Web service adaptation: A decade’s overview

A prototype and demonstrator of Akogrimo’s architecture: An approach of merging grids, SOA, and the mobile Internet

For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

BackgroundDiagnosing HIV and/or TB is not sufficient; linkage to care and treatment is conditional to reduce the burden of disease. This study aimed to determine factors associated with linkage to HIV care and TB treatment at community-based services in Cape Town, South Africa.MethodsThis retrospective cohort study utilized routinely collected data from clients who utilized stand-alone (fixed site not attached to a health facility) and mobile HIV testing services in eight communities in the City of Cape Town Metropolitan district, between January 2008 and June 2012. Clients were included in the analysis if they were ≥12 years and had a known HIV status. Generalized estimating equations (GEE) logistic regression models were used to assess the association between determinants (sex, age, HIV testing service and co-infection status) and self-reported linkage to HIV care and/or TB treatment.ResultsLinkage to HIV care was 3 738/5 929 (63.1%). Linkage to HIV care was associated with the type of HIV testing service. Clients diagnosed with HIV at mobile services had a significantly reduced odds of linking to HIV care (aOR 0.7 (CI 95%: 0.6–0.8), p<0.001. Linkage to TB treatment was 210/275 (76.4%). Linkage to TB treatment was not associated with sex and service type, but was associated with age. Clients in older age groups were less likely to link to TB treatment compared to clients in the age group 12–24 years (all, p-value<0.05).ConclusionA large proportion of clients diagnosed with HIV at mobile services did not link to care. Almost a quarter of clients diagnosed with TB did not link to treatment. Integrated community-based HIV and TB testing services are efficient in diagnosing HIV and TB, but strategies to improve linkage to care are required to control these epidemics.

PurposeCloud computing becomes mobile when a mobile device tries to access the shared pool of computing resources provided by the cloud, on demand. Mobile applications may enrich their functionality by delegating heavy tasks to the clouds as the remote processing and storage have become possible by adding asynchronous behavior in the communication. However, developing mobile cloud applications involves working with services and APIs from different cloud vendors, which mostly are not interoperable across clouds. Moreover, by adding asynchronicity, mobile applications must rely on push mechanisms which are considered to be moderately reliable, and thus not recommended in scenarios that require high scalability and quality of service (QoS). To counter these problems, and the purpose of this paper, is to design a middleware framework, Mobile Cloud Middleware (MCM), which handles the interoperability issues and eases the use of process‐intensive services from smartphones by extending the concept of mobile host.Design/methodology/approachMCM is developed as an intermediary between the mobile and the cloud, which hides the complexity of dealing with multiple cloud services from mobiles. Several applications are presented to show the benefits of mobiles going cloud‐aware. Moreover, to verify the scalability of MCM, load tests are performed on the hybrid cloud resources using well known load balancing mechanisms like HAProxy and Tsung.FindingsFrom the study it was found that it is possible to handle hybrid cloud services from mobiles by using MCM. The analysis demonstrated that the MCM shows reasonable performance levels of interaction with the user, thus validating the proof of concept. Moreover, MCM decreases the effort in developing mobile cloud applications and helps in keeping soft‐real time responses by using its asynchronous approach.Originality/valueMCM fosters the utilization of different types of cloud services rather than the traditional mobile cloud services based on data synchronization. By offloading heavy tasks to the clouds, the framework extends the processing power and storage space capabilities of the constrained smart phones. The applications mentioned in the paper bring an added value by being success stories for mobile cloud computing domain in general.

As more and more mobile devices rely on cloud services since the introduction of cloud computing, data privacy has emerged as one of the most pressing security concerns. Users typically encrypt their important data before uploading it to cloud servers to safeguard data privacy, which makes data usage challenging. On the other side, this also increases the possibility of brand-new issues in cities. A clever, effective and efficient urban monitoring system is required to address possible challenges that may arise in urban settings. In the smart city concept, which makes use of sensors, one strategy that might be used in IoT and cloud computing is to monitor and gather data on problems that develop in cities in real-time. However, it will take a while and be rather expensive to install IoT and sensors throughout the city. The Mobile Crowd-Sensing (MCS) method is proposed to be used in this study to retrieve and gather data on issues that arise in metropolitan areas from citizen reports made using mobile devices. And we suggest a budget-constrained, reputation-based collaborative user recruitment (RCUR) procedure for a MCS system. To construct an edge-assisted MCS system in urban situations, we first integrate edge computing into MCS. We also examine how user reputation affects user recruitment. Finally, we create a collaborative sensing approach using the edge nodes’ sensing capabilities.

Computation offloading frameworks partition an application's execution between a cloud server and the mobile device to minimize its completion time on the mobile device. An important component of an offloading framework is the partitioning algorithm that decides which tasks to execute on mobile device or cloud server. The partitioning algorithm schedules tasks of a mobile application for execution either on mobile device or cloud server to minimize the application finish time. Most offloading frameworks partition parallel applications devices using an optimization solver which takes a lot of time. We show that by allowing duplicate execution of selected tasks on both the mobile device and the remote cloud server, a polynomial algorithm exists to determine a schedule that minimizes the completion time. We use simulation on both random data and traces to show the savings in both finish time and scheduling time over existing approaches. Our trace-driven simulation on benchmark applications shows that our algorithm reduces the scheduling time by 8 times compared to a standard optimization solver while guaranteeing minimum makespan.

Digital library cloud service is the development and extension of traditional digital library knowledge services under the environment of mobile cloud. Meanwhile, it’s also the new stage of knowledge services development. Based on the analysis of mobile cloud technology services model and its advantages, taking “end”, ”tube”, ”cloud” as the main line, this paper build up digital library mobile cloud knowledge services model from three aspects: knowledge collecting, knowledge processing and knowledge using. Thus, it point out the opportunities and challenges faced by mobile cloud services and present the strategies of implementing knowledge cloud services.

Mobile cloud computing is high technology that extends existing IT capabilities and requirements. And it can also access to shareable remote computing resources pool through the network. As the concept of mobile cloud, many providers have served the mobile cloud services using their own service policies. In other words, there is no formal definition of quality criteria for mobile cloud service evaluation. To solve this problem, some quality models are proposed for cloud service evaluation. However, those did not include many metrics to evaluate the services. Even if the model included a number of criteria, it is difficult to identify whether the metrics are proper or not. Furthermore, most existing models were not concerned about mobile characteristics. Therefore, we propose a cloud service integration model to solve the problem as we mentioned above. First, we select additional metrics to satisfy the mobile characteristics. Second, we present an extended SLA model for modeling complex service-dependencies in mobile cloud services. Third, we describe a method of discovering relations between the metrics of service belonging to mobile cloud services and then using these relations for establishing newly generated SLA.

Along with the popularity of mobile devices and advances in wireless technology, mobile services have become more and more prevalent. To the best of knowledge, despite the potential importance of lifestyle, little research has been performed on the effect of various lifestyle factors on mobile services diffusion, particularly in the Chinese context. This study examines the relationship between the lifestyles of Chinese consumers and the adoption of mobile services. Based on a sample of 313 respondents from the biggest city in central China, one can show that consumers with different lifestyles have different preferences related to a number of the identified mobile services. Furthermore, Chinese consumers are clustered into four lifestyle segments by two dimensions: the quality-awareness fashionable dimension and the economical dimension. The findings demonstrate that the quality-awareness fashionable dimension has stronger impact than the economical dimension toward the adoption of all the five types of mobile services.

A common vision of Augmented Reality (AR) is that of a person immersed in a diverse collection of virtual information, superimposed on their view of the world around them. If such a vision is to become reality, an ecosystem for AR must be created that satisfies at least these properties: multiple sources (or channels of interactive information) must be able to be simultaneously displayed and interacted with, channels must be isolated from each other (for security and stability), channel authors must have the flexibility to design the content and interactivity of their channel, and the application must fluidly integrate with the ever-growing cloud of systems and services that define our digital lives. In this paper, we present the design and implementation of the Argon AR Web Browser and describe our vision of an AR application environment that leverages the WWW ecosystem. We also describe KARML, our extension to KML (the spatial markup language for Google Earth and Maps), that supports the functionality required for mobile AR. We combine KARML with the full range of standard web technologies to create a standards-based web browser for mobile AR. KARML lets users develop 2D and 3D content using existing web technologies and facilitates easy deployment from standard web servers. We highlight a number of projects that have used Argon and point out the ways in which our web-based architecture has made previously impractical AR concepts possible.

Objectives: To implement a Service based Test Automation framework for testing web services in automotive Human Machine Interface (HMI) application. In this paper, we propose a service based test automation framework for web service testing in automotive HMI (Human Machine Interface). Methods: There are number of testing frameworks available for testing mobile application; In this proposal, an automated testing framework has been designed to test services which communicate between mobile application and real vehicles (cars) using differenttechnologies web services and mobility testing tools. Findings: In our findings, a customized mobile test automation framework has been developed which can eliminate most of the errors that may exist in manual testing by human and reduce a lot of time. Moreover, the framework can easily be extended to find a broader range of testing in automotive mobile applications. The techniques and mechanisms used in creating a test automation framework leads to analyze how they can do regression testing on mobile services in an efficient manner.

In the big data era, servitization becomes one of the important development trends of the IT world. More and more software resources are developed and existed in the format as services on the Internet. These services from multi-domains and multi-networks are converged as a huge complicated service network or ecosystem, which can be called as Big Service. How to reuse the abundant open service resources to rapidly develop the new applications or comprehensive service solutions to meet massive individualized customer requirements is a key issue in the big data and big service ecosystem. Based on analyzing the ecosystem of big service, this paper presents a new paradigm of software service engineering, Requirement-Engineering Two-Phase of Service Engineering Paradigm (RE2SEP), which includes service oriented requirement engineering, domain oriented service engineering, and the development approach of software services. By means of the RE2SEP approach, the adaptive service solutions can be efficiently designed and implemented to match the requirement propositions of massive individualized customers in Big Service ecosystem. A case study of the RE2SEP applications, which is a project on citizens mobility service in smart city environment, is also given in this paper. The RE2SEP paradigm will change the way of traditional life-cycle oriented software engineering, and lead a new approach of software service engineering.

This essay discusses the important role of deep learning technology in optimizing the mobile visual search function of digital library. On this basis, it analyzes the necessity and feasibility of constructing mobile visual search mechanism based on deep learning technology, and expounds the application of deep neural network to construct mobile visual search. The main content of the visual search mechanism, including mobile visual big data input layer, mobile visual resource organization layer, deep neural network analysis layer, and mobile visual service interaction layer, etc., are designed to enhance the value of mobile visual search in a big data environment and promote big data The realization of personalized intelligent services of the Times Digital Library.

Quality of cloud service (QoS) is one of the crucial factors for the success of cloud providers in mobile cloud computing. Context-awareness is a popular method for automatic awareness of the mobile environment and choosing the most suitable cloud provider. Lack of context information may harm the users’ confidence in the application rendering it useless. Thus, mobile devices need to be constantly aware of the environment and to test the performance of each cloud provider, which is inefficient and wastes energy. Crowdsourcing is a considerable technology to discover and select cloud services in order to provide intelligent, efficient, and stable discovering of services for mobile users based on group choice. This article introduces a crowdsourcing-based QoS supported mobile cloud service framework that fulfills mobile users’ satisfaction by sensing their context information and providing appropriate services to each of the users. Based on user's activity context, social context, service context, and device context, our framework dynamically adapts cloud service for the requests in different kinds of scenarios. The context-awareness based management approach efficiency achieves a reliable cloud service supported platform to supply the Quality of Service on mobile device.

With the progressions in Information and Communication Technology (ICT), the innumerable electronic devices (like smart sensors) and several software applications can proffer notable contributions to the challenges that are existent in monitoring plants. In the prevailing work, the segmentation accuracy and classification accuracy of the Disease Monitoring System (DMS), is low. So, the system doesn't properly monitor the plant diseases. To overcome such drawbacks, this paper proposed an efficient monitoring system for paddy leaves based on big data mining. The proposed model comprises 5 phases: 1) Image acquisition, 2) segmentation, 3) Feature extraction, 4) Feature Selection along with 5) Classification Validation. Primarily, consider the paddy leaf image which is taken as of the dataset as the input. Then, execute image acquisition phase where 3 steps like, i) transmute RGB image to grey scale image, ii) Normalization for high intensity, and iii) preprocessing utilizing Alpha-trimmed mean filter (ATMF) through which the noises are eradicated and its nature is the hybrid of the mean as well as median filters, are performed. Next, segment the resulting image using Fuzzy C-Means (i.e. FCM) Clustering Algorithm. FCM segments the diseased portion in the paddy leaves. In the next phase, features are extorted, and then the resulted features are chosen by utilizing Multi-Verse Optimization (MVO) algorithm. After completing feature selection, the chosen features are classified utilizing ANFIS (Adaptive Neuro-Fuzzy Inference System). Experiential results contrasted with the former SVM classifier (Support Vector Machine) and the prevailing methods in respect of precision, recall, F-measure,sensitivity accuracy, and specificity. In accuracy level, the proposed one has 97.28% but the prevailing techniques only offer 91.2% for SVM classifier, 85.3% for KNN and 88.78% for ANN. Hence, this proposed DMS has more accurate detection and classification process than the other methods. The proposed DMS evinces better accuracy when contrasting with the prevailing methods.