Rich Cloud Research Articles

Development and Application of Concentric Lock Platform Under Digital Cultural Tourism Product Innovation

With the global spread of the COVID-19 epidemic, the traditional tourism industry has suffered an unprecedented impact, pushing the tourism industry towards digital transformation. As an important direction of this transformation, digital cultural tourism (digital Literature & Tourism) utilizes the latest digital technologies such as 3D simulation, Virtual Reality (VR), Augmented Reality (AR), etc., to provide a brand-new form of cultural and tourism experience. Taking the concentric lock platform as an example, this study explores the development and application under the innovation of digital cultural and tourism products, analyzes its contribution to the recovery of tourism, cultural inheritance and innovation, and puts forward the outlook for future development. Designed with the concepts of emotional interaction, cultural inheritance and technological innovation, the concentric lock platform provides a space where users can padlock their locks in a virtual environment to express their emotions and share their stories. The platform not only redefines the emotional connection between couples, but also provides a new profit model and cultural inheritance for tourist attractions. In terms of technical realization, the platform adopts cloud computing to support high concurrent access, and utilizes technologies such as WebGL and Three.js to render complex 3D graphics in web browsers to create realistic virtual attraction models for users, while focusing on data security and privacy protection. The platform's functional modules include the padlocking and viewing of virtual concentric locks, the withdrawal process of breaking up locks, as well as a rich cloud tourism experience and cultural heritage content, which meets the diversified needs of users. By cooperating with tourist attractions, the concentric lock platform developed the virtual concentric lock function with attractions as the background, promoting cultural heritage and tourism recovery. Especially during the epidemic, the platform provided global users with the possibility of cloud tourism and virtual emotional interaction, effectively mitigating the impact of the tourism industry affected by the epidemic. In the Discussion and Outlook section, this study points out that the concentric lock platform and similar digital cultural and tourism products face challenges and opportunities in terms of technological development, market demand, cultural heritage and social impact. In the future, with the application of new technologies such as 5G and artificial intelligence, more personalized and interactive cultural tourism experiences are expected to be developed. At the same time, how to effectively integrate online experiences with on-the-ground tourism activities, protect user privacy and ensure data security will be the key issues to be solved for digital cultural tourism products such as the concentric lock platform. To summarize, the concentric lock platform, as a typical case of digital cultural tourism, not only shows the potential of digital technology application in the field of cultural tourism, but also provides an effective strategy and practice case for the digital transformation of the tourism industry. With the continuous progress of technology and increasing consumer demand, the future development of the digital cultural tourism field will be broader, providing people with richer and deeper cultural tourism experience.

A Survey on Edge Intelligence and Lightweight Machine Learning Support for Future Applications and Services

As the number of devices connected to the Internet has grown larger, so too has the intensity of the tasks that these devices need to perform. Modern networks are more frequently working to perform computationally intensive tasks on low-power devices and low-end hardware. Current architectures and platforms tend towards centralized and resource-rich cloud computing approaches to address these deficits. However, edge computing presents a much more viable and flexible alternative. Edge computing refers to a distributed and decentralized network architecture in which demanding tasks such as image recognition, smart city services, and high-intensity data processing tasks can be distributed over a number of integrated network devices. In this article, we provide a comprehensive survey for emerging edge intelligence applications, lightweight machine learning algorithms, and their support for future applications and services. We start by analyzing the rise of cloud computing, discuss its weak points, and identify situations in which edge computing provides advantages over traditional cloud computing architectures. We then divulge details of the survey: the first section identifies opportunities and domains for edge computing growth, the second identifies algorithms and approaches that can be used to enhance edge intelligence implementations, and the third specifically analyzes situations in which edge intelligence can be enhanced using any of the aforementioned algorithms or approaches. In this third section, lightweight machine learning approaches are detailed. A more in-depth analysis and discussion of future developments follows. The primary discourse of this article is in service of an effort to ensure that appropriate approaches are applied adequately to artificial intelligence implementations in edge systems, mainly, the lightweight machine learning approaches.

Dynamic Multiworkflow Offloading and Scheduling Under Soft Deadlines in the Cloud-Edge Environment

Computing offloading and scheduling are emerging, accompanied by smart city services, Internet of Things, and other rich cloud services. Cloud-assisted mobile edge computing provides malleable resources but introduces considerable overhead. Due to the geographical distributions and distances of the edge, cloud, and mobile terminal, promoting cost efficiency and reducing the makespan of applications are urgent targets. For multiple mobile applications, the soft deadline constraint for each application is common in the edge-cloud environment. When multiple users initiate application requests, scheduling on the target offloaded platform commences. Some workflows, especially newly arrived workflows, might suffer from “starving to death,” which has the risk of being missed. This article describes a dynamic, multiworkflow offloading, and scheduling, adaptive heterogeneous earliest-finish-time-based algorithm, which can lightly run on a distributed network. We explore an innovative model composed of a multiobjective makespan and cost for a soft deadline constraint optimization problem. Abundant data are adopted to test the offloading and scheduling effect on a variety of platform parameters, and comparative experiments demonstrate the validity and efficiency of the adaptive system.

A Pair-Task Heuristic for Scheduling Tasks in Heterogeneous Multi-cloud Environment

Heterogeneous multi-cloud environments make use of a collection of diverse performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications in the multi-cloud environment require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Scheduling tasks to distributed heterogeneous clouds is termed NP-complete which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of ‘n’ tasks in two groups among a set of 'm' clouds, three heuristicsPair-Task Threshold Limit (PTL), PTMax-Min, and PTMin-Max are proposed. Firstly, proposedheuristics calculate tasks threshold valuebased on the tasks attributes to determine the tasks scheduling order and then tasks are sorted in descending order of threshold value. Group 1 comprises ([n/2]) tasks ordered in descending value of threshold. Group 2 comprises remaining tasks ([n/2] − 1) ordered in ascending value of threshold. Secondly, tasks form group 1 are scheduled first based on minimum completion time, and then tasks in group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. The proposed approaches PTL, PTMax-Min, and PTMin-Max explicitly shows the better results in terms of reduced makespan, completion time, response time and more resource utilization compared to MCT, MET, and Min-min.

ECG diagnosis for arrhythmia detection with a cloud-based service and a wearable sensor network in a smart city environment

Irregular heartbeats are a primary indicator of Cardiovascular Disease (CVD), which is the leading cause of death in a developing smart city environment. Wearable devices can reliably monitor cardiac beats by producing Electrocardiogram (ECG) readings. The considerable value gained from a wireless wearable system allows for remote ECG assessment with continuous real-time functionality. The data collected from the wearable sensor network in the smart city platform gives timely alarms and treatment that could save lives. Cloud-based ECG methods can be accurate to a certain extent, as latency is still an existing problem. Cloud-based portals linked immediately to wearable devices can provide numerous advantages, such as reduced latency and a good level of service. Therefore, a novel cloud-based arrhythmia detection using the Recurrent Neural Network (RNN) (NC-RNN) method has been proposed for the ECG diagnosis with a wearable sensor in the smart city environment. The ECG signal collected from the wearable sensor involves three phase diagnosis stage. R-peak detection techniques are used for preliminary diagnostics in edge devices. The ECG signals are then classified using RNN at the edge device, with the severity of irregular beat detected in the ECG signal. Finally, a cloud platform classification method can evaluate the obtained ECG signals. While the proposed method's training session is runnable on the technically rich Cloud data centers, the interpretation unit is deployed over the cloud infrastructure for evaluating the ECG signals and setting off the emergency remedies with minimum latency. The simulation results of the suggested framework can accomplish effective ECG detection via wearable devices with high accuracy and less latency.

Hybrid multi criteria decision methods for optimal cloud selection in mobile cloud computing

Cloud computing provides the users with flexibility of using computational systems on demand at nominal cost. Execution of resource intensive task on mobile devices is still a big challenge due to resource constraints of mobile devices. Utilizing the servi ces of resource rich cloud servers for offloading the task from mobile device can overcome the resource constraint issues of mobile devices. When offloading the task, there is a need to choose an optimal cloud server from a pool of available cloud servers offering similar services. The proposed work uses three hybrid multicriteria decision methods for choosing the optimal cloud server and comparative analysis of the three methods are presented. Real time cloud servers located at four different regions are c onsidered with six quality of service ( QoS) attributes. The results indicate that all the three methods are viable solutions in selecting the optimal cloud server with multi - objective optimization on the basis of ratio anal ysis (MOORA) providing a faster response compared to other two methods. Sensitivity analysis performed proves the correctness and effectiveness of the proposed methodology.

Voices of the Rainforest: A Day in the Life of Bosavi Papua New Guinea

Voices of the Rainforest: A Day in the Life of Bosavi Papua New Guinea

Interaction of transmutation products with precipitates, dislocations and grain boundaries in neutron irradiated W

Tungsten is the primary candidate materials for the high neutron flux, high temperature components of a future demonstration fusion reactor. Despite this, there is a lack of data on W under fusion relevant neutron doses and irradiation temperatures. In this study, single crystal and polycrystalline W samples irradiated at the High Flux Reactor (HFR) at 900 ∘C were characterised using Atom Probe Tomography (APT) and Scanning Transmission Electron Microscopy (STEM). Bulk chemical and isotopic concentration predictions were validated by analysing the mass spectrum from APT experiments. A post irradiation composition of W-1.26 ± 0.15 at.%Re - 0.08 ± 0.02 at.%Os - 0.01 ± 0.01 at.%Ta was measured in the single crystal sample, whereas W-1.09 ± 0.07 at.%Re - 0.08 ± 0.02 at.%Os - 0.01 ± 0.01 at.%Ta was measured for the polycrystalline. APT and STEM showed that a high number density of Re and Os rich precipitates had formed under neutron irradiation. These typically consisted of a core rich in Re and Os, surrounded by a less dense Re rich cloud. Multiple analysis methods were applied to investigate the composition of these clusters. APT showed that the centres of some of the precipitates had a rod shaped core which were rich in both Re and Os. Line profile analysis suggests that in the centre of the precipitates, the threshold composition for σ phase formation may have been reached, as has been observed in higher transmutation rate experiments. In addition, dislocations, sub grain boundaries and dislocation loops were all shown to be decorated with both Re and Os, in agreement with predictions from DFT simulations.

A Double Auction Mechanism for Resource Allocation in Coded Vehicular Edge Computing

The development of smart vehicles and rich cloud services have led to the emergence of vehicular edge computing. To perform the distributed computation tasks efficiently, Coded Distributed Computing (CDC) was proposed to reduce communication costs and mitigate the straggler effects through the use of coding techniques. In this paper, we propose a double auction mechanism to allocate the resources of the edge servers to the vehicles in order to complete the CDC tasks. Specifically, the vehicles use the PolyDot codes to manage the tradeoff between communication costs and recovery threshold. Given the requirements of various vehicles, the double auction mechanism matches the edge servers with the required resources to the vehicles. Besides, the double auction mechanism also determines the prices that the vehicles need to pay for the resources of the edge servers. The analyses show that the double auction mechanism satisfies the properties of individual rationality, incentive compatibility and budget-balance. From the simulation, the utility of auctioneer increases when the number of vehicles and edge servers increases.

Study for Food Recognition System Using Deep Learning

Accurate dietary appraisal has been found by literature to be very significant in the evaluation of weight loss treatments. Most current methods of dietary evaluation, however, depend on recollection. The development of a modern computer-based food recognition system for reliable food evaluation is now possible across comprehensive mobile devices as well as rich Cloud services. Fixing the problem of food detection and identification in photos of different kinds of foods. Given the variety of food products with low inter-and high intra-class variations and the limited information in a single picture, the problem is complicated. By propose the overall application of multiple fusion-trained classifiers to achieve increased identification and recognition capabilities on characteristics obtained from various deep models. This paper studied various techniques of food recognition using different approaches and based on several variables, compared their effectiveness. Our study results demonstrate that deep learning overcomes other strategies like manual feature extractors, standard ML algorithms, as well as DL as a practical tool for food hygiene and safety inspections.

A Review on Offloading and Security Schemes in Mobile Cloud Computing

In recent years, Mobile Cloud Computing (MCC) has become a major computing system where the mobile device users utilize the multiple services from Cloud Service Providers. MCC can provide flexible delivery of services with cost effectiveness to mobile cloud users using the internet. In MCC, the limitations such as processing capabilities, memory requirements and battery utilization in Smart Mobile Devices can be tackled by Offloading the Computations to resource rich Cloud Infrastructures. Due to the benefits of cost efficiency and flexibility several, investigations and studies have been of great interest to address issues in MCC. In this context, this paper reviews the major critical issues of research in computation offloading and security schemes in MCC. Each of this issue is investigated critically considering recent trends and comparative assessments. Analysis of parameters and metrics of the existing techniques are presented.

A real-world service mashup platform based on data integration, information synthesis, and knowledge fusion

ABSTRACT Service-oriented architecture (SOA) provides a flexible IT infrastructure in order to deal with global competition. However, real-world services change their architectures and functions frequently. This work uses data integration, information synthesis, and knowledge fusion to analyze SOA evolution and complexity. A quantificational approach is provided to estimate the change effects on a service-oriented system. We established a cloud service community to provide rich cloud services and a service mashup platform that connects API service providers and service users. The platform user can invoke all API services on the platform without jumping to the third-party website. The platform users can filter and accept the services recommended by the platform, and also freely compose the services which are real-world services. Data integration, information synthesis, and knowledge fusion are applied to mashups that users constructed, helping users avoid complexity and unreliability that is our objective. We also showed that our method achieved significant quantitative results.

Secure Deduplication System with Active Key Update and Its Application in IoT

The rich cloud services in the Internet of Things create certain needs for edge computing, in which devices should be able to handle storage tasks securely, reliably, and efficiently. When processing the storage requests from edge devices, each cloud server is supposed to eliminate duplicate copies of repeating data to reduce the amount of storage space and save on bandwidth. To protect data confidentiality while supporting deduplication, some convergent-encryption-based techniques have been proposed to encrypt the data before uploading. However, all these works cannot meet two requirements while preventing brute-force attacks: (i) power-constrained edge nodes should update encryption keys efficiently when an edge node is abandoned; and (ii) the access privacy of edge nodes should be guaranteed. In this article, we propose a novel encryption scheme for secure chunk-level deduplication. Based on this scheme, we present two constructions of the secure deduplication system that support an efficient key update protocol. The key update protocol does not involve any edge node in computational tasks, so that the deduplication system can adopt an active key update strategy. Moreover, one of our constructions, which is called advance construction, can provide access privacy assurances for edge nodes. The security analysis is given in terms of the proposed threat model. The experimental analysis demonstrates that the proposed deduplication system is practical.

Guest Editor's Introduction: Special Section on Fog/Edge Computing and Services

The papers in this special section focus on fog computing and services. The emerging Internet of Things (IoT) and rich cloud services have helped create the need for fog computing (also known as edge computing), in which data processing occurs in part at the network edge or anywhere along the cloud-to-endpoint continuum that can best meet user requirements, rather than completely in a relatively small number of massive clouds. Fog computing could address latency concerns, devices’ limited processing and storage capabilities and battery life, network bandwidth constraints and costs, and many security and privacy concerns that arise from the emerging IoT.

Life-Tags

We introduce "Life-Tags," a wearable, smartglasses-based system for abstracting life in the form of clouds of tags and concepts automatically extracted from snapshots of the visual reality recorded by wearable video cameras. Life-Tags summarizes users' life experiences using word clouds, highlighting the "executive summary" of what the visual experience felt like for the smartglasses user during some period of time, such as a specific day, week, month, or the last hour. In this paper, we focus on (i) design criteria and principles of operation for Life-Tags, such as its first-person, eye-level perspective for recording life, passive logging mode, and privacy-oriented operation, as well as on (ii) technical and engineering aspects for implementing Life-Tags, such as the block architecture diagram highlighting devices, software modules, third-party services, and dataflows. We also conduct a technical evaluation of Life-Tags and report results from a controlled experiment that generated 21,600 full HD snapshots from six indoor and outdoor scenarios, representative of everyday life activities, such as walking, eating, traveling, etc., with a total of 180 minutes of recorded life to abstract with tag clouds. Our experimental results and Life-Tags prototype inform design and engineering of future life abstracting systems based on smartglasses and wearable video cameras to ensure effective generation of rich clouds of concepts, reflective of the visual experience of the smartglasses user.

Automated Detection of Explosive Volcanic Eruptions Using Satellite‐Derived Cloud Vertical Growth Rates

AbstractAsh rich clouds, produced by explosive volcanic eruptions, are a major hazard to aviation. Unfortunately, explosive volcanic eruptions are not always detected in a timely manner in satellite data. The large optical depth of emergent volcanic clouds greatly limits the effectiveness of multispectral infrared‐based techniques for distinguishing between volcanic and nonvolcanic clouds. Shortwave radiation‐based techniques require sufficient sunlight and large amounts of volcanic ash, relative to hydrometeors, to be effective. Given these fundamental limitations, a new automated technique for detecting emergent clouds, produced by explosive volcanic eruptions, has been developed. The Cloud Growth Anomaly (CGA) technique utilizes geostationary satellite data to identify cloud objects, near volcanoes, that are growing rapidly in the vertical relative to clouds that formed through meteorological processes. Explosive volcanic events are shown to frequently be a source of rapidly developing clouds that, at a minimum, reach the upper troposphere. As such, the CGA algorithm is effective at determining when a recently formed cloud is possibly the result of an explosive eruptive event. While the CGA technique can be applied to any geostationary satellite sensor, it is most effective when applied to latest generation of meteorological satellites, which provide more frequent images with improved spatial resolution. Using a large collection of geographically diverse explosive eruptions, and several geostationary satellites, the CGA technique is described and demonstrated. A CGA‐based eruption alerting tool, which is designed to improve the timeliness of volcanic ash advisories, is also described.

A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure

Literature has indicated that accurate dietary assessment is very important for assessing the effectiveness of weight loss interventions. However, most of the existing dietary assessment methods rely on memory. With the help of pervasive mobile devices and rich cloud services, it is now possible to develop new computer-aided food recognition system for accurate dietary assessment. However, enabling this future Internet of Things-based dietary assessment imposes several fundamental challenges on algorithm development and system design. In this paper, we set to address these issues from the following two aspects: (1) to develop novel deep learning-based visual food recognition algorithms to achieve the best-in-class recognition accuracy; (2) to design a food recognition system employing edge computing-based service computing paradigm to overcome some inherent problems of traditional mobile cloud computing paradigm, such as unacceptable system latency and low battery life of mobile devices. We have conducted extensive experiments with real-world data. Our results have shown that the proposed system achieved three objectives: (1) outperforming existing work in terms of food recognition accuracy; (2) reducing response time that is equivalent to the minimum of the existing approaches; and (3) lowering energy consumption which is close to the minimum of the state-of-the-art.

Virtual Fog: A Virtualization Enabled Fog Computing Framework for Internet of Things

The prosperity of Internet of Things (IoT) and the success of rich Cloud services have expedited the emergence of a new computing paradigm called Fog computing, which promotes the processing of data at the proximity of their sources. Complementary to the Cloud, Fog promises to offer many appealing features, such as low latency, low cost, high multitenancy, high scalability, and to consolidate the IoT ecosystem. Although the Fog concept has been widely adopted in many areas, a comprehensive realization has yet been adequately researched. To address all these issues, in this paper, object virtualization is investigated to overcome obstacles resulting from resource constraints on sensory-level nodes while service virtualization is explored to easily create tailored applications for end users. Moreover, network function virtualization is studied to perform the flexibility of network service provisioning. Grounded on object virtualization, network function virtualization and service virtualization, a layered framework that encompasses smart objects, Fog and Cloud is presented to illustrate the realization of virtual Fog along IoT continuum. This proposed virtual Fog framework is applied to a smart living case for verification, then quantitative analysis is conducted to demonstrate the low latency, low operating expense, high multitenancy and scalability, followed by an experimental evaluation to further confirm that delay and jitter can be decreased through virtualization.

Copying ancient woodlands: a positive perspective

The Convention on Biological Diversity’s Aichi Target 15 requires global restoration of degraded ecosystems including complex examples. A case is made for the deliberate creation of woodlands with ancient characteristics. In the temperate biome, efforts to restore such communities will be accelerated by intervention facilitating natural succession. Negative rhetoric regarding the irreplaceability of ancient communities may hinder such considerations. Some facets of ancient woods are irreplaceable and their destruction is discouraged. Nevertheless, as human population and impact increase, this resource will degrade. Though understanding of many ecological processes is incomplete, ecological restoration requires proactivity. Physicians need not fully understand the healing process to rely upon it and, fortunately, ecology carries similar intangible reliables; natural succession occurs and complex communities actively self-assemble. Accepting this, then woodland with many characteristics of ancientness can be deliberately created. Thermodynamic principles may assist and seeking to initiate a biodiversity cascade is proposed as a pragmatic approach. The intricacy that defines ancient woodland might thus be assembled in timescales of less than a century. Ascension Island’s anthropogenic cloud forest provides a precedent. For utilitarian purposes, human-mediated plant dispersal (deliveries of plants to the island), led to the transformation of species-poor, fern-dominated hillsides to species–rich cloud forest in about 100 years. How much more could be achieved with appropriate ecological intention?

Efficient Algorithms for Capacitated Cloudlet Placements

Mobile cloud computing is emerging as a main ubiquitous computing platform to provide rich cloud resources for various applications of mobile devices. Although most existing studies in mobile cloud computing focus on energy savings of mobile devices by offloading computing-intensive jobs from mobile devices to remote clouds, the access delays between mobile users and remote clouds usually are long and sometimes unbearable. Cloudlet as a new technology is capable to bridge this gap, and can enhance the performance of mobile devices significantly while meeting the crisp response time requirements of mobile users. In this paper, we study the cloudlet placement problem in a large-scale Wireless Metropolitan Area Network (WMAN) consisting of many wireless Access Points (APs). We first formulate the problem as a novel capacitated cloudlet placement problem that places $K$ cloudlets to some strategic locations in the WMAN with the objective to minimize the average access delay between mobile users and the cloudlets serving the users. We then propose an exact solution to the problem by formulating it as an Integer Linear Programming (ILP). Due to the poor scalability of the ILP, we instead propose an efficient heuristic for the problem. For a special case of the problem where all cloudlets have identical computing capacities, we devise novel approximation algorithms with guaranteed approximation ratios. We also devise an online algorithm for dynamically allocating user requests to different cloudlets, if the $K$ cloudlets have already been placed. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising and scalable.