Cloud-based Multimedia Research Articles

USE OF CLOUD-BASED MULTIMEDIA EDUCATIONAL RESOURCES IN THE PREPARATION OF FUTURE PRIMARY SCHOOL TEACHERS

Most educational support materials include graphics, animation, audio and video snippets, infographics, and text that can be accessed through the Internet. In recent years attention paid to the pedagogical design of teaching and learning tools using cloud-based technologies (CBT) has been increasing in the world. Such facilitates as the friendly interface of computer-based and web-based applications, the speed, availability and security of CBT provoke higher interest among educators to the constant use of products offered by these technologies. The above-mentioned points to the problem of designing a quality cloud-based multimedia educational resource (CBMER), as well as the need to determine the effectiveness of the use of projected CBMER in the educational process. Based on the analysis of scientific articles and research by specialists in the educational sphere the stages of designing were distinguished, the basic principles (didactic, qualitative and technical) which should be observed during introduction in the educational process were described; the functions that perform CBMER and the operating modes were specified. For the use of the author's CBMER in the educational activities of teachers of higher education institutions, a number of recommendations have been put forward and the author's system of evaluation of the quality of the multimedia educational resource has been proposed. The peculiarities of teaching with the use of CBMER are described in detail based on the case of the discipline «Special Workshop on Informatics» for students of specialty «013. Primary education» of Borys Grinchenko Kyiv University. It is statistically justified that the author's CBMER containing interactive tasks, dynamic elements, audio and video elements contribute to the formation of professional competences.

An auto-scaling mechanism for cloud-based multimedia storage systems: a fuzzy-based elastic controller

An auto-scaling mechanism for cloud-based multimedia storage systems: a fuzzy-based elastic controller

Advanced framework for highly secure and cloud‐based storage of colour images

The evergrowing virtualised information technology infrastructure is powered by cloud-centric technology around the world. Cloud-based multimedia storage has become an essential aspect for users and business behemoths. However, as per a survey of Norton, around 3800 breaches have been publicly disclosed with 4.1 billion numbers of records exposed in 2019, which is a 54% rise when compared to 2018. So the data security is a widely quoted barrier for cloud storage. Ciphering the confidential images before transmission and subsequent storage in cloud database needs critical attention for techno-specific applications. In image encryption, chaos-based keys can do better confusion, but the diffusion process using XOR is vulnerable to chosen plain text attack. The proposed colour image encryption scheme innately uses Deoxyribo Nucleic Acid coding that blends well with chaotic cryptosystem for an efficient statistical shift. The ciphered images are stored in authenticated and authorised cloud storage facilities. The experimentation is carried out with the help of Amazon Web Services storage instances. The proposed image encryption scheme offers a strong resistance towards the brute force, occlusion, statistical and differential attacks and yields near-zero correlation and good entropy.

Optimal service placement using Pseudo service chaining mechanism for cloud-based multimedia services

Network function virtualization based service chaining has recently received significant attention. In cloud environment, virtual machine typically be scheduled to load balancing among computing nodes. Therefore, the relation between virtual machines is not considered when it is scheduled. However, the location of virtual machines be able to affects network performance for intra virtual machines. Video/image processing and transmission are key processes for cloud-based over-the-top content provider. In this paper, we propose a pseudo service chaining mechanism for efficiently allocating computing nodes in a service chain to guarantee quality of services. The mechanism defines a pseudo service chain to reduce the network overhead which mainly causes performance degradation and places highly correlated virtual machines on the nearest computing node. The performance of proposed mechanism was evaluated on real cloud testbed.

A smart network and compute-aware Orchestrator to enhance QoS on cloud-based multimedia services

Rich-media applications deployed on cloud lead the use of the internet by people and organisations around the world. The advent of Software-Defined Networking (SDN), Networking Function Virtualisation (NFV), and Machine Learning (ML) techniques brings new possibilities to address carrier environment challenges, especially QoS. The literature does not show a smart and flexible solution that brings scalability with holistic management of network, compute, and application resources taking into account different approaches to enhance QoS. This paper presents the Orchestrator that manages agnostic network, compute, and applications resources hosted in the cloud. Through ML algorithms, our solution enhances QoS towards network resilience, bandwidth allocation based on real-time traffic, and end-to-end QoS mechanism to the event-driven scenario. The Orchestrator operation spans across different domains providing applications, virtual functions, and cloud resources management elastically. Our experimental evaluation in a large-scale testbed shows Orchestrator's capability to provide a smart jitter decrease using AI techniques.

A smart network and compute-aware Orchestrator to enhance QoS on cloud-based multimedia services

Rich-media applications deployed on cloud lead the use of the internet by people and organisations around the world. The advent of Software-Defined Networking (SDN), Networking Function Virtualisation (NFV), and Machine Learning (ML) techniques brings new possibilities to address carrier environment challenges, especially QoS. The literature does not show a smart and flexible solution that brings scalability with holistic management of network, compute, and application resources taking into account different approaches to enhance QoS. This paper presents the Orchestrator that manages agnostic network, compute, and applications resources hosted in the cloud. Through ML algorithms, our solution enhances QoS towards network resilience, bandwidth allocation based on real-time traffic, and end-to-end QoS mechanism to the event-driven scenario. The Orchestrator operation spans across different domains providing applications, virtual functions, and cloud resources management elastically. Our experimental evaluation in a large-scale testbed shows Orchestrator's capability to provide a smart jitter decrease using AI techniques.

Cloud-based multi-media systems for patient education and adherence: a pilot study to explore patient compliance with colonoscopy procedure preparation

ABSTRACT Technology based patient education and adherence approaches are increasingly utilized to instruct and remind patients to prepare correctly for medical procedures. This study examines the interaction between two primary factors: patterns of patient adherence to challenging medical preparation procedures; and the demonstrated, measurable potential for cloud-based multi-media information technology (IT) interventions to improve patient adherence. An IT artifact was developed through prior design science research to serve information, reminders, and online video instruction modules to patients. The application was tested with 297 patients who were assessed clinically by physicians. Results indicate modest potential (43.4% relative improvement) for the IT-based approach for improving patient adherence to endoscopy preparations. Purposively designed cloud-based applications hold promise for aiding patients with complex medical procedure preparation. Health care provider involvement in the design and evaluation of a patient application may be an effective strategy to produce medical evidence and encourage the adoption of adherence apps.

Energy-Efficient Device-to-Device Edge Computing Network: An Approach Offloading Both Traffic and Computation

Edge computing is expected to enable the cloud computing services in proximity and leverage the computational capacities at the edge of a network. Driven by massive data-hungry and energy-hungry applications, future wireless networks will need to provide various cloud-based multimedia services in a greener manner. However, limited computing capacity, wireless resources, and power supply prevent edge computing from evolving and maturing in terms of high sustainability and energy efficiency. In this article, we attempt to further improve the energy-efficient communication in mobile edge computing by proposing a device-to-device (D2D) offloading architecture. The D2D technique, in line with mobile edge computing, is responsible for offloading wireless traffic as well as computational tasks. Cooperative relay-aided transmission is also used in this architecture to pursue further improvements in a multi-cell scenario. Finally, offloading and balancing technologies are introduced to increase the total energy efficiency, decrease the inter/ intra-cell interference, deal with traffic/computational congestion, and provide better services to end users.

Toward Encrypted Video Tampering Detection and Localization Based on POB Number System Over Cloud

The unlimited growth in the amount of multimedia content has shifted the global infrastructure to the cloud-based multimedia hosting. However, the high probability of security breaches of the content is increasing demand for secure solutions toward this end. One such feasible solution is to encrypt the content to unreadable form before outsourcing to the cloud-based servers. In this paper, the media information, specifically the video content is distributed into multiple random shares based on the permutation ordered binary number system. The information remains fully concealed without any leakage at the cloud servers. Even if the attacks endanger the integrity of the information, the proposed scheme is enriched with the capability of chalking out accurately the altered pixels. These tampered regions are subsequently reflected in the reconstructed video frames obtained at authentic entity end possessing the secret keys required to build back the original content. Moreover, the proposed scheme is capable of detecting temporal attacks on the video frames by employing sequence-based authentication bits. The robustness of the proposed scheme has been validated under different attack scenarios and the scheme is found to be performing satisfactorily well.

Unevenness measurement using the support vector machine and dynamic multiservice load balancing with modified genetic algorithm in cloud-based multimedia system

Cloud computing is the most concerned and growing field in the real world, which is used by various fields to handle and manage the multimedia application due to availability of more amount of resources. At the time of multiple multimedia requests entering into the cloud server, fining and provisioning the required resources for the incoming multimedia requests of different king would be a more difficult task. In the existing system centralised hierarchical cloud-based multimedia system (CMS) is introduced which consists of elements such as resource manager, cluster head and server clusters. This word can assign the user required resources effectively with reduced cost. Also, existing research overcomes the load balancing problem which might occur at the time of multiple multimedia services with different characteristics entering into the system and allocating them in the server without considering their load capacity level using genetic algorithm. However, genetic algorithm would fail to find the optimal resource with optimal load level due to local search optimisation problem. A, load balancing cannot be done effectively in case of arrival of multimedia tasks with varying characteristics. This problem is resolved in the proposed research methodology by introducing the novel load balancing system in which both task unevenness problems and the genetic algorithms local search optimisation problems are resolved. This paper proposed an effective multimedia load balancing (MLB) scheme for CMS using support vector machine (SVM) and dynamic multiservice load balancing with adaptive genetic algorithm (AGA) (MLB-SVM-AGA). In this work, SVM is introduced for the purpose of quantifying the unevenness in the utilisation of multiple resources on a resource manager on the client side and confirmed at the server side in the each cluster. Unevenness scenario can be modelled as a mathematical hyperplane problem, but in most cases it is computationally intractable. Here, AGA is used for the purpose of solving the problem of dynamic load balancing. The experimental evaluation is conducted in the CloudSim toolkit for both proposed and existing research methodologies. The performance evaluation is done and the results demonstrate that AGA approach is able to dynamically spread the multimedia task load equally.

Unevenness measurement using the support vector machine and dynamic multiservice load balancing with modified genetic algorithm in cloud-based multimedia system

Unevenness measurement using the support vector machine and dynamic multiservice load balancing with modified genetic algorithm in cloud-based multimedia system

A (n, n) threshold non-expansible XOR based visual cryptography with unique meaningful shares

Cloud-based multimedia systems are attracting end users with high end infrastructure resources and enormous storage facilities. However, there persists a high chance of leakage of sensitive information if outsourced to the remote cloud servers without encryption. In this article, a secure way of obscuring the information based on XOR based Visual Cryptography (VC) has been proposed. It obscures the secret information into multiple meaningful shares prior to outsourcing which reduces the vulnerability of random looking shares to cryptanalysis. The scheme also overcomes the problems existing in the traditional VC schemes like pixel expansion, share alignment problem, contrast loss, explicit codebook requirement, limitation on number of participants and lossy recovery of the secret. Proposed scheme ensures perfect recovery of the secret with high boost in contrast at the authentic entity end. Three novel algorithms have been proposed for generation of basis matrices, generation of random shares and conversion of random shares to meaningful shares to achieve this objective. The visual quality of the meaningful shares and the recovered secret image is quite good as evaluated by various objective error metrics. Comparative results based on various aspects of visual cryptography prove the efficiancy of the proposed approach over existing state-of-the-art approaches.

Secure Cloud-Based Image Tampering Detection and Localization Using POB Number System

The benefits of high-end computation infrastructure facilities provided by cloud-based multimedia systems are attracting people all around the globe. However, such cloud-based systems possess security issues as third party servers become involved in them. Rendering data in an unreadable form so that no information is revealed to the cloud data centers will serve as the best solution to these security issues. One such image encryption scheme based on a Permutation Ordered Binary Number System has been proposed in this work. It distributes the image information in totally random shares, which can be stored at the cloud data centers. Further, the proposed scheme authenticates the shares at the pixel level. If any tampering is done at the cloud servers, the scheme can accurately identify the altered pixels via authentication bits and localizes the tampered area. The tampered portion is also reflected back in the reconstructed image that is obtained at the authentic user end. The experimental results validate the efficacy of the proposed scheme against various kinds of possible attacks, tested with a variety of images. The tamper detection accuracy has been computed on a pixel basis and found to be satisfactorily high for most of the tampering scenarios.

Multiservice Load Balancing with Hybrid Particle Swarm Optimization in Cloud-Based Multimedia Storage System with QoS Provision

Load balancing is a method of workload distribution across various computers or instruction data centres for maximizing throughput and minimizing work load on resources. To perform load balancing techniques in cloud computing environments, various challenges such as data security, and proper distribution exist which requires serious attention. The most important challenge posed by cloud applicationsis the provision of Quality of Service (QoS) provision as it develops the problem of resource allocation to the application so as to guarantee a service level along dimensions such as performance, availability and reliability. A centralized hierarchical Cloud-based Multimedia System (CMS) consisting of a resource manager, cluster heads, and server clusters is being considered by which the resource manager assigns clients' requests to server clusters for performing multimedia service tasks based on the job features after which each the job is assigned to the servers within its server cluster by the cluster head. Designing an effective load balancing algorithm for CMS however being a complicated and challenging task, enables spreading of multimedia service job load on servers at the minimal cost for transmitting multimedia data between server clusters and clients without exceeding the maximal load limit of each server cluster. In the present work, the Multiple Kernel Learning with Support Vector Machine (MKL-SVM) approach is proposed to quantify the disturbance in the utilization of multiple resources on a resource manager at client side and then verifying at the server side in the each cluster. Also, Fuzzy Simple Additive Weighting (FSAW) method is introduced for QoS provision for improving the system performance. The proposed model CMSdynMLB serves as the multiservice load balancing while considering the integer linear programming problem having unevenness measurement. In order to solve the problem of dynamic load balancing, Hybrid Particle Swarm Optimization (HSPO) is proposed as it holds well for dynamic problems. From the simulation results, it is determined that proposed MKL-SVM algorithm can efficiently manage the dynamic multiservice load balancing.

Fully Reversible Privacy Region Protection for Cloud Video Surveillance

Privacy becomes one of the major concerns of cloud-based multimedia applications such as cloud video surveillance. Privacy protection of surveillance videos aims to protect privacy information without hampering normal processing tasks of the cloud. Privacy Region Protection only protects the privacy region while keeping the non-privacy region visually intact to facilitate processing in the cloud. However, full reversibility, i.e. the complete recovery of the original video which is critical to digital investigation and law enforcement has not been properly addressed in privacy region protection. In this paper, we introduce fully reversible privacy region protection into cloud video surveillance and propose a novel fully reversible privacy protection method for H.264/AVC compressed video. All the operations are performed in the compressed domain and avoid lossy re-encoding, so the original H.264/AVC compressed video can be fully recovered. To our best knowledge, the proposed scheme is the first fully reversible one for privacy region protection. Experimental results and performance comparison demonstrate the effectiveness and efficiency of the proposed approach.

Literature Review of Faculty-perceived Usefulness of Instructional Technology in Classroom Dynamics

This article provides a literature review of the research concerning the role of faculty perspectives about instructional technology. Learning management systems, massive open online courses (MOOCs), cloud-based multimedia applications, and mobile apps represent the tools and the language of academia in the 21st century. Research examined illustrates how important understanding user attitudes can be in the technology deliberation process. Consequently, the faculty opinions should not be underestimated in technology acceptance discussions as coupled with ongoing education technological developments is the challenge of innovation adoption.

Time-Domain Attribute-Based Access Control for Cloud-Based Video Content Sharing: A Cryptographic Approach

With the ever-increasing demands on multimedia applications, cloud computing, due to its economical but powerful resources, is becoming a natural platform to process, store, and share multimedia contents. However, the employment of cloud computing also brings new security and privacy issues as few public cloud servers can be fully trusted by users. In this paper, we focus on how to securely share video contents to a certain group of people during a particular time period in cloud-based multimedia systems, and propose a cryptographic approach, a provably secure time-domain attribute-based access control (TAAC) scheme, to secure the cloud-based video content sharing. Specifically, we first propose a provably secure time-domain attribute-based encryption scheme by embedding the time into both the ciphertexts and the keys, such that only users who hold sufficient attributes in a specific time slot can decrypt the video contents. We also propose an efficient attribute updating method to achieve the dynamic change of users’ attributes, including granting new attributes, revoking previous attributes, and regranting previously revoked attributes. We further discuss on how to control those video contents that can be commonly accessed in multiple time slots and how to make special queries on video contents generated in previous time slots. The security analysis and performance evaluation show that TAAC is provably secure in generic group model and efficient in practice.

Distributed two-level cloud-based multimedia task scheduling

Recently, Multimedia cloud is emerging as a promising technology to effectively process multimedia services. A key problem in multimedia cloud is how to deal with task scheduling and load balancing to satisfy the quality of service demands of users. In this paper, we propose a two levels task scheduling mechanism for multimedia cloud to addresses the problem. The first level scheduling is from the users’ multimedia application to the data centers, and the second is from the data centers to servers. The data centers and virtual machines both are modeled as M/M/1 queuing systems. The algorithm proposed formulates the task-scheduling problem as cooperative game among data centers. Then we allocate the tasks received by a data center to servers using cooperative game again among servers. Various simulations are conducted to validate the efficiency of the proposed task scheduling approaches. The results showed that the proposed solutions provided better performance as compared to the existing approaches.

Security in Cloud based Multimedia System: A Review

Security in Cloud based Multimedia System: A Review

Pricing and resource allocation for multimedia social network in cloud environments

Distributed resource allocation is critical for efficient sharing multimedia contents in cloud-based wireless multimedia social network environments. In this paper, a cloud-based multimedia service system architecture is proposed to overcome the limited bandwidth allocation problem in the context of social network, in which bandwidth limited mobile users are allowed to directly acquire live multimedia streaming from the desktop users rather than the cloud based on their social relationships. We also present a theoretical framework for bandwidth allocation from desktop users to mobile users by a dynamic resource pricing process in the proposed bandwidth management system, where price-sensitive users and QoS-sensitive users are considered, and all users target at maximizing their total utilities. Finally, an iterative allocation algorithm is designed to simulate the bandwidth allocation process with respect to shared bandwidth and price. Simulation results verify the effectiveness of our proposed pricing model and allocation algorithm in terms of convergence and efficiency.