Enabling Efficient Data Transmission in Wireless Sensor Networks-Based IoT Application

The use of the Internet of Things(IoT)is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices.In critical infrastructure domains like oil and gas supply,intelligent transportation,power grids,and autonomous agriculture,it is essential to guarantee the confidentiality,integrity,and authenticity of data collected and exchanged.However,the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques.Consequently,designing a lightweight secure data transmission scheme is becoming essential.In this article,we propose lightweight secure data transmission(LSDT)scheme for IoT environments.LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol.We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs.Security and performance analysis indicates that the LSDT scheme is secure,suitable for IoT applications,and performs better in comparison to other related security schemes.

Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent years.The mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all things.The variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication environments.Ensuring data secure transmission is critical for mobile IIoT networks.This paper investigates the data secure transmission performance prediction of mobile IIoT networks.To cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first derived.Then,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction algorithm.For mobile signals,the important features may be removed by the pooling layers.This will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is designed.Out of the input and output layers,it removes the pooling layer and contains six convolution layers.Elman,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed algorithm.Through simulation analysis,good prediction accuracy is achieved by the CNN algorithm.The prediction accuracy obtains a 59%increase.

Secured Data Transmission Using Modified LEHS Algorithm in Wireless Sensor Network

In the ancient block Hill cipher, the cipher text is obtained by multiplying the blocks of the plain text with the key matrix. To strengthen the keymatrix, a double guard Hill cipher was proposed with two key matrices, a private key matrix and its modified key matrix along with permutation. In the ancient block Hill cipher, the cipher text is obtained by multiplying the blocks of the plain text with the key matrix. To strengthen the key matrix, a double guard Hill cipher was proposed with two key matrices, a private key matrix and its modified key matrix along with permutation. In this paper a novel modification is performed to the double guard Hill cipher in order to reduce the number of calculation to obtain the cipher text by using non-square matrices. This modified double guard Hill cipher uses a non-square matrix of order (p × q) as its private keymatrix.

Steganography-Based Transmission of Medical Images Over Unsecure Network for Telemedicine Applications

Steganography is one of the best techniques to hide secret data.Several steganography methods are available that use an image as a cover object,which is called image steganography.In image steganography,the major features are the cover object quality and hiding data capacity.Due to poor image quality,attackers could easily hack the secret data.Therefore,the hidden data quantity should be improved,while keeping stego-image quality high.The main aim of this study is combining several steganography techniques,for secure transmission of data without leakage and unauthorized access.In this paper,a technique,which combines various steganographybased techniques,is proposed for secure transmission of secret data.In the pre-processing step,resizing of cover image is performed with Pixel Repetition Method(PRM).Then DES(Data Encryption Standard)algorithm is used to encrypt secret data before embedding it into cover image.The encrypted data is then converted to hexadecimal representation.This is followed by embedding using Least Signification Bit(LSB)in order to hide secret data inside the cover image.Further,image de-noising using Convolutional Neural Network(CNN)is used to enhance the cover image with hidden encrypted data.Embedded Zerotrees of Wavelet Transform is used to compress the image in order to reduce its size.Experiments are conducted to evaluate the performance of proposed combined steganography technique and results indicate that the proposed technique outperforms all existing techniques.It achieves better PSNR,and encryption/decryption times,than existing methods for medical and other types of images.

Secured Framework for Assessment of Chronic Kidney Disease in Diabetic Patients

With the emergence of cloud technologies,the services of healthcare systems have grown.Simultaneously,machine learning systems have become important tools for developing matured and decision-making computer applications.Both cloud computing and machine learning technologies have contributed significantly to the success of healthcare services.However,in some areas,these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease(DKD)while ensuring privacy preservation of the medical data.To address the cloud data privacy problem,we proposed a DKD prediction module in a framework using cloud computing services and a data control scheme.This framework can provide improved and early treatment before end-stage renal failure.For prediction purposes,we implemented the following machine learning algorithms:support vector machine(SVM),random forest(RF),decision tree(DT),naïve Bayes(NB),deep learning(DL),and k nearest neighbor(KNN).These classification techniques combined with the cloud computing services significantly improved the decision making in the progress of DKD patients.We applied these classifiers to the UCI Machine Learning Repository for chronic kidney disease using various clinical features,which are categorized as single,combination of selected features,and all features.During single clinical feature experiments,machine learning classifiers SVM,RF,and KNN outperformed the remaining classification techniques,whereas in combined clinical feature experiments,the maximum accuracy was achieved for the combination of DL and RF.All the feature experiments presented increased accuracy and increased F-measure metrics from SVM,DL,and RF.