Recent Patents on Engineering - Volume 15, Issue 4, 2021

Background: Data that are in the form of text, audio, image, and video are used everywhere in our modern scientific world. These data are stored in physical storage, cloud storage and other storage devices. Some of these data are very sensitive and require efficient security while storing as well as in transmitting from the sender to the receiver. Objective: With the increase in data transfer operation, enough space is also required to store these data. Many researchers have been working to develop different encryption schemes, yet there exist many limitations in their works. There is always a need for encryption schemes with smaller cipher data, faster execution time and low computation cost. Methods: A text encryption based on Huffman coding and ElGamal cryptosystem is proposed. Initially, the text data is converted into its corresponding binary bits using Huffman coding. Next, the binary bits are grouped and again converted into large integer values which will be used as the input for the ElGamal cryptosystem. Results: Encryption and Decryption are successfully performed where the data size is reduced using Huffman coding and advance security with the smaller key size is provided by the ElGamal cryptosystem. Conclusion: Simulation results and performance analysis specify that our encryption algorithm is better than the existing algorithms under consideration.

Background: Cloud computing is one of the prominent technology revolutions around us. It is changing the ways the consumer expends services, changing the ways the organization develop and run applications and is completely reshaping the old business models in multiple industries. Cloud service providers need large-scale data centers for offering cloud resources to users, the electric power consumed by these data centers has become a concrete and prudential concern. Most of the energy is dissipated in these data centers due to under-utilized hosts, which also subsidies to global warming. The broadly adept technology is virtual machine migration in cloud computing, therefore, the main focus is to save energy. Objective: Virtual Machine (VM) migration can reap various objectives like load balancing, ubiquitous computing, power management, fault tolerance, server maintenance, etc. This paper presents an energy-oriented mechanism for VM migration based on firefly optimization that reduces energy consumption and the number of VM migrations to a great extent. Methods: A Firefly Optimization (FFO) oriented VM migration mechanism has been proposed, which allocates tasks to the physical machines in cloud data centers. It strives to migrates high loaded VMs from one physical node to another, which induces minimum energy consumption after VM migration. Results: The empirical result shows that the FFO based mechanism, implemented in the CloudSim simulator, performs better in terms of the number of hosts saved up to 13.91% in contrast to the First Fit Decreasing (FFD) algorithm and 8.21% as compared to Ant Colony Optimization (ACO). It reduced energy consumption up to 12.76% as compared to FFD and 7.78% as compared to ACO and, ultimately lesser number of migrations up to 52.49% when compared to FFD and 44.51% as compared to ACO. Conclusion: The proposed scheme performs better in terms of saving hosts, reducing energy consumption, and decreasing the number of migrations in contrast to FFD and ACO techniques. The research paper also presents challenges and issues in cloud computing, VM migration process, VM migration techniques, their comparative review as well.

Although there are many reciprocal recommenders based on different strategies which have found applications in different domains but in this paper we aim to design a common framework for both symmetric as well as asymmetric reciprocal recommendation systems (in Indian context), namely Job recommendation (asymmetric) and Online Indian matrimonial system (symmetric). The contributions of this paper is multifold: i) Iterative framework for Reciprocal Recommendation for symmetric as well as asymmetric systems. ii) Useful information extracted from explicit as well as implicit sources which were not explored in the existing system (Free-text mining in Indian Matchmaking System). iii) Considering job-seekers’ personal information like his marital status, kids, current location for suggesting recommendation. iv) Proposed Privacy preservation in the proposed framework for Reciprocal Recommendation system. These parameters are very important from practical viewpoint of a user and we have achieved improved efficiency through our framework as compared to the existing system.

Aim: The aim of the paper is to analyse the security strength of image encryption schemes which are based on pixel rotation and inversion functions. The key independent image decryption methodologies are presented to obtain original images with intelligible contents from encrypted images using neighbourhood similarity characteristics and divide-and-conquer attack. Background: The efficiency and security strength of secure communication of sensitive data depend on the computing resources and cryptographic strength of encryption schemes. An encryption scheme is cryptographically strong if it does not leave any weakness, vulnerability or pattern which could be exploited by cryptanalyst to obtain the original image from an encrypted image. Prior to the use of any image encryption scheme for multimedia security applications, it should be analysed for its security strength to ensure the safety of information so that an adversary could not extract intelligible information from encrypted image data. A number of encryption schemes developed for image security applications and claimed highly secure but some of these are cryptanalysed successfully and found insecure. Objective: The analysis of image ciphers which encrypt plain images by transforming its pixels using circular rotation or inversion function in a random fashion is carried out for decrypting encrypted images to obtain original images. The encryption schemes, namely ‘Chaotic Image Encryption (CIE)’ and ‘Graphical Image Encryption (GIE)’, were reported secure but we find that these schemes are insecure which can be exploited to obtain meaningful information from the ciphered images. We apply the similarity characteristics of images to mount cryptanalytic attacks on these ciphers and obtain original images without any knowledge of the encryption/decryption keys. These encryption schemes encrypting the specified region-of-interest (ROI) are also analysed to decrypt ROI encrypted images. Methods: The methodology of decryption is key independent and based on divide-and-conquer strategy to obtain original images from the given encrypted images. It utilizes the neighbourhood similarity of pixels in an image which is measured in terms of pixel-to-pixel difference between adjacent pixels for pixel inversion based image cipher (GIE) and line-to-line correlation between adjacent lines for pixel rotation based image cipher (CIE). The ROI encrypted and masked encrypted images are also decrypted. Results: Experimental test results show that the decrypted images obtained are quite intelligible and one can understand the contents of decrypted images. It is also seen that an image cipher encrypting the ROI can be decrypted by utilizing unencrypted region surrounding encrypted ROI part of an image. Conclusion: It has been shown that CIE, GIE, ROI and masked encryption schemes reported for image security applications are insecure and not providing adequate security. Such encrypted images can be decrypted successfully without any key knowledge with high intelligibility by considering image similarity characteristics of neighbouring pixels and applying divide-and-conquer attack strategy. Future work: The key independent decryption methodology can be considered to cryptanalyse the encryption schemes under noise attack scenario as future work to see the applicability of decryption methods with respect to increase in noise in encrypted images. Moreover, other modern encryption schemes based on pixel inversion and rotation functions can be analysed for their security strength.

Background: The future computations need parallelism, and a way to achieve the same is through quantum-based specific properties. Quantum computation supports exponential operation to be performed in parallel over a single execution step and hence achieves computational speedup. Objective: One of the quantum-based algorithms allows to search the key element over an index based unstructured database and succeeds to obtain the speedup. In the whole context of article writing, our approach reviews the significance of quantum computing, the basics required for quantum computation and their quantum logic-based circuit operations. Methods: The article focuses on Grover’s search algorithm and its variations, and then illustrates the relevant amplitude amplification & phase matching techniques in accordance with the advantages and limitation to the specific perspective. Results: We made the comparative analysis between the amplitude amplification-based static & dynamic Grover’s searching and phase estimation technique. Along with this, we justified the possibilities of the empirical results and observed facts over Grover’s variant approaches. Conclusion: Finally, we conclude the analytical comparison between quantum-based searching techniques along with the applicability of Grover’s algorithm in practical computational science.

Background: Recently, the Internet of Things (IoT) has brought various changes in the existing research field by including new areas such as smart transportation, smart home facilities, smart healthcare, etc. In smart transportation systems, vehicles contain different components to access information related to passengers, drivers, vehicle speed, and many more. This information can be accessed by connecting vehicles with the Internet of Things leading to new fields of research known as Internet of Vehicles. The setup of the Internet of Vehicle (IoV) consists of many sensors to establish a connection with several other sensors belonging to different environments by exploiting different technologies. The communication of the sensors faces a lot of challenging issues. Some of the critical challenges are to maintain security in information exchanges among the vehicles, inequality in sensors, quality of internet connection, and storage capacity. Objective: To overcome the challenging issues, we have designed a new framework consisting of seven-layered architecture, including the security layer, which provides seamless integration by communicating with the devices present in the IoV environment. Further, a network model consisting of four components such as Cloud, Fog, Connection, and Clients has been designed. Finally, the protocol stack which describes the protocol used in each layer of the proposed seven-layered IoV architecture has been shown. Methods: In this proposed architecture, the representation and the functionalities of each layer and types of security have been defined. Case studies of this seven-layer IoV architecture have also been performed to illustrate the operation of each layer in real-time. The details of the network model including all the elements inside each component have also been shown. Results: We have discussed some of the existing communication architectures and listed a few challenges and issues occurring in present scenarios. Considering these issues, we have developed the seven-layered IoV architecture and the network model with four essential components known as the cloud, fog, connection, and clients. Conclusion: This proposed architecture provides a secure IoV environment and provides life safety. Hence, safety and security will help to reduce the cybercrimes occurring in the network and thereby provide good coordination and communication of the vehicles in the network.

Internet of Things has proven to maximize workflow and data sensing capabilities. Contrarily, Distributed Denial of Service attacks that employ compromised Internet of Things devices have caused considerable damage to the Information Technology infrastructure since their advent. More specifically, Application-level attacks facilitated using affected Internet of Things devices are found to be difficult to detect and defend against. Seemingly benign traffic from infected devices exits the network edge to target a remote server. The conceptual framework described in this paper attempts to mitigate malicious Internet of Things traffic at the source network. Fog nodes at the source Autonomous System are utilized in tandem with a ratiometric that flags traffic as well as the originating device as suspicious or benign based on traffic ratios calculated in real-time. Subsequently, malicious traffic is blocked inside the Autonomous System. The burden of protecting the external network from Distributed Denial of Service attacks is transferred to Fog nodes inherent to every source network. The proposed conceptual defense framework is proactive and performs in real-time attack scenarios.

Background: As crime rates are increasing all over the world, many methods for crime prediction based on data mining have been proposed in the past. Crime prediction finds application in areas like predictive policing, Hotspot evaluation and geographic profiling. It has been observed in the past that crime is closely related to geographical location, time, weather conditions and day of the week. Objective: Thus, to tackle crime events, a proactive policing approach can be developed using crime prediction. The main objective of this study is to provide a heuristic approach to crime prediction. Methods: In this work, a crime prediction approach is proposed which utilizes a crime history dataset which contains multiple categories of crime. And a heuristic approach based on the generalization of crime categories is proposed. A spatiotemporal crime prediction technique based on machine learning techniques is proposed. State-of-the-art classification approaches along with ensemble learning approach are used for prediction. Results: The performance of the proposed model is compared using state-of-the-art classification techniques without a heuristic approach and with a heuristic approach, and it is found that the model with heuristics achieves better accuracy. Conclusion: Crime events dataset can be utilized to predict future crime events in an area because crime shows geographical patterns. These spatial patterns might vary with the category of crime and it is challenging to deal with lots of crime categories. Thus, a generalization based approach can be a vital asset in crime prediction.

To detect network attacks more effectively, this study uses Honeypot techniques to collect the latest network attack data and proposes network intrusion detection classification models, based on deep learning, combined with DNN and LSTM models. Experiments showed that the data set training models gave better results than the KDD CUP 99 training model’s detection rate and false positive rate. The DNN-LSTM intrusion detection algorithm, proposed in this study, gives better results than KDD CUP 99 training model. Compared to other algorithms, such as LeNet, DNNLSTM intrusion detection algorithm exhibits shorter classification test time along with better accuracy and recall rate of intrusion detection.

With the development of integrated circuits and microelectronics, integrated and miniaturized implantable medical devices are increasingly used in modern medical technologies, e.g., cardiac pacemakers, vasodilators, and cochlear implants. However, the normal operation of these devices is inseparable from the availability of sufficient energy supply and the bidirectional transmission of internal and external signals. Due to the limitation of the working environment of sensors, there is only a small space for most implanted electronic devices, which is a challenge faced by existing technology. In this paper, current wireless implantable energy supply and communication technologies are reviewed to determine the best available technologies, thereby providing a reference for method selection in designing implantable medical systems.

Aims and Objectives: Usually, the increase in temperature of an individual indicates the possibility of being infected with a disease that might be risky to other people, such as coronavirus. Traditional techniques for monitoring body core-temperature require body contact either by oral, rectum, axillary, or tympanic means, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing the level of medical sectors is a necessary target for research operations, especially the development of integrated circuits, sensors, and cameras, to make life easier. Methods: The solution is proposed as an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the actual body temperature. Results: The Arduino microcontroller was connected to an Infrared-Thermal sensor named MLX90614 as an input signal and was connected to the LCD to display the CCT. To evaluate the proposed system, experiments were conducted on 31 subjects, and contactless temperature from the three face sub-regions was sensed, including forehead, nose, and cheek. Conclusion: Experimental results demonstrated that CCT could be measured remotely from the human face, including three face sub-regions, among which the forehead region should be preferred (a smallest error rate of 2.3%), rather than nose and cheek regions (2.6 % and 3.2% error rate, respectively) for CCT measurement due to the lowest error rates achieved.

Background: The torsional vibration of the power transmission system has heavy effect on the ride comfort and safety of the vehicle, which has attracted plenty of research. Therefore, aiming at the torsional vibration problem of the electric vehicle power transmission system, enough study on how to suppress the torsional vibration is clearly of great benefit. Objective: The goal of the work is to explore the new method to suppress the torsional vibration. The main contribution is that the parameter of the power transmission system is optimized and the adaptive fuzzy PID controller is proposed to be utilized and optimized to suppress the torsional vibration. Methods: An optimization objective function including the angular acceleration of the motor shaft, the decelerator and differential assembly and the half shaft is established as the output of the genetic algorithm fitness value. The error combination of the square root of the angular acceleration of the motor shaft, the decelerator and differential assembly and the half shaft is adopted as the input of the fuzzy inference controller and the PID controller, which can significantly simplify the fuzzy rule and the structure of the controllers. Results: The proposed method significantly reduces the torque amplitude of the motor shaft, the decelerator and differential assembly, the transmission half shaft, and the overall vibration amplitude. The maximum reduced vibration amplitude change is up to 41.22% and 87.04% respectively after parameter optimization and fuzzy PID control in the example. Conclusion: The comprehensive utilization of parameter optimization and adaptive fuzzy PID control can successfully suppress the torsional vibration phenomenon of the power transmission system, and the torsional amplitude of the angular velocity and angular acceleration decreases significantly. Therefore, the vehicle noise can be reduced and the stability can be improved. The study lays a foundation for solving the torsional vibration problem and the mechanical optimal design of the vehicle transmission system.

Background: It has been observed that part of the tunnel of rail transit of line 1# in Lanzhou city passes through loess. Loess has obvious water sensitivity and anisotropy, these obvious characteristics have different degrees of influence on different projects. Objective: This study aims to compare the differences of shear strength with the vertical and horizontal direction through the triaxial and direct shear test, and permeability of samples in different directions. Methods: The Q4 loess undisturbed sample from vertical and horizontal directions near tunnel face were selected in order to study the physical mechanical properties of Lanzhou rail transit section, and many related experiments were also carried out. Results: The results show that the anisotropy of the shear strength of the surrounding rock (Q4 loess) is significant near the tunnel face with a depth of 15m. The failure stress in the vertical direction is 1.33 times to that in the horizontal direction, and the permeability coefficient in the vertical direction is 1.37 times to that in the horizontal direction. In different directions, the internal friction angle of loess is basically the same. The cohesion force is different in different directions and different test methods. In general, the vertical strength of Q4 loess is greater than the horizontal strength. Conclusion: This characteristic is due to the structural characteristics of loess. Therefore, in the design and construction of loess tunnel, the mechanical parameters in different directions of loess should be considered, and the influence of different test methods on the measured parameters should also be considered.