Prioritized Service Scheme with QOS Provisioning in a Cloud Computing System

Cloud computing is a compilation of existing techniques and technologies, packaged within a new infrastructure paradigm that offers improved scalability, elasticity, business agility, faster startup time, reduced management costs, and just-in-time availability of resources. It is based on the pay as you use policy and virtual servers are used in this technology. This technology is capturing the market at a rapid rate and is an advancement over the distributed computing technology. There is a scheduling issue in this technology as in case of normal scheduling the service with the more burst time blocks the service of less burst time hence we need to prioritize the service in the way that every service gets equal opportunity to execute. A priority scheme is proposed in which the prioritized customers are categorized into different priority queues. These prioritized customers have guaranteed Quality of Service (QoS) by the cloud computing system in terms of less response time. The concept of selection probability is introduced according to which the cloud metascheduler chooses the next query for execution. The priority queues are modeled as M/M/1/K/K queues and an analytical model is developed for the calculation of selection probabilities. Two algorithms are proposed for explaining the processing at the users’ end and at the Cloud Computing server’s end. The results obtained are validated using the numerical simulations. DOI: 10.17762/ijritcc2321-8169.15024

Automatic Solar Tracking System with AVR Microcontroller based Street Light

This paper presents the design of a solar tracking system driven by an AVR microcontroller. This project is done by two ways of tracking system, manual and auto tracking. This project is very useful for street light in the campus and villages. The solar panel converts the sun light into the electrical energy, because sun is a very good source of different energies. And the solar energy is the best technique for renewable energy. Basically the energy sources are two types such as conventional energy sources and non-conventional energy sources. Coal, petroleum, natural gas etc. are example of conventional energy sources and solar cell, fuel cells, thermo-electric generator, thermionic converter, solar power generation, wind power generation, geo-thermal energy generation etc. are example of non-conventional energy sources. In developing countries where electricity supplies are inadequate, renewable energy can offer an alternative to expensive extensions of the grid to sparsely populated or rural areas, or a contribution to the grid-based energy mix to meet rapidly expanding electricity demand in urban areas. This work presents an autonomous street lighting system based on solar energy as primary source, batteries as secondary source, and light emitting diodes as lighting source. This system is being presented as an alternative for remote localities, like roads and crossroads

A Review Approach on various form of Apriori with Association Rule Mining

Data mining is a computerized technology that uses complicated algorithms to find relationships in large databases Extensive growth of data gives the motivation to find meaningful patterns among the huge data. Sequential pattern provides us interesting relationships between different items in sequential database. Association Rules Mining (ARM) is a function of DM research domain and arise many researchers interest to design a high efficient algorithm to mine ass ociation rules from transaction database. Association Rule Mining plays a important role in the process of mining data for frequent pattern matching. It is a universal technique which uses to refine the mining techniques. In computer science and data min ing, Apriori is a classic algorithm for learning association rules Apriori algorithm has been vital algorithm in association rule mining. . Apriori alg orithm - a realization of frequent pattern matching based on support and confidence measures produced exc ellent results in various fields. Main idea of this algorithm is to find useful patterns between different set of data. It is a simple algorithm yet having man y drawbacks. Many researches have been done for the improvement of this algorithm. This paper sho ws a complete survey on few good improved approaches of Apriori algorithm. This will be really very helpful for the upcoming researchers to find some new ideas from these approaches. The paper below summarizes the basic methodology of association rules alo ng with the mining association algorithms. The algorithms include the most basic Apriori algorithm along with other algorithms such as AprioriTi d, AprioriHybrid

Assessing Household Solid Fuel Use: Multiple Implications for the Millennium Development Goals

OBJECTIVE: The World Health Organization is the agency responsible for reporting the Millennium Development Goal (MDG) indicator “percentage of population using solid fuels.” In this article, we present the results of a comprehensive assessment of solid fuel use, conducted in 2005, and discuss the implications of our findings in the context of achieving the MDGs. METHODS: For 93 countries, solid fuel use data were compiled from recent national censuses or household surveys. For the 36 countries where no data were available, the indicator was modeled. For 52 upper-middle or high-income countries, the indicator was assumed to be < 5%. RESULTS: According to our assessment, 52% of the world’s population uses solid fuels. This percentage varies widely between countries and regions, ranging from 77%, 74%, and 74% in Sub-Saharan Africa, Southeast Asia, and the Western Pacific Region, respectively, to 36% in the Eastern Mediterranean Region, 16% in Latin America and the Caribbean and in Central and Eastern Europe. In most industrialized countries, solid fuel use falls to the < 5% mark. DISCUSSION: Although the “percentage of population using solid fuels” is classified as an indicator to measure progress towards MDG 7, reliance on traditional household energy practices has distinct implications for most of the MDGs, notably MDGs 4 and 5. There is an urgent need for development agendas to recognize the fundamental role that household energy plays in improving child and maternal health and fostering economic and social development

Comparative Study of RDBMS, NOSQL and Graph Databases

The paper aims at analysis and comparison of various forms of databases particularly computer database Management System (RDBMS), Not solely SQL (NOSQL), Graph Databases. The Structured source language is employed by applications to access computer database systems containing informative during a semi declarative language whereas NOSQL databases area unit supported the key-value pairs. Graph info uses graph structures for resolution queries and to represent and store knowledge

Energy Aware Channel Allocation with Spectrum Sensing in Pilot Contamination Analysis for Cognitive Radio Networks

Cognitive radio (CR) is an innovative and contemporary technology that has been making an effort to overcome the problems of bandwidth reduction by rising the usage of mobile cellular bandwidth connections. The reallocation and distribution of channels is a fundamental characteristic of cellular mobile networks (CMN) to exploit the consumption of CMS. Meanwhile, throughput maximization might lead to higher power utilization, the spectrum sensing system must tackle the energy throughput tradeoff. The spectrum sensing time should be defined by the residual battery energy of secondary user (SU). In that context, energy effective algorithm for spectrum sensing should be developed for meeting the energy constraint of CRN. This study designs a new quantum particle swarm optimization-based energy aware spectrum sensing scheme (QPSO-EASSS) for CRNs. Here, the presented QPSO-EASSS technique dynamically estimates the sensing time depending upon the battery energy level of SUs and the transmission power can be computed based on the battery energy level and PU signal of the SUs. In addition, in this work, the QPSO-EASSS technique applies the QPSO algorithm for throughput maximization with energy constraints in the CRN. The detailed set of experimentations take place and reported the improvements of the QPSO-EASSS technique compared to existing models

Centralized Cloud Service Providers in Improving Resource Allocation and Data Integrity by 4G IoT Paradigm

Due to the expansion of Internet of Things (IoT), the extensive wireless, and 4G networks, the rising demands for computing calls and data communication for the emergent EC (EC) model. By stirring the functions and services positioned in the cloud to the user proximity, EC could offer robust transmission, networking, storage, and transmission capability. The resource scheduling in EC, which is crucial to the accomplishment of EC system, has gained considerable attention. This manuscript introduces a new lighting attachment algorithm based resource scheduling scheme and data integrity (LAARSS-DI) for 4G IoT environment. In this work, we introduce the LAARSS-DI technique to proficiently handle and allot resources in the 4G IoT environment. In addition, the LAARSS-DI technique mainly relies on the standard LAA where the lightning can be caused using the overall amount of charges saved in the cloud that leads to a rise in electrical intensity. Followed by, the LAARSS-DI technique designs an objective function for the reduction of cost involved in the scheduling process, particularly for 4G IoT environment. A series of experimentation analyses is made and the outcomes are inspected under several aspects. The comparison study shown the improved performance of the LAARSS-DI technology to existing approaches

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model

Recommendation Model-Based 5G Network and Cognitive System of Cloud Data with AI Technique in IOMT Applications

Recommender system provides the significant suggestion towards the effective service offers for the vast range of big data. The Internet of Things (IoT) environment exhibits the value added application services to the customer with the provision of the effective collection and processing of information. In the extension of the IoT, Internet of Medical Things (IoMT) is evolved for the patient healthcare monitoring and processing. The data collected from the IoMT are stored and processed with the cognitive system for the data transmission between the users. However, in the conventional system subjected to challenges of processing big data while transmission with the cognitive radio network. In this paper, developed a effective cognitive 5G communication model with the recommender model for the IoMT big data processing. The proposed model is termed as Ranking Strategy Internet of Medical Things (RSIoMT). The proposed RSIoMT model uses the distance vector estimation between the feature variables with the ranking. The proposed RSIoMT model perform the recommender model with the ranking those are matches with the communication devices for improved wireless communication quality. The proposed system recommender model uses the estimation of direct communication link between the IoMT variables in the cognitive radio system. The proposed RSIoMT model evaluates the collected IoMT model data with the consideration of the four different healthcare datasets for the data transmission through cognitive radio network. Through the developed model the performance of the system is evaluated based on the deep learning model with the consideration of the collaborative features. The simulation analysis is comparatively examined based on the consideration of the wireless performance. Simulation analysis expressed that the proposed RSIoMT model exhibits the superior performance than the conventional classifier. The comparative analysis expressed that the proposed mode exhibits ~3 – 4% performance improvement over the conventional classifiers. The accuracy of the&nbsp; developed model achieves 99% which is ~3 – 9% higher than the conventional classifier. In terms of the channel performance, the proposed RSIoMT model exhibits the reduced recommender relay selection count of 1 while the other technique achieves the relay value of 13 which implies that proposed model performance is ~4-6% higher than the other techniques

Regulatory Elements within the Prodomain of Falcipain-2, a Cysteine Protease of the Malaria Parasite Plasmodium falciparum

Falcipain-2, a papain family cysteine protease of the malaria parasite Plasmodium falciparum, plays a key role in parasite hydrolysis of hemoglobin and is a potential chemotherapeutic target. As with many proteases, falcipain-2 is synthesized as a zymogen, and the prodomain inhibits activity of the mature enzyme. To investigate the mechanism of regulation of falcipain-2 by its prodomain, we expressed constructs encoding different portions of the prodomain and tested their ability to inhibit recombinant mature falcipain-2. We identified a C-terminal segment (Leu155–Asp243) of the prodomain, including two motifs (ERFNIN and GNFD) that are conserved in cathepsin L sub-family papain family proteases, as the mediator of prodomain inhibitory activity. Circular dichroism analysis showed that the prodomain including the C-terminal segment, but not constructs lacking this segment, was rich in secondary structure, suggesting that the segment plays a crucial role in protein folding. The falcipain-2 prodomain also efficiently inhibited other papain family proteases, including cathepsin K, cathepsin L, cathepsin B, and cruzain, but it did not inhibit cathepsin C or tested proteases of other classes. A structural model of pro-falcipain-2 was constructed by homology modeling based on crystallographic structures of mature falcipain-2, procathepsin K, procathepsin L, and procaricain, offering insights into the nature of the interaction between the prodomain and mature domain of falcipain-2 as well as into the broad specificity of inhibitory activity of the falcipain-2 prodomain