scholarly journals Robotics and Automation

2021 ◽  
Vol 9 (VII) ◽  
pp. 2852-2856
Author(s):  
Sahil Kamleshwar
Keyword(s):  
Cloud Computing ◽  
Open Source Software ◽  
Tracking Control ◽  
Data Access ◽  
Movement Planning ◽  
Data Sets ◽  
Flexible Systems ◽  
Cloud Infrastructure ◽  
Descriptive Data ◽  
Simulation Tools

Cloud infrastructure and its extensive set of Internet-enabled resources have the potential to provide significant benefits to robots and flexible systems. We look for robots and data-switching programs or code from the network to support their performance, that is, when not all sense, calculation, and memory are integrated into the standalone system. This survey is designed for four possible Cloud benefits: 1) Big Data: access to photo libraries, maps, trajectories, and descriptive data; 2) Cloud Computing: access to the same grid computer with the demand for mathematical analysis, reading, and movement planning; 3) Integrated Robots Learning: robots that share tracking, control policies, and results; and 4) Census: use of crowdourcing to tap people's skills for image and video analysis, classification, reading, and error retrieval. The cloud can also improve robots and flexible systems by providing access to: a) data sets, publications, models, measurements, and simulation tools; b) open competitions for designs and programs; and c) open source software.

Home Healthcare in Cloud Computing

2013 ◽  
pp. 614-634
Author(s):  
Mina Deng ◽  
Milan Petkovic ◽  
Marco Nalin ◽  
Ilaria Baroni
Keyword(s):  
Cloud Computing ◽  
Digital Rights Management ◽  
Data Access ◽  
Home Healthcare ◽  
Security And Privacy ◽  
Policy Enforcement ◽  
Cloud Infrastructure ◽  
Development Lifecycle ◽  
Application Architecture ◽  
Private And Public

Cloud computing is one of the emerging technologies that has an increasing impact on both private and public sectors. It represents an on-demand service model for delivering computing resources ranging from storage and data access, via computation and software provisioning. This chapter describes an application of Cloud Computing in home healthcare by introducing several use cases and application architecture based on the cloud. A comprehensive methodology is used to integrate security and privacy engineering process into the development lifecycle and to identify challenges for building security and privacy in the proposed cloud-based home healthcare system. Moreover, a functional infrastructure plan is provided to demonstrate the integration between the proposed application architecture with the cloud infrastructure. Finally, this chapter discusses several mitigation techniques putting the focus on patient-centric control and policy enforcement via cryptographic technologies, and consequently on digital rights management and attribute-based encryption technologies.

scholarly journals Secure Record Linkage of Large Health Data Sets: Evaluation of a Hybrid Cloud Model

10.2196/18920 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18920
Author(s):  
Adrian Paul Brown ◽  
Sean M Randall
Keyword(s):  
Cloud Computing ◽  
Record Linkage ◽  
Social Issues ◽  
Cloud Model ◽  
Synthetic Data ◽  
Health Data ◽  
Data Sets ◽  
Hybrid Cloud ◽  
Cloud Infrastructure ◽  
Amazon Web Services

Background The linking of administrative data across agencies provides the capability to investigate many health and social issues with the potential to deliver significant public benefit. Despite its advantages, the use of cloud computing resources for linkage purposes is scarce, with the storage of identifiable information on cloud infrastructure assessed as high risk by data custodians. Objective This study aims to present a model for record linkage that utilizes cloud computing capabilities while assuring custodians that identifiable data sets remain secure and local. Methods A new hybrid cloud model was developed, including privacy-preserving record linkage techniques and container-based batch processing. An evaluation of this model was conducted with a prototype implementation using large synthetic data sets representative of administrative health data. Results The cloud model kept identifiers on premises and uses privacy-preserved identifiers to run all linkage computations on cloud infrastructure. Our prototype used a managed container cluster in Amazon Web Services to distribute the computation using existing linkage software. Although the cost of computation was relatively low, the use of existing software resulted in an overhead of processing of 35.7% (149/417 min execution time). Conclusions The result of our experimental evaluation shows the operational feasibility of such a model and the exciting opportunities for advancing the analysis of linkage outputs.

scholarly journals Secure Record Linkage of Large Health Data Sets: Evaluation of a Hybrid Cloud Model (Preprint)

2020 ◽  
Author(s):  
Adrian Paul Brown ◽  
Sean M Randall
Keyword(s):  
Cloud Computing ◽  
Record Linkage ◽  
Social Issues ◽  
Cloud Model ◽  
Synthetic Data ◽  
Health Data ◽  
Data Sets ◽  
Hybrid Cloud ◽  
Cloud Infrastructure ◽  
Amazon Web Services

BACKGROUND The linking of administrative data across agencies provides the capability to investigate many health and social issues with the potential to deliver significant public benefit. Despite its advantages, the use of cloud computing resources for linkage purposes is scarce, with the storage of identifiable information on cloud infrastructure assessed as high risk by data custodians. OBJECTIVE This study aims to present a model for record linkage that utilizes cloud computing capabilities while assuring custodians that identifiable data sets remain secure and local. METHODS A new hybrid cloud model was developed, including privacy-preserving record linkage techniques and container-based batch processing. An evaluation of this model was conducted with a prototype implementation using large synthetic data sets representative of administrative health data. RESULTS The cloud model kept identifiers on premises and uses privacy-preserved identifiers to run all linkage computations on cloud infrastructure. Our prototype used a managed container cluster in Amazon Web Services to distribute the computation using existing linkage software. Although the cost of computation was relatively low, the use of existing software resulted in an overhead of processing of 35.7% (149/417 min execution time). CONCLUSIONS The result of our experimental evaluation shows the operational feasibility of such a model and the exciting opportunities for advancing the analysis of linkage outputs.

Home Healthcare in Cloud Computing

2015 ◽  
pp. 619-639
Author(s):  
Mina Deng ◽  
Milan Petkovic ◽  
Marco Nalin ◽  
Ilaria Baroni
Keyword(s):  
Cloud Computing ◽  
Digital Rights Management ◽  
Data Access ◽  
Home Healthcare ◽  
Security And Privacy ◽  
Policy Enforcement ◽  
Cloud Infrastructure ◽  
Development Lifecycle ◽  
Application Architecture ◽  
Private And Public

Cloud computing is one of the emerging technologies that has an increasing impact on both private and public sectors. It represents an on-demand service model for delivering computing resources ranging from storage and data access, via computation and software provisioning. This chapter describes an application of Cloud Computing in home healthcare by introducing several use cases and application architecture based on the cloud. A comprehensive methodology is used to integrate security and privacy engineering process into the development lifecycle and to identify challenges for building security and privacy in the proposed cloud-based home healthcare system. Moreover, a functional infrastructure plan is provided to demonstrate the integration between the proposed application architecture with the cloud infrastructure. Finally, this chapter discusses several mitigation techniques putting the focus on patient-centric control and policy enforcement via cryptographic technologies, and consequently on digital rights management and attribute-based encryption technologies.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

2017 ◽  
Vol 7 (9) ◽  
pp. 30 ◽  
Author(s):  
Ramandeep Kaur
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Heuristic Algorithms ◽  
Hybrid Approach ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Bee Colony ◽  
Effective Manner ◽  
Effective Performance

A lot of research has been done in the field of cloud computing in computing domain.  For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.

A Research of GIS Software Application Based on Cloud Computing

2014 ◽  
Vol 513-517 ◽  
pp. 2107-2110 ◽  
Author(s):  
Zhi Jian Diao ◽  
Song Guo
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Information Storage ◽  
Application Layer ◽  
Cloud Infrastructure ◽  
Network Resource ◽  
Software Application ◽  
Computing Model ◽  
Two Layer Model ◽  
Gis Software

Cloud computing is a novel network-based computing model, in which the cloud infrastructure is constructed in bottom level and provided as the support environment for the applications in upper cloud level. The combination of clouding computing and GIS can improve the performance of GIS, and it can also provide a new prospect of GIS information storage, processing and utilization. By integrating cloud computing and GIS, this paper presented a cloud computing based GIS model based on two features of cloud computing: data storage and transparent custom service. The model contains two layers: service layer and application layer. With this two-layer model, GIS can provide stable and efficient services to end users by optimized network resource allocation of underlying data and services in cloud computing.

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