# A Secure IoT and Cloud Computing-Enabled e-Health Management System

Authors: Chanapha Butpheng; Kuo-Hui Yeh; Jia-Li Hou
Journal: Security and Communication Networks
 (2022)

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## Abstract

Utilization of the Internet of Things (IoT) technology is virtually ubiquitous across various life disciplines. When the IoT is integrated into e-health system to enable more real-time on-demand services, it brings significant convenience to the physicians and patients. However, the risk of encountering unreliable information and potential security threat is promptly raised at the same time. Therefore, in this research, we establish a secure IoT and cloud computing-enabled e-health management system in which a distinctive authentication scheme is adopted. The proposed system ensures all major security requirements, such as confidentiality, entity anonymity, repudiation tracking, data integrity, and innate resistance to man-in-the-middle, location spoofing, and replay attacks. Moreover, our system can effectively be executed on low-powered processing units and simultaneously guarantees greater security than historically available alternatives.


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## Body

## 1. Introduction

The adoption of smart object technology has rapidly driven a huge advancement in wireless sensor-based application development. These applications deliver efficiency of data transfer, and their nature of ubiquity enables real-time and on-demand Internet of Things- (IoT-) based services into daily life. The most important variable to consider when developing novel IoT-based applications is security during data processing. Potential threats constantly emerge as technology evolves. Hello Barbie is an example of a consumer product with IoT functionality that could be utilized as a possible threat vector by bad actors to spy on consumers utilizing the built-in camera and voice-interaction capabilities. In addition, hackers might be able to use network-monitoring-driven devices to collect personal sensitive information from network attached IoT devices.IoT continued to emerge as one of the fastest accumulations of communication standards by which smart objects used in our daily lives are integrated with the Internet. The onboard computing and communication capabilities are the drivers of this trend. However, all new technology has potential security vulnerabilities that emerge as the technology matures. Every IoT object may be a potential threat vector in the sense that each IoT device represents a possible vulnerable entry point that can be exploited by malicious actors. There are two security aspects we may consider: (a) the assurance of robust physical protection of IoT devices; (b) the assurance of data availability, confidentiality, integrity, and privacy while engaging in normal operation. Given the advanced nature and rapid innovation of IoT technologies, there is a great demand and expectation that revolutionary security solutions will be adapted specifically to IoT-based objects and their specific applications. The demand stems from the shortcomings of traditional security protocols which are not designed for IoT devices. Firewalls that integrate protocols to manage network traffic at the application level are an example of a solution that is capable of regulating high-level Internet traffic. Nevertheless, firewalls are not a panacea in regard to end-point IoT devices. Moreover, IoT objects are frequently mission-specific and possess limited resources to accomplish their respective missions. Hence, traditional security solutions must be restructured for the specific security requirements of IoT devices to secure IoT-based applications and systems as a whole. For instance, Yousefnezhad et al. [1] defined fundamental security elements: data availability, confidentiality, and integrity. Moreover, they outlined a lifecycle security solution comprised of three stages: beginning, middle, and end of life stages. Thus, the required privacy and security elements have been classified utilizing the concept of IoT network architecture. More concretely, the authors developed a security architecture which categorizes the primary security requirements for the fundamental layer, the perceptual layer, the network layer, the support layer, and the application layer. The security requirements for each layer are agreement of encryption/key, verification of identification, protected cloud computing, robust antivirus abilities, authentication verification, and data privacy assurance. Furthermore, Hathaliya and Tanwar [2] presented the importance of maintaining privacy/security within a e-health system and an extensive summary of privacy/security solutions. In regard to data communication, with the popularity of IoT applications, the Internet is utilized to communicate a huge amount of generated data. This method of communication is vulnerable to attack from bad actors. An IoT device may be most vulnerable when communicating, storing, or processing data. Gardašević et al. [3] identified security and privacy threats/attacks for IoT smart healthcare systems, such as jamming, eavesdropping, spoofing, man-in-the-middle attacks, and so on. In order to secure the system against these attacks, a solution has been developed to support the advancement and widespread utilization of distributed computing.Typically, security is implemented and aligned with the target system’s operation process, including device authentication, firewall integration, secure booting, efficient network management, access control, and updates/patches. Secure interdevice communication/authentication offers significant security benefit for IoT crucial elements. On the other hand, the academic community has exhaustively dedicated time and resources to utilize cloud computing to solve IoT efficiency and scalability issues. The cloud computing model is an extremely powerful tool because of its ability to be quickly implemented and scaled through the use of virtualized resources management. The utilization of IoT devices and its amalgamation with cloud computing provides network benefits, including communication efficiency, quick access, and intelligent interconnection capabilities, and provides complementary capabilities when integrated as a component in an efficient system that is scalable and flexible. Therefore, our study proposes a secure e-health management system operated with IoT and cloud computing. We begin by defining fundamental required security elements for an e-health system that utilizes IoT-cloud resources. Then, we will introduce a robust authentication mechanism that operates within Body Sensor Networks (BSN) technology to ensure that the fundamental security elements, including attack resistance and data integrity/confidentiality, are satisfied.Our work is structured accordingly: section 2 summarizes security as it is today and outlines fundamental security elements necessary for e-health systems that utilize IoT-cloud technology. Then, section 3 introduces our proposed system with an authentication protocol, and section 4 examines the robustness of our proposed system. Next, section 5 evaluates the effectiveness of the proposed system through a proof-of-concept implementation. Lastly, the conclusion and suggestions for future research can be found in section 6.


## 2. Related Works

This section summarizes security as it is today and outlines fundamental security elements necessary for e-health systems that utilize IoT-cloud technology.


### 2.1. The Current State of the Art

The primary security priorities for e-health systems that utilize IoT-cloud technology are robust preservation of security/privacy in regard to patient health information. Hence, many systems, integrating privacy policies and multilevel security mechanisms, have been proposed to be more capable of managing patient health information. Recently, Butpheng et al. [4] presented a practical amalgamation of cloud computing and IoT-technologies in an e-health setting. The authors demonstrated that integration/implementation of cloud computing and IoT-technologies (CIoT) is achievable in an efficient manner. This integration improves health diagnostics, treatment timeliness, and health outcomes and reduces costs. CIoT is comprised of interconnected smart hardware, custom systems, and custom applications that communicate over the Internet. The authors provided a comprehensive technical architecture with an inclusive list of privacy/security requirements for e-health systems that utilize IoT-cloud technology including identification, authentication, and authorization. Aligning with the proposed architecture, security solutions can be effectively designed to decrease the probability of an attack via examining and identifying vulnerabilities from each security aspect.Similarly, Wang and Cai [5] proposed a secure communication protocol for incorporating healthcare data and Named Data Networking (NDN) based on IoT technology and Edge-cloud computing (SHNIE) to secure and expedite the delivery of medical data and reduce latency and cost. SHNIE can efficiently deliver aggregated NDN-medical data to multiple users from the nearest edge devices that own the required medical data. The security of communications is realized through a hash ciphertext of the provider’s name and IDs as a verification token in the NDN to preserve privacy and security. Moreover, Ray et al. [6] designed a novel solution to deal with issues of interoperability, heterogeneity, and Internet-aware resistances. Proposed devices will collect patient data instantaneously and then process and analyze it with embedded Internet connected electronics. The proposed devices utilize the Internet for all interactions and communication and are capable of remote monitoring and controlling. Then, the authors further propose an e-healthcare architecture that incorporates blockchain technology based on IoT that is classified as follows: IoT e-healthcare; blockchain platform; connectivity; and IoT devices. Furthermore, Shewale and Sankpal [7] utilized various medical technologies such as real-time monitoring, patient data administration, and healthcare supervision via BSN technology to observe patients’ heartbeats and blood pressure. They introduced a monitoring system to detect the status of each patient’s health data, collect it, then forward the data to the server using Wi-Fi module based wireless communication. In 2019, Chenthara et al. [8] identified essential privacy/security elements for e-health systems that utilize cloud computing. They engineered a strengthened security and privacy system to manage numerous types of vulnerable/private e-health data including user/patient data, diagnostic data, and treatment data. Next, Koutli et al. [9] outlined a VICINITY security framework that can be integrated into e-health applications that utilize IoT devices to service elderly patients. In the scheme, they demonstrated how Ambient-Assisted-Living (AAL) and mHealth (VICINITY IoT platform) can be used in conjunction to satisfy all required security/privacy elements. Their e-health applications are allowed for the secure processing of personal medical data and remote data storage.In 2018, Chattopadhyay et al. [10] coined the term Internet of Medical Things (IoMT) since IoT object adoption in healthcare has become ubiquitous. IoMT provides an unparalleled level of patient/doctor accessibility through the utilization of instantaneous management of patient data, treatment support, data processing, and other services. The authors utilized a cryptosystem to safeguard communication and authentication between local processing units (LPU), smart sensors, and transmission gateways. The proposed cryptosystem is integrated with a BSN comprising mainly of wearable and pervasive IoT devices as a healthcare monitoring system. Alihamidi et al. [11] proposed a Blockchain architecture enabling fog computing technology to secure e-health systems. They designed a three-layered IoT-based solution for e-Health systems with a Wireless Sensor Network (WSN). These three major classes are differentiated by their programmed level of access and connectivity between the connected system units. Various vectors of access can be used for evaluating the security effectiveness of any IoT architecture. Rahmani et al. [12] applied fog-computing to e-health systems that utilize IoT-cloud technology by introducing a geographically distributed intermediate intelligence layer in between the cloud devices and the sensor devices. Their solution addresses problems with agility, expandability, and dependability. The prototype, called a smart e-health gateway health monitoring system, was found effective at enhancing system intelligence, energy efficiency, mobility, performance, interoperability of security protocols, and reliability.


### 2.2. Security Requirement for IOT-Cloud-Based E-Health Systems

In section B, we introduce required security elements for e-health systems that utilize IoT-cloud technology management frameworks. The following are the major security requirements.


#### 2.2.1. Access Control: Required for Secure Communication

Researchers have thoroughly examined dynamic identity-based authentication protocols and identified their advantages, such as user convenience and protocol efficiency, for more than 20 years. In addition, secure communication requires anonymous and unpredictable identities and robust session keys must be predetermined to ensure secure communication between objects. Moreover, simple authentication and login without the use of session keys is easily vulnerable to security threats. Regardless of claims that various security schemes such as Secure Sockets Layer (SSL) and Transport Layer Security (TLS) can be utilized to ensure that security remains strong after authentication, they are ultimately inefficient due to exorbitant computational cost. Therefore, a solution with session key agreement scheme is essential to guarantee robust and secure communication. For the purpose of this study, our team attempts to demonstrate a protected communication protocol for e-health systems that utilize IoT-cloud technology for IoT-cloud-based e-health systems. However, e-health systems that utilize IoT-cloud technology always rely on various technologies such as sensors, actuators, and communication modules for data acquisition, manipulation, transmission, real-time data analysis, data storage, and application functions. Hence, it is essential to develop robust protocols being perfectly capable of operational conditions for IoT medical devices and achieving the required security as well.


#### 2.2.2. Data Encryption

As suggested by Yeh [13] and Yeh [14], the one-way hash operations maintain qualified security in an effective manner, while the exclusive-or protocol may be vulnerable to attackers. As exclusive-or operation is capable of resisting ciphertext only attacks, these protocols are the minimum acceptable level of cryptanalytic security. As a result, the integration of exclusive-or operation must be given careful consideration when designing security protocols. For example, publicly transmitted text must utilize volatile cipher formulae in conjunction with exclusive-or procedures. Moreover, these two constructs must not simply and directly be applied to the cipher. In addition, Ding et al. [15] determined that in order to protect network communication, data encryption must be utilized. Data encryption technology can deliver security within a computer network and its communication. Data encryption is utilized for the transformation of data based on predetermined bespoke rules/algorithms. This enables secure data transmission and the data can be decrypted at the destination through the use of unique unlocking methods. Then, Al-Haija et al. [16] suggested the use of flow cryptography to secure information between communicated parties through the use of encryption to transform plaintext into ciphertext. Subsequently, a designated key can be utilized to decipher the ciphertext into plaintext. The following is a list of standard data encryption algorithms utilized to secure network traffic: Data Encryption Standard (DES) algorithm, MD5 algorithm, and Rivest–Shamir–Adelman (RSA) algorithm [15, 16].


#### 2.2.3. Resistance against Spoofing Attacks

IoT-cloud-based communication architectures are primarily built atop a variant of traditional wireless sensor networks, called body sensor networks (BSN), consisting of many different body biosensors. The primary problem to address is the security of these individual biosensors considering that they collect and retain private health-related biodata. Hence, our proposed secure e-health system that utilizes IoT-cloud technology should carefully consider several different security requirements such as user identification, secure network switching, data anonymity/untraceability, and spoofing attack resistance at both the device and system level. These required security elements could be addressed via an anonymous authentication protocol. Stepien et al. [17] claimed that the most efficient method to secure IoT-based systems is to embed security solutions into the IoT devices directly. The best practice is that the IoT devices should be capable of accommodating and maintaining authenticity. That is, IoT-based systems must be capable of preventing unauthorized access while allowing flexible interoperability between connected devices under ad hoc network conditions. Moreover, to safeguard the data security at the IoT device end, it is imperative that the devices should be manufactured with the ability to en/decryption functions. This will ensure that malicious entities cannot physically retrieve any raw data from IoT device directly. There are multitudes of security, privacy, and safety risks associated with IoT-device utilization in e-health systems. One example is that personal data can be stolen and used to harm users or patients in different ways. Another example is that bad actors can initiate an insider/outsider attack to acquire patient registration information and biometric data. In this paper, the user authentication process is incorporated into cloud computing to protect against spoofing attacks.


#### 2.2.4. Resistance to Man-in-the-Middle Attacks

One of the primary security vulnerabilities to overcome is man-in-the-middle attack resistance during authentication. An immoral actor could possibly intercept transmitted authentication messages to spoof the communicating devices into believing they are legitimate users. This allows the attacker to communicate with the server as an authorized user and collect or modify data. Spoofing can also be used against real authorized users. For example, the attacker can mimic a legitimate server and communicate with the authorized user to steal their credentials. Embedding all communicating device IDs into the protocol messages for device authentication is an effective method of preventing man-in-the-middle infiltrations. Yu et al. [18] introduced an authentication/key consensus protocol in an IoT-cloud-based setting. They designed registration/authentication stages that utilizes a fuzzy-verifier technique as well as added verification within cloud servers. This method can effectively resist insider attacks and DoS assaults.


#### 2.2.5. Multiple Security and Privacy Properties Must Be Managed Simultaneously

The security/privacy of both data and devices are the primary concerns of any e-health systems that utilize IoT-cloud technology. This is due to the wireless communication protocols utilized by most BSNs, which tend to be inherently insecure. This insecurity leaves the system vulnerable to outside attacks that could cause grievous harm. The following are three of the most common principles for protecting IoT-cloud-based e-Health systems from outside attackers. Firstly, in order to defend the system from spoofing and the unauthorized access of data, a mutual authentication is utilized between commination devices. Secondly, the system must achieve and maintain anonymity and remain untraceable to ensure that data collected by biosensors, private personal data, or patient diagnostic data is not disclosed. Third, the system must be hardened against forgery and replay attacks. In addition, in study [2], the authors presented an extensive security/privacy framework for Healthcare 4.0, which established an integrated blockchain solution for different healthcare applications. Their framework improved patient diagnostics and ensured security/privacy of collected healthcare information.


## 2.1. The Current State of the Art

The primary security priorities for e-health systems that utilize IoT-cloud technology are robust preservation of security/privacy in regard to patient health information. Hence, many systems, integrating privacy policies and multilevel security mechanisms, have been proposed to be more capable of managing patient health information. Recently, Butpheng et al. [4] presented a practical amalgamation of cloud computing and IoT-technologies in an e-health setting. The authors demonstrated that integration/implementation of cloud computing and IoT-technologies (CIoT) is achievable in an efficient manner. This integration improves health diagnostics, treatment timeliness, and health outcomes and reduces costs. CIoT is comprised of interconnected smart hardware, custom systems, and custom applications that communicate over the Internet. The authors provided a comprehensive technical architecture with an inclusive list of privacy/security requirements for e-health systems that utilize IoT-cloud technology including identification, authentication, and authorization. Aligning with the proposed architecture, security solutions can be effectively designed to decrease the probability of an attack via examining and identifying vulnerabilities from each security aspect.Similarly, Wang and Cai [5] proposed a secure communication protocol for incorporating healthcare data and Named Data Networking (NDN) based on IoT technology and Edge-cloud computing (SHNIE) to secure and expedite the delivery of medical data and reduce latency and cost. SHNIE can efficiently deliver aggregated NDN-medical data to multiple users from the nearest edge devices that own the required medical data. The security of communications is realized through a hash ciphertext of the provider’s name and IDs as a verification token in the NDN to preserve privacy and security. Moreover, Ray et al. [6] designed a novel solution to deal with issues of interoperability, heterogeneity, and Internet-aware resistances. Proposed devices will collect patient data instantaneously and then process and analyze it with embedded Internet connected electronics. The proposed devices utilize the Internet for all interactions and communication and are capable of remote monitoring and controlling. Then, the authors further propose an e-healthcare architecture that incorporates blockchain technology based on IoT that is classified as follows: IoT e-healthcare; blockchain platform; connectivity; and IoT devices. Furthermore, Shewale and Sankpal [7] utilized various medical technologies such as real-time monitoring, patient data administration, and healthcare supervision via BSN technology to observe patients’ heartbeats and blood pressure. They introduced a monitoring system to detect the status of each patient’s health data, collect it, then forward the data to the server using Wi-Fi module based wireless communication. In 2019, Chenthara et al. [8] identified essential privacy/security elements for e-health systems that utilize cloud computing. They engineered a strengthened security and privacy system to manage numerous types of vulnerable/private e-health data including user/patient data, diagnostic data, and treatment data. Next, Koutli et al. [9] outlined a VICINITY security framework that can be integrated into e-health applications that utilize IoT devices to service elderly patients. In the scheme, they demonstrated how Ambient-Assisted-Living (AAL) and mHealth (VICINITY IoT platform) can be used in conjunction to satisfy all required security/privacy elements. Their e-health applications are allowed for the secure processing of personal medical data and remote data storage.In 2018, Chattopadhyay et al. [10] coined the term Internet of Medical Things (IoMT) since IoT object adoption in healthcare has become ubiquitous. IoMT provides an unparalleled level of patient/doctor accessibility through the utilization of instantaneous management of patient data, treatment support, data processing, and other services. The authors utilized a cryptosystem to safeguard communication and authentication between local processing units (LPU), smart sensors, and transmission gateways. The proposed cryptosystem is integrated with a BSN comprising mainly of wearable and pervasive IoT devices as a healthcare monitoring system. Alihamidi et al. [11] proposed a Blockchain architecture enabling fog computing technology to secure e-health systems. They designed a three-layered IoT-based solution for e-Health systems with a Wireless Sensor Network (WSN). These three major classes are differentiated by their programmed level of access and connectivity between the connected system units. Various vectors of access can be used for evaluating the security effectiveness of any IoT architecture. Rahmani et al. [12] applied fog-computing to e-health systems that utilize IoT-cloud technology by introducing a geographically distributed intermediate intelligence layer in between the cloud devices and the sensor devices. Their solution addresses problems with agility, expandability, and dependability. The prototype, called a smart e-health gateway health monitoring system, was found effective at enhancing system intelligence, energy efficiency, mobility, performance, interoperability of security protocols, and reliability.


## 2.2. Security Requirement for IOT-Cloud-Based E-Health Systems

In section B, we introduce required security elements for e-health systems that utilize IoT-cloud technology management frameworks. The following are the major security requirements.


### 2.2.1. Access Control: Required for Secure Communication

Researchers have thoroughly examined dynamic identity-based authentication protocols and identified their advantages, such as user convenience and protocol efficiency, for more than 20 years. In addition, secure communication requires anonymous and unpredictable identities and robust session keys must be predetermined to ensure secure communication between objects. Moreover, simple authentication and login without the use of session keys is easily vulnerable to security threats. Regardless of claims that various security schemes such as Secure Sockets Layer (SSL) and Transport Layer Security (TLS) can be utilized to ensure that security remains strong after authentication, they are ultimately inefficient due to exorbitant computational cost. Therefore, a solution with session key agreement scheme is essential to guarantee robust and secure communication. For the purpose of this study, our team attempts to demonstrate a protected communication protocol for e-health systems that utilize IoT-cloud technology for IoT-cloud-based e-health systems. However, e-health systems that utilize IoT-cloud technology always rely on various technologies such as sensors, actuators, and communication modules for data acquisition, manipulation, transmission, real-time data analysis, data storage, and application functions. Hence, it is essential to develop robust protocols being perfectly capable of operational conditions for IoT medical devices and achieving the required security as well.


### 2.2.2. Data Encryption

As suggested by Yeh [13] and Yeh [14], the one-way hash operations maintain qualified security in an effective manner, while the exclusive-or protocol may be vulnerable to attackers. As exclusive-or operation is capable of resisting ciphertext only attacks, these protocols are the minimum acceptable level of cryptanalytic security. As a result, the integration of exclusive-or operation must be given careful consideration when designing security protocols. For example, publicly transmitted text must utilize volatile cipher formulae in conjunction with exclusive-or procedures. Moreover, these two constructs must not simply and directly be applied to the cipher. In addition, Ding et al. [15] determined that in order to protect network communication, data encryption must be utilized. Data encryption technology can deliver security within a computer network and its communication. Data encryption is utilized for the transformation of data based on predetermined bespoke rules/algorithms. This enables secure data transmission and the data can be decrypted at the destination through the use of unique unlocking methods. Then, Al-Haija et al. [16] suggested the use of flow cryptography to secure information between communicated parties through the use of encryption to transform plaintext into ciphertext. Subsequently, a designated key can be utilized to decipher the ciphertext into plaintext. The following is a list of standard data encryption algorithms utilized to secure network traffic: Data Encryption Standard (DES) algorithm, MD5 algorithm, and Rivest–Shamir–Adelman (RSA) algorithm [15, 16].


### 2.2.3. Resistance against Spoofing Attacks

IoT-cloud-based communication architectures are primarily built atop a variant of traditional wireless sensor networks, called body sensor networks (BSN), consisting of many different body biosensors. The primary problem to address is the security of these individual biosensors considering that they collect and retain private health-related biodata. Hence, our proposed secure e-health system that utilizes IoT-cloud technology should carefully consider several different security requirements such as user identification, secure network switching, data anonymity/untraceability, and spoofing attack resistance at both the device and system level. These required security elements could be addressed via an anonymous authentication protocol. Stepien et al. [17] claimed that the most efficient method to secure IoT-based systems is to embed security solutions into the IoT devices directly. The best practice is that the IoT devices should be capable of accommodating and maintaining authenticity. That is, IoT-based systems must be capable of preventing unauthorized access while allowing flexible interoperability between connected devices under ad hoc network conditions. Moreover, to safeguard the data security at the IoT device end, it is imperative that the devices should be manufactured with the ability to en/decryption functions. This will ensure that malicious entities cannot physically retrieve any raw data from IoT device directly. There are multitudes of security, privacy, and safety risks associated with IoT-device utilization in e-health systems. One example is that personal data can be stolen and used to harm users or patients in different ways. Another example is that bad actors can initiate an insider/outsider attack to acquire patient registration information and biometric data. In this paper, the user authentication process is incorporated into cloud computing to protect against spoofing attacks.


### 2.2.4. Resistance to Man-in-the-Middle Attacks

One of the primary security vulnerabilities to overcome is man-in-the-middle attack resistance during authentication. An immoral actor could possibly intercept transmitted authentication messages to spoof the communicating devices into believing they are legitimate users. This allows the attacker to communicate with the server as an authorized user and collect or modify data. Spoofing can also be used against real authorized users. For example, the attacker can mimic a legitimate server and communicate with the authorized user to steal their credentials. Embedding all communicating device IDs into the protocol messages for device authentication is an effective method of preventing man-in-the-middle infiltrations. Yu et al. [18] introduced an authentication/key consensus protocol in an IoT-cloud-based setting. They designed registration/authentication stages that utilizes a fuzzy-verifier technique as well as added verification within cloud servers. This method can effectively resist insider attacks and DoS assaults.


### 2.2.5. Multiple Security and Privacy Properties Must Be Managed Simultaneously

The security/privacy of both data and devices are the primary concerns of any e-health systems that utilize IoT-cloud technology. This is due to the wireless communication protocols utilized by most BSNs, which tend to be inherently insecure. This insecurity leaves the system vulnerable to outside attacks that could cause grievous harm. The following are three of the most common principles for protecting IoT-cloud-based e-Health systems from outside attackers. Firstly, in order to defend the system from spoofing and the unauthorized access of data, a mutual authentication is utilized between commination devices. Secondly, the system must achieve and maintain anonymity and remain untraceable to ensure that data collected by biosensors, private personal data, or patient diagnostic data is not disclosed. Third, the system must be hardened against forgery and replay attacks. In addition, in study [2], the authors presented an extensive security/privacy framework for Healthcare 4.0, which established an integrated blockchain solution for different healthcare applications. Their framework improved patient diagnostics and ensured security/privacy of collected healthcare information.


## 2.2.1. Access Control: Required for Secure Communication

Researchers have thoroughly examined dynamic identity-based authentication protocols and identified their advantages, such as user convenience and protocol efficiency, for more than 20 years. In addition, secure communication requires anonymous and unpredictable identities and robust session keys must be predetermined to ensure secure communication between objects. Moreover, simple authentication and login without the use of session keys is easily vulnerable to security threats. Regardless of claims that various security schemes such as Secure Sockets Layer (SSL) and Transport Layer Security (TLS) can be utilized to ensure that security remains strong after authentication, they are ultimately inefficient due to exorbitant computational cost. Therefore, a solution with session key agreement scheme is essential to guarantee robust and secure communication. For the purpose of this study, our team attempts to demonstrate a protected communication protocol for e-health systems that utilize IoT-cloud technology for IoT-cloud-based e-health systems. However, e-health systems that utilize IoT-cloud technology always rely on various technologies such as sensors, actuators, and communication modules for data acquisition, manipulation, transmission, real-time data analysis, data storage, and application functions. Hence, it is essential to develop robust protocols being perfectly capable of operational conditions for IoT medical devices and achieving the required security as well.


## 2.2.2. Data Encryption

As suggested by Yeh [13] and Yeh [14], the one-way hash operations maintain qualified security in an effective manner, while the exclusive-or protocol may be vulnerable to attackers. As exclusive-or operation is capable of resisting ciphertext only attacks, these protocols are the minimum acceptable level of cryptanalytic security. As a result, the integration of exclusive-or operation must be given careful consideration when designing security protocols. For example, publicly transmitted text must utilize volatile cipher formulae in conjunction with exclusive-or procedures. Moreover, these two constructs must not simply and directly be applied to the cipher. In addition, Ding et al. [15] determined that in order to protect network communication, data encryption must be utilized. Data encryption technology can deliver security within a computer network and its communication. Data encryption is utilized for the transformation of data based on predetermined bespoke rules/algorithms. This enables secure data transmission and the data can be decrypted at the destination through the use of unique unlocking methods. Then, Al-Haija et al. [16] suggested the use of flow cryptography to secure information between communicated parties through the use of encryption to transform plaintext into ciphertext. Subsequently, a designated key can be utilized to decipher the ciphertext into plaintext. The following is a list of standard data encryption algorithms utilized to secure network traffic: Data Encryption Standard (DES) algorithm, MD5 algorithm, and Rivest–Shamir–Adelman (RSA) algorithm [15, 16].


## 2.2.3. Resistance against Spoofing Attacks

IoT-cloud-based communication architectures are primarily built atop a variant of traditional wireless sensor networks, called body sensor networks (BSN), consisting of many different body biosensors. The primary problem to address is the security of these individual biosensors considering that they collect and retain private health-related biodata. Hence, our proposed secure e-health system that utilizes IoT-cloud technology should carefully consider several different security requirements such as user identification, secure network switching, data anonymity/untraceability, and spoofing attack resistance at both the device and system level. These required security elements could be addressed via an anonymous authentication protocol. Stepien et al. [17] claimed that the most efficient method to secure IoT-based systems is to embed security solutions into the IoT devices directly. The best practice is that the IoT devices should be capable of accommodating and maintaining authenticity. That is, IoT-based systems must be capable of preventing unauthorized access while allowing flexible interoperability between connected devices under ad hoc network conditions. Moreover, to safeguard the data security at the IoT device end, it is imperative that the devices should be manufactured with the ability to en/decryption functions. This will ensure that malicious entities cannot physically retrieve any raw data from IoT device directly. There are multitudes of security, privacy, and safety risks associated with IoT-device utilization in e-health systems. One example is that personal data can be stolen and used to harm users or patients in different ways. Another example is that bad actors can initiate an insider/outsider attack to acquire patient registration information and biometric data. In this paper, the user authentication process is incorporated into cloud computing to protect against spoofing attacks.


## 2.2.4. Resistance to Man-in-the-Middle Attacks

One of the primary security vulnerabilities to overcome is man-in-the-middle attack resistance during authentication. An immoral actor could possibly intercept transmitted authentication messages to spoof the communicating devices into believing they are legitimate users. This allows the attacker to communicate with the server as an authorized user and collect or modify data. Spoofing can also be used against real authorized users. For example, the attacker can mimic a legitimate server and communicate with the authorized user to steal their credentials. Embedding all communicating device IDs into the protocol messages for device authentication is an effective method of preventing man-in-the-middle infiltrations. Yu et al. [18] introduced an authentication/key consensus protocol in an IoT-cloud-based setting. They designed registration/authentication stages that utilizes a fuzzy-verifier technique as well as added verification within cloud servers. This method can effectively resist insider attacks and DoS assaults.


## 2.2.5. Multiple Security and Privacy Properties Must Be Managed Simultaneously

The security/privacy of both data and devices are the primary concerns of any e-health systems that utilize IoT-cloud technology. This is due to the wireless communication protocols utilized by most BSNs, which tend to be inherently insecure. This insecurity leaves the system vulnerable to outside attacks that could cause grievous harm. The following are three of the most common principles for protecting IoT-cloud-based e-Health systems from outside attackers. Firstly, in order to defend the system from spoofing and the unauthorized access of data, a mutual authentication is utilized between commination devices. Secondly, the system must achieve and maintain anonymity and remain untraceable to ensure that data collected by biosensors, private personal data, or patient diagnostic data is not disclosed. Third, the system must be hardened against forgery and replay attacks. In addition, in study [2], the authors presented an extensive security/privacy framework for Healthcare 4.0, which established an integrated blockchain solution for different healthcare applications. Their framework improved patient diagnostics and ensured security/privacy of collected healthcare information.


## 3. Proposed IOT-Cloud-Based E-Health System

This section introduces a communication environment, a designated trust border, and outline the purpose of our suggested e-health systems that utilize IoT-cloud technology. Then, we illustrate the detailed communication steps for the proposed system that contains both discreet initialization and authentication phases.As mentioned in the literature review, e-health systems that utilize IoT-cloud technology have the capability of managing a large variety of data, such as heart rate, temperature, diagnosis, and outcomes. This data can be analyzed to identify and calculate the severity of particular diseases. e-health systems leverage the capabilities of various hardware devices and software components to achieve the organized, controlled, and universal cloud computing incorporation. This enables fast/accurate transfer and data analysis. Moreover, it ensures high overall system quality. The analyzed data could be utilized to create graphic visualizations and warnings corresponding to a given patients health condition and stored confidentially. Moreover, data integrity and authentication are ensured through the utilization of cloud networks which implement appropriate security mechanisms for a given system.


### 3.1. The Communication Environment

Here, we introduce our proposed communication environment for e-health systems that utilize IoT-cloud technology. There are three essential components of our e-health communication: wearable body biosensors, LPU (Local Processing Unit), and BSN (Body Sensor Networks) server. Wearable body biosensors are always fixed onto users and classified as edge devices. These edge devices collect and transmit biodata from a given patient to LPU and BSN server for analysis. This communication is highly efficient and enables fast and accurate diagnosis and treatment in which real-time biodata such as electrocardiograph (ECG), electroencephalograph (EEG), electromyograph (EMG), and blood pressure (BP) are retrieved. After the data is collected, the BSN servers can process the data and provide bespoke treatment recommendations for patients in a timely manner. This reduced delay in diagnosis, and the treatment would greatly improve patient outcomes. Our suggested communication framework for e-health systems that utilize IoT-cloud technology employs a designated LPU and numerous biosensors to complete advanced registration on a designated BSN server. Once advanced registration is completed, assigned security authorizations shall be distributed and held onboard the biosensors, designated LPU, and designated BSN server. Moreover, assigned security authorizations are utilized to establish protected channels of communication channels and to authenticate devices. This will ensure that data confidentiality and data integrity are maintained.Our proposed system is comprised of two phases: a system initialization phase and an authentication phase. In the initialization phase, approval will be determined for all required security elements then distributed between the communication objects (body sensors, LPU, and BSN server) through protected channels. After that, the authentication phase will be utilized for the protection of all exchanged communication/data between communicating devices. Before outlining our proposed communication protocols, we comprehensively illustrate the symbols and abbreviations utilized within this paper (Table1).Table 1
Notations.
SymbolDefinitionP, QPrincipalsX,YStatementsKLong-term secrets or secret keysP believes XP believes that X is correctP sees XA message is transmitted that containsX to PP said XP transmits a message that includes X within the present protocol or beforehandP controls XP holds authority over S; P exerts control over X and should be believedFresh (X)X has not been transmitted in a message prior to the present protocol sessionP↔KQKeyK is shared among both P and QXKRepresents formulaX, encrypted/secured under key KBSiIdentity of wearable biosensoriLPUjIdentity of the local processing unitjServerIdentity of the BSN server (cloud computing)IDiPublic identity ofBSiIDjPublic identity ofLPUjIDsPublic identity of serverIDdjPrivate identity ofLPUjIDssPrivate identity ofServerAIDiOne-time alias identity of the wearable biosensor iSIDA set of unlinkable shadow identitiesSID=sid1,sid2,…,sidnTrseqTrack sequence numberRis, Ri, Rj, Rsid1, RsidnRandom number⊕Bitwise operators‖Concatenation operation


### 3.2. System Initialization

Firstly, body biosensorBSi transmits its device identity IDi as a registration request to a designated BSN server (cloud-based server). Once the request is received from BSi, the designated Server will then generate a random number (Ris) then utilizes its unique identity IDs to calculate the secret key, Kis=IDsRisIDi. Subsequently, the designated Server will compute multiple unlinkable shadow identities SID=sid1,sid2,…,sidn for BSi. Then, to achieve expedient identification of BSi and avert replay attacks, a track sequence number Trseq is designated. During each authentication session, Trseq must be altered and the new Trseq will be stored both on the BSi and Server. During the authentication session, the freshness of inbound BSi requests can be verified and directly identified by the Server through the use of Trseq stored in a designated backend database. Then, the Server automatically rejects any inbound demands and then terminates communication if the integrity of the Trseq in the demand is not preserved in the backend database. If the demand remains intact, the Server instructs BSi to transmit a new request while simultaneously embedding a fresh SID as the anonymized identity of BSi . Lastly, the Server issues a security certificate IDi,Kis,SID,Trseq to BSi. Meanwhile, the Server maintains the same security certificate IDi,Kis,SID,Trseq corresponding to each BSi in the backend database. Moreover, registration between the Server and LPUj is accomplished through a similar protocol; LPUj transmits its identity IDj in the form of a registration request to a designated Server. The designated Server then calculates Kis=IDsRisIDi utilizing a freshly generated public key Rjs. It then shares a security certificate IDs,Kjs,IDj. Lastly, the Server maintains the security certificate IDs,Kjs,IDj and its corresponding LPUj in a backed database.The compiler will verify the token and either deny authorization or add the sensor to a list of authorized devices. The compiler is connected to the physical LAN network or wireless network to receive the token key, save it to the device list, and transmit the encrypted token key and data to authorized network nodes. Once the token key is verified and validated, the token key is reset to generate a new record. Individual body sensors will receive authorization from the compiler and start gathering data. Gathered data will be encrypted and transmitted wirelessly to the compiler and stored.


### 3.3. Authentication Phase

In our proposed e-health systems that utilize IoT-cloud technology, we assume healthcare professionals with intelligent devices, endeavor to deliver instantaneous healthcare benefits through the use of an autonomous, noncontact information gathering and recovery apparatus. Given that our communications network based on IoT is public, an authentication protocol that is both strong and resilient is necessary to protect information exchanged between the biosensors, LPU, and BSN server. Due to the public nature of hospital networks, the network is inherently insecure. Accordingly, a robust IoT communication architecture is required for the creation of a secure channel that can be utilized for the safe exchange of data betweenBSi, LPUj, and the Server.The authentication phase communication protocols are detailed in Figure1.Figure 1
Authentication phase.Stage 1.
BSi⟶LPUj:MA1=AIDi,M1,Ri,Trseq,IDj
BSi first generates random number Ri and calculates M1=Kis⊕Ri and AIDi=M1IDjTrseq. Next, BSi sends MA1=AIDi,M1,Ri,Trseq,IDj as an authentication request to LPUj . If the shared Trseq value between the Server and BSi is not synchronized, BSi selects a new sid1 from the master SID list and consequently sets sid1 as AIDi . Lastly, BSi transmits an authentication request (MA1=AIDi,M1,Ri,IDj) to LPUj.Stage 2.
LPUj⟶Server:MA2=M2,Rj,IDj,V1,MA1.
Once the authentication request fromBSi is received, LPUj will produce a random number Rj and calculate M2=Kjs⊕Rj and V1=MA2TrseqIDjRjKjs. Then, LPUj sends MA2=M2,Rj,IDj,V1,MA1 to the Server.Stage 3.
Server⟶LPUj:MA3=Rs2,Tr,V3,V2.
Once theServer obtains MA2=M2,Rj,IDj,V1,MA1, then the Server initially confirms if the tracking sequence number Trseq remains present within a given request. Supposing Trseq has concluded in MA2, the Server executes condition (1), or else condition (2) shall be invoked.(i)
Condition (1): testTrseq legitimacy; then seek an analogous tuple through Trseq from a designated backed database. If Trseq legitimacy is confirmed, the Server recovers Kis . If legitimacy is rejected, the Server will terminate the current session. Alternatively, the Server shall validate M2, V1, and AIDi utilizing these subsequent algorithms:(ii)
Is the acquiredM2 equivalent to calculate M2 expressed as M2=Kjs⊕Rj?(iii)
Is the acquiredV1 equivalent to calculate MA1 expressed as MA1=Trseq‖IDjRjKjs?(iv)
Condition (2): in the event that theServer is unable to locate Trseq within the request (MA2, MA1), the Server shall determine the validity/freshness of AIDi=sidi from SID. In the event that the Server is unable to recognize sidi harvested from the backend database, the Server eliminates all connections and then directs BSi to attempt validation once more utilizing a different shadow identity sidi.
In the event that at least one inspection agrees, theServer shall produce random numbers Rs1, Rs2 and then designate Rs1 as the new tracking sequence number TrseqTrseq−new=Rs1. Subsequently, the Server computes Tr=Kis‖Rs2⊕Trseq−new, V3=Tr‖Kis, V2=IDjRs2Kjs, SKis=Kis‖Tr, and SKjs=Kjs‖IDjRs2Rj. It should be noted that the designated session key is SKis and shall be applied to the subsequent protected commination between BSi and the Server. Moreover, SKjs is designated as the session key that will be utilized by the LPUj and Server. Thereafter, S transmits MA3=Rs2,Tr,V3,V2 to the LPUj as a response.Stage 4.
LPUj⟶BSi:MA4=Rs2,Tr,V3.
After receivingMA4=Rs2,Tr,V3,V2, LPUj first calculates IDjRjKjs; next, it confirms that the accepted value V2 and the calculated value IDjRs2Kjs are equivalent. In the event that reciprocal authentication between the LPUj and the Server is confirmed, bothLPUj and the Server approve the utilization of secure session key SKjs. Lastly, LPUj sends MA4=Rs2,Tr,V3 to BSi . Upon obtaining MA4, BSi calculates Tr‖Kjs and cross-references it with the obtained value V3 . In the event that the two values are equivalent, BSi obtains Trseq−new=Kis‖Rs2⊕Tr and sets Trseq=Trseq−new; the next new authentication session will utilize this technique. Lastly, BSi calculates an SKis=Kis‖Tr, session key that is utilized in cooperation with the Server. This guarantees mutual authentication between the Server and BSi.


## 3.1. The Communication Environment

Here, we introduce our proposed communication environment for e-health systems that utilize IoT-cloud technology. There are three essential components of our e-health communication: wearable body biosensors, LPU (Local Processing Unit), and BSN (Body Sensor Networks) server. Wearable body biosensors are always fixed onto users and classified as edge devices. These edge devices collect and transmit biodata from a given patient to LPU and BSN server for analysis. This communication is highly efficient and enables fast and accurate diagnosis and treatment in which real-time biodata such as electrocardiograph (ECG), electroencephalograph (EEG), electromyograph (EMG), and blood pressure (BP) are retrieved. After the data is collected, the BSN servers can process the data and provide bespoke treatment recommendations for patients in a timely manner. This reduced delay in diagnosis, and the treatment would greatly improve patient outcomes. Our suggested communication framework for e-health systems that utilize IoT-cloud technology employs a designated LPU and numerous biosensors to complete advanced registration on a designated BSN server. Once advanced registration is completed, assigned security authorizations shall be distributed and held onboard the biosensors, designated LPU, and designated BSN server. Moreover, assigned security authorizations are utilized to establish protected channels of communication channels and to authenticate devices. This will ensure that data confidentiality and data integrity are maintained.Our proposed system is comprised of two phases: a system initialization phase and an authentication phase. In the initialization phase, approval will be determined for all required security elements then distributed between the communication objects (body sensors, LPU, and BSN server) through protected channels. After that, the authentication phase will be utilized for the protection of all exchanged communication/data between communicating devices. Before outlining our proposed communication protocols, we comprehensively illustrate the symbols and abbreviations utilized within this paper (Table1).Table 1
Notations.
SymbolDefinitionP, QPrincipalsX,YStatementsKLong-term secrets or secret keysP believes XP believes that X is correctP sees XA message is transmitted that containsX to PP said XP transmits a message that includes X within the present protocol or beforehandP controls XP holds authority over S; P exerts control over X and should be believedFresh (X)X has not been transmitted in a message prior to the present protocol sessionP↔KQKeyK is shared among both P and QXKRepresents formulaX, encrypted/secured under key KBSiIdentity of wearable biosensoriLPUjIdentity of the local processing unitjServerIdentity of the BSN server (cloud computing)IDiPublic identity ofBSiIDjPublic identity ofLPUjIDsPublic identity of serverIDdjPrivate identity ofLPUjIDssPrivate identity ofServerAIDiOne-time alias identity of the wearable biosensor iSIDA set of unlinkable shadow identitiesSID=sid1,sid2,…,sidnTrseqTrack sequence numberRis, Ri, Rj, Rsid1, RsidnRandom number⊕Bitwise operators‖Concatenation operation


## 3.2. System Initialization

Firstly, body biosensorBSi transmits its device identity IDi as a registration request to a designated BSN server (cloud-based server). Once the request is received from BSi, the designated Server will then generate a random number (Ris) then utilizes its unique identity IDs to calculate the secret key, Kis=IDsRisIDi. Subsequently, the designated Server will compute multiple unlinkable shadow identities SID=sid1,sid2,…,sidn for BSi. Then, to achieve expedient identification of BSi and avert replay attacks, a track sequence number Trseq is designated. During each authentication session, Trseq must be altered and the new Trseq will be stored both on the BSi and Server. During the authentication session, the freshness of inbound BSi requests can be verified and directly identified by the Server through the use of Trseq stored in a designated backend database. Then, the Server automatically rejects any inbound demands and then terminates communication if the integrity of the Trseq in the demand is not preserved in the backend database. If the demand remains intact, the Server instructs BSi to transmit a new request while simultaneously embedding a fresh SID as the anonymized identity of BSi . Lastly, the Server issues a security certificate IDi,Kis,SID,Trseq to BSi. Meanwhile, the Server maintains the same security certificate IDi,Kis,SID,Trseq corresponding to each BSi in the backend database. Moreover, registration between the Server and LPUj is accomplished through a similar protocol; LPUj transmits its identity IDj in the form of a registration request to a designated Server. The designated Server then calculates Kis=IDsRisIDi utilizing a freshly generated public key Rjs. It then shares a security certificate IDs,Kjs,IDj. Lastly, the Server maintains the security certificate IDs,Kjs,IDj and its corresponding LPUj in a backed database.The compiler will verify the token and either deny authorization or add the sensor to a list of authorized devices. The compiler is connected to the physical LAN network or wireless network to receive the token key, save it to the device list, and transmit the encrypted token key and data to authorized network nodes. Once the token key is verified and validated, the token key is reset to generate a new record. Individual body sensors will receive authorization from the compiler and start gathering data. Gathered data will be encrypted and transmitted wirelessly to the compiler and stored.


## 3.3. Authentication Phase

In our proposed e-health systems that utilize IoT-cloud technology, we assume healthcare professionals with intelligent devices, endeavor to deliver instantaneous healthcare benefits through the use of an autonomous, noncontact information gathering and recovery apparatus. Given that our communications network based on IoT is public, an authentication protocol that is both strong and resilient is necessary to protect information exchanged between the biosensors, LPU, and BSN server. Due to the public nature of hospital networks, the network is inherently insecure. Accordingly, a robust IoT communication architecture is required for the creation of a secure channel that can be utilized for the safe exchange of data betweenBSi, LPUj, and the Server.The authentication phase communication protocols are detailed in Figure1.Figure 1
Authentication phase.Stage 1.
BSi⟶LPUj:MA1=AIDi,M1,Ri,Trseq,IDj
BSi first generates random number Ri and calculates M1=Kis⊕Ri and AIDi=M1IDjTrseq. Next, BSi sends MA1=AIDi,M1,Ri,Trseq,IDj as an authentication request to LPUj . If the shared Trseq value between the Server and BSi is not synchronized, BSi selects a new sid1 from the master SID list and consequently sets sid1 as AIDi . Lastly, BSi transmits an authentication request (MA1=AIDi,M1,Ri,IDj) to LPUj.Stage 2.
LPUj⟶Server:MA2=M2,Rj,IDj,V1,MA1.
Once the authentication request fromBSi is received, LPUj will produce a random number Rj and calculate M2=Kjs⊕Rj and V1=MA2TrseqIDjRjKjs. Then, LPUj sends MA2=M2,Rj,IDj,V1,MA1 to the Server.Stage 3.
Server⟶LPUj:MA3=Rs2,Tr,V3,V2.
Once theServer obtains MA2=M2,Rj,IDj,V1,MA1, then the Server initially confirms if the tracking sequence number Trseq remains present within a given request. Supposing Trseq has concluded in MA2, the Server executes condition (1), or else condition (2) shall be invoked.(i)
Condition (1): testTrseq legitimacy; then seek an analogous tuple through Trseq from a designated backed database. If Trseq legitimacy is confirmed, the Server recovers Kis . If legitimacy is rejected, the Server will terminate the current session. Alternatively, the Server shall validate M2, V1, and AIDi utilizing these subsequent algorithms:(ii)
Is the acquiredM2 equivalent to calculate M2 expressed as M2=Kjs⊕Rj?(iii)
Is the acquiredV1 equivalent to calculate MA1 expressed as MA1=Trseq‖IDjRjKjs?(iv)
Condition (2): in the event that theServer is unable to locate Trseq within the request (MA2, MA1), the Server shall determine the validity/freshness of AIDi=sidi from SID. In the event that the Server is unable to recognize sidi harvested from the backend database, the Server eliminates all connections and then directs BSi to attempt validation once more utilizing a different shadow identity sidi.
In the event that at least one inspection agrees, theServer shall produce random numbers Rs1, Rs2 and then designate Rs1 as the new tracking sequence number TrseqTrseq−new=Rs1. Subsequently, the Server computes Tr=Kis‖Rs2⊕Trseq−new, V3=Tr‖Kis, V2=IDjRs2Kjs, SKis=Kis‖Tr, and SKjs=Kjs‖IDjRs2Rj. It should be noted that the designated session key is SKis and shall be applied to the subsequent protected commination between BSi and the Server. Moreover, SKjs is designated as the session key that will be utilized by the LPUj and Server. Thereafter, S transmits MA3=Rs2,Tr,V3,V2 to the LPUj as a response.Stage 4.
LPUj⟶BSi:MA4=Rs2,Tr,V3.
After receivingMA4=Rs2,Tr,V3,V2, LPUj first calculates IDjRjKjs; next, it confirms that the accepted value V2 and the calculated value IDjRs2Kjs are equivalent. In the event that reciprocal authentication between the LPUj and the Server is confirmed, bothLPUj and the Server approve the utilization of secure session key SKjs. Lastly, LPUj sends MA4=Rs2,Tr,V3 to BSi . Upon obtaining MA4, BSi calculates Tr‖Kjs and cross-references it with the obtained value V3 . In the event that the two values are equivalent, BSi obtains Trseq−new=Kis‖Rs2⊕Tr and sets Trseq=Trseq−new; the next new authentication session will utilize this technique. Lastly, BSi calculates an SKis=Kis‖Tr, session key that is utilized in cooperation with the Server. This guarantees mutual authentication between the Server and BSi.


## 4. Security Analysis

In this section, we present our security analysis in terms of the aforementioned system criteria. The analysis of authentication is based on a logic model. Before we conduct the formal logic analysis on the proposed authentication mechanism, we define basic constructs and logic postulates. Hereafter,P and Q range over principals, X and Y range over statements, and K ranges over long-term secret keys (i.e., Table 1).


### 4.1. Claim 1: Robust Access Control Can Be Achieved through Mutual Authentication among Communication Entities in a BSN

We demonstrate some basic logical assumptions and constraints, where variablesP and Q range over principals, X and Y range over statements, and K ranges over long-term secret keys.(i)
Statement 1: SupposeP believes P↔KQ and P perceives XK; next, we presume that P believes Q said X.(ii)
Statement 2: SupposeP believes fresh X and P believes Q said X; next, we presume that P believes that Q believes X.(iii)
Statement 3: SupposeP believes Q holds dominion over X and P believes that Q believes X; next, we presume that P believes X.(iv)
Statement 4: SupposeP observes X,Y then P observes X. In addition, provided that P believes P↔KQ and P observes XK, then P observes X.(v)
Statement 5: Supposing a given formula contains one component that is fresh, it is assumed that the entirety of the formula is similarly fresh. SupposingP believes fresh X, then P believes fresh X,Y.Prior to analyzing the authentication process of the BSN, the following constraints must be considered:(i)
Constraint 1:BSi and Server believe BSi↔IDi,Kis,SID,TrseqServer.(ii)
Constraint 2:LPUj and Server believe LPUj↔IDj,KjsServer.(iii)
Constraint 3:Server believes fresh Ri,Rj.(iv)
Constraint 4:BSi and LPUj believe fresh Rs1,Rs2.(v)
Constraint 5:Server believes BSi controls Ri.(vi)
Constraint 6:Server believes LPUj controls Rj.(vii)
Constraint 7:BSi and LPUj believe Server controls Rs1,Rs2.The tangible BSN authentication protocols and procedures are defined below. Each symbol is defined, and the BSN communication protocol is illustrated in Figure1.(i)
Stage 1:BSi⟶LPUj:MA1=AIDi,M1,Ri,Trseq,IDj, where M1=Kis⊕Ri, and AIDi=M1IDjTrseq.(ii)
Stage 2:LPUj⟶Server:MA2=M2,Rj,IDj,V1,MA1, where M2=Kjs⊕Rj, V1=MA2TrseqIDjRjKjs, and MA1=AIDi,M1,Ri,Trseq,IDj(iii)
Stage 3:Server⟶LPUj:MA3=Rs2,Tr,V3,V2, where Tr=Kis‖Rs2⊕Trseq−new, V3=Tr‖Kis, and V2=IDjRs2Kjs.(iv)
Stage 4:LPUj⟶BSi:MA4=Rs2,Tr,V3, where Tr=Kis‖Rs2⊕Trseq−new and V3=Tr‖Kis.The official analysis of the BSN joint authentication protocol is detailed below:(1)
LPUj perceives MA3=Rs2,Tr,V3,V2; based on Stage 3, it is apparent that LPUj has received and perceived Rs2,Tr,V3,V2.(2)
LPUj believes LPUj↔IDj,KjsServer; based on Constraint 2, LPUj perceives that it shares IDj and Kjs with the designated server.(3)
Based on (1) and (2),LPUj perceives that the Server said V2; therefore, we can derive that LPUj perceives that the Server said V2 utilizing Statement 1.(4)
LPUj perceives fresh Rj; as Rj which is dispensed by LPUj; LPUj has the ability to evaluate the freshness of Rj and perceives Rj as fresh if it meets the specified criteria.(5)
Based on (3) and (4),LPUj perceives that the Server perceives V2; if it is confirmed that LPUj perceives that the Server perceives V2 based on Rule 2, then the claim is supported.(6)
LPUj perceives that the Server governs Rs2; based on Constraint 7, LPUj perceives that the random number Rs2 is directly governed by the Server.(7)
Based on (5), (6), and Statement 3,LPUj perceives V2; therefore, it can be confirmed that LPUj believes V2.(8)
BSi perceives MA4=Rs2,Tr,V3; in Stage 4, BSi is confirmed to perceive MA4=Rs2,Tr,V3.(9)
Based on Constraint 1,BSi perceives BSi↔IDj,Kjs,SID,TrseqServer; therefore, BSi perceives that IDi, Kis, SID, and Trseq are shared with the Server.(10)
Based on (8) and (9),BSi perceives that the Server transmitted Tr,V3; therefore, we can determine that BSi perceives that Server transmitted Tr,V3 utilizing Statement 1.(11)
Based on Constraint 4,BSi perceives Rs2 as fresh; BSi accepts the freshness of Rs2.(12)
Based on (10) and (11),BSi perceives that the Server perceives Tr,V3; it is guaranteed that BSi perceives that the Server perceives Tr,V3 because of Statement 2.(13)
Based on Constraint 7,BSi perceives that the Server governs Rs2; BSi perceives that the random number Rs2 is directly governed by the Server.(14)
Based on (12) and (13),BSi perceives Tr,V3; therefore, we can determine that BSi perceives Tr,V3.The finalized results of our simulation are shown below:(i)
LPUj perceives that the Server perceives V2 based on (5).(ii)
LPUj perceives V2 based on (7).(iii)
BSi perceives that Server perceives Tr,V3 based on (12).(iv)
BSi perceives Tr,V3 based on (14).Assuming the designatedserver is reliable/trustworthy and based on the results of (5), (7), (12), and (14), both BSi and LPUj can authenticate each other on the designated Server.


### 4.2. Claim 2:BSi Anonymity and Untraceability Can Be Ensured

During the authentication phase, we adopt random numbers,Ri, Rj,Rs2, and utilize them to randomize any communicated messages, namely, AIDi, M1, M2, V1, Trseq, Tr, V2, and V3, where some are involved with BSi. Firstly, in the BSN, Trseq is employed as a unique single-use token that enables the rapid identification of BSi due to the fact that Trseq does not contain any data relevant to BSi. Moreover, Trseq is actively refreshed following every successfully completed authentication session. Trseq does not reveal any information about BSi. Secondly, a secret identity AIDi (occasionally a single-use/valueless sid shall be utilized) will be circulated between BSi and the Server and then shall be analyzed by the Server. Nobody is capable of tracing or identifying BSi with the exception of the Server. Thirdly, based on the following equation expressed as M1=Kis⊕Ri⊕Tr=‖KisRs2⊕Trseq−new and V3=Tr‖Kis, it is clear that these three variables are randomized through the use of the random numbers Ri and Rs2. The random numbers cannot be recycled between sessions. Given the aforementioned arguments, we believe that the anonymity and untraceability of BSi can be ensured and maintained.


### 4.3. Claim 3: Resistance against Spoofing Attacks and Data Confidentiality Can Be Ensured

In the aforementioned unit, basic authentication/login mechanisms that do not utilize session key agreements are insufficient and cannot ensure security in any way. The primary element required to ensure security is the establishment of two session keys. The first session key is approved byBSi and the Server, and the second key is appointed by the LPUj and the Server.Within the proposed BSN, two unique session keys, (SKis=Kis‖Tr and SKjs=KjsIDjRs2‖Rj), will ultimately be created and utilized to secure data transmissions between BSi, LPUj, and Server. Moreover, we assert that all the secure communication channels between BSi, LPUj, and Server are provided by the proposed scheme. Additionally, the Server selects two highly volatile secrets (Kis, Kjs) in order to secure all communication transpiring within the authentication phase of the proposed scheme. Without knowledge or perception of the two selected secrets, it is exceedingly hazardous for bad actors to disrupt the proposed authentication schemes or salvage any meaningful data from communicated ciphertexts. Therefore, confidentiality of the data is ensured and maintained.


### 4.4. Claim 4: Resistance against Common Electronic Attacks (Man-in-the-Middle, Location Spoofing, and Replay) Can Be Ensured

Bad actors could intend to deceive/bypass authorized communication objects (BSi, LPUj, and Server) through the use of imitation messages. The expedient and efficient identification of counterfeit messages and the elimination/mitigation of possible threats within an IoT-based healthcare setting is growing increasingly imperative. Messages counterfeited by bad actors come in a variety of novel forms. Moreover, these malicious tricks could be initiated from within the IoT systems inherently heterogeneous network architectures. It follows that an inspection scheme with the capability of identifying fake communications and then, subsequently, preventing harmful attacks would be essential. In the proposed scheme, it is virtually impossible for a bad actor to forge genuine messages such as AIDi, M1, M2, V1, Tr, V2, and V3 in the absence of the knowledge of Kis and Kjs. Therefore, it is exceedingly difficult to launch forgery attacks. Additionally, the Server selects two highly volatile secrets because their volatile characteristics are highly resistant to brute-force attacks. Assuming the bad actor successfully harvests and transmits validated recycled messages used in an earlier authentication session to a potential target. Then, a robust verification protocol utilizes a unique single-use key for all discrete sessions. A cursory inspection of the random number’s freshness will determine their verification and validity. Consequently, the proposed scheme has incorporated a high degree of replay attack resistance. Moreover, our proposed scheme, respectively, incorporates details of BSi and LPUj into M1 and MA1. In regard to untraceability, BSi is secure within M1. Only the Server is capable of executing an untraceable attack on BSi, given that Kis is unknown to other entities. In regard to the Server, we present a robust verification protocol that is capable of identifying location-spoofing attacks. Within our proposed scheme, we incorporated a natural resistance to location-spoofing attacks. Lastly, we examine our schemes’ toughness and ability to resist man-in-the-middle attacks. Moreover, we investigate if a bad actor could intrude on the authentication session and attempt to deceive authorized communication objects by impersonating legitimate by utilizing forged/scavenged messages. Our proposed scheme incorporates discrete communication object identities into all protocol messages; this enables mutual authentication between authorized communication objects: Kis=IDsRisIDi, Kis=IDsRisIDj, M1=Kis⊕Ri, Tr=Kis‖Rs2⊕Trseq−new′, V3=Tr‖Kis, and V2=IDjRs2Kjs to ensure that such an event does not occur. It is evident that each discrete communication object’s identity is contained in all communication within the authentication session. Forged/scavenged messages that contain illegal identities are unusable due to the irrevocability our scheme’s authentication protocols. Moreover, previously sent messages cannot be modified or recycled for reuse due to our robust authentication protocol that utilizes two large random numbers with incorporated auxiliary values. Messages can be encrypted by anyone via the public key cryptosystem but can only be decoded by someone who knows the designated random numbers. Therefore, resistance against man-in-the-middle attacks is ensured.


### 4.5. Claim 5: Simultaneous Management of Security and Privacy Properties Can Be Achieved through the Use of Nonrepudiation Transaction Mechanisms

Within an IoT environment, a protocol with the ability to ensure that a collection of mobile objects, such as gateways and sensors, can coexist within the same time and place is integral at foiling probable fraud events. Our scheme’s protocol requires the identity information for bothBSi and LPUj to be incorporated into all authentication messages (MA1 and MA2) to verify all object identities and the location of the ongoing transaction session. According to our proposed scheme, an authorized timestamp is contributed by the Server, MA2=M2,Rj,IDj,V1,MA1 which will be utilized as evidence in the ongoing transaction between BSi and LPUj after the validity of MA2 is established. Therefore, it is evident that the proposed scheme contains robust nonrepudiation transaction characteristics.


## 4.1. Claim 1: Robust Access Control Can Be Achieved through Mutual Authentication among Communication Entities in a BSN

We demonstrate some basic logical assumptions and constraints, where variablesP and Q range over principals, X and Y range over statements, and K ranges over long-term secret keys.(i)
Statement 1: SupposeP believes P↔KQ and P perceives XK; next, we presume that P believes Q said X.(ii)
Statement 2: SupposeP believes fresh X and P believes Q said X; next, we presume that P believes that Q believes X.(iii)
Statement 3: SupposeP believes Q holds dominion over X and P believes that Q believes X; next, we presume that P believes X.(iv)
Statement 4: SupposeP observes X,Y then P observes X. In addition, provided that P believes P↔KQ and P observes XK, then P observes X.(v)
Statement 5: Supposing a given formula contains one component that is fresh, it is assumed that the entirety of the formula is similarly fresh. SupposingP believes fresh X, then P believes fresh X,Y.Prior to analyzing the authentication process of the BSN, the following constraints must be considered:(i)
Constraint 1:BSi and Server believe BSi↔IDi,Kis,SID,TrseqServer.(ii)
Constraint 2:LPUj and Server believe LPUj↔IDj,KjsServer.(iii)
Constraint 3:Server believes fresh Ri,Rj.(iv)
Constraint 4:BSi and LPUj believe fresh Rs1,Rs2.(v)
Constraint 5:Server believes BSi controls Ri.(vi)
Constraint 6:Server believes LPUj controls Rj.(vii)
Constraint 7:BSi and LPUj believe Server controls Rs1,Rs2.The tangible BSN authentication protocols and procedures are defined below. Each symbol is defined, and the BSN communication protocol is illustrated in Figure1.(i)
Stage 1:BSi⟶LPUj:MA1=AIDi,M1,Ri,Trseq,IDj, where M1=Kis⊕Ri, and AIDi=M1IDjTrseq.(ii)
Stage 2:LPUj⟶Server:MA2=M2,Rj,IDj,V1,MA1, where M2=Kjs⊕Rj, V1=MA2TrseqIDjRjKjs, and MA1=AIDi,M1,Ri,Trseq,IDj(iii)
Stage 3:Server⟶LPUj:MA3=Rs2,Tr,V3,V2, where Tr=Kis‖Rs2⊕Trseq−new, V3=Tr‖Kis, and V2=IDjRs2Kjs.(iv)
Stage 4:LPUj⟶BSi:MA4=Rs2,Tr,V3, where Tr=Kis‖Rs2⊕Trseq−new and V3=Tr‖Kis.The official analysis of the BSN joint authentication protocol is detailed below:(1)
LPUj perceives MA3=Rs2,Tr,V3,V2; based on Stage 3, it is apparent that LPUj has received and perceived Rs2,Tr,V3,V2.(2)
LPUj believes LPUj↔IDj,KjsServer; based on Constraint 2, LPUj perceives that it shares IDj and Kjs with the designated server.(3)
Based on (1) and (2),LPUj perceives that the Server said V2; therefore, we can derive that LPUj perceives that the Server said V2 utilizing Statement 1.(4)
LPUj perceives fresh Rj; as Rj which is dispensed by LPUj; LPUj has the ability to evaluate the freshness of Rj and perceives Rj as fresh if it meets the specified criteria.(5)
Based on (3) and (4),LPUj perceives that the Server perceives V2; if it is confirmed that LPUj perceives that the Server perceives V2 based on Rule 2, then the claim is supported.(6)
LPUj perceives that the Server governs Rs2; based on Constraint 7, LPUj perceives that the random number Rs2 is directly governed by the Server.(7)
Based on (5), (6), and Statement 3,LPUj perceives V2; therefore, it can be confirmed that LPUj believes V2.(8)
BSi perceives MA4=Rs2,Tr,V3; in Stage 4, BSi is confirmed to perceive MA4=Rs2,Tr,V3.(9)
Based on Constraint 1,BSi perceives BSi↔IDj,Kjs,SID,TrseqServer; therefore, BSi perceives that IDi, Kis, SID, and Trseq are shared with the Server.(10)
Based on (8) and (9),BSi perceives that the Server transmitted Tr,V3; therefore, we can determine that BSi perceives that Server transmitted Tr,V3 utilizing Statement 1.(11)
Based on Constraint 4,BSi perceives Rs2 as fresh; BSi accepts the freshness of Rs2.(12)
Based on (10) and (11),BSi perceives that the Server perceives Tr,V3; it is guaranteed that BSi perceives that the Server perceives Tr,V3 because of Statement 2.(13)
Based on Constraint 7,BSi perceives that the Server governs Rs2; BSi perceives that the random number Rs2 is directly governed by the Server.(14)
Based on (12) and (13),BSi perceives Tr,V3; therefore, we can determine that BSi perceives Tr,V3.The finalized results of our simulation are shown below:(i)
LPUj perceives that the Server perceives V2 based on (5).(ii)
LPUj perceives V2 based on (7).(iii)
BSi perceives that Server perceives Tr,V3 based on (12).(iv)
BSi perceives Tr,V3 based on (14).Assuming the designatedserver is reliable/trustworthy and based on the results of (5), (7), (12), and (14), both BSi and LPUj can authenticate each other on the designated Server.


## 4.2. Claim 2:BSi Anonymity and Untraceability Can Be Ensured

During the authentication phase, we adopt random numbers,Ri, Rj,Rs2, and utilize them to randomize any communicated messages, namely, AIDi, M1, M2, V1, Trseq, Tr, V2, and V3, where some are involved with BSi. Firstly, in the BSN, Trseq is employed as a unique single-use token that enables the rapid identification of BSi due to the fact that Trseq does not contain any data relevant to BSi. Moreover, Trseq is actively refreshed following every successfully completed authentication session. Trseq does not reveal any information about BSi. Secondly, a secret identity AIDi (occasionally a single-use/valueless sid shall be utilized) will be circulated between BSi and the Server and then shall be analyzed by the Server. Nobody is capable of tracing or identifying BSi with the exception of the Server. Thirdly, based on the following equation expressed as M1=Kis⊕Ri⊕Tr=‖KisRs2⊕Trseq−new and V3=Tr‖Kis, it is clear that these three variables are randomized through the use of the random numbers Ri and Rs2. The random numbers cannot be recycled between sessions. Given the aforementioned arguments, we believe that the anonymity and untraceability of BSi can be ensured and maintained.


## 4.3. Claim 3: Resistance against Spoofing Attacks and Data Confidentiality Can Be Ensured

In the aforementioned unit, basic authentication/login mechanisms that do not utilize session key agreements are insufficient and cannot ensure security in any way. The primary element required to ensure security is the establishment of two session keys. The first session key is approved byBSi and the Server, and the second key is appointed by the LPUj and the Server.Within the proposed BSN, two unique session keys, (SKis=Kis‖Tr and SKjs=KjsIDjRs2‖Rj), will ultimately be created and utilized to secure data transmissions between BSi, LPUj, and Server. Moreover, we assert that all the secure communication channels between BSi, LPUj, and Server are provided by the proposed scheme. Additionally, the Server selects two highly volatile secrets (Kis, Kjs) in order to secure all communication transpiring within the authentication phase of the proposed scheme. Without knowledge or perception of the two selected secrets, it is exceedingly hazardous for bad actors to disrupt the proposed authentication schemes or salvage any meaningful data from communicated ciphertexts. Therefore, confidentiality of the data is ensured and maintained.


## 4.4. Claim 4: Resistance against Common Electronic Attacks (Man-in-the-Middle, Location Spoofing, and Replay) Can Be Ensured

Bad actors could intend to deceive/bypass authorized communication objects (BSi, LPUj, and Server) through the use of imitation messages. The expedient and efficient identification of counterfeit messages and the elimination/mitigation of possible threats within an IoT-based healthcare setting is growing increasingly imperative. Messages counterfeited by bad actors come in a variety of novel forms. Moreover, these malicious tricks could be initiated from within the IoT systems inherently heterogeneous network architectures. It follows that an inspection scheme with the capability of identifying fake communications and then, subsequently, preventing harmful attacks would be essential. In the proposed scheme, it is virtually impossible for a bad actor to forge genuine messages such as AIDi, M1, M2, V1, Tr, V2, and V3 in the absence of the knowledge of Kis and Kjs. Therefore, it is exceedingly difficult to launch forgery attacks. Additionally, the Server selects two highly volatile secrets because their volatile characteristics are highly resistant to brute-force attacks. Assuming the bad actor successfully harvests and transmits validated recycled messages used in an earlier authentication session to a potential target. Then, a robust verification protocol utilizes a unique single-use key for all discrete sessions. A cursory inspection of the random number’s freshness will determine their verification and validity. Consequently, the proposed scheme has incorporated a high degree of replay attack resistance. Moreover, our proposed scheme, respectively, incorporates details of BSi and LPUj into M1 and MA1. In regard to untraceability, BSi is secure within M1. Only the Server is capable of executing an untraceable attack on BSi, given that Kis is unknown to other entities. In regard to the Server, we present a robust verification protocol that is capable of identifying location-spoofing attacks. Within our proposed scheme, we incorporated a natural resistance to location-spoofing attacks. Lastly, we examine our schemes’ toughness and ability to resist man-in-the-middle attacks. Moreover, we investigate if a bad actor could intrude on the authentication session and attempt to deceive authorized communication objects by impersonating legitimate by utilizing forged/scavenged messages. Our proposed scheme incorporates discrete communication object identities into all protocol messages; this enables mutual authentication between authorized communication objects: Kis=IDsRisIDi, Kis=IDsRisIDj, M1=Kis⊕Ri, Tr=Kis‖Rs2⊕Trseq−new′, V3=Tr‖Kis, and V2=IDjRs2Kjs to ensure that such an event does not occur. It is evident that each discrete communication object’s identity is contained in all communication within the authentication session. Forged/scavenged messages that contain illegal identities are unusable due to the irrevocability our scheme’s authentication protocols. Moreover, previously sent messages cannot be modified or recycled for reuse due to our robust authentication protocol that utilizes two large random numbers with incorporated auxiliary values. Messages can be encrypted by anyone via the public key cryptosystem but can only be decoded by someone who knows the designated random numbers. Therefore, resistance against man-in-the-middle attacks is ensured.


## 4.5. Claim 5: Simultaneous Management of Security and Privacy Properties Can Be Achieved through the Use of Nonrepudiation Transaction Mechanisms

Within an IoT environment, a protocol with the ability to ensure that a collection of mobile objects, such as gateways and sensors, can coexist within the same time and place is integral at foiling probable fraud events. Our scheme’s protocol requires the identity information for bothBSi and LPUj to be incorporated into all authentication messages (MA1 and MA2) to verify all object identities and the location of the ongoing transaction session. According to our proposed scheme, an authorized timestamp is contributed by the Server, MA2=M2,Rj,IDj,V1,MA1 which will be utilized as evidence in the ongoing transaction between BSi and LPUj after the validity of MA2 is established. Therefore, it is evident that the proposed scheme contains robust nonrepudiation transaction characteristics.


## 5. Implementation

This section summarizes the implementation of our prototype such that we could validate the practicality and usefulness of our proposed e-health system that utilizes IoT-cloud technology. Our communication process requires coordinated interaction among all the prototype hardware and software components. This communication protocol must be customized based on the intelligent devices, transmissions, interoperability, internal protocols, access control requirements, security encryption/decryption requirements, and privacy requirements of any given system. We implement security protocols at the component level (Raspberry PI, biosensors) and then determine the equivalent computational toll for each component of the system. Table2 illustrates the implementation environment, in which we utilized a Raspberry PI as the designated local server that is connected to a wireless router/access point and a smart object (body sensor). The essential security elements are all programmed using Arduino Software (C and JavaScript) and executed natively on the Raspberry PI. Lastly, all data is encrypted at the Raspberry PI and transmitted to a cloud API.Table 2
Implementation environment.
EnvironmentDetailsRaspberry PI32-bit microcontroller Node MCU ESP8266Micro 26 MHz to 52 MHz, memory 125 MB instruction4 MB storage, built-in flash, 10 GPIO-Pins, SPI, I2CIntegrated TCP/IP protocol stackSoftware platformArduino softwareC, JavaScriptCloud APIPHP, HTML, MySQLFirst, we construct and execute a private network on a laptop computer with an Intel i5-6200U CPU, Nvidia GeForce 930M GPU, and 12 GB of RAM running Windows 10. Second, we utilize a TP-Link Soft Access Point to connect the laptop and Raspberry PI via a Direct (P2P) WiFi connection. The Raspberry PI has an ESP 8266 microcontroller and 125 MB RAM, which is capable of wakeup and packet transmission in less than 2 milliseconds and standby power consumption of less than 1.0 milliamperes. The Raspberry PI microcontroller can be programmed in C language to act as a hub. Therefore, we utilize the Raspberry PI as an intelligent LPU object that can process and manage communication amongst IoT objects (body sensors) and cloud servers. Third, we designed a client application to be implemented on user facing devices such as smartphones, laptop/desktop computers, or any devices that can run cloud API’s. This cloud API stores the data using MySQL to control a database that authorized users can access at any time. Therefore, our API platform is easily customizable, flexible, and scalable and can be combined with our Raspberry PI platform to perform authentication. This system is then tested for practicability, efficiency, and feasibility. Lastly, to balance system efficiency, scalability, and security robustness, a secure RSA algorithm [15, 16] that utilizes a random number generator and a bitwise exclusive operation are integrated into the system. In order to be effective, the communications system channel must be coupled to at least one terminal that has an encoding device and to at least one terminal that has a decoding device. However, during the implementation phase of the system, the values IDi, IDs, IDj, Ris, Rjs, sid, Trseq, Ri, Rj, Rs1, and Rs2 are all set to a 32-bit random number generator operation to ensure security density. Bear in mind that within our experiment, IoT object access, is stringently governed in accordance with the designated privacy policy. In the absence of user consent, access to IoT objects is prohibited.Our implementation integrates readily available hardware devices, microcontrollers, and communication hardware to transmit data to the cloud system. The demonstrated computation cost required in terms of the cryptosystem using a programmable microcontroller is reduced compared to conventional methods. Our prototype utilizes Arduino software that can be programmed to communicate in a reader/sender configuration via the I2C synchronous serial protocol. The information will be sent bit by bit until the receiving device executes the request and transmits data back while the I2C protocol allows for each authorized device to have its own unique address and for both devices to take turns communicating over a single line. The computation cost of the proposed cryptographic operation in which the experimental conditions are the same as that of [16]. In addition, our Raspberry PI microcontroller board is programmed using Arduino software. Then, we can program control of the physical systems via read/write or analog/digital signals by integrating C programming, JavaScript programming, and RSA encryption and decryption to work in conjunction with Arduino software to significantly improve efficiency and security. Cryptography is defined as a mathematical means by which data can be secured between two parties, whereby the sender and receiver use the same designated algorithm for protection. Therefore, potential intruders can neither understand the content of the messages nor alter them. Any party that knows the designated key (receiver) has the capability of decrypting the received ciphertext. The complete design is divided into stages that start by utilizing a random number generator to produce two large prime numbers. Then, a primality-testing unit is initialized, after which the last stage is the encryption/decryption processes. The system contains text-number conversions that were executed based on ASCII coding/decoding with extended lists that include 255 different codes, as shown in Figures 2(a) and 2(b). Furthermore, we illustrate our communication process utilized in our proposed e-health system that utilizes IoT-cloud technology in Figure 3.Figure 2
(a) Client encryption/decryption. (b) Server encryption/decryption.
(a)(b)Figure 3
Communication process.In the following, we simplify our proposed system by dividing it into two main storage types: cloud computing and local storage, as presented in Figure4.(i)
Local storage: it refers to dedicated IoT devices with on-site or local network memory to store applications, services, and collected data. This design improves reliability, interoperability, processing, and transmission during offline operation. Local storage is a link between the Raspberry PI module storage and host system storage that allows for streamlined protocols and local data processing. Moreover, RSA encryption/decryption algorithms are integrated into the local system to secure it from external attacks, specifically, from attacks against the communication between devices via routers, Wi-Fi networks, LAN networks, or cellular networks.(ii)
Cloud computing storage: cloud API web services are applications, services, and physical devices that can store collected data. Cloud computing reduces the implementation complexity of the system. Moreover, the security of the system is ensured through the utilization of RSA en/decryption at the cloud security layer. This ensures that only authorized users, such as administrative users, developers, and patients, can access or manipulate stored data. The presentation layer enables users and patients to access their medical information via a web server or authorized Internet session.Figure 4
Main storage types for IoT devices.The implementation architecture includes IoT sensor devices, equipment, and application servers. It allows these interconnected devices to communicate while utilizing custom user interfaces responsible for collecting data from network-connected sensors and equipment. When a user session is completed, any updates or modifications to the data or information are synced with the cloud servers. e-Health systems can utilize the aforementioned user interfaces to obtain and assess the health condition of its users then counsel them accordingly. Furthermore, the user interfaces are connected to cloud servers through the use of middleware that facilitates intercloud communication with the cloud database. The middleware utilizes cloud API web services to transfer secure information between the web-based interfaces and the cloud servers. Moreover, middleware facilitates communication with the required databases based on user-generated requests. The cloud API is specifically designed to control authentication, authorization, and data processing. Lastly, data flow is consistent throughout the layers and robust cloud security mechanisms, such as RSA, will be implemented at every layer.We implemented our system for the purpose of IoT-cloud-based e-health management during the worldwide COVID-19 pandemic. The implemented system reduces the complexity of storing information, increases communication efficiency, and improves the performance and accuracy of a healthcare system. Results were verified using COVID-19 spread as a case study scenario. The proposed implementation architecture utilizes various web services and security services which are exclusively cloud-driven and are further implemented as middleware. We analyze the security of communication procedures to identify weaknesses so that improvements can be implemented to achieve the principal security requirements. Intelligent body sensors were issued to patients in the field. This enables nurses/physicians to more effectively monitor and administer care to their patients. Nurses/physicians can utilize their mobile gateways for the collection of live data and to deliver quicker, superior-quality healthcare services to patients. Further patient data mining and analysis can be utilized to build predictive models. It is obvious that the implemented system offers robust security density with a reasonable computational cost. The implementation simplifies the system, from the capture of data to the storage/analysis of data. Our system utilizes embedded sensors and equipment sensors controlled by an LPU node. The LPU processes the health data, displays it locally, and transmits it to the cloud servers. The designated physician will receive daily emails with notifications in the application or webserver, containing the updated health data of their patients. The application enables both the health care personnel and the patients to access and monitor relevant health data. Additionally, security/privacy protocols including the authentication of users and services, encryption of communication, accountability tracking, and data anonymization are utilized to secure the system from attacks. Finally, users who interacted with the e-health system that utilizes IoT-cloud technology are allowed to track potential COVID-19 symptoms. In addition, users can receive alarms and visualizations of the collected live data and react to developing situations, as shown in Figure5.Figure 5
Utilizing IoT-cloud-based e-health system for COVID-19 detection and tracking.In our implemented system, first, the system identifies a user’s ID and collects initial baseline measurements of the user’s temperature, heart rate, blood pressure, and so on. Then, it uploads the data to a designated private network and sends it to the cloud API database. We utilize wearable body sensors to collect temperature data over time. If a user’s data is abnormal, that is, body temperature measures greater than 37.5°C, then the user will be classified as possibly having contracted COVID-19, based on observed symptoms. We separate symptoms into two categories: the normal group (no symptoms during the 14-day reporting period) and the abnormal group (displaying symptoms during the 14-day reporting period). In the event that symptoms are confirmed in a user, all that user’s data will be transferred to designated Cloud API servers and their health status will be updated. Once a user displays COVID-19 symptoms, we recommend immediate quarantine and contact-tracing. Additionally, disinfection of affected areas, active monitoring of any people who had direct contact with the patient, and possible quarantine of contacted individuals are required to control the potential spread of the virus. All patient data will be continually collected, transmitted, and updated on our cloud servers. Lastly, at-risk groups, such as physicians who have direct contact with many possibly infected individuals, should track all symptoms and report them to relevant health authorities in a timely manner. Many users, including physicians, nurses, and other stakeholders can access collected data from the public cloud and private cloud via authorized web portals.


## 6. Conclusion and Future Work

The growth/development of IoT applications has been exceptionally fast. This growth has proven to be useful in a multitude of industries. Moreover, there have been progressive changes in the administration and operation models of medical organizations across the world. Numerous intelligent IoT objects are all interconnected in order to create a widespread IoT-based network architecture. However, a new network helps expose new security challenges. Our research has established a secure e-health management system utilizing cloud computing and IoT-based BSN structures. We propose an authentication mechanism to ensure that major security requirements are satisfied. Based on our implementation, our proposed system can be effectively executed on low-powered LPU testbeds, such as a Raspberry PI, and the system ensures greater security than historically available alternatives. Moreover, we performed a rigorous formal analysis to confirm the security, privacy, efficiency, and robustness. The implementation results prove that our IoT-cloud-based e-health management system is practicable. We used Arduino microcontroller units to implement and test RSA encryption and decryption keys. Therefore, it is evident that the security system is considered beneficial for sensor networks, as it can be connected while near a sink, particularly if it supports rechargeable batteries with easily interchangeable cells. However, system efficiency could be further improved with the future development of more efficient leaner codes, algorithms, and architectures. In the future, this design could be modified to utilize a larger bit size for encryption and decryption by modifying the algorithm. Lastly, numerous public and symmetric key cryptographic algorithms may be designed and verified based on design sets, such as DES, AES, and many others.



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*Source: 5300253-2022-06-02.xml*