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The cloud computing center can dynamically respond to various needs, schedule computing resources, and provide users with convenient IT services. As the demand for cloud computing services continues to increase, the scale of the data center is getting larger and larger, and the problem of high energy consumption of equipment is becoming more and more prominent. Therefore, building a green data center is key to ensuring the development of the technology industry. Virtual machine online migration technology has been widely used in energy consumption management, which plays an important role in the energy‐saving management of large‐scale data centers. Considering the problem of energy consumption in a multi‐data center environment, a cross‐data center virtual machine migration strategy is proposed, EVMA. First, the target data center of the virtual machine migration is determined according to the bandwidth between data centers, and then the overload host and virtual machine selection strategy is determined according to the historical CPU load. The experimental results showed that the algorithm had a good performance in reducing the energy consumption of the data center and ensuring the quality of service. We proposed a virtual machine migration method for multiple data centers, as it related to the energy consumption and service quality of multiple data centers.

Accurate clustering of IoT devices is a promising challenge. We have observed that a few studies have been performed to address this challenge. However, they are expensive or do not shape accurate clustering. To fill this gap, in this study, we first solve a geometric version of a big challenge in pure mathematics: the NP-hard “Almost ” problem. Then, we solve it in a polynomial time. To clarify the concept, we present it as the “Two Disjoint Convex Hulls” challenge. We solve this challenge using two algorithms: the first is “Naive” and the second is faster than the “Naive” one can solve it in polynomial order, . In addition to providing a mathematical proof of our solution, we demonstrate its superior performance within an IoT industrial ecosystem.

Remote monitoring of large development projects using optical imagery is always challenging due to the spatial and temporal characteristics of the imagery and the spectral nature of detected projects. Hence, most urban projects are typically observed only after they have already undergone development. This study introduces a SAR coherence-based model that allows monitoring ongoing development projects using long time series. This model can approximately define when the project has been started and/or finished, periods of intensive work, and slow progress during the investigation period. The model relies on Sentinel-1 imagery to detect areas of development projects by classifying InSAR coherence over long time series using unsupervised learning. The model starts with interferometric coherence estimation, followed by a two-step unsupervised learning method: principal component analysis (PCA) and ISODATA clustering algorithms. The interpretation of the unsupervised classes was done using statistical trends of coherence and long-time series information. The classified map was interpreted into five main classes: stable, seasonal trend, positive first-order trend, negative first-order trend, and negative second-order trend. This model was tested on Greater Cairo, Egypt, by analyzing three years of monthly InSAR coherence time series, and the accuracy of the unsupervised classification map in the tested area was 92.7%. To validate the proposed model, information was gathered from 39 development projects in Greater Cairo, and the model’s efficacy in detecting these projects was evaluated. The validation demonstrated that the model was able to detect changes in 38. The model shows a strong ability to detect building construction projects, building demolition-induced new-axis roads, and most bridge constructions. This study extended the importance of InSAR coherence to a new level by utilizing long time series information for detailed monitoring of urban project development from space. With the global coverage advantage of Sentinel-1 imagery, this model is applicable for monitoring development projects in any city worldwide.

This study presents a novel workflow for automated Digital Terrain Model (DTM) extraction from Airborne LiDAR point clouds based on a convolutional neural network (CNN), considering a transfer learning approach. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and interpolation. First, each point is transformed into a featured image based on its elevation differences with neighboring points. Then, the feature images are classified into ground and non-ground using ImageNet pretrained ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Last, the extracted ground points are interpolated to generate a continuous elevation surface. We compared the proposed workflow with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progressive Triangulated Irregular Network Densification (PTD). Our results show that the proposed workflow establishes an advantageous DTM extraction accuracy with yields of only 0.52%, 4.84%, and 2.43% for Type I, Type II, and the total error, respectively. In comparison, Type I, Type II, and the total error for PMF are 7.82%, 11.60%, and 9.48% and for PTD 1.55%, 5.37%, and 3.22%, respectively. The root means square error (RMSE) for the 1 m resolution interpolated DTM is only 7.3 cm. Moreover, we conducted a qualitative analysis to investigate the reliability and limitations of the proposed workflow.

In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. With the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. In this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. Mobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)-based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19. Mobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients.

[This corrects the article DOI: 10.2196/44406.].

The tracking problem for continuous-time systems is investigated. It is assumed that the states of the systems are not available. An observer is firstly designed to estimate the states by using the ℋ∞ method. The control action is consist of a state-feedback control, an integral component, and a feedforward loop. The linear-matrix-inequality region is used to constrain the eigenvalue location for the closed-loop systems. The control gains can be obtained by solving a sequence of linear matrix inequalities (LMIs) which can guarantee the mixed ℋ2/ℋ∞ performance for the closed-loop systems.

Personalized information retrieval is an effective tool to solve the problem of information overload. Along with the rapid development of emerging network technologies such as cloud computing, however, network servers are becoming more and more untrusted, resulting in a serious threat to user privacy of personalized information retrieval. In this paper, we propose a basic framework for the comprehensive protection of all kinds of user privacy in personalized information retrieval. Its basic idea is to construct and submit a group of well-designed dummy requests together with each user request to the server, to mix up the user requests and then cover up the user privacy behind the requests. Also, the framework includes a privacy model and its implementation algorithm. Finally, theoretical analysis and experimental evaluation demonstrate that the framework can comprehensively improve the security of all kinds of user privacy, without compromising the availability of personalized information retrieval.

PurposeIn this paper, the authors propose an effective mechanism for the protection of digital library readers' lending privacy under a cloud environment.Design/methodology/approachThe basic idea of the method is that for each literature circulation record, before being submitted to the untrusted cloud database of a digital library for storage, its reader number has to be encrypted strictly at a client, so as to make it unable for an attacker at the cloud to know the specific reader associated with each circulation record and thus protect readers' lending privacy. Moreover, the authors design an effective method for querying the encrypted literature circulation records, so as to ensure the accuracy and efficiency of each kind of database queries related to the encrypted reader number field of literature circulation records.FindingsFinally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed methods.Originality/valueThis paper presents the first study attempt to the privacy protection of readers' literature circulations, which can improve the security of readers' lending privacy in the untrusted cloud, without compromising the accuracy and efficiency of each kind of database queries in the digital library. It is of positive significance to construct a privacy-preserving digital library platform.

PurposeFirst, the authors analyze the key problems faced by the protection of digital library readers' data privacy and behavior privacy. Second, the authors introduce the characteristics of all kinds of existing approaches to privacy protection and their application limitations in the protection of readers' data privacy and behavior privacy. Lastly, the authors compare the advantages and disadvantages of each kind of existing approaches in terms of security, efficiency, accuracy and practicality and analyze the challenges faced by the protection of digital library reader privacy.Design/methodology/approachIn this paper, the authors review a number of research achievements relevant to privacy protection and analyze and evaluate the application limitations of them in the reader privacy protection of a digital library, consequently, establishing the constraints that an ideal approach to library reader privacy protection should meet, so as to provide references for the follow-up research of the problem.FindingsAs a result, the authors conclude that an ideal approach to reader privacy protection should be able to comprehensively improve the security of all kinds of readers' privacy information on the untrusted server-side as a whole, under the premise of not changing the architecture, efficiency, accuracy and practicality of a digital library system.Originality/valueAlong with the rapid development of new network technologies, such as cloud computing, the server-side of a digital library is becoming more and more untrustworthy, thereby, posing a serious threat to the privacy of library readers. In fact, the problem of reader privacy has become one of the important obstacles to the further development and application of digital libraries.