Cloud Framework Research Articles

Smart vehicular traffic management: An edge cloud centric IoT based framework

Urbanization is imposing many challenges, and vehicular traffic management is one such challenge. It hampers smooth traffic flow, wastes time, and threats of road safety. Moreover, it also impacts the environment, economy, health, and other essential services. The root cause of traffic mismanagement is the dynamic nature of the traffic on roads and the incapability of legacy systems to interpret such dynamics in real-time. The data analysis services based on Edge Cloud frameworks in technology outfitted urban spaces provide real-time robust and smart solutions to ever facing challenges from urbanization. Hence, Edge Cloud-based traffic management can be used to manage urban vehicular traffic in real-time. In this paper, Edge Cloud-centric IoT based smart traffic management system is developed for traffic inflow prediction and time optimized smart navigation of the vehicles. The traffic inflow prediction adapts the traffic movement phase time accordingly and avoids long waiting queues and congestions at intersections. The smart navigation enables the optimal distribution of traffic to possible paths and subsequently improves road safety at intersections. Baseline classifiers are used to predict the traffic inflow, and the statistical analyses acknowledge the prediction efficiency of the J48 decision tree as compared to other utilized classifiers. Edge Computing is used for time optimized smart navigation of vehicles and, subsequently, optimal traffic load balancing in real-time. The road safety perspective of vehicles at intersections is also get benefited from the optimal traffic load balancing. The results depict the efficiency of the proposed system for smart navigation, optimal traffic load balancing, and improved road safety at intersections.

Adaptive cloud resource management through workload prediction

Resource management strategy in adaptive cloud provisions the needed resources dynamically to the end-users. To improve the runtime performance of adaptive cloud for service-based applications, two aspects of technical issues are required to be addressed. The first one is the balancing of a large amount of data on existing resources and the second is resource provisioning which can adjust the number of resources optimally to adapt the time-varying workload. As the growth of data is increasing tremendously, efficient resource management is the need in cloud computing. We build a cloud framework to process data in automation with adaptive resource and workload management strategy. Numbers of approaches are reviewed and applied for workload prediction. We developed the model Auto-Regressive Integrated Moving Average-workload Prediction for Efficient Resource Provisioning (ARIMA-PERP) and evaluated the results that can satisfy the on-demand need of end-users for efficient resource utilization. To serve the maximum number of user requests, performance metrics of the proposed approach are evaluated. It is observed that our evaluated results achieved an accurate prediction by 91.11%, which meets the efficient resource utilization for the demanded workload. As compared with the exiting approach, we achieved better performance by 0.11% for accurate prediction. The proposed architecture is intended to provide the resources dynamically and efficiently satisfying the demands of the user. To achieve this objective of efficient resource provisioning, algorithms are developed for workload prediction which helps in deciding optimum resource provisioning. Our system uses proactive approach resource management and deployment of the adaptive cloud system. In traditional systems, resources are managed based on demand, availability and the strategy of scheduling which results in delayed response time at large. We configured the ARIMA model to predict the future workload for provisioning the resources dynamically and remove the problem of over-provisioning and under-provisioning in a cloud environment.

The path to the Hotel of Things: Internet of Things and Big Data converging in hospitality

研究目的本论文开发了酒店管理中的物联网（IoT）的使用, 并且检验了其与大数据的关系。根据其理论和实际考虑, 这方面的科技缺乏应用理论基础和未来研究方向。研究设计/方法/途径本论文采用理论讲述的方式, 通过例子来阐述IoT的应用和大数据对于酒店管理的影响。本论文还拓展了IoT、大数据、以及酒店管理在文献范畴、价值属性、以及供应商考量中的认识。本论文借鉴了信息系统和商业管理中的理论模型以支撑提出的理论概念。研究结果本论文综合审视了IoT在酒店管理中的应用, 提出了一个输入-处理-输出的模型, 指出了其与大数据的关联。其相关价值维度通过IoT-大数据三重影响强度模型来显示。研究理论限制/意义本论文提出了对IoT使用的未来展望, 拓展了著名的SMAC模型（social, mobile, analytics, cloud）, 加入了IoT成分。研究实际意义本论文对IoT和大数据在酒店IT架构、商业模型、安保和标准化等方面的使用, 强调了其未来应用研究的方向。研究原创性/价值本论文通过酒店管理中的IoT应用综述, 以及其与大数据分析的关联, 展示了IoT、大数据、酒店覆盖等概念如何融合, 这方面的研究还未真正被开发。本论文对于酒店领域中的IoT和大数据应用以及相关领域的未来研究, 有着奠基作用。

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.

Soft-computing-centric framework for wildfire monitoring, prediction and forecasting

Wildfires are exorbitantly cataclysmic disasters that lead to the destruction of forest cover, wildlife, land resources, human assets, reduced soil fertility and global warming. Every year wildfires wreck havoc across the globe. Therefore, there is a need of an efficient and reliable system for real-time wildfire monitoring to dilute their disastrous effects. Internet of Things (IoT) has demonstrated remarkable evolution and has been successfully adopted in environmental monitoring domain. This paper proposes a collaborative IoT–Fog–Cloud framework based on soft computing techniques for real-time wildfire monitoring, prediction and forecasting. The framework includes proposals for classifying a forest terrain into its appropriate wildfire proneness class using fuzzy K-nearest-neighbor classifier by analyzing wildfire influent attributes and wildfire consequent attributes. Moreover, real-time emergency alert generation mechanism based on temporal mining has been proposed in event of adverse wildfire conditions. Estimation of future wildfire proneness levels of a forest terrain using Holt–Winter’s forecasting model also forms an integral part of the proposed framework. Implementation results reveal that high values of accuracy, specificity, sensitivity and precision averaging to 93.97%, 92.35%, 93.01% and 91.24% are attained for determination of wildfire proneness of a forest terrain. Low values of mean absolute error (MAE), mean square error (MSE), mean absolute percentage error and root mean square error (RMSE) averaging to 0.665, 2, 11.705 and 1.405, respectively, for real-time alert generation are registered, thereby increasing the utility of the proposed framework. Wildfire proneness forecasting also yields highly accurate results with low values of MAE, MSE and RMSE averaging to 0.166667, 0.25 and 0.492799, respectively.

A cloud-based operation optimization of building energy systems using a hierarchical multi-agent control

This work presents an agent-based control concept that we integrate into a cloud framework for controlling building energy systems. The agents are arranged in a hierarchical structure, where a coordinator agent sends optimized set point values to sub-agents. Each sub-agent controls a subsystem in order to reach the given set points and provides the coordinator agent with a cost function for the overall set point optimization. The multi-agent system and the building energy system exchange data via the cloud framework FIWARE. The components of the building energy system (e.g. boiler, air-handling unit) are connected to the cloud framework and send measurements (e.g. temperature values or volume flow) via a scalable IoT-Interface to the corresponding data object in the platform. The sub-agents of the agent control receive the measurements and send the calculated control action back to the corresponding components of the energy system. In a simulation study, we use the framework to control an air-handling unit. The aim of the agent control is to reach a given supply air temperature while reducing the total consumed energy. The implemented cloud-based control is capable of efficiently reaching the given temperature set points while communicating via the IoT interface.

Semantic tracking and recommendation using fourfold similarity measure from large scale data using hadoop distributed framework in cloud

PurposeThe purpose of this paper is to propose a fourfold semantic similarity that results in more accuracy compared to the existing literature. The change detection in the URL and the recommendation of the source documents is facilitated by means of a framework in which the fourfold semantic similarity is implied. The latest trends in technology emerge with the continuous growth of resources on the collaborative web. This interactive and collaborative web pretense big challenges in recent technologies like cloud and big data.Design/methodology/approachThe enormous growth of resources should be accessed in a more efficient manner, and this requires clustering and classification techniques. The resources on the web are described in a more meaningful manner.FindingsIt can be descripted in the form of metadata that is constituted by resource description framework (RDF). Fourfold similarity is proposed compared to three-fold similarity proposed in the existing literature. The fourfold similarity includes the semantic annotation based on the named entity recognition in the user interface, domain-based concept matching and improvised score-based classification of domain-based concept matching based on ontology, sequence-based word sensing algorithm and RDF-based updating of triples. The aggregation of all these similarity measures including the components such as semantic user interface, semantic clustering, and sequence-based classification and semantic recommendation system with RDF updating in change detection.Research limitations/implicationsThe existing work suggests that linking resources semantically increases the retrieving and searching ability. Previous literature shows that keywords can be used to retrieve linked information from the article to determine the similarity between the documents using semantic analysis.Practical implicationsThese traditional systems also lack in scalability and efficiency issues. The proposed study is to design a model that pulls and prioritizes knowledge-based content from the Hadoop distributed framework. This study also proposes the Hadoop-based pruning system and recommendation system.Social implicationsThe pruning system gives an alert about the dynamic changes in the article (virtual document). The changes in the document are automatically updated in the RDF document. This helps in semantic matching and retrieval of the most relevant source with the virtual document.Originality/valueThe recommendation and detection of changes in the blogs are performed semantically using n-triples and automated data structures. User-focussed and choice-based crawling that is proposed in this system also assists the collaborative filtering. Consecutively collaborative filtering recommends the user focussed source documents. The entire clustering and retrieval system is deployed in multi-node Hadoop in the Amazon AWS environment and graphs are plotted and analyzed.

To Effectively Control the Workload by Hierarchical Approach

The productive administration of geologically dispersed server farms has turned into an imperative issue not just for huge organizations that claim a few destinations, yet additionally because of the rising of between Cloud foundations that permit heterogeneous server farms to coordinate. These conditions open phenomenal roads for the help of a colossal measure of workload, yet they require the meaning of novel calculations and methodology for their administration, where adaptability is a need. The intricacy determines by the span of the framework and by the requirement for achieving a few and some of the time clashing objectives, among which: stack adjusting among various destinations, anticipation of dangers, workload union, and decrease of costs, expended vitality and carbon outflows. In this paper a various leveled approach is displayed, which protects the self-rule of single server farms and in the meantime takes into consideration a coordinated administration of heterogeneous platforms. The system is deliberately nonexclusive yet can be customized to the particular prerequisites of single situations. Exhibitions are examined for a particular Cloud framework made out of four server farms.

1581 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number C4409098319/2019©BEIESP DOI: 10.35940/ijrte.C4409.098319 Journal Website: www.ijrte.org Cloud-Implementation of E-Healthcare Framework

Healthcare Systems are gaining ground around the world, seeking to provide viable and affordable healthcare to all sections of society. With the recent advancements in the Information and Communication Technology (ICT) around the globe, the Healthcare Frameworks can be made more robust with the use of more novel techniques and modern-day technologies. With the rise of Mobile Cloud Computing resources and easy availability of smartphones and net services, a proposed framework referred to as “Cloud Implementation of E-Healthcare Framework” built upon a Cloud Framework authenticated by personal biometrics would allow the agencies at work to store a patient’s credentials and medical history on the Cloud which will then be accessible throughout the network which will be a significant shift from the Paper-Based Record System as well as the new and more modern Electronic-Based Record Systems. Our proposed framework is expected to enhance administrative machinery in Healthcare agencies, a more robust data frame to store patient’s credentials and also time-saving procedures to provide the necessary treatment to the patient.

Data Privacy Preservation using Aes-Gcm Encryption in Heroku Cloud

The increasing popularity of cloud data storage and its ever-rising versatility, shows that cloud computing is one of the most widely excepted phenomena. It not only helps with powerful computing facilities but also reduce a huge amount of computational cost. And with such high demand for storage has raised the growth of the cloud service industry that provides an affordable, easy-to-use and remotely-accessible services. But like every other emerging technology it carries some inherent security risks associated and cloud storage is no exception. The prime reason behind it is that users have to blindly trust the third parties while storing the useful information, which may not work in the best of interest. Hence, to ensure the privacy of sensitive information is primarily important for any public, third-party cloud. In this paper, we mainly focus on proposing a secure cloud framework with encrypting sensitive data’s using AES-GCM cryptographic techniques in HEROKU cloud platform. Here we tried to implement Heroku as a cloud computing platform, used the AES-GCM algorithm and evaluate the performance of the said algorithm. Moreover, analyses the performance of AES/GCM execution time with respect to given inputs of data

An Extended Cloud Framework to Monitor and Control Wireless Sensors Networks

wireless network is any kind of type of local area network that makes use of wireless data hookups to plug network nodes. Wireless networks are actually computer networks who are not linked through cable televisions no matter the type. The use of a wireless network permits enterprises to prevent the pricey methods of presenting cords in to buildings or as a hookup in between various tools locations. This remote triggering system offers the user a flexible setting for the testing. A trial and error create of 150 wireless sensor nodes are actually cultivated to satisfy the style of both interior and exterior experiments.

Secure Data Query Framework for Cloud and Fog Computing

Fog computing is mainly used to process a large amount of data produced by terminal devices. As fog nodes are the closest acquirers to the terminal devices, the processed data may be tampered with or illegally captured by some malicious nodes while the data is transferred or aggregated. When some applications need to require real-time process with high security, cloud service may sample some data from fog service to check final results. In this paper, we propose a secure data query framework for cloud and fog computing. We use cloud service to check queried data from fog network when fog network provides queried data to users. In the framework, cloud server pre-designates some data aggregation topology trees to fog network, and then fog network may acquire related data from fog nodes according to one of the pre-designated data aggregation trees. Additionally, some fog nodes are assigned as sampled nodes that can feed back related data to cloud server. Based on the security requirements of fog computing, we analyze the security of our proposed framework. Our framework not only guarantees the reliability of required data but also effectively protects data against man-in-the-middle attack, single node attack and collusion attack of malicious users. Also, the experiments show our framework is effective and efficient.

An integrated TOE–DoI framework for cloud computing adoption in the higher education sector: case study of Sub-Saharan Africa, Ethiopia

This research paper describes an integrated framework based on both the ‘Technology–Organization–Environment (TOE) framework’ and ‘Diffusion of Innovation (DoI) theory’. The study explores the noteworthy factors and sub-factors which are pertinent for the adoption of cloud computing in the Ethiopian Higher Education (EHE) sector. The technology literature herein was based on technology adoption frameworks and theories which were studied in order to identify a set of factors and sub-factors relevant to cloud computing adoption. It resulted in conceptualizing an integrated TOE–DoI framework for cloud computing adoption in higher education at the university in Ethiopia and in developing its reliable measures. Accordingly, a quantitative study was done with a questionnaire survey comprising 500 respondents in connection with 4 factors (technological, organizational, environmental and socio-cultural). Consequently, the cloud computing adoption in Ethiopia was established using factors and concepts adopted from the study. It confirmed that the TOE–DoI approach to higher education in Ethiopia is authenticated. Thus, the four factors’ reliability statistics validated with a Cronbach’s alpha a = 0.739, 0.712, 0.761, 0.841, and Cronbach’s alpha ‘a’ based on standard items a = 0.740, 0.713, 0.762 and 0.842 for technology, organizational, environmental, and socio-cultural factors. This indicates that scaling the four aspects therein suggests profound evidence to determine a cloud computing adoption in EHE with TOE–DoI integration.

CUPS: Secure opportunistic cloud of things framework based on attribute‐based encryption scheme supporting access policy update

AbstractThe ever‐growing number of internet connected devices, coupled with the new computing trends, namely within emerging opportunistic networks, engenders several security concerns. Most of the exchanged data between the internet of things (IoT) devices are not adequately secured due to resource constraints on IoT devices. Attribute‐based encryption is a promising cryptographic mechanism suitable for distributed environments, providing flexible access control to encrypted data contents. However, it imposes high decryption costs, and does not support access policy update, for highly dynamic environments. This paper presents CUPS, an ABE‐based framework for opportunistic cloud of things applications, that securely outsources data decryption process to edge nodes in order to reduce the computation overhead on the user side. CUPS allows end‐users to offload most of the decryption overhead to an edge node and verify the correctness of the received partially decrypted data from the edge node. Moreover, CUPS provides the access policy update feature with neither involving a proxy‐server, nor re‐encrypting the enciphered data contents and re‐distributing the users' secret keys. The access policy update feature in CUPS does not affect the size of the message received by the end‐user, which reduces the bandwidth and the storage usage. Our comprehensive theoretical analysis proves that CUPS outperforms existing schemes in terms of functionality, communication and computation overheads.

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

Abstract Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on. The main objective of the paper is to formulate optimal resource provisioning policies by efficiently handling the uncertainties in the jobs and resources with the application of Neutrosophic Soft-Set (NSS) and Fuzzy Neutrosophic Soft-Set (FNSS). The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering the RUBiS, RUBBoS, and Olio benchmark applications.

Ensure Data Protection in Cloud Computing using ASCII Based Encryption Technique

Cloud computing is an engineering discipline which is concerned with all aspect of storing and sharing useful data. Today, millions of users are able to upload and download useful information on cloud environment from anywhere at any time. Recently, many security issues are encountered in real time applications with a great loss of data and information. Subsequently, many research works have been done to highjack the security issues of cloud computing. Still, all feasible solutions may not applicable at all time and some practical situations. In this paper, we designed and implemented an ASCII based encryption technique that securely uploads, sharing the data on cloud framework with a minimum computational overhead and entropy. The proposed technique will increase the throughput of the system and it can be deployed on a cloud environment for sharing and securing useful data information.

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Including the expanding fame of the cloud model and quick multiplication of cloud frameworks there are expanding concerns about energy utilization and subsequent effect of cloud as a supporter of worldwide CO2 discharges. Until now, little is thought about how to fuse energy utilization and CO2 worries into cloud application. Energy consumption has become an important cost factor for computing resources. In this research article, we proposed an algorithm to VM Allocation and VM migrations in the context of power utilisation in the data centers. This mechanism is to minimize the energy utilization in the cloud computing environment. We validate our results with the help of prediction based faster energy efficient VMs approach and modified Best Fit approach which shows the faster assignments and increase the performance when consumption of the energy is optimised. As well as we also simulate our results in the cloudsim in the multiple numbers of host and virtual machine to reduce the energy consumptions.

EeadSelfCloud: Energy Efficient Adaptive Depth Self Cloud Mechanism for VM Migration in Data Centers

Cloud computing, with its great potential in low cost and demanding services, is a good computing platform. Modern data centers for cloud computing are facing the difficulty of consistently increasing complexity because of the expanding quantity of clients and their enlarging resource demands. A great deal of efforts are currently focused on giving the cloud framework with autonomic behavior , so it can take decision about virtual machine (VM) management over the datacenter without intervention of human beings. Most of the self-organizing solutions results in eager migration, which attempts to diminish the amount of working servers virtual machines. These self-organizing resolution produce needless migration due to unpredictable workload. So also it consume huge amounts of electrical energy during unnecessary migration process. To overcome this issue, this project develop one novel VM migration scheme called eeadSelfCloud. The proposed schema is used to change the virtual machine in a cloud center that requires a lot of factors, such as basic requirements for resources during virtual machine setup, dynamic resource allocation, top software loading, software execution, and power saving at the Data Center. Data Center Utilization, Average Node Utilization, Request Rejection Ration, Number of Hop Count and Power Consumption are taken as constraint for measuring the proposed approach. The analysis report depicted that the proposed approach performs best than the other existing approaches.

Electromagnetic properties of the d*(2380) hexaquark

Experiments with intense photon and electron beams have the potential to provide access to nontrivial properties of the recently discovered $d^*(2380)$ hexaquark including its size, structure, magnetic moment, quadrupole and octupole deformations. In this paper we investigate the sensitivity of ongoing and planned experiments to various properties of the $d^*(2380)$, employing models based on both constituent quark and pion cloud frameworks. Our calculations indicate that for photoinduced reactions on the deuteron the $d^*(2380)$ is predominantly produced from the $D-wave$ component of the deuteron. We confirm earlier findings that the intrinsic quadrupole deformation of the $d^*(2380)$ should be small. We also demonstrate an ability to extract the $d^*(2380)$ magnetic moment and put constrains on the $d^*(2380)$ $M3/E2$ ratio.

Big Data Analysis for Sustainable Agriculture on a Geospatial Cloud Framework

The grand challenge confronting agriculture is the development of technologies for sustainable intensification of crop production systems to feed the estimated future human population of 9 to 10 billion. It is thought that crop production must be increased by 60 to 100% by the year 2050 to meet these nutritional needs. Crop production systems that yield more food of higher nutritional content are needed that at the same time have a diminished impact on the environment. Prior agricultural intensification was through substantial use of fertilizer, pesticides, and irrigation all at significant environmental cost. As a part of Sustainable Agriculture, next-generation, on cropping systems that couple biologically based technologies (plant-beneficial microbes, cover crops) and precision agriculture need to be developed to decrease fertilizer, pesticide, and water inputs. Over the past two decades, Information Technology has been the disruptive force in industries by driving out market inefficiencies through automation and better decision support tools that require the inclusion of both the citizens and consumers in the process. Like all industries, Agriculture has not been immune to the constant disruptions over the past century. However, recent advances in the computing infrastructure, sensor technology, big data and advanced algorithms ( suggest that a major disruption or paradigm shift is on the horizon, leading to opportunities for sustainable agriculture entering the mainstream. Specifically, solutions based on these new technologies will be needed for mass transfer of genomic and other genetic data for development of these advanced crop cultivars, and for the management of agronomic data for the development of these next-generation production systems. Geospatial solutions based on imagery, IoT, AI, mobile data collection, etc. will be critical for the operation of precision agriculture systems where intelligent application of resource inputs are applied at precise geo-specific field locations based on crop need. Finally, solutions will be needed to allow immediate feedback from digital farm communities regarding the performance of these new cropping systems; speeding their development. Here we describe a “system of systems” approach to building a scientific network that integrates the scientific and farming communities, based on a Geoinformatics cloud framework.