The proposed work clear the problem which are arises in existing systems. The work flow of proposed work has been shown in the Figure.1, in the flow diagram of proposed work has been developed like a layered architecture. In that the total work has been partitioned in to 5 layer architecture. The overall process of the proposed work has been designed in the 5 layered structure. The layers are categorised based on their role and work. The five layers of proposed work are
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Device Layer
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Protocol Layer
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Intermediate Laye
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Data Communication Layer
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Data Access Laer
Device Layer:
The device layer is the bottom layer of the overall architecture. In which the medical devices like sensing devices, blood pressure monitoring device and analyzing devices are having the major role. Actuators, such as electronic motors and medication pumps, are tools that can carry out certain activities in response to data collected by sensors. Nevertheless, in light of These devices can be categorized into three groups based on how they are installed (i.e., implantable, both stationary and wearable devices. Here is a quick discussion of these categories. The pacemaker and neuro stimulators are the most popular devices in this category. Gastric stimulators, Insulin Pumps, Glucose Monitoring Systems, Drug Pumps, Foot Drop Implants, Cochlear Implants, etc. The third class of devices that can be employed for are the Fixed Medical Devices. many laboratory testing, including X-ray machines. The physical characteristics in their environment, such as temperature, pressure, etc. ventilators, and ECG equipment installed in medical labs and diagnostics rooms, are sensed by sensors to produce data. Due to their substantial physical bulk, these devices are immobile. These devices are network-connected, computer-controlled, and allow for remote monitoring. These devices send data that is insecure and vulnerable to threats of data theft. In contrast to devices that operate remotely, it is crucial to protect the security of these devices because they collect and transmit the data of several patients hourly. As a result, the intruders pay these gadgets more attention.
Protocol Layer:
In this layer the type of communication protocols used are discussed, in recent communication development measured data from medical devices and patient details are communicated to the cloud data storage by advanced communication protocols like Wi-Fi, Zig-bee, blue tooth, etc. the above mentioned protocols are used to communicate between the health care devices to intermediate layer. The wireless standards and communication protocols needed to enable wireless device communication make up the protocol layer. The IEEE 802.15.6 standard is the first specifically designed for Wireless Body Area Networks (WBANs). According to the needs of the application, IEEE 802.15.6 can be simply customized to support both medical and non-medical applications. It guarantees communication within and outside of the human body and was especially created with low-power, short-range sensor devices in mind. In this respect, IEEE 802.15.6 is a future standard that will join existing widely adopted ones like IEEE 802.15.1 (Bluetooth), IEEE 802.15.4 (ZigBee), IEEE 802.11 (WiFi), Bluetooth Low Energy (BLE), NFC, LoRaWAN, UWB [36], RuBee, and others. The European Telecommunications Standards Institute (ETSI) Smart Ban explains that the choice of the standard is based on a variety of variables, including data rate, transmission range, the number of devices supported, interference from the coexistence of various technologies, etc.
Intermediate Layer:
This layer's devices are in charge of transmitting data to powerful computational resources like cloud servers. These gadgets function as gateways that allow data to move from the sensor hardware to central servers or the cloud for archiving and additional analysis. These Devices can transmit data to the server using a variety of communication protocols, including WiFi, Bluetooth, GSM, and others. Data can be stored in some intermediary devices as a pre-processing algorithm's assessment of the data's clinical relevance or otherwise not. Here, a few of the intermediary devices are briefly discussed. Smart Hub is used to facilitate communication between smart IoHTs devices but has many other uses as well. Flaws that encourage attackers to sniff the traffic of smart devices hub, if possible. Access Points provide wireless connections between various medical devices and link them to distant servers. In order to enable protocol translation and device administration, IoHTs gateways primarily serve as the bridge to link sensor networks with traditional communication networks. The System on Chip (SoC) is a piece of hardware that unifies every part of a computer system, aids in normalizing data gathered from various sensors, and regulates actuators in accordance with applications. This lessens communication and lessens the burden on a central server. To assist healthcare providers, personal digital assistants (PDAs) are widely employed in the industry. These systems process data using a range of software applications after receiving it from various wearable and implanted devices.
Data Communication Layer:
The management and database servers are the primary component of the healthcare system that collects and updates the patient data gathered by the sensors. These servers can also assist the doctors in controlling the patient's medication dosage or in prescribing a different medication. The servers notify doctors and the emergency response teams when there is an emergency so that they can take the necessary measures.
Data Access Laer:
In any emergency, doctors and emergency response teams offer input and are important components of the healthcare system. Doctors and ERT respond to the system at this layer when it issues an alarm. The healthcare system is not complete without doctors, who are able to examine their patients whenever and wherever they are and adjust their care as needed. An IoT device for a doctor provides real-time data on the patient being watched. The IoT gateway gadget collects data from various healthcare devices and sends it to PDAs used by doctors. Team for Emergency Response. The ERT is in charge of offering patients on-site or remotely medical care in the event of any medical emergency. Medical equipment keeps track of patients' conditions in the context of IoHT. The five-layered architecture model used to develop the proposed work is depicted in Fig. 2. Prior to sending the data to storage, the IoHTs sensing devices monitor the patient's health and analyse the IoHTs data that have been acquired.
Medical records include sensitive information that should never be compromised by an uninvited third party. Therefore, data needs to be transformed into a secure form before being communicated. Encryption techniques are used to protect those data. Original patient data are sent to the RSA Encryption procedure as an input during the encryption phase. With the help of the encryption key, the RSA method transforms the original data into a cypher text. The cypher text and encryption key must be transmitted in a secure manner when the encryption procedure has been successfully completed. In the conventional method of transmission and encryption, the encryption key and cypher text are both sent over the same channel. The original data that is transmitted by the sender can readily be obtained by hackers with the aid of cutting-edge computing technologies.
The EFL-QKD algorithm is employed in the suggested architecture to protect the data while it is being communicated. Initially, the cloud infrastructure stores the encryption text. through means of cloud services If a medical emergency arises, anyone with the encryption key can access the patient's data from anywhere. Another piece of information, the encryption key, is transmitted in a sophisticated manner in that. Here, the advanced technique known as the QKD method is employed. The traditional QKD method has the potential for side channel assaults, hence additional features have been included. The encryption key is first transformed into a quantum bit using any available polarizer. Then, using the fuzzification process, the quantum bits are changed into fuzzy bits. Finally, the fuzzified qubits appear. The fuzzified qubits then communicated with one another via a quantum server. Defuzzification is used to transform fuzzy bits into quantum bits at the receiver side. The quantum bits are then transformed into classical data using the proper polarizer utilized at the transmitter side. The receiver will be informed if any unauthorized users attempt to access, at which point he will cease the communication process. Due to the fuzzification process, even if the hacker tries to access the data through a side channel, he will not be able to obtain the original encryption key. With the aid of the decryption procedure, the receiver side can quickly obtain the original data that was transmitted from the sender side from the cloud after successfully receiving the encryption key. When compared to previous ways, this system will communicate the patient data in a way that is more than 99 percent secure.
3.1 Algorithm:
EFL – QKD Framework |
Input Data: Cipher Text(Encryption Key) Output: Fuzzified Qubit Attributes: CT, LCT, count, In, Rl, Or, PD,FQD // CT – Cipher Text, LCT-Length of Cipher Text, Count, In = index value, Rl –Rectilinear, Or- Orthogonal, PD – Polarized Data, FQD – Fuzzified Quantum Data . Begin Input Cipher Text // load the encryption key as input data LCT = Length(CT) Select type of Polarizer Rl || Or For In in range LCT PD = Pol(CT) // Polarization of Cipher Text Print PD END //Fuzzification Process For In in range LCT FQD = Fuz(PD) // Fuzzification of Polarized data Print FQD END |
Algorithm Description |
The EFL-QKD technique transforms the supplied input data into a fuzzified qubit in the EFL-QKD framework, which are carried out in the subsequent steps. After the encryption procedure, the user in the suggested model may receive two different sorts of information: the encryption key and the cypher text. The encryption key is conveyed through a quantum channel while the cypher text is transmitted through a conventional classical channel. However, the encryption key must first be transformed into a secure form using the architecture stated above. At stage 1 of the EFL-QKD architecture, the traditional data, which is the encryption key, is provided as an input. where the polarization process that transformed classical data into quantum data took place. The two polarizers that could be used in that technique to turn the classical data into quantum data are orthogonal and rectilinear polarizers. Following the successful conversion of quantum data, the qubit is handled in stage 2, where it undergoes the fuzzification process to become a fuzzified qubit. In the receiver end, the defuzzification and depolarization processes are used to recover the original encryption key that was sent by the user after the converted qubit has been fuzzified and relayed to the receiver over a quantum server. The recipient will affirm the sender's ability to transfer the encrypted data once he has received the successful key, at which point he will also receive the original data.
Performance Validation:
The suggested model tackles the security concerns with IoHTs and cloud data processing. The proposed model EFL-QKD provides high level of security for the encrypted information and encryption key. When compared to the current methods, the suggested EFL-QKD offers more than 99 percent security. The calculations that are provided below explain that. Even though the patient information in a current IoHTs is transmitted over a standard channel and is encrypted, sophisticated modern computers can still easily access it. In the IoHTs models the security issues can be calculated by multiplying the number of medical devices with 2.
Security Issue = Number of Medical Devices in Communication * 2
Table 2
Number of Medical devices vs Security Issue
S.No | Number of Devices in Communication (NDC) | Security Issue Count (SI) |
1 | 1 | 2 |
2 | 2 | 4 |
3 | 3 | 6 |
4 | 4 | 8 |
5 | 5 | 10 |
6 | 6 | 12 |
7 | 7 | 14 |
8 | 8 | 16 |
9 | 9 | 18 |
10 | 10 | 20 |
Table 2 reveals that as the number of medical IoT devices increases, so does the security risk, as seen in the diagram in Fig. 3.
With the help of the suggested EFL-QKD method, that can be resolved. The formula below can be used to calculate Security Issue in EFL-QKD.
Security Issue in EFL-QKD = number of communication devices / (n * 4)
Where n is the number of polarizers used (2), and that value is multiplied by the 4(fuzzy set)
So that the final security issues can be calculated by the following table .
Table 3
Security Issue in EFL-QKD Framework
S.No | Number of Devices in Communication (NDC) | Security Issue (SI) |
1 | 1 | 0.125 |
2 | 2 | 0.25 |
3 | 3 | 0.375 |
4 | 4 | 0.5 |
5 | 5 | 0.625 |
6 | 6 | 0.75 |
7 | 7 | 0.875 |
8 | 8 | 1 |
9 | 9 | 1.125 |
10 | 10 | 1.25 |
From that above diagram we can say that, the security issue in the EFL-QKD is more secure when compare with existing algorithms, that is depicted in the Fig. 4.
The above diagram clearly shows that the increases in number of Medical does not made any drastically changes in the Security, which is highlighted in different colors in the above diagram. Also it indicates that the proposed EFL-QKD provides more that 99 percent of security when compared with the existing models. If the value of a scaling component in the test statistic is known, it is quite likely that the test statistic will follow the normal distribution. When the scaling term is unknown, an approximation based on the data that follows a Student's T distribution is used in place of the test statistic.
\({\stackrel{-}{x}}_{1}=\sum \frac{{x}_{1}}{n}\) = 0.6875
\({\stackrel{-}{x}}_{2}=\sum \frac{{x}_{2 }}{n}\)= 11
According to the findings of the previous analysis, the mean value of security losses in EFL-QKD is 0.6875, and the mean value of security losses in existing method is 11, with standard deviations of 0.11353 and 1.8165 for EFL-QKD and existing method, respectively. The differences are statistically significant in both cases, according to the two-tailed t value of 5.37504 and the P value of.000042, which is less than 0.05. Compared to Existing IoHTs, the mean value of EFL-QKD is substantially lower (0.0865). It is evident that the security losses of the EFL-QKD framework are much lower and seem to be better when compared to the security losses of the existing IoHTs systems.