(\mathrm{t}\mathrm{p}+\mathrm{f}\mathrm{n}\right)}}$$

(15) $$\mathbf{F}\mathbf{\text{-}}\mathbf{M}\mathbf{e}\mathbf{a}\mathbf{s}\mathbf{u}\mathbf{r}\mathbf{e}=2\ast {\displaystyle \frac{\left(\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}\ast \mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\right)}{\left(\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}+\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\right)}}$$

The gradient boost decision-making classification method is applied to the healthcare data of a user. The trained classifier verifies the dataset and is classified into k-an exclusive mutual unit using the k-fold cross-validation method. K-fold cross-validation is applied with values of 1, 5, 10, 15, and 20 for k. Here the k resampling samples called cross-validation are used to assess machine-learning models on a small data sample. The process contains a single parameter, k, that designates how many groups should be created from a given data sample. As a result, the process is frequently referred to as k-fold cross-validation. It aids in preventing overfitting. We know that the highest performance accuracy is achieved when a model is trained utilizing all the data in a single brief run. We can design a generalized model by avoiding this k-fold cross-validation.

There are typically three methods to choose k: let k equal 25 or 250. It has been discovered via experimenting that choosing k to be 25 or 250 produces good enough results. Let k equal n, where n is the dataset’s size. Thus, the test data set will include each sample. Another option is to pick k so that each split data sample has a sufficient size to ensure statistical representation in the larger dataset. A technique for thorough cross-validation is leave-p-out cross-validation. The training dataset, in this case, contains the p observations (or sample dataset items); everything else is regarded as training data. Figure 5 will make it more straightforward that p is equal to 25, as indicated by the 25 circles in the “test data” section. A unique variation of the leave-p-out exhaustive cross-validation method, when p = 10, is called leave-one-out cross-validation. In the situation of k-fold cross-validation, where k = N, this is likewise a particular case (number of elements in the sample dataset) (Table 2).

Figure 5 illustrates the various classification methods’ precision values with different k-cross folds. The curves in the graph show that the gradient boost method has the highest precision value of 99% compared to other classification methods. The decision tree and naïve Bayes method have 87% and 84% precision, respectively.

As demonstrated in Fig. 6, the proposed hybrid gradient boost technique method has reached the highest recall value of 99% compared to the decision-making with 92% and the NB with 90% for the recall factor.

As illustrated in Fig. 7 exposes that the gradient boost classifier has gotten the highest f-measure value of 96% compared to the decision-making with 88% and the NB with 83% for this f-score factor.

Figure 8 shows how the suggested hybrid gradient boost approach effectively outperformed decision-making and NB in recall factor, each of which had recall values of 97% and 98%, respectively. As shown in Fig. 8, the gradient boost classifier obtained the highest accuracy score (96%), followed by the decision-making classifier (81%) and the NB classifier (73%).

Table 3 shows the comparison measure of the various decision-making methods with the proposed gradient boost decision-making method corresponding to the execution accuracy with execution time. From the table, the accuracy of the gradient boost is higher than the other methods. After that two algorithms, the decision tree classifier has higher accuracy and execution time than the naïve Bayes method. The naïve Bayes has the most negligible accuracy value compared with other methods.

As shown in Fig. 9, the naïve Bayes and decision tree methods were performed with the highest execution time among the other classifiers. Finally, the gradient boost classifier has performed with a minimum execution time of 12 ms over the same dataset. As shown in Fig. 9, the naïve Bayes and decision-making method has been performed with the lowest execution accuracy among the gradient boost classifier. And finally, the gradient boost classifier has performed the highest accuracy of 97.9% over the same dataset. The user needs privacy for their health data and access by the doctor and denies access from third parties.

As illustrated in Fig. 10, the number of data and average response time for data submission and the number of data and average response time for data validation, respectively. When the data is submitted to the blockchain ledger, this takes time for the process; this processing time must be less or higher. This is only based on the amount of data. If the data count is the lesser time taken for the process is minor; similarly, if the data is large, then the time taken for the operation or response time might increase. Besides, the validating portion of the blockchain follows this condition. The data submission analysis and validation for the existing decision tree took an average response time of 3,212 ms (on data submission). It took an average response time of 17.640 ms (on data validation). This average response time is 10% higher than the existing approach.

Conclusion

The blockchain method and proposed hybrid gradient boost that was suggested concentrated on data transfer to compare several new technologies employing wireless communication. The use of decision trees and blockchain-based decision-making were used to optimize wireless communication. The recommended system produces the following results in terms of prediction accuracy, execution time, execution accuracy, and average response time of data submission and data validation. The suggested hybrid gradient boost methodology offers the best precision value of 99% compared to the prior classification method. The decision tree and the naïve Bayes algorithm have 87 and 84% accuracy rates, respectively. Over the same datasets, the hybrid gradient boost classifier has the fastest execution time, clocking in at 12 ms. Naïve Bayes and the decision tree method have the most prolonged execution durations compared to the other classifiers. However, naïve Bayes and the decision-making strategy had the lowest execution accuracy when using the gradient boost classifier. Performance for the gradient boost classifier, which offers the highest level of precision, is 97.9%. The research on data submission and validation for an existing decision tree found that average reaction times were 3,212 and 17.640 ms, respectively. The average reaction time of this approach is 10% longer than the present method. In the future efficient data transmission, such as existing File Transfer Protocols (FTP) and Secure File Transfer Protocols (SFTP), will be handled concerning machine learning on wireless communication through an enhanced hybrid blockchain process.

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