Radial kernelized regressive merkle–damgård cryptographic hash blockchain for secure data transmission with IoT sensor node

Abstract

The concept of the Internet of Things (IoT) has drawn noteworthy attention to use the collected data anywhere in the world. Wireless Sensor Node (WSN) is integrated into the IoT for data collection. The IoT is very intense towards the security investigation. Patient data is medical information about an individual patient. Patient data contains their past and current health, treatment history, lifestyle options and genetic data. The patient data security is a most important issue in IoT. When a collected data get transmitted from the sensor node to the server it theft or altered by various intruders and it may cause a lack of security. In order to address these issues, in this research work, a novel technique called Radial Kernelized Regressive Merkle–Damgård Cryptographic Hash Blockchain (RKRMDCHB) technique is introduced to improve secure data transmission. The wireless sensor nodes are deployed to sense and monitor the data. At first, the sensing data are collected for secure transmission to the server. The RKRMDCHB technique uses the Radial Basis Kernelized Regression Function to perform the data classification. The regression function analyzes the input data and is categorized into various types of classes based on the radial basis kernel function. The classified data is given to the data block of the blockchain for secure transmission. The RKRMDCHB technique uses the Merkle–Damgård Cryptographic technique to generate the hash for each input data with the help of the one-way compression function. The data in the form of a hash is transmitted to the server through the internet. As a result, the RKRMDCHB technique ensures security and confidentiality to preserve the data and provide better communication. Experimental assessment is carried out on certain factors such as data confidentiality rate, data integrity, and processing time, with respect to a number of data sensed from the sensor device. The results demonstrate that the proposed RKRMDCHB technique provides an efficient solution for secure data transmission while preserving sensitive information against potential threats.

Specification

Description:FIELD OF INVENTION

An emerging IoT technology is a significant part to guarantee the security of information exchange. Small sensor devices are integrated with IoT for sensing, processing the information from the various environmental conditions. With the rapid development in sensor devices and their integration in the Internet of Things (IoT), security becomes an essential factor for authorized users able to access reliable data. A Lightweight Scalable Block chain (LSB) technology was introduced in for providing end-to-end security with IoT devices. The LSB technology minimizes the processing time. However, the performance of the data confidentiality rate was not improved. A lightweight IoT information sharing security framework was developed in using block chain technology to improve the IoT information sharing security. However, it failed to solve the risk of privacy leakage. In addition, it did not efficiently focus on the privacy protection of block chain data.


BACKGROUND OF THE INVENTION

Secure transmission with Full-duplex IoT devices was per- formed in for multiuser information communication using a heuristic suboptimal algorithm. However, it failed to increase the secrecy performance of the system. A new distributed key management system was designed in to en- sure data transmission from one source to another. But, any cryptographic hash-based technique was not used to achieve better data integrity. A secure and efficient access control approach was designed in for sensor networks along with the structure of the IoT. However, the better security issue was not solved.
A Lightweight Certificate less Key Agreement (Like) method was developed in for a secure device-to-device communication. The Like method reduces the average time required to perform the cryptographic operations for Secure IoT Communications. However, the data integrity rate was not achieved. An ultra- lightweight device-to-device secure protocol was developed in using the symmetric key-based scheme to preserve the data transmissions. But, the different security parameters were not evaluated. A Constrained Application Protocol (CoAP) was developed in to implement secure data transmission between the IoT de- vices. But, CoAP failed to effectively discuss the secure data transmission.
A Deep Learning approach was presented in for a secure smart city application with the help of blockchain through the IoT device. However, the time consumption for secure data transmission was not minimized. A se- cured data collection approach was designed in for IoT based secure healthcare data transmission with mini- mum the computation cost. But, the detailed execution of the algorithm with protection performance metrics was not estimated. A secure wireless method was introduced in using blockchain technology. However, it failed to estimate the time consumption for secure communication. A Blockchain technique was developed for se-
cure communication in Hybrid Industrial IoT. The technique is also used to maintain confidentiality and transparency between the workers in Industrial IoT. However, the transaction time and cost incurred during the secure communication process was not estimated. A patient privacy-protected data collection method was developed in for improving the secret sharing of patients' data. But, the various security performance metrics such as confidentiality and integrity remained unaddressed. A deep learning-based approach was introduced to improve the secure transmission in IoT connect- ed system. However, it failed to implement any crypto- graphic technique in the deep learning approach to achieve higher security.

A novel lightweight and secure architecture were designed in for IoT by using Ethereum Blockchain to provide secure access. However, the different security performance metrics were not evaluated. A flexible and efficient authentication method was designed in based on the consideration of heterogeneous IoT devices. However, it failed to provide the better security. A privacy-preserving scheme was developed in for the smart home systems. This scheme achieves a better security level in terms of achieving higher confidential- ity and lesser communication overhead. However, data integ- rity was not evaluated.
Secure and lightweight authentication and key agreement protocol were designed in for IoT based WSNs for increasing the security. The designed scheme only analyzes the running time. But, the security performance metrics were not evaluated. A secure and light- weight triple-trusting architecture (SLTA) was developed in uses blockchain technology for providing trusted services. The Lightweight Secure IoT (LS-IoT) and Lightweight Access Control (LAC) were developed in for ECG monitoring and improving the secure data transmission. However, advanced machine learning algorithms were not applied to minimize secure data transmission.
Block-chain-based IoT device was introduced in for secure authentication scheme. Optimal Privacy- Multihop Dynamic Clustering Routing Protocol (OP- MDCRP) was introduced in to protect the data privacy with aid of cryptographic based clustering struc- ture. But, the data confidentiality rate was not measured. A layered model of IoT routing security was introduced in for examining the vulnerabilities connected at each phase of the routing process. But, it failed to calculate the data integrity.
A novel blockchain enabled cyber-security framework and algorithm was introduced in for industrial IoT. However, it failed to provide an adaptive decentralized approach for the healthcare industry. Designing and implementing blockchain security was introduced into secure the data transmission. But, the processing time was not reduced. In the special focus on security and privacy in blockchain-based applications was presented with the recent advances related to blockchain and covering a wide array of topics.


SUMMARY OF THE INVENTION

In this section, the system model with security require- ments is described. Security plays a vital role to protect the communication from a sensor node to a server. A system model comprises the two entities such as WSN and the internet server. A wireless sensor network (WSN) is a distributed network that comprises a variety of sensor nodes and is responsible for collecting the data.Figure 1 illustrates the overview of the network model which includes a WSN and Internet server. The Sensor Nodes sn1;sn2; sn3; :snn are distributed in the objects and it is connected by using the Internet of Things (IoT). The sensor nodes sense the information from the objects and send them to the Internet server. The server receives the information and analyzing, managing, storing the data for further processing. During the data transmission, the sensor nodes need to transmit collected data to the server in a secure way. This communication satisfies confidentiality and integrity. Confidentiality is the process of main- taining secret information from an unauthorized entity. Integrity is ensuring that the collected patient data from the sensor nodes have not been altered or modified by unauthorized entities. Therefore, the secure communica- tion system ensures that confidentiality and integrity during the transmission from the sensor node to the server.











Figure 2 given below portrays the basic structure of WSN enabled IoT healthcare solutions. The proposed RKRMDCHB technique uses the MHEALTH dataset to monitor the person's activities through multimodal body sensing. Here, the sensor is a device that is placed on the patient's chest, right wrist, and left ankle for monitoring and sensing the activities experienced by various body parts. After that, the sensed data is sent to the server (i.e., hospital) through the internet for further processing. During the communication, security needs to guarantee by preventing unauthorized access. The proposed RKRMDCHB technique is used to per- form the different processes namely data collection, classification, and secure communication to achieve secure communication.





The flow chart of proposed RKRMDCHB technique is given below, initially, the number of patient data as input is gathered from a MHEALTH dataset by different sensor nodes placed in the different parts of the patient body. The sensor devices are places on the patient's chest, right wrist, and left ankle in the dissimilar parts of the patient's body to sense and gather the information on various activities (Fig. 3).
Then the gathered data is send to the hospital server via the internet. The mean value is assigned for each class. Radial basis kernelized regression function is used to categorize the data. The regression function is used to analyze the patient data by using mean of the dissimilar classes. The threshold is fixed in the kernel function. When the value is higher than 0.5, then the data is categorized into a particular class. Otherwise, the then the data is not categorized into a particular class. Next, Block chain is created for each transaction depended on the Merkle-Damgård hash cryptographic func- tion. Divide the data into message block, with aid of hash function. For every message block is generates the hash value. The final hash value of patient data is transfer to server. Finally, this proposed RKRMDCHB technique is achieves the secure data transmission.





















Fig. 3 Flowchart of proposed RKRMDCHB technique , Claims:1.We provides the capability of providing higher security by using achieving a higher confidentiality rate, integrity rate, and lesser processing time.
2.The classified data is given to the data block of the block chain for secure transmission.
3.It provides an efficient solution for secure data transmission while preserving sensitive information against potential threats.
4.Radial basis kernelized regression function is applied to analyze the patient data and categorized into different classes with to minimum the processing time. Then the classified data is given to the data block of blockchain for secure data transmission. The Merkle-Damgård Cryptographic technique is applied for converting the input patient data into a fixed- length of hash by the means of a one-way compression func tion.

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