Computational Analysis of Run-time Monitoring: Fundamentals of Java-MaC

A run-time monitor shares computational resources, such as memory and CPU time, with the target program. Furthermore, heavy computation performed by a monitor for checking target program's execution with respect to requirement properties can be a bottleneck to the target program's execution. Therefore, computational characteristics of run-time monitoring cause a significant impact on the target program's execution.We investigate computational issues on run-time monitoring. The first issue is the power of run-time monitoring. In other words, we study the class of properties run-time monitoring can evaluate. The second issue is computational complexity of evaluating properties written in process algebraic language. Third, we discuss sound abstraction of the target program's execution, which does not change the result of property evaluation. This abstraction can be used as a technique to reduce monitoring overhead. Theoretical understanding obtained from these issues affects the implementation of Java-MaC, a toolset for the run-time monitoring and checking of Java programs. Finally, we demonstrate the abstraction-based overhead reduction technique implemented in Java-MaC through a case study.     

CE2RV: Commissioned energy-efficient virtualization for large-scale heterogeneous wireless sensor networks

The Internet of Things (IoT) comprises sensor networks, intelligent things, devices, and humans for heterogeneous services and applications. Energy constraints in conventional wireless networks impact IoT performance resulting in service failures. For reducing the adverse impact of energy, this article introduces a commissioned energy-efficient resource virtualization (CE2RV) scheme. This proposed scheme classifies the sensor nodes as fast and slow-depleting for identifying service failures. The fast-depleting nodes are discontinued from the service replications, and the remaining energy high-node-connected resources are identified. Such resources are virtualized for thwarting the existing energy failures over various services. The node classifications are performed using a tree-learning algorithm. The classifications are performed for node replacement and service virtualization under different energy depletion rates. This is required for preventing sensor network disconnections between the users and service providers. The classification is required for overcoming multiple virtualizations between common nodes across different service providers. The proposed scheme's performance is analyzed using the metrics of service disconnections, energy utilization, energy efficiency, and service delay.     

Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

Medical image classification becomes a vital part of the design of computer aided diagnosis (CAD) models. The conventional CAD models are majorly dependent upon the shapes, colors, and/or textures that are problem oriented and exhibited complementary in medical images. The recently developed deep learning (DL) approaches pave an efficient method of constructing dedicated models for classification problems. But the maximum resolution of medical images and small datasets, DL models are facing the issues of increased computation cost. In this aspect, this paper presents a deep convolutional neural network with hierarchical spiking neural network (DCNN-HSNN) for medical image classification. The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images. In addition, region growing segmentation technique is involved to determine the infected regions in the medical image. Moreover, NADAM optimizer with DCNN based Capsule Network (CapsNet) approach is used for feature extraction and derived a collection of feature vectors. Furthermore, the shark smell optimization algorithm (SSA) based HSNN approach is utilized for classification process. In order to validate the better performance of the DCNN-HSNN technique, a wide range of simulations take place against HIS2828 and ISIC2017 datasets. The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures. Please type your abstract here.     

Co-simulation Based Finite State Machine for Telematic and Data Compression Microservices in IoT

Wireless Personal Communications - Despite the wide adoption of Internet of things (IoT) with several webs standards and cloud technologies; building of city wide IoT based smart city platform for...     

Elliptic key cryptography with Beta Gamma functions for secure routing in wireless sensor networks

Wireless Networks - The security of data communicated through wireless networks is a challenging issue due to the presence of malicious and unauthenticated users whose intention is either to...     

Improved Secure Identification-Based Multilevel Structure of Data Sharing in Cloud Environments

The Cloud Computing Environment (CCE) developed for using the dynamic cloud is the ability of software and services likely to grow with any business. It has transformed the methodology for storing the enterprise data, accessing the data, and Data Sharing (DS). Big data frame a constant way of uploading and sharing the cloud data in a hierarchical architecture with different kinds of separate privileges to access the data. With the requirement of vast volumes of storage area in the CCEs, capturing a secured data access framework is an important issue. This paper proposes an Improved Secure Identification-based Multilevel Structure of Data Sharing (ISIMSDS) to hold the DS of big data in CCEs. The complex file partitioning technique is proposed to verify the access privilege context for sharing data in complex CCEs. An access control Encryption Method (EM) is used to improve the encryption. The Complexity is measured to increase the authentication standard. The active attack is protected using this ISIMSDS methodology. Our proposed ISIMSDS method assists in diminishing the Complexity whenever the user’s population is increasing rapidly. The security analysis proves that the proposed ISIMSDS methodology is more secure against the chosen-PlainText (PT) attack and provides more efficient computation and storage space than the related methods. The performance of the proposed ISIMSDS methodology provides more efficiency in communication costs such as encryption, decryption, and retrieval of the data.     

Synthesis and thermal stability of potassium substituted hydroxyapatites and hydroxyapatite/β-tricalciumphosphate mixtures

Hydroxyapatite (HAP) and biphasic ceramics of two different HAP and β-tricalcium phosphate (β-TCP) proportions with substituted potassium were prepared through the aqueous precipitation method. The prepared powders were characterized using XRD, FT-IR and elemental analysis. The results have shown that potassium added during the synthesis was found present in the apatite structure even after calcination at 1300 °C without showing any significant change in the phase behaviour of resultant apatites. The substitution of potassium in the calcium deficient apatites was accompanied by the formation of biphasic mixture of HAP and β-TCP ceramics, upon calcination beyond 700 °C and the resultant mixtures were dependent on the initial Ca/P ratios of the precursors. The calculated cell constant values for potassium substituted apatites have shown contraction in a-axis and irregular changes in the c-axis relative to those of pure HAP prepared under the same experimental conditions.     

Synthesis of a polyacrylamide chelating resin and applications in metal ion extractions

A series of differently crosslinked polyacrylamide chelating resins were prepared, using N,N′-methylene-bis-acrylamide as the crosslinking agent. The crosslinked resins were functionalized by the reaction with ethylene diamine to get amino group capacities varying from 4.2 to 1.1 mequiv/g. The maximum adsorption capacities of the lightly crosslinked resin for Fe³⁺, Fe²⁺, Ni²⁺, and Cu²⁺ are 3.62, 4.22, 1.48, and 1.67 mequiv/g, respectively. The adsorbed metal ions can be quantitatively desorbed. The adsorption rate and the influence of pH on the adsorption of metal ions were examined. The resins can be used for the separation between different metal ions as well as the ions in different valence states (e.g., Fe³⁺ and Fe²⁺). The resin is amenable for continuous process and can be regenerated several times.