Mobile Cloud Services Research Articles

Exploring Key Factors Influencing the Adoption of Mobile Cloud Computing in Higher Education: Case study at An-Najah University

The integration of mobile learning applications has emerged as indispensable in higher education, yet the widespread adoption of mobile cloud computing technology within university settings remains limited in developing countries. This research seeks to bridge this gap by elucidating the primary factors influencing the decision to adopt mobile cloud computing in universities. To attain this objective, a comprehensive model is proposed, encompassing seven established technological factors along with novel considerations such as quality of service and social influence. The study involves data collection from 210 students and academic staff An-Najah National University in Palestine. The investigation underscores the paramount influence of quality of service, perceived usefulness, perceived ease of use, facilitating conditions, social influence, management support, and privacy on mobile cloud adoption. Furthermore, the research brings to light the persistent concerns surrounding security and privacy, serving as significant impediments to mobile cloud adoption in Palestinian Universities. The implications of these findings extend to higher educational institutions, mobile cloud service providers, and decision-makers. The identified factors serve as critical considerations for successful implementation of mobile cloud computing technology. Moreover, the research sheds light on the societal and scientific significance by addressing the motivation behind the title—exploring the utility of Mobile Computing Technology in enhancing educational processes.

Time-Controllable Keyword Search Scheme With Efficient Revocation in Mobile E-Health Cloud

Electronic health (e-health) systems may outsource data such as patient e-health records to mobile cloud servers for efficiency gains (e.g., minimizing local storage and computation costs). However, such a move may result in privacy implications in the presence of semi-honest cloud servers. Searchable Encryption (SE) can potentially facilitate privacy-preserving searches based on keywords for encrypted data stored in the mobile cloud, but most existing SE solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users for specified time ranges). Hence, in this paper we design a time-controllable keyword search scheme by using an attribute-based comparable access control. This allows users to match indexes encrypted at specified time intervals. Then, we improve the basic framework to support efficient user revocation using secret sharing. We then formally prove the security of our proposed frameworks against chosen-keyword attack and key collusion attack, as well as achieving keyword secrecy. We also evaluate the performance of our proposed approach using a real-world dataset to demonstrate their practical utility.

ВИКОРИСТАННЯ ШІ ДЛЯ ЗАХИСТУ ЦИФРОВОГО АКТИВУ ЩОДО УПРАВЛІННЯ БІЗНЕС ПРОЦЕСАМИ В УМОВАХ ЦИФРОВИХ ТРАНСФОРМАЦІЙ: ЕВОЛЮЦІЯ ТА МАЙБУТНЄ

It is important to note that in the conditions of the digital transformation of the economy, the management of business processes requires new innovative tools, including artificial intelligence (AI), which is actively being used in business and is becoming a competitive advantage. The relevance of the research lies in the fact that in the conditions of the formation of the digital economy, modern methods of managing business processes with the use of AI go beyond the framework of business infrastructure. After all, the use of mobile devices, cloud and hybrid services and the transfer of data about a business company to contractors and customers, and therefore information for solving business problems acquires commercial business value and due to the existing risks of its loss, needs protection. The purpose of the article is to study the use of artificial intelligence for the protection of a digital asset in terms of business process management in the context of digital transformations in the context of the evolution and future of AI. In the research process, methods of analysis, systematization and generalization were used. The risks of the loss of a digital asset in the conditions of digitalization due to cyberattacks, which the business receives in the event of information insecurity, are characterized. It is proposed to introduce a new economic term – a digital innovative asset, which is based on business, commercial value in information and communication technologies for managing business processes in the conditions of digital transformation of the economy and requires a reliable protection system. The stages of the evolution of artificial intelligence in the context of digital asset protection are defined. The methods used by AI to identify and predict threats to a digital asset for the purpose of protection are described. The practical value of the research lies in the effectiveness of the use of intelligent AI and machine learning in the protection of a digital innovative asset, which will become a practice and is especially important in the conditions of the formation of a digital economy. After all, predictive AI optimizes threat detection and creates effective cybersecurity solutions that are closely related to the threat landscape, which tends to constantly change and create danger for the digital asset, which is the basis of business process management, which will reduce risks and provide business opportunities. The vector of further research is the development of a global digital strategy for the protection of a digital asset.

Edge Computing Driven Low-Light Image Dynamic Enhancement for Object Detection

We focus on performing object detection in images taken under low-light conditions, which is critical task and often occurs in mobile multimedia computing environment. Unlike former methods to obtain enhanced images before detection with variant kinds of manually designed filters, we propose an edge computing and multi-task driven framework to complete tasks of image enhancement and object detection with fast response. The proposed framework consists of two stages, namely cloud-based enhancement stage and edge-based detection stage. In cloud-based enhancement stage, we establish connection between mobile users and cloud servers to input rescaled and small-size illumination parts of lowlight images, where enhancement subnetworks are dynamically combined to output several enhanced illumination parts and corresponding weights based on low-light context of input images. During edge-based detection stage, cloud-computed weights offers informativeness information on extracted feature maps to enhance their representation abilities, which results in accurate predictions on labels and positions for objects. By applying the proposed framework in cloud computing system, experimental results show it significantly improves detection performance in mobile multimedia and low-light environment.

Effects of Users’ Motivation on Their Usage of Mobile Cloud Computing

Mobile cloud computing (MCC) is one of the most visible efforts to take computing technology beyond the use of tools and achieve universal penetration. Mobile and cloud computing are becoming increasingly important in people’s daily lives, highlighting the urgent need for new approaches. The motivation of mobile users and the nature of cloud computing make it ideal for developing mobile cloud services. The focus of this paper is on the cognitive, social, and technical motivations that mobile users experience when owning, interacting with, and using mobile devices through MCC. We also explore the moderating role of network externalities between perceived utility of MCC, usage attitudes, and actual use of MCC. The results show that the positive effects of perceived utility and attitudes on mobile cloud computing usage increase gradually with network externalities. This study provides a theoretical basis for academics and practical guidance for service providers to promote MCC.

A Survey on Fingerprinting Technologies for Smartphones Based on Embedded Transducers

Smartphones are a vital technology, they improve our social interactions, provide us a great deal of information and bring forth the means to control various emerging technologies, like the numerous IoT devices that are controlled via smartphone apps. In this context, smartphone fingerprinting from sensor characteristics is a topic of high interest not only due to privacy implications or potential use in forensics investigations, but also because of various applications in device authentication. In this work we review existing approaches for smartphone fingerprinting based on internal components, focusing mostly on camera sensors, microphones, loudspeakers and accelerometers. Other sensors, i.e., gyroscopes and magnetometers, are also accounted, but they correspond to a smaller body of works. The output of these transducers, which convert one type of energy into another, e.g., mechanical into electrical, leaks through various channels such as mobile apps and cloud services, while there is little user awareness on the privacy risks. Needless to say, miniature physical imperfections from the manufacturing process make each such transducer unique. One of the main intentions of our study is to rank these sensors according to the accuracy they provide in identifying smartphones and to give a clear overview on the amount of research that each of these components triggered so far. We review the features which can be extracted from each type of data and the classification algorithms that have been used. Last but not least, we also point out publicly available datasets which can serve for future investigations.

Genetic Programming for Dynamic Workflow Scheduling in Fog Computing

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Joint Task Offloading and Resource Allocation for Device-Edge-Cloud Collaboration With Subtask Dependencies

With more computational intensive applications deployed involving mobile edge computing (MEC), the collaboration among mobile devices, edge and cloud servers becomes an effective mechanism to fully utilize all available distributed computing resources. However, two main challenges have yet to be addressed to enable this three-way collaboration for securing necessary computational resources and further guaranteeing the quality of service (QoS) of task handling. The first challenge is related to the partitioning of an application task into several dependent subtasks and schedule them among the collaborating device-edge-cloud (DEC). The second is focused on the allocation of necessary computing resources of device, edge and cloud servers for effective subtask handling. To this end, we study the joint task offloading and resource allocation for DEC collaboration in this paper by formulating a new optimization problem with the objective of minimizing the task handling latency. To solve this problem, we decompose the original problem into two subproblems, which include the first one of calculating the optimal task partitioning ratio by mathematical analytical method, as well as the second on using the Lagrangian dual (LD) method for obtaining the optimal task offloading and resource allocation policy. Finally, we conduct simulation experiments on a real-life dataset obtained from the central business district (CBD) of Melbourne, Australia, and the experimental results validate the efficacy of our approach in minimizing latency.

An efficient dynamic decision-based task optimization and scheduling approach for microservice-based cost management in mobile cloud computing applications

The use of smartphones and mobile devices has increased significantly, as have Mobile Cloud Applications based on cloud computing. These applications are used in various fields, including Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. While existing cloud-based frameworks provide such services on Virtual Machines, they incur problems such as overhead, lengthy boot time, and high costs. To address these issues, the paper proposes a Dynamic Decision-Based Task Scheduling Approach for Microservice-based Mobile Cloud Computing Applications (MSCMCC) that can run delay-sensitive applications and mobility with less cost than existing approaches. The study focuses on Task Offloading problems on heterogeneous Mobile Cloud servers. It proposes a Task Offloading and Microservices based Computational Offloading (TSMCO) framework to solve Task Scheduling in steps such as Resource Matching, Task Sequencing, and Task Offloading. Experimental results show that the proposed MSCMCC and TSMCO enhance Mobile Server Utilization while minimizing costs and improving boot time, resource utilization, and task arrival time for various applications. Specifically, the proposed system effectively reduces the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

Up-conversion Luminescence System for Quantitative Detection of IL-6.

Interleukin-6 (IL-6) is a very important cytokine and an early predictor of survival in febrile patients (eg, patients with COVID-19). With the outbreak of the COVID-19 in the world, the significance of medical detection of interleukin 6 has gradually become prominent. A method to point-of-care(POCT) diagnosis and monitoring of IL-6 levels in patients is urgently needed. In this work, an up-conversion luminescence system (ULS) based on upconverting nanoparticles (UCNs) for quantitative detection of IL-6 was designed. The ULS consists of Micro Controller Units (MCU), transmission device, laser, image acquisition module, Bluetooth module, etc. Through hardware system acquisition and image software algorithm processing, we obtain a limit of detection (LOD) of IL-6 at 1 ng/mL, and the quantitative range is from 1 to 200 ng/mL. The system is handheld and has great detection accuracy. The detection time is 10 minutes. In addition, the system can access mobile device terminals (smartphones, personal computers, etc.) or 5G cloud servers via Bluetooth and WIFI. Patients and family members can view medical data through mobile terminals, and the data stored in the 5G cloud server can be used for edge computing and big data analysis. It is suitable for the early diagnosis of infectious diseases such as COVID-19 and has good application prospects.

Mobile CT and cloud service makes CT inspection fast and affordable

Industrial processes should be capable and controlled. In industrial mass production, however, undesirable deviations regularly occur which must not affect the quality of the finished product. In such cases, a temporarily increased inspection effort can also be justified using industrial CT. Under these circumstances, the test equipment must be available quickly and at a reasonable cost. To achieve this aim, a mobile CT was built and supplemented by a flexible, automatically working evaluation software that was adapted to the specific test task at short notice. The software system is cloud-based and is characterized by very high availability and scalable performance. In combination with a CT available on short call directly on the production line, this solution can quickly check the part quality and provide important feedback for process optimization.

SPDL: A Blockchain-Enabled Secure and Privacy-Preserving Decentralized Learning System

Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies have shown the feasibility of preserving privacy, enhancing training performance or introducing Byzantine resilience, but none of them simultaneously considers all of them. Therefore we face the following problem: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how can we efficiently coordinate the decentralized learning process while simultaneously maintaining learning security and data privacy for the entire system?</i> To address this issue, in this paper we propose SPDL, a blockchain-secured and privacy-preserving decentralized learning system. SPDL integrates blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR), and differential privacy seamlessly into one system, ensuring efficient machine learning while maintaining data privacy, Byzantine fault tolerance, transparency, and traceability. To validate our approach, we provide rigorous analysis on convergence and regret in the presence of Byzantine nodes. We also build a SPDL prototype and conduct extensive experiments to demonstrate that SPDL is effective and efficient with strong security and privacy guarantees.

Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems

The emergence of fog-based Internet of Things (IoT) systems have played a significant role in enhancing the applicability of the IoT paradigm. In such systems, fog-nodes are proficient enough to retain, process and transmit the data coming from IoT devices. Nevertheless, as an extension of cloud computing, inheriting the security and privacy concerns of cloud computing is also inevitable in fog-based IoT systems. To deal with such challenges, a diverse range of security solutions are reported in the literature. However, most of them have several limitations (i.e., vulnerability to known security attacks and high computation overhead) that curtail their practical implementation applicability. Keeping these limitations in mind, this paper propose a privacy-preserving hash-based authenticated key agreement protocol using XOR and concatenation operations for fog-driven IoT systems. Using healthcare as a case study, the security of the novel protocol is evaluated by using informal and formal security analysis. In order to obtain the experimental results, the key cryptographic operations used at the user, fog node and cloud server-side are implemented on a mobile device, Arduino and cloud server, respectively. Findings from the performance evaluation results show that the proposed protocol has the least computation cost compared to several related competing protocols.

MOBILE CLOUD COMPUTING SERVICES AUTHENTICATION SCHEME

Because of the rapid growth of cloud computing and the expansion of mobile phone users in recent years, mobile cloud computing has attracted wide attention. In the mobile cloud, wireless computing networks are the basics of sharing data between mobile devices and cloud services. Since air is the communication medium, it must be properly protected; otherwise, it will be subject to a variety of security threats, for example, attacks from middle-man, identity tracking, etc... Furthermore, mobile devices are limited in storage, resources, and computing powers. Hence, designing an efficient and secure balance of authentication schemes is extremely important. First of all, a multi-factor authentication scheme based on biometric (fingerprint information), hash function, and fuzzy vault algorithm is presented in that paper. Secondly, the Validation and Analysis tool of AVISPA Security was approved. Thirdly, the security of the scheme proposed is compared to other related schemes.

Updating Linked Data practices for FAIR Digital Object principles

Updating Linked Data practices for FAIR Digital Object principles

Optimal Pricing and Introduction Timing of Technology Upgrades in Subscription-Based Services

Many technologies that fuel subscription-based services improve over time. Examples are mobile phones, software suites (Microsoft Office, Adobe Creative Cloud), subscription services (Netflix), and cloud service providers. Additionally, modern subscription services are increasingly personalized to individual subscribers, and as a result, discriminatory pricing is ever-present in the marketplace, allowing firms to reward existing customers with discounts and special offers on upgrades. However, customers may be averse to switching to improved services because of costs related to redesigning business processes, downtime, or customer inertia. We propose a model of technology upgrades featuring discriminatory pricing based on customers’ upgrade experience. We characterize the optimal pricing policy for the service provider and develop an efficient algorithm for computing optimal prices. We also characterize the optimal timing of technology introductions and show that it is generally optimal to introduce new technologies in periodic intervals after some time.

Secured Optimized Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) uses multiple mobiles to compute several complex tasks that are unable to compute on a single device. Taking advantage of all abundant mobile resources and making a mobile cloud from them will be useful. This study aims to propose and implement a novel framework to cover challenges raised by application execution on resource-constrained devices. The purpose is to overcome the waste of resources in MEC and provide time efficiency to the data packets that are sent and received between offloaded and offloading devices. The main task of MEC is to offload tasks by first selecting resources and then allocating tasks to selected resources. A multiple linear regression algorithm is used for the selection of compatible devices. Particle swarm optimization techniques are used as a benchmark technique to design an algorithm for optimized resource allocation. Multiple mobile devices acted as a major component for making edge clouds. The study finds that response time of processing tasks is reduced, ineffectual resources become beneficial, increase in demand for mobile devices, and usage of mobile resources as a replacement for mobile edge cloud servers. Abundant resource usage of two or more edge clouds, that is, interedge resource usage is the originality of this research, while others are using intraedge resources only.

Quick Block Transport System for Scalable Hyperledger Fabric Blockchain Over D2D-Assisted 5G Networks

This paper presents a quick block transport system for scalable Hyperledger Fabric (HLF) blockchain over device-to-device (D2D)-assisted 5G networks. The objective of the proposed system is to increase the transaction throughput and further enable the application of the blockchain to delay-sensitive 5G services. To this end, the system rapidly disseminates the block data to all the peers on the blockchain network. The blockchain network is organized with mobile edge cloud (MEC) server and mobile peers in the 5G network. To ensure a scalable and reliable blockchain architecture, the core functionalities of the HLF blockchain are appropriately distributed among the mobile edge cloud and each mobile peer. The MEC server handles core functionalities for the blockchain network, such as block generation and membership management. In addition, it is responsible for gathering the network status of all peers and organizing the overlay structure to be utilized to rapidly propagate the block with a low network cost. On the other hand, the mobile peers handle the endorsing and validating functionalities to prevent block data tampering in the blockchain network. Furthermore, locality-aware device-to-device networking is utilized in 5G network coverage to achieve faster block dissemination. The proposed system is designed to work seamlessly in an existing HLF blockchain framework with consideration of scalability and compatibility. It is fully implemented using the open-source HLF and OpenAirInterface. To verify the effectiveness of the proposed block transport system, it was evaluated in an actual 5G network as well as 802.11 network. The experimental results verified its feasibility.

SecNetworkCloudSim: An Extensible Simulation Tool for Secure Distributed Mobile Applications

Fueled by the wide interest for achieving rich-storage services with the lowest possible cost, cloud computing has emerged into a highly desired service paradigm extending well beyond Virtualization technology. The next generation of mobile cloud services is now manipulated more and more sensitive data on VM-based distributed applications. Therefore, the need to secure sensitive data over mobile cloud computing is more evident than ever. However, despite the widespread release of several cloud simulators, controlling user’s access and protecting data exchanges in distributed mobile applications over the cloud is considered a major challenge. This paper introduces a new NetworkCloudSim extension named SecNetworkCloudSim, a secure mobile simulation tool which is deliberately designed to ensure the preservation of confidential access to data hosted on mobile device and distributed cloud’s servers. Through high-level mobile users’ requests, users connect to an underlying proxy which is considered an important layer in this new simulator, where users perform secure authentication access to cloud services, allocate their tasks in secure VM-based policy, manage automatically the data confidentiality among VMs and derive high efficiency and coverage rates. Most importantly, due to the secure nature of proxy, user’s distributed tasks can be executed without alterations on different underlying proxy’s security policies. We implement a scenario of follow-up healthcare distributed application using the new extension.

Secure and Efficient Data Storage Operations by Using Intelligent Classification Technique and RSA Algorithm in IoT-Based Cloud Computing

In mobile cloud services, smartphones may depend on IoT-based cloud infrastructure and information storage tools to conduct technical errands, such as quest, information processing, and combined networks. In addition to traditional finding institutions, the smart IoT-cloud often upgrades the normal impromptu structure by treating mobile devices as corporate hubs, e.g., by identifying institutions. This has many benefits from the start, with several significant problems to be overcome in order to enhance the unwavering consistency of the cloud environment while Internet of things connects and improves decision support system of the entire network. In fact, similar issues apply to monitor loading, resistance, and other security risks in the cloud state. Right now, we are looking at changed arrangement procedures in MATLAB utilizing cardiovascular failure information and afterward protecting that information with the assistance of RSA calculation in mobile cloud. The calculations tried are SVM, RF, DT, NB, and KNN. In the outcome, the order strategies that have the best exactness result to test respiratory failure information will be recommended for use for enormous scope information. Instead, the collected data will be transferred to the mobile cloud for preservation using the RSA encryption algorithm.