Take advantage of blockchain and IOMT for secure and interoperable electronic health files

This section assesses the EHRGUARD system proposed, starting with an overview of the material platform, the “e-SALTH SENSOR SHIELD v2.0”, followed by a visual analysis of the experimental results. Ehrguard’s performance is compared to two other systems, Biothr²¹ And ehdhe²³By focusing on measurements such as latency, execution time and flow. Table 3 provides a summary of the battery technology used in the development of the proposed system.

Data set

The data set used to assess the EHRGUARD model consists of vital patients collected from IOMT sensors, including heart rate, blood pressure, oxygen levels and glucose levels, as well as associated diagnostics, prescriptions and blockchain transaction hash for the management of secure data. The data is recorded in real time, allowing continuous monitoring of patients and immediate detection of abnormal conditions such as hypertension, diabetes and critical alerts. If abnormal readings are detected, alerts are triggered for medical intervention, while normal readings are stored safely in a database out of chain, with chopped references recorded on the blockchain to ensure integrity and immutability. Each patient file is linked to a doctor’s ID for medical supervision and prescribed drugs are safely recorded. This data set demonstrates Ehrguard’s ability to safely manage electronic health files using a combination of IOMT, blockchain and real -time surveillance, guaranteeing data confidentiality, safety and effective health care management. Table 4 takes up the data set used to assess the Ehrguard.

Hardware platform

The SHIELD V2.0 of the e-health health sensor is a material module specially designed for projects based on Arduino, in particular those focused on medical and medical supervision. It serves as a versatile platform to integrate a wide range of sensors and devices to measure and monitor physiological parameters. The shield has connectors and interfaces to connect various health sensors, such as those to measure the heart rate, oxygen saturation (SPO2), body temperature, and even more, as illustrated in Figure 5. In the proposed system, four types of health sensors are connected to the patient’s body: a temperature sensor, a SPO2 sensor, a pulse sensor and a pulse EMG. The shield allows real -time data collection of these sensors, which can then be processed and used to monitor an individual’s health parameters. It supports analog and digital connectivity, which makes it compatible with sensors of different types of output. In addition, the shield offers flexible power supply options, allowing users to configure food for sensors and shield according to the needs of their project. In this system, a Raspberry Pi equipped with the SHIELD V2.0 of the electronic health sensor operates like an IoT gateway, buying vital signature data to a healthcare health care server. The e-health health sensor platform also provides a C + + Open Source library for reading the sensor data. For detailed information on the use of the shield, users must consult the documentation and specifications of the manufacturer’s product³³.

SHIELD DE SE-SALECH SEMELTH V2.0.

Overflow

The assessment of the system rate proposed using Jmeter is essential to assess its performance under variable loads. The flow rate, measured in KB / SEC, indicates the effectiveness of the system in the management of user requests and data transfer rates. Figure 6 compares the flow of our system proposed with Biothr and EHDHE, demonstrating a significant improvement in the rate of flow. This improvement highlights the higher capacity of the system to facilitate faster and more efficient data transmission, which directly contributes to improving overall system performance and a better user experience. The results validate the effectiveness of the system proposed to manage high data charges and ensure transparent operation in real health care scenarios.

Flow of the proposed frame.

Latence of the proposed system

In the EHRGUARD system proposed, latency is a critical factor, especially in health care environments where timely access to patient files is essential for effective patient care. The latency of the system is influenced by several factors, including network latency, consensus mechanisms, block size and transaction flow. To assess the performance of the system, we measured the average latency using Jmeter, a tool widely used for performance tests. During the evaluation, we simulated a variable number of users and measured the latency in seconds. The results, illustrated in Figure 7, compare the latency performance of our system with reference models such as BIOTR and EHDHE. The proposed framework demonstrates a significantly lower latency compared to these landmarks, highlighting its effectiveness and robustness. This latency reduction guarantees faster and more reliable access to patient files, improving the overall effectiveness of the system and supporting decision -making in health environments.

Latency of the proposed system.

Calculation cost

The calculation cost, which reflects the resources necessary to perform tasks in a system or algorithm, is a critical factor to assess the effectiveness of our proposed Ehrguard system. It includes various components, including user authentication, data recovery, processing of sensor data, integration of blockchain and data storage. Thanks to an in -depth experiment, our EHRGUARD model has demonstrated higher profitability compared to reference systems such as BIOTR and EHDHE. As illustrated in Figure 8, Ehrguard reaches a total calculation cost of approximately 7 s per 1000 intelligent knots, representing a reduction of 38% compared to BIOTHR and a reduction of 24% compared to EHDHE. These results highlight Ehrguard’s ability to considerably minimize calculation costs while maintaining scalability, ensuring effective use of improved resources and performance in health care applications. This profitability makes Ehrguard a robust and practical solution for the implementation of the real world.

Calculation cost of the proposed system.

Above communication

The general communication costs, which refer to additional resources and latency incurred by a system due to communication activities such as data transmission, messaging and transactions between components or nodes within a blockchain network, is a critical factor in system performance. In our analysis, as shown in Figure 9, the proposed scheme demonstrates significantly greater efficiency in the management of general communication costs compared to BIOTR and EHDHE. This improved efficiency indicates that our system uses communication resources more effectively, reducing unnecessary delays and optimizing resource allocation. By minimizing the general communication costs, the proposed system improves overall performance, guaranteeing more smooth and more reliable operations within the blockchain network, in particular in health care applications where the exchange of data in timely and efficient time is essential.

The general communication costs of the proposed system.

Calculation time analysis

In this section, we analyze the effectiveness of calculating the EHRGUARD system by assessing the time required for key operations involved in the management of patient health data. The analysis focuses on four basic processes: data encryption, blockchain transactions processing, the execution of the consensual mechanism (proof of work) and the generation of alert in real time. Given the critical nature of health care applications, guaranteeing both safety and performance is essential for the feasibility of the system in real world parameters. We define the following parameters to measure the calculation time of each operation:

Or:

\ (\: {T} _ {Enc, i}: \: \) is the time to encrypt patient data III -Th.

\ (\: n \): is the total number of patients.

• T_TX: The treatment of a blockchain transaction implies the creation of a new block, the haste and add it to the blockchain. The calculation time for blockchain transactions is given by:

\ ({T_ {tx}} = {t_ {block \ _creation}} + {t_ {hashing}} + {t_ {block \ _Append}} \)

Or:

\ (\: T _ {{block \ _creation}} \): is time to create the new block.

\ (\: {T} _ {chopping} \): The time is taken to generate the hash of the block.

\ (\: {T} _ {block \ _Append} \): is time to add the blockchain block.

Or:

\ (\: {T} _ {Solve} \): is time to resolve the calculation puzzle.

N: is the number of nodes in the blockchain network.

• T_Alert: When the sensor data fall outside the predefined thresholds (for example, abnormal heart rate or blood pressure), real -time alerts are generated for health professionals. The generation of alerts is calculated as:

Or:

\ (\: {T} _ {detection} \): The time is taken to detect abnormal readings from the sensor data.

\ (\: {T} _ {notification} \): The time is taken to inform health professionals via the alert system.

Or:

\ (\: {T} _ {db, i} \): is the time to recover the I-Th patient database data.

Table 5 presents the analysis of the calculation time of key operations in the EHRGUARD system, with its effectiveness in the management of patient health data. The data encryption operation takes an average of 150 ms, guaranteeing rapid and secure handling of patient information. The treatment of blockchain transactions requires 250 ms on average, reflecting the time necessary to create, chop and store a new block in the blockchain to maintain data integrity. The consensus mechanism of proof of work (POW), essential for blockchain safety, on average 500 ms, introducing a certain latency because of its calculation complexity. The real -time alert generation for abnormal sensor readings only takes an average of 80 ms, guaranteeing appropriate notifications for healthcare professionals. Finally, recovery of data from the database excluding chain on average 60 ms, allowing rapid access to patient files. Overall, the system demonstrates effective performance, the balance between safety, real -time monitoring and data integrity with a minimum of delays, which makes Ehrguard suitable for real -time health care applications.