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As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services.

In recent years, governments have paid much more attention to online learning platforms. This study establishes a high-performance agricultural digital learning platform (hereafter referred to as the Platform), in an attempt to (1) achieve learning diversity, improve users' learning ability and willingness to learn, and unblocking geo-restrictions, and (2) design cloud-based e-learning materials and assess their learning effectiveness through the use of satisfaction surveys. A cloud-based high-efficiency online course platform for the agricultural community uses a real-time streaming analysis to determine the network string flow during users' video playback and enables dynamic allocation to obtain the best server effectiveness. Users can remain motivated while taking high-quality online courses. With the establishment of cloud-based course materials, and the specificity of agricultural information, we hope to improve users' learning motivation and learning effectiveness. Furthermore, this study uses satisfaction surveys and the UTAUT model to assess the research findings according to whether the users' performance expectancy, effort expectancy, social influence, and facilitating conditions have a positive impact on the perceived satisfaction and perceived usefulness. We verified this point and inferred that digital learning can support the agricultural community significantly. [ABSTRACT FROM AUTHOR]

While cloud computing services such as cloud storage are fairly mature, it remains challenging to design efficient and secure cryptographic schemes to facilitate fine‐grained access control and achieve other features. For example, existing ciphertext‐policy attribute‐based encryption schemes do not generally have in‐place limits on the number of access or access duration, which can consequently be exploited to perform economic denial of sustainability and other attacks. In addition, improving system efficiency can be challenging in large‐scale operations. Thus, in this paper, we present a blockchain‐based access control with k $k$‐times tamper resistance. Our proposed approach allows one to set and enforce quota/limits on the number of accesses allowed for each user, whose integrity is ensured using blockchain. In addition, our proposed approach also allows multiple attribute authorities to coexist and work together with a central authority to facilitate secret key distribution and improve system efficiency. We then evaluate the security and the efficiency of our proposed approach to demonstrate its utility. [ABSTRACT FROM AUTHOR]

Cognitive computing over big data brings more development opportunities for enterprises and organizations in industrial informatics, and can make better decisions for them when they face data security challenges. To satisfy the requirement of real-time data storage in industrial Internet of Things (IoT), the remote unconstrained storage cloud is usually used to store the generated big data. However, the characteristic of semitrust of the cloud service provider determines that the data owners will worry about whether the data stored in cloud computing has been corrupted. In this article, a secure storage auditing is proposed, which supports efficient key updates and can be well used in cognitive industrial IoT environment. Moreover, the proposed basic auditing can be extended to support batch auditing that is suitable for multiple end devices to audit their data blocks simultaneously in practice. In addition, a hybrid data dynamics method is proposed, which employs a hash table to store the data blocks and uses a linked list to locate the operated data block. Compared with previous methods, the data block location time in the proposed data dynamics can be reduced by 40%. The security analysis results demonstrate that the proposed scheme can be proved to be correct, and is secure under computational differ-hellman (CDH) and discrete logarithm (DL) assumptions. [ABSTRACT FROM AUTHOR]

Since traffic in networks is growing rapidly, it is difficult for the existing network architecture to support the huge traffic requirement. This article proposes a novel intelligent architecture as a promising paradigm for B5G heterogeneous networks to optimize network resource usage and network performance. The main idea is to build a suitable network model through AI, and integrate edge computing and cloud computing to improve computing performance. In addition, this article gives appropriate recommendations of the deep learning method for different network issues. Since the deep learning method requires a large amount of computing resources, the network resource allocation needs to be paid attention to in this architecture. For complex environments of B5G heterogeneous networks, integrated packet forwarding is one potential technology to improve quality of service. Moreover, we discuss the challenges and open issues for B5G. [ABSTRACT FROM AUTHOR]

With the growing popularity of mobile devices, a new type of peer-to-peer communication mode for mobile cloud computing has been introduced. By applying a variety of short-range wireless communication technologies to establish connections with nearby mobile devices, we can construct a mobile cloudlet in which each mobile device can either works as a computing service provider or a service requester. Although the paradigm of mobile cloudlet is cost-efficient in handling computation-intensive tasks, the understanding of its corresponding service mode from a theoretic perspective is still in its infancy. In this paper, we first propose a new mobile cloudlet-assisted service mode named Opportunistic task Scheduling over Co-located Clouds (OSCC), which achieves flexible cost-delay tradeoffs between conventional remote cloud service mode and mobile cloudlets service mode. Then, we perform detailed analytic studies for OSCC mode, and solve the energy minimization problem by compromising among remote cloud mode, mobile cloudlets mode and OSCC mode. We also conduct extensive simulations to verify the effectiveness of the proposed OSCC mode, and analyze its applicability. Moreover, experimental results show that when the ratio of data size after task execution over original data size associated with the task is smaller than 1 (i.e., [ABSTRACT FROM AUTHOR]

Nowadays, Software Defined Network (SDN) develops rapidly for its novel structure which separates the control plane and the data plane of network devices. Many researchers devoted themselves to the study of such a special network. However, some limitations restrict the development of SDN. On the one hand, the single controller in the conventional model bears all threats, and the corruption of it will result in network paralysis. On the other hand, the data will be increasing more in SDN switches in the data plane, while the storage space of these switches is limited. In order to solve the mentioned issues, we propose two corresponding protocols in this paper. Specifically, one is an anonymous protocol in the control plane, and the other is a verifiable outsourcing protocol in the data plane. The evaluation indicates that our protocol is correct, secure, and efficient. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud technology, many services have been transferred from local computers to the cloud-based platform, which decreases the amount of computation done on the former. The local computer could thus be developed in the direction of portability and power saving. Graphics processing, apart from providing user interfaces featuring diversified special effects, is also significant in terms of application programs and play interactions. It is exactly on the basis of the concept of graphics processing that cloud-support rendering is developed, which is aimed to improve the graphics efficiency in mobile devices, via the graphics processing units in the cloud-based platform. The cloud-based platform and the mobile devices are usually connected by the Internet; however, as remote rendering might call for greater network bandwidth, its efficiency will be compromised if the network bandwidth is not stable. Given this limitation, this paper sets out to propose a quality-of-service-aware resource allocation strategy for mobile 3D graphics rendering, which is a hybrid rendering technology combining the client-side graphics processing capabilities with the graphics processing units in the cloud-based platform. When network bandwidth is not stable, the technology is able to assess the current network bandwidth, and dynamically configure the rendered frames on the client side and cloud-based platforms. Even when the client side could not access the network, it would still be possible to carry out the drawing through the graphics processing units on the local computer. Three applications are tested in this research: the technology can increase the frame rate by an average of 44.99% when the bandwidth is 10% greater than the minimum limit, by an average of 44.57% when the bandwidth is less than the minimum limit, by an average of 30.86% when the bandwidth is 10% less than the minimum limit, and by an average of 33.74% when the bandwidth is not stable. [ABSTRACT FROM PUBLISHER]

With hundreds of TV channels, a good TV program recommendation system can save time. Hadoop fair scheduler cloud computing is designed to make information processing and filtering effective and scalable. In cloud computing, computers are connected over a network and perform computation simultaneously; more computation power can be obtained by adding more computer nodes. In the present study, cloud computing is used to build a TV program recommendation system. A fair scheduler cloud structure is applied to improve the system performance. For program recommendation, the K-means recursive clustering algorithm is used for user clustering, the term frequency/inverse document frequency algorithm is applied for finding related popular programs, and k-nearest neighbor is used to recommend programs. Most TV program recommendation systems focus on providing a personal recommendation system. The proposed system also considers user groups and the program watching preferences of the majority. The proposed fair scheduler cloud-based architecture is scalable; a massive amount of information can be processed in real-time to obtain program recommendation results that can represent almost all users. [ABSTRACT FROM AUTHOR]

As mobile devices are equipped with more memory and computational capability, a novel peer-to-peer communication model for mobile cloud computing is proposed to interconnect nearby mobile devices through various short range radio communication technologies to form mobile cloudlets, where every mobile device works as either a computational service provider or a client of a service requester. Though this kind of computation offloading benefits compute-intensive applications, the corresponding service models and analytics tools are remaining open issues. In this paper we categorize computation offloading into three modes: remote cloud service mode, connected ad hoc cloudlet service mode, and opportunistic ad hoc cloudlet service mode. We also conduct a detailed analytic study for the proposed three modes of computation offloading at ad hoc cloudlet. [ABSTRACT FROM PUBLISHER]

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction. [ABSTRACT FROM PUBLISHER]

Abstract: In recent years, cloud computing technology has matured significantly, as has the development of digital TV services. This, therefore, has led to an increased demand for improved quality TV services. In this paper, cloud computing technology is used to build a program recommendation system for digital TV programs, and the Hadoop Fair Scheduler is utilized to improve processing performance. Historical data of watched TV programs are collected through an electronic program guide, and then processed using K-means clustering, term frequency/inverse document frequency and k-nearest neighbor algorithms, to obtain clusters of audience groups and to find popular TV programs for each cluster. The proposed system can process massive amounts of user data in real-time, and can easily be scaled up. [Copyright &y& Elsevier]

Cloud services have attracted much attention in recent years as networking systems for the information connection devices of organizations or enterprises around the world have become increasingly sophisticated. Cloud services also provide a variety of services for users, including virtual hosting services, storage services, and Web application services. Distributed computing and the peer-to-peer (P2P) network are two important elements in cloud computing. However, the huge numbers of information devices currently in use present some new problems, such as the lack of IPv4 protocol addresses. Therefore, the IPv6 protocol was proposed. During the transition period from pure IPv4 to pure IPv6, IPv4 and IPv6 will coexist on the Internet. To link independent dual-stack P2P networks around the world, we herein propose a mechanism to improve the service discovery in dual-stack cloud file service, thus decreasing the amount of protocol translation and improving the efficiency of the bandwidth and load balance. From the simulation results, we conclude that the proposed mechanism can achieve higher connection success rate, efficient bandwidth, and a lower connection build time. Our paper indicates that the connection success rate and efficient bandwidth outperforms the other schemes by approximately 20% and 24%, respectively. [ABSTRACT FROM PUBLISHER]