DESIGN AND EVALUATION OF AN IOTA-BASED MEDICAL INFORMATION SYSTEM

The traditional medical information systems are plagued by issues such as data breaches, lack of privacy, and data integrity concerns. This paper presents the design and evaluation of an IOTA-based medical information system aimed at addressing these challenges. In recent years, blockchain technology has emerged as a powerful tool for securing and managing data in a decentralized manner. One area where this technology has the potential to revolutionize the way we do things is in e-medicine. E-medicine, or electronic medicine, refers to the use of technology to deliver healthcare services remotely. This includes telemedicine, online consultations, and remote monitoring of patients' health status. IOTA blockchain technology, in particular, has a lot of potential in e-medicine. IOTA is a distributed ledger technology that uses a directed acyclic graph (DAG) instead of a traditional blockchain. The main ad-vantage of this approach is that it eliminates the need for miners and makes the system more scalable, fast, and energy-efficient. IOTA is also designed


INTRODUCTION
Medical information systems play a crucial role in the healthcare industry, as they facilitate the efficient management and sharing of patient data among healthcare providers. However, traditional systems have been plagued by issues such as data breaches, lack of privacy, and data integrity concerns. Moreover, these systems often struggle with interoperability and scalability, which hinder their overall performance and effectiveness.
Because blockchain technology is based on the concept of distributed ledgers, it enables medical records to be easily exchanged between hospitals, doctors, and researchers for a variety of reasons, including maintaining a patient's data [1]. As a result, the healthcare industry is one of the most important industries that stands to benefit from blockchain technology. MedRec is an implementation that is built on Ethereum that keeps and maintains an auditable history and records of medical transaction for providers, regulators, and patients [2]. MedRec was developed by the Ethereum Foundation. As a result of its foundation in Ethereum, miners that verify transactions are eligible for a variety of rewards. They also take into consideration a second incentive strategy that incorporates the participation of medical professionals in the mining process. Now, things get a little more complicated as a result of mining because it boosts both the cost of the gas required to run the function of smart contracts and the possibility of security breaches. [3] Nguyen D. C, and other authors [4] examine the deployment of data sharing through mobile applications that use blockchain. Nevertheless, this implementation does not consider the sharing of data, such as how corporations share information with one another. The exploitation of health data for the sake of research is prevented by the design of this system. In addition, the Hyperledger blockchain and off-chain storage are both utilized by the Medichain system, which was designed specifically for the purpose of archiving data associated with medical care [5,6].
In addition, the framework that has been developed places an emphasis on providing users with privacy and confidentiality. Yet, it considers Hyperledger Composer but does not consider the results of the implementation. Blockchain technology is being discussed by Patel V. A, and the rest of the team as a potential system and framework for securing and protecting healthcare systems [7]. There is a presentation and discussion of a permission-based blockchain that is presented with authority verification for the purpose of exchanging healthcare data [8]. In [9], with the assistance of a Hyperledger composer, an emergency access control management system (EACMS) is presented. In [10], Tith, D. and his team presented a framework based on Hyperledger that was installed on a local network of four Linux-based computers and served as a user interface for patients and clinicians. In [11], a framework for the administration of electronic health records consent that makes use of the Hyperledger Fabric running on the IBM blockchain platform is described. The analysis included the specifics of the deployments of three different providers (one patient and two providers). CrowdMed overcame the problem of insufficient information-sharing motivation by offering reward tokens to patients who contributed more data for the purposes of research [12], so incentivizing them to share more data with the organization. There has been no discussion regarding the findings of the evaluation of the suggested framework.

METHODS AND MATERIALS
The paper aims to address these challenges by proposing a novel solution that leverages blockchain technology. Blockchain, a decentralized and distributed ledger technology, has shown promising poten-tial in various industries due to its inherent security, immutability, and transparency features.
In this study, we focus on designing and implementing a permissioned blockchain-based medical information system that allows controlled access to authorized participants. We are integrating IOTA technology to automate and secure various processes, such as patient consent, access control, and data sharing. The evaluation of the system involves performance tests, security analysis, and a comparison with existing solutions.
The primary objectives of the research are: − to enhance the security and privacy of medical information systems through blockchain technology; − to ensure data integrity by maintaining an immutable record of data transactions; − to facilitate efficient and secure data sharing among healthcare providers using smart contracts; − to improve transparency and auditability within the system; − to identify and address the challenges of scalability and interoperability in blockchain-based medical information systems. This paper contributes to the growing body of research on the application of blockchain technology in the healthcare industry. By addressing the key challenges faced by traditional medical information systems, the proposed solution has the potential to revolutionize patient care and transform the way healthcare providers manage and share sensitive medical data.

RESULTS
The architecture of the suggested solution is broken down in great detail in this part of the report. It is structured in such a way that personal information can be stored off-chain in a cloud database, and that the blockchain will only be used to record information relating to consent. The architecture of the new system under consideration is dissected in great detail.
Because personal data cannot be saved on the blockchain, this option was not taken into consideration. Figure 1 presents an illustration of the system's overall architecture [13,14,15]. Consumers are able to use blockchain technology to record the specifics of their consent (via blockchain transactions), and they can also use it as an access log, as was shown earlier. Users are able to retain the safety and integrity of their data in the cloud. The users' permission to use their data can be solicited from the organizations that collect it. The administrator is allowed to disclose the user's data if the user has provided necessary approval to the organization. In conclusion, users of a blockchain transaction have the ability to withdraw an organization's access to the ledger at any time. The proposed model of a medical information system ( Fig. 1) includes: 2 types of system users (patient and Doctor), 3 web servers on which the corresponding software solutions are deployed, and 2 databases.
To log in, the patient goes through the authorization stage, where they specify the necessary data (last name, first name, patient ID). Thanks to PatientApp-WebSite, the patient can search for doctors, view their medical data, and consult with medical professionals. The PatientDB database will store all the necessary medical information of the patient. When logging in to their system at the authorization stage, doctors indicate the doctor's ID issued by the Ministry of health after checking for professional aptitude. However, medical professionals can also log in as a patient. Doc-torAppWebSite allows a doctor to advise patients seeking help. The DoctorDB database stores data about all patients who have applied for help, and the data of the doctor himself. PatientRegister and Doc-torRegister are suitable solutions that store and provide patients and Doctors With login details.
In the proposed system, node types relative to roles can be classified into: a. Patient node. This node allows you to perform the following actions: -fill out the patient's medical card; -create / view seed and public / private keys; -view the patient's medical data: tests, medical prescriptions, diagnoses, symptoms; -view the patient's personal data; -search for a doctor for further consultation; -view the data of each doctor (specialty, length of Service, medical skills, etc.); -send a list of symptoms that are bothering the patient for further consultation; -view the result of the consultation. b. Doctor's node. This node allows you to perform the following actions: -set up a doctor's profile; -create / view seed and public / private keys; -view the list of patients who were treated; -view the patient's medical data: medical prescriptions, diagnoses, etc.; -view the medical data of patients who have applied for help; -make a diagnosis and send it to the patient; -write out a medical prescription and send it to the patient.
c. This is a node that stores all the relevant information that a doctor/patient needs to log in to their system.
Every doctor who wants to use this system must pass professional aptitude. This is necessary so that an incompetent doctor cannot use the system and put human life in trouble. After the check, the doctor receives a special number (DoctorID) that will allow you to log in to the system. All Doctor data and this number are stored on this node. In addition, the data of any patient and their special number (PatientID) are also stored on this node. Therefore, this node allows you to perform the following actions: -stores the doctor's personal data and DoctorID for authorization in the system; -stores the patient's personal data and PatientID for authorization in the system.
IOTA is a permissionless, scalable, and feeless distributed ledger technology that utilizes a directed acyclic graph (DAG) called the Tangle. This architecture allows IOTA to achieve high throughput and secure data transactions without the need for mining or transaction fees. In this study, we explore the design and evaluation of an IOTA-based medical information system that leverages the benefits of the Tangle to overcome the challenges faced by traditional systems.
We designed and implemented an IOTA-based medical information system that allows for the secure and efficient management of patient data. The system architecture includes the following components: − IOTA Tangle: The core infrastructure that underpins the system, enabling secure and feeless data transactions.
− Data Storage and Access Control: Patient data is encrypted and stored on distributed nodes, ensuring data privacy and security. Access to the data is granted to authorized healthcare providers using cryptographic techniques.
The user enters their medical data (body temperature, heart rate, symptoms, laboratory tests, etc.). To send data to the doctor, the patient creates a transaction (transaction). Transactions are broadcast in an envelope called bundle. Next, you will need to sign (signature) transactions with a private key to confirm ownership. Then a bundle hash should be generated. Now you need to calculate the Proof-of-Work for each transaction in the bundle. The POW result is a nonce value that is added to the transaction. The last step is to send the bundle to the network.

Experimental Evaluation
When sending a transaction to the IOTA Tangle network, there are 3 main steps that take a lot of time to complete. These main stages are: − time to search for tips; − POW execution time; − total time to create and send the bundle. For testing, we will take 10 different diseases and their symptoms, which the patient can send to the provider of medical services. We will send the symptoms of these diseases to the doctor's node. We will draw this text 20 times. Figure 3 shows the average execution time for each stage described above and the number of transactions generated during bundle formation. The graph shows that it takes less than a minute to create and send a bundle to the network. The least time is spent searching for two transactions to confirm. The POW execution time depends on the number of transactions in the bundle, since each transaction calculates its own POW. The more transactions, the more time is spent at this stage. If we draw analogies with the blockchain network, then the transaction time interval depends on the network load. The average transaction time is between ten minutes and an hour, but when the network is overloaded, the transaction time increases.
Performance metrics Average response time This is the most important indicator to understand how a website works from the user's point of view. Simply put, the average response time is the time spent passing a specific packet of information sent from the user's browser to the server, and the time spent returning the packet back to the user's computer. The application must be checked under various circumstances (for example, the number of concurrent users, the number of requested transactions). As a rule, this indicator is measured from the beginning of the request to the moment when the last byte is sent.
Other factors, such as the geographical location of the user and the complexity of the requested information, may affect the average response time for users and should be considered when evaluating the overall performance of the application. The researchers call the most acceptable response value 0.5 s. considering geography, this value can be about 1-3 seconds. If the response value is consistently higher than the specified value, then you need to think about changing hosts or optimizing the site [44]. Figure 4 shows the average response time value. The figure 4 shows the average Avg value. Response time does not exceed 3 seconds. From this we can conclude that Avg. Response time does not exceed the specified norm. Although the maximum value is 36.8 seconds. To reduce this value, we have changed the methods that are responsible for sending transactions, calculating POW, and searching for confirmation transactions. The Average Response time after the changes decreased by 2.3 times to 15.9 seconds.
One of the most important parameters of any server is its bandwidth, since it is this characteristic that largely determines the speed and loading speed of the entire resource, which in turn affects the number of the users. Bandwidth is the number of records that the server can complete in one second. Band-width is measured in requests per second (RPS). Figure 5 shows the bandwidth under a load of 25 users. Throughput can be affected by several factors. These include the number of users, the complexity and frequency of user operations, and managing and configuring pages and web parts. Each of these factors can have a significant impact on throughput.  Figure 5 shows that on average, the server processes 5 requests per second at a constant load of 25 users. Figure 6 shows the bandwidth under a load of 50 users. The IOTA-based medical information system demonstrated significant improvements over traditional systems in several key areas: 1. Security and Privacy: The use of the Tangle, combined with cryptographic techniques, resulted in enhanced security and privacy for patient data.
2. Scalability: IOTA's Tangle architecture allowed for high throughput and scalability, addressing one of the main challenges faced by traditional medical information systems.
3. Interoperability: The system was designed with standardized data formats and communication protocols to ensure seamless interaction with existing healthcare systems.
4. Feeless Transactions: The absence of transaction fees in IOTA enabled the system to carry out microtransactions at no additional cost, which is crucial for large-scale medical data management.

CONCLUSION
The design and evaluation of an IOTA-based medical information system show promising potential for addressing the challenges faced by traditional healthcare systems. By leveraging the unique features of IOTA's Tangle, the proposed system enhances security, privacy, scalability, and interoperability in medical information management. Future research should focus on refining the system architecture, conducting real-world pilot studies, and further exploring the potential benefits of IOTA and other distributed ledger technologies in the healthcare industry.

INTRODUCTION
E-learning has become an increasingly popular approach to learning thanks to the rapid growth of web technologies. COVID-19 has forced countries to introduce online or hybrid learning to catch up with expected learning targets. However, many countries remain ineffective in the transition to online or hybrid education. In addition, while some countries are succeeding in improving student achievement (e.g., Italy increased student progress with online tutoring by 4.7% compared to traditional instruction), some others are not achieving the same results with online learning. Recently, however, education industry leaders have begun to identify use cases for using process intelligence to improve online learning platforms, teaching methodology, and student learning habits [1].
The Internet and related web technologies offer excellent solutions for presenting, publishing, and sharing learning content and information. This is special software called a Learning Management System (LMS). Learning management systems such as Moodle are a popular tool in higher education. They allow teachers to present their courses virtually. In these virtual courses, they can provide learning objects such as lecture slides or videos, quizzes, or forums where students can interact with each other.