ABSTRACT
Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., computational grids is a NP-complete problem. Many researchers have proposed several task scheduling algorithms on grids based on formulating and solving it as an optimization problem with different objective functions such as makespan, cost, energy etc. Further to address the requirements/demands/needs of the users (lesser cost, lower latency etc.) and grid service providers (high utilization and high profitability), a task scheduler needs to be designed based on solving a multi-objective optimization problem due to several trade-offs among the objective functions. In this direction, we propose an efficient multi-objective task scheduling framework to schedule computationally intensive tasks on heterogeneous grid networks. This framework minimizes turnaround time, communication, and execution costs while maximizing grid utilization. We evaluated the performance of our proposed algorithm through experiments conducted on standard, random, and scientific task graphs using the GridSim simulator.
ABSTRACT
The cultivation of most crops depends upon the regional weather conditions. So, the analysis of the agro-climatic conditions of a zone contributes significantly to deciding the right crop for the right land in the right season to obtain a better yield. Machine learning algorithms facilitate this process to a great extent for better results. In this paper, the authors proposed an ML-based crop selection model based on the weather conditions and soil parameters, collectively. Weather analysis is done using LSTM RNN and the process of crop selection is completed using Random Forest Classifier. This model gives better results for weather prediction in comparison to ANN. With LSTM RNN, the RMSE observed in Min. Temp. prediction is 5.023%, Max. Temp. Prediction is 7.28%, and Rainfall Prediction is 8.24%. In the second phase, the Random Forest Classifier showed 97.235% accuracy for crop selection, 96.437% accuracy in predicting resource dependency, and 97.647 accuracies in giving the appropriate sowing time for the crop. The model construction time taken with a random forest classifier using mentioned data size is 5.34 s. The authors also suggested the future research direction to further improve this work.
ABSTRACT
A mobile agent is a software application that moves naturally among hosts in a uniform and non-uniform environment; it starts with one host and then moves onto the next in order to divide data between clients. The mobile paradigm is utilized in a wide assortment of medical care applications such as the medical information of a patient, the recovery of clinical information, the incorporation of information pertaining to their wellbeing, dynamic help, telemedicine, obtaining clinical data, patient administration, and so on. The accompanying security issues have grown in tandem with the complexity and improvements in mobile agent technologies. As mobile agents work in an insecure environment, their security is a top priority when communicating and exchanging data and information. Data integrity, data confidentiality and authentication, on-repudiation, denial of service, and access control, are all key security concerns with mobile agent migration. This paper proposes a Verifiable, Secure Mobile Agent Migration model, based on two polynomials (t, n), and an edge secret imparting plan with Blowfish encryption, to enable secure information transmission in clinical medical care.
Subject(s)
Computer Security , Telemedicine , Humans , Confidentiality , Algorithms , SoftwareABSTRACT
This paper aims to comprehensively review 891 documents in the Scopus database about Internet of Things (IoT) in Ind 4.0 to understand the historical growth, current state, and potential expansion trend. From 2014 to 2020, a systematic methodology gathered information on IoT in Ind 4.0 documents in various Scopus databases. The relevant IoT research in Ind 4.0 documents is provided, and their types, publications, citations, and predictions are discussed. The VOSviewer 1.16.6 and Biblioshiny 2.0 applications display IoT status in Ind 4.0 publications for visualization research. The citation review aims to find the most prominent and influential authors, sources, papers, countries, and organizations. For citation analysis and ranking, document selection criteria were well defined. The author keywords, index keywords, and text data content analysis were used to identify the hotspots and development trends. The yearly IoT in Ind 4.0 article publications presented a speedily increasing trend, and a curve was fitted employing an exponential function. The paper "Intelligent manufacturing in the context of Industry 4.0: a review" was rated first with 754 citations. With 1629 citations, the "International Journal of Production Research" was ranked #1 along with Wan J. The South China University of Technology in Guangzhou, China, was placed first along with the United States as the most prolific and influential country. 'Industry 4.0' appeared the first time in 2014 with an application of IoT in Ind 4.0 with an overall appearance of 528, followed by the 'internet of things' in 2015, three times with a total count of 220 up to 2020. The IoT in Ind 4.0 assessment and bibliometric analysis intended to provide intellectuals a broad perspective working in IoT in Ind 4.0. Scholars should understand the hotspots in this area as soon as possible. This is also the first paper to thoroughly use bibliometric research to examine the IoT in Ind 4.0 literature. It will assist researchers of IoT in Ind 4.0 in expanding their knowledge and quickly comprehending the development status and pattern.
