Journal articles: 'MOBILE COMPUTING MODEL'

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Jararweh, Yaser, Lo’ai Tawalbeh, Fadi Ababneh, Abdallah Khreishah, and Fahd Dosari. "Scalable Cloudlet-based Mobile Computing Model." Procedia Computer Science 34 (2014): 434–41. http://dx.doi.org/10.1016/j.procs.2014.07.051.

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Wojarnik, Grzegorz. "MODEL OF MOBILE DATA SYNCHRONIZATION WITHIN CLOUD COMPUTING SERVICES." Zeszyty Naukowe Uniwersytetu Szczecińskiego. Studia Informatica 44 (2017): 63–72. http://dx.doi.org/10.18276/si.2017.44-06.

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Mora, Higinio, Francisco J. Mora Gimeno, María Teresa Signes-Pont, and Bruno Volckaert. "Multilayer Architecture Model for Mobile Cloud Computing Paradigm." Complexity 2019 (February 11, 2019): 1–13. http://dx.doi.org/10.1155/2019/3951495.

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Mobile Cloud Computing is one of today's more disruptive paradigms of computation due to its effects on the performance of mobile computing and the development of Internet of Things. It is able to enhance the capabilities of devices by outsourcing the workload to external computing platforms deployed along the network, such as cloud servers, cloudlets, or other edge platforms. The research described in this work presents a computational model of a multilayer architecture for increasing the performance of devices using the Mobile Cloud Computing paradigm. The main novelty of this work lies in defining a comprehensive model where all the available computing platforms along the network layers are involved to perform the outsourcing of the application workload. This proposal provides a generalization of the Mobile Cloud Computing paradigm which allows handling the complexity of scheduling tasks in such complex scenarios. The behaviour of the model and its ability of generalization of the paradigm are exemplified through simulations. The results show higher flexibility for making offloading decisions.

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Alakbarov, R. G. "Model of Optimal Placement of Cloudlets in a Wireless Metropolitan Area Network." Informacionnye Tehnologii 29, no. 4 (April 18, 2023): 182–88. http://dx.doi.org/10.17587/it.29.182-188.

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Cloud computing has recently emerged as a new paradigm for processing and storing large amounts of data. The rapid increase in the number of mobile phones and IoT devices benefitingfrom cloud computing services reduces the Internet bandwidth, resulting in delays in delivering data processed on remote cloud servers to the user. Mobile devices use edge computing systems (cloudlet, fog computing, etc.) to overcome resource shortages, power consumption and delays in communication channels. Edge computing systems place processing devices (cloudlets) close to users. The closer the cloudlets to mobile devices, the lower the processing time and energy consumption of the mobile device, and the higher the bandwidth of communication channels. Thus, cloudlet-based mobile computing clouds are widely used to reduce the latency in the Internet communication channels and energy consumption on mobile devices. This article identifies the most popular places for cloud servers in metropolitan mobile networks and discusses the optimal placement of a limited number of cloudlets in those places.

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El-Sofany, Hosam, and Samir Abou El-Seoud. "A Novel Model for Securing Mobile-based Systems against DDoS Attacks in Cloud Computing Environment." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 01 (January 29, 2019): 85. http://dx.doi.org/10.3991/ijim.v13i01.9900.

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the increasing growth of mobile devices technology and Mobile-based systems with the emerging of cloud computing technology, created a Mobile Cloud Computing field to be the recent future technology for different wireless services. The development of Mobile-based system under cloud computing environment solve some performance and environment related issues include: bandwidth, storage capacity, availability, scalability and heterogeneity. The Mobile-based cloud computing apps are different comparing to mobile computing apps, since in the first model the devices run cloud based web applications not as mobile computing native apps. Services of Mobile-based systems via cloud are accessing and sharing through internet connection thus they are open for attacker to attack on its security. Distributed Denial of Service (DDoS) attacks can cause a big problem in mobile cloud computing security. The main objective of DDoS attacks is to infect wireless devises resources (e.g., software applications, wireless network, etc.) and make them unavailable to the authorized user. In DDoS, the attacker tries to overload the Mobile-based service with traffic. The main objective of this research paper is to introduce novel model for securing Mobile-based systems against DDoS attacks. Efficiency and performance analysis evaluations of the proposed model are presented. The feedbacks of the experimental results were highly promising, for protecting mobile-based cloud computing systems against DDoS attacks.

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Han, Songyue, Dawei Ma, Chao Kang, Wei Huang, Chaoying Lin, and Chunyuan Tian. "Optimization of Mobile Edge Computing Offloading Model for Distributed Wireless Sensor Devices." Journal of Sensors 2022 (February 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/9047737.

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The development and popularization of mobile Internet and wireless communication technology have spawned a large number of computation-intensive and delay-intensive applications. Limited computing resources and existing technologies cannot meet the performance requirements of new applications. Mobile edge computing technology can use wireless communication technology to offload data to be stored and computing tasks to the nearby assistant or edge server with idle resources. Based on the data offloading of distributed wireless sensor device to device communication, the architecture is designed and the basic framework of distributed mobile edge computing is constructed. To solve the problem of high mobile cloud computing technology, the offloading model of optimized mobile edge computing was proposed, and the stability and convergence of the proposed algorithm were proved. Finally, the system performance of the proposed algorithm is verified by simulation. The results show that the proposed algorithm can converge within a finite number of steps. Compared with other benchmark schemes, the proposed algorithm has better performance in reducing system energy consumption, reducing moving edge response delay and system total delay.

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Sun, Chang Li, Chang Liu, and Dan Shi. "Study on the Architecture of Mobile Campus Digital Library System." Applied Mechanics and Materials 608-609 (October 2014): 425–29. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.425.

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Cloud computing and mobile Internet is a new computing model based on Internet business that is a mixture of many technical results, it has the advantage of safe and reliable, massive information storage, fast and convenient. This paper mainly discusses the advantages of mobile digital library, and puts forward the mode of constructing the cloud computing and mobile Internet library based on information platform, analyzes the basic architecture of mobile library, in-depth study cloud computing and mobile Internet in library information platform access model and database model, finally the paper gives general idea to construct digital library using mobile WAP Technology.

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Pham, Hai Son, Hoang Son Le, and The Anh Hoang. "Propose technology and management model of mobile cloud computing for the Vietnam national digital transformation program." Ministry of Science and Technology, Vietnam 65, no. 8 (August 25, 2023): 35–39. http://dx.doi.org/10.31276/vjst.65(8).35-39.

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Cloud computing is considered a digital platform to develop mobile applications for the National Digital Transformation Program 2025 with a vision to 2030. There are many solutions to build a cloud computing platform, but those solutions consume hardware resources, high communication latency and lack of mobility, thus increasing the cost of investment in building a cloud computing platform. To address the disadvantages of the above solutions, this study proposes a mobile cloud computing model aimed at providing services that only implement on mobile devices, enhancing the calculation capacity on mobile devices, minimising resource shortages and performing efficiently for resource-intensive mobile applications. To do that, the solution proposed in this study is to enable the use of the nearest resource-rich mobile devices that can provide computing services to mobile users in the vicinity. Utilising leisure resources to serve resource-intensive mobile applications helps the government to reduce hardware investment for building cloud computing platforms. In addition, this study also proposes a management model for mobile applications through the App Store that is owned and managed by the Vietnamese government. From there, the government will monitor mobile applications and not let them develop spontaneously, causing difficulties and loss of trust for users.

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P., Pavithra, and Balamurugan K. "Enhanced Secure Big Data in Distributed Mobile Cloud Computing Using Fuzzy Encryption Model." Bonfring International Journal of Software Engineering and Soft Computing 8, no. 2 (April 30, 2018): 21–25. http://dx.doi.org/10.9756/bijsesc.8391.

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Jiang, Weijin, Yang Wang, Yirong Jiang, Yuhui Xu, Jiahui Chen, Lina Tan, and Guo Liang. "Mobile Internet Mobile Agent System Dynamic Trust Model for Cloud Computing." Computers, Materials & Continua 62, no. 1 (2020): 123–36. http://dx.doi.org/10.32604/cmc.2020.05933.

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Kim, Seung-Hyun, and Jin Ki Kim. "Determinants of the adoption of mobile cloud computing services." Information Development 34, no. 1 (October 12, 2016): 44–63. http://dx.doi.org/10.1177/0266666916673216.

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The quantity and popularity of mobile cloud computing services have increased significantly. From the principal-agent perspective, this study proposes a model to determine the adoption of mobile cloud computing services. In the model, three uncertainty antecedents of mobile cloud computing utilization are included: fears of service provider opportunism, perceived information asymmetry, and information privacy and security concerns. Likewise, three motivators are included: trust, convenience, and social presence. Based on 263 observations of mobile cloud computing users, the study identifies that trust and convenience are motivators and perceived uncertainty is a mitigator for adopting mobile cloud computing services.

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Deng, Rong. "Research on Key Technology of Web Network Based on Mobile Computing." Advanced Materials Research 989-994 (July 2014): 4612–16. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4612.

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Mobile computing environment is relatively complex, and the structure dynamic degree is high, and the heterogeneous is changeable. Therefore, the development of mobile network based on web techniques must be adapted to the complex environment. In order to get a better network structure supporting mobile computing, based on the context aware consciousness theory, combined with the VC programming software, we design the algorithm of mobile computing network node, obtain the spatial statistical node model of mobile network, and use GPRS/3G base station to establish mobile network model based on the core of multimedia services. In order to verify the effectiveness and reliability of mobile computing algorithm, we calculate the network performance using VC programming software, obtain the network data transmission and delay results, and use mobile algorithm to develop multimedia licensing scheme. It provides a new algorithm for researching on mobile computing.

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Danwei Chen, Ji Zhang, and Qinghan Xue. "Virtual Resources Scheduling Model for Mobile Cloud Computing." Journal of Convergence Information Technology 7, no. 23 (December 31, 2012): 656–63. http://dx.doi.org/10.4156/jcit.vol7.issue23.76.

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Ahmad, Salah Al, Syed Ahmad Aljunid, and Normaly Kamal Ismail. "Mobile cloud computing applications penetration testing model design." International Journal of Information and Computer Security 13, no. 2 (2020): 210. http://dx.doi.org/10.1504/ijics.2020.108849.

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Al-Turkistany, Mohammad, Abdelsalam (Sumi) Helal, and Mark Schmalz. "Adaptive wireless thin-client model for mobile computing." Wireless Communications and Mobile Computing 9, no. 1 (January 2009): 47–59. http://dx.doi.org/10.1002/wcm.603.

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Gai, Keke, Meikang Qiu, Hui Zhao, Lixin Tao, and Ziliang Zong. "Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing." Journal of Network and Computer Applications 59 (January 2016): 46–54. http://dx.doi.org/10.1016/j.jnca.2015.05.016.

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Shin, Young-Rok, and Eui-Nam Huh. "mCSQAM: Service Quality Assessment Model in Mobile Cloud Services Environment." Mobile Information Systems 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/2517052.

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Cloud computing is high technology that extends existing IT capabilities and requirements. Recently, the cloud computing paradigm is towards mobile with advances of mobile network and personal devices. As concept of mobile cloud, the number of providers rapidly increases for various mobile cloud services. Despite development of cloud computing, most service providers used their own policies to deliver their services to user. In other words, quality criteria for mobile cloud service assessment are not clearly established yet. To solve the problem, there were some researches that proposed models for service quality assessment. However, they did not consider various metrics to assess service quality. Although existing research considers various metrics, they did not consider newly generated Service Level Agreement. In this paper, to solve the problem, we proposed a mobile cloud service assessment model called mCSQAM and verify our model through few case researches. To apply the mobile cloud, proposed assessment model is transformed from ISO/IEC 9126 which is an international standard for software quality assessment. mCSQAM can provide service quality assessment and determine raking of the service. Furthermore, if Cloud Service Broker includes mCSQAM, appropriate services can be recommended for service users using user and service conditions.

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Hameed, Shihab A., Ali Nirabi, Mohamed Hadi Habaebi, and Alaa Haddad. "Application of mobile cloud computing in emergency health care." Bulletin of Electrical Engineering and Informatics 8, no. 3 (September 1, 2019): 1088–95. http://dx.doi.org/10.11591/eei.v8i3.1498.

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Mobile applications in emergency health care help maintain patient confidentiality and manage patient records, data storage. Compiles and analyzes care of better quality care. new implementations come with new goals and technologies like using mobile application with cloud computing system and reducing the responding time to safe the patient life and give the patient best health care professional service transition to using of mobile application in emergency healthcare, this paper will present (MCCEH) mobile cloud computing in emergency health care model, mainly reducing the wasting time in emergency health care, The process starting once the accident occurred and the patient run the application, mobile application will detect the patient location and allow him to book nearest medical center or specialist in some emergency cases once the patient did the booking will send help request to medical center this process will include an online pre-register patient in the medical center to save time of patient registration, MCCEH model allows the patients to review the previous feedback and experiences of each specialist or medical center and allows doctors to be able to stay in contact with their patients more often and by communication through mobiles applications and share messages and photos of the accident or emergency case itself.

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Zhao, Jun, and Tingyu Sheng. "Construction of Shared OCCA Model Based on Mobile Agent Technology." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 6 (November 4, 2020): 795–803. http://dx.doi.org/10.2174/2352096512666191016120432.

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Background: The Open Cloud Computing Alliance (OCCA) strives for more Cloud Computing Service Providers (CCSP) to join the alliance. OCCA only requires CCSP to provide virtual computing resources and does not care about the methods of the underlying implementation, which leads the open-source cloud computing to a larger scale and more efficient. Due to the differences in service modes and service categories, the cloud computing platforms formed by CCSP are heterogeneous. How to implement tasks across platforms and ensure the quality of migration are the key issue for sharing the OCCA platform. Methods: The Mobile Agent technology based on a domain is introduced. User tasks are encapsulated into Mobile agent packets by domain client, which realizes the migration of user tasks from one platform to another, and makes it possible to interoperate between OCCA virtual machines. To ensure the service quality of OCCA better, a five-layer logical model of R-OCCA with high commercial availability is proposed, which defines the service content of each layer and gives the setting of key parameters. This paper introduces the architectural composition and operational mechanism of the model, which carries out a qualitative analysis of the model, and establishes an experimental prototype to verify the feasibility of the model on the virtual machine platform. Results: Experiments show that it is feasible to implement Cloud Computing Alliance among cloud computing platforms through Mobile Agent under the existing technical conditions. Conclusion: To better guarantee the quality of OCCA service, a five-level R-OCCA logic model with strong commercial availability is proposed. The service content of each level is defined and the key parameters are given. From the CCSP income, the rationality of the model set is explained. The feasibility of the model was analyzed. The architectural composition and operational mechanisms of the model are introduced. The performance of the model was also analyzed.

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Zhou, Hang, Yong Xiang, Hao-Feng Li, and Rong Yuan. "Task Offloading Strategy of 6G Heterogeneous Edge-Cloud Computing Model considering Mass Customization Mode Collaborative Manufacturing Environment." Mathematical Problems in Engineering 2020 (September 15, 2020): 1–8. http://dx.doi.org/10.1155/2020/1059524.

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With the continuous integration of cloud computing, edge computing, and Internet of things (IoT), various mobile applications will emerge in future 6G network. Driven by real-time response and low energy consumption requirements, mobile edge-cloud computing (MECC) will play an important role to improve user experience and reduce costs. However, due to the complexity of applications, the computing capacity of devices cannot meet the low-latency and low energy consumption requirement. Meanwhile, subject to the limited supplement of power and energy system, the heterogeneous multilayer mobile edge-cloud computing (HetMECC) is proposed to join cloud server, edge server, and terminal devices for data calculation and transmission. By dividing computing tasks, terminal applications can receive reliable and efficient computing services. The simulation results show that the proposed model can achieve the low-latency requirement of data calculation and transmission and improve the robustness of architecture.

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Muralidhar K. and Madhavi K. "Setting Up Ad Hoc Computing as a Service in Mobile Ad Hoc Cloud Computing Environment." International Journal of Interdisciplinary Telecommunications and Networking 13, no. 1 (January 2021): 1–12. http://dx.doi.org/10.4018/ijitn.2021010101.

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Despite the rapid growth in popularity and hardware capacity in mobile devices, they suffer from resource poverty, which limits their ability to meet increasing mobile users' demands. Computation offloading may give a prominent solution. But it relies on the connection to the remote cloud and may fail in situations where there is poor or no connectivity. Cloudlet was introduced to cover this problem, but mobile users miss free mobility when using cloudlets. Offloading to the cloud or cloudlet is not always the preferred solution. An alternative is to utilize the nearby mobile devices as local resource suppliers and pull their capabilities as a mobile device cloud. In this paper, the authors present such an approach known as ad hoc computing as a service (AhCaaS) model for computation offloading in an ad hoc manner by connecting to nearby mobile devices. They define a multi-attribute selection strategy to determine the optimal computation offloadee. They evaluated the proposed model, and the result shows that AhCaaS reduces execution time, battery consumption, and avoids task reassignment.

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David, Beaulah, and Dr R. Santhosh. "Fault Tolerance and QoS based Pervasive Computing using Markov State Transition Model." International Journal of Engineering & Technology 7, no. 4 (September 17, 2018): 2403. http://dx.doi.org/10.14419/ijet.v7i4.12664.

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Fault-tolerance is significant in pervasive computing environments. Recently, few research works has been developed for reducing the fault, occurring in pervasive computing. However, there is a need for a fault tolerance mechanism to reduce the link failures and unwanted mobile node access (in pervasive computing environment). In order to overcome these limitations, Markov State Transition Based Fault Tolerance (MST-FT) Model is proposed. The main objective of MST-FT Model is to achieve resource efficient QoS in pervasive computing environment by avoiding the link failures and unwanted mobile node usages. Initially, the optimization of link failures is achieved by maintaining Markov chain of high energy mobile nodes on the wireless network communication path. The mobile nodes with higher energy and minimal drain rate are combined to form a chain in its corresponding path of communication in order to minimize the link failures in pervasive computing. Next, the inappropriate mobile node usage is avoided by selecting only the authorized mobile nodes for Markov chain construction to effective network communication, which resulting in improved fault tolerant rate. Therefore, MST-FT Model provides higher resource efficient QoS as compared to existing works. The performance of MST-FT Model is measured in terms of fault tolerant rate, execution time, energy consumption rate and quality of service level. The simulation results show that the MST-FT Model is able to improve the fault tolerant rate by 13% and also reduces the energy consumption rate of resource efficient QoS by 25%, when compared to previous works.

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Lin, Qiang. "Dynamic Resource Allocation Strategy in Mobile Edge Cloud Computing Environment." Mobile Information Systems 2021 (August 7, 2021): 1–10. http://dx.doi.org/10.1155/2021/8381998.

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With the development of the mobile Internet, smart mobile terminals have become an indispensable tool for people's lives and mobile applications are becoming more and more powerful. This research mainly discusses the dynamic resource allocation strategy of the mobile edge cloud computing environment. The physical resource layer in the network model is responsible for providing specific resources that are actually available, such as hardware resources, computing resources, storage resources, mainly including base stations, mobile edge computing servers, spectrum, power, and other communications of different infrastructure vendor basic components of the system. The functions of the virtual machine monitor include resource virtualization and resource management. As an important component of wireless network virtualization, virtual machine monitors are usually deployed in physical base stations to provide physical resources and to consider the connection between the virtual machine stations. The business of the business cache model is an application that is requested by users running on the mobile edge computing server or cloud at the base station. The computing task scheduling in the mobile edge environment can be classified as a wireless interaction model. This model captures the user throughput in cellular network interaction. The physical layer channel access strategy (CDMA) allows all mobile users to efficiently share the same spectrum resources at the same time. When the preference coefficient for task energy consumption varies between 0.35–0.55 and 0.65–1, the superior range of maximum system efficiency achieved by RAOM accounts for 55% of the entire range. This research contributes to the reasonable allocation of resources, and the mobile edge computing model improves the fairness of users with a lower transmission cost.

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Wang, Yue Ming, Rui Li Wang, and De Xun Xu. "Mobile E-Commerce Personalized Information Recommendation Model." Applied Mechanics and Materials 687-691 (November 2014): 2136–39. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2136.

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In recent years, the mobile Internet got swift and violent development, business gradually permeate into almost every a of people's work and life, personalized recommendation system model has important application value in mobile commercial activities, this article expounds the mobile commercial personalized recommendation model, and analyzed its structure, discussed the method of using cloud computing for mobile business and the necessity of large data processing.

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Dong, Jing, Jun Han, Jing Liu, and Hui Bin Yin. "Shallow Analysis of Mobile Learning Environment under Cloud Computing." Advanced Materials Research 403-408 (November 2011): 1447–50. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1447.

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Mobile learning is a new way of application of mobile communications technology. As a new way of learning mode and technology it must be pay much attention. It is severely affected by network technology. Cloud computing, a new computing model, is proposed based on a distributed system, grid computing, etc. It is a new way of sharing infrastructure. Cloud computing will develop a new environment of learning space, information sharing, collaboration and learning to the field of education. This article explores the impact of Cloud computing to mobile learning from several factors of the mobile learning environment.

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Salama, Ahmed S. "A Swarm Intelligence Based Model for Mobile Cloud Computing." International Journal of Information Technology and Computer Science 7, no. 2 (January 8, 2015): 28–34. http://dx.doi.org/10.5815/ijitcs.2015.02.04.

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Cho, Sung-Je, and Seong-Soo Han. "Recovery Model Improvement using BISLD in Mobile Computing Environment." Journal of the Korea Academia-Industrial cooperation Society 13, no. 10 (October 31, 2012): 4786–93. http://dx.doi.org/10.5762/kais.2012.13.10.4786.

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Pederson, Thomas, Lars-Erik Janlert, and Dipak Surie. "A Situative Space Model for Mobile Mixed-Reality Computing." IEEE Pervasive Computing 10, no. 4 (April 2011): 73–83. http://dx.doi.org/10.1109/mprv.2010.51.

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Khan, Atta ur Rehman, Mazliza Othman, Abdul Nasir Khan, Shahbaz Akhtar Abid, and Sajjad Ahmad Madani. "MobiByte: An Application Development Model for Mobile Cloud Computing." Journal of Grid Computing 13, no. 4 (April 23, 2015): 605–28. http://dx.doi.org/10.1007/s10723-015-9335-x.

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Jin, Yingwei, Wenyu Qu, Yong Zhang, and Yong Wang. "A mobile agent-based routing model for grid computing." Journal of Supercomputing 63, no. 2 (May 25, 2011): 431–42. http://dx.doi.org/10.1007/s11227-011-0616-2.

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Mukherjee, Anwesha, Deepsubhra Guha Roy, and Debashis De. "Mobility-aware task delegation model in mobile cloud computing." Journal of Supercomputing 75, no. 1 (January 2019): 314–39. http://dx.doi.org/10.1007/s11227-018-02729-x.

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Alonso-Monsalve, Saúl, Félix García-Carballeira, and Alejandro Calderón. "A heterogeneous mobile cloud computing model for hybrid clouds." Future Generation Computer Systems 87 (October 2018): 651–66. http://dx.doi.org/10.1016/j.future.2018.04.005.

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Xu, Yuanyuan, Kun Zhu, and Shan Li. "Hierarchical Combinatorial Auction in Computing Resource Allocation for Mobile Blockchain." Wireless Communications and Mobile Computing 2020 (August 14, 2020): 1–14. http://dx.doi.org/10.1155/2020/8874862.

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The mobile blockchain has been recognized as an emerging solution to address the security and privacy issues in a mobile application system. The mining process in mobile blockchain requires high computing resources which could overwhelm that which mobile devices can offer. In this case, mobile edge computing servers (MESs) can be involved to offer computing services to miners in mobile blockchain. Note that the resources of MESs are also limited; MESs could further request resources from the cloud computing server (CCS). Accordingly, the issue of hierarchical computing resource allocation arises. In this paper, we first consider a simple case with single-seller multiple buyers and a hierarchical single-seller multibuyer combinatorial auction model is proposed to solve this problem, based on which efficient and truthful frameworks are provided. We then extend the model to consider multiple CCSPs and propose a hierarchical multiple-seller multiple-buyer combinatorial auction model. For both models, the winner determination problems are formulated and computationally tractable algorithms are proposed. Also, pricing schemes are proposed to ensure the property of incentive compatibility and individual rationality. Finally, we evaluate the proposed schemes via simulations.

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Hussein, Karim Qassim. "Multimedia Information Time Balance Management in Mobile Cloud Environment Supported By Case Study." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 19 (October 19, 2022): 118–32. http://dx.doi.org/10.3991/ijim.v16i19.33615.

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Mobile cloud computing is used to define and determine computing services with a structure model. The data and resource of any service will be retrieved from cloud computing through internet service, some tools, and user interface (web-based or application). Mobile Cloud Computing (MCC) is a hybrid of cloud computing and mobile computing. Multimedia Information is the core of Mobile Cloud information because of the sizable information of multimedia particularly video streaming. Mobile Cloud mostly handles and processes that information. MCC is one of the business expressions with the real environment in the IT world. The concept of the MCC is still in the beginner stage of advancement. So, the handle of the innovation in a careful way especially in the bearing of future research should be provide. In this paper, an algorithm is throttled load balancing for mobile clouds has been presented within an example of Multimedia information. The results has shown that the load balancing of cloud computing environment. In this scenario, load balancing techniques in mobile cloud computing can be employed and can successfully manage time through the cloud.

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Yao, Min. "Digital Library Knowledge Service Model Research Based on Mobile Cloud Computing." Applied Mechanics and Materials 631-632 (September 2014): 200–203. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.200.

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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.

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Wan Mohd Isa, Wan Abdul Rahim, Ahmad Iqbal Hakim Suhaimi, Nurulhuda Noordin, Afdallyna Fathiyah Harun, Juhaida Ismail, and Rosshidayu Awang Teh. "Cloud computing adoption reference model." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (October 1, 2019): 395. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp395-400.

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<span>This paper presents a study to conceptualize a cloud computing adoption reference model. The case study on the cloud computing adoption was done in one local public university in Malaysia. The case study was conducted to understand in-depth and real context phenomenon by investigating the influencing factors of cloud computing adoption. The main objective of this study is to design a cloud computing adoption reference model. This study applied Technology-Organizational-Environmental (TOE) Framework by Tornatzky & Fleischer and Diffusion of Innovation by Rogers as the theoretical background of the Cloud Computing Adoption Reference. Ten interviews were conducted with key informants. The theme pattern analysis of data were done by using qualitative computer programs, Atlas.ti. The findings are shown in summarize patterns that supports the conceptualization of cloud computing adoption reference model. Future work include the adaption of cloud computing adoption reference model specifically for the niche area of mobile computing.</span>

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Wei, Huixian, and Jia Liu. "Computer Mathematical Modeling Based on the Improved Genetic Algorithm and Mobile Computing." Wireless Communications and Mobile Computing 2021 (December 26, 2021): 1–7. http://dx.doi.org/10.1155/2021/1584435.

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In order to change the problem of data redundancy in a genetic algorithm, this paper proposes a computer mathematical model based on the combination of an improved genetic algorithm and mobile computing. Combined with the least square method, MATLAB software is used to solve the equations, determine the range of parameters, and solve the estimation parameter range and identification problems. The improved genetic algorithm combined with mobile computing and least square method to establish a mathematical model greatly increased the individual search space and increased the operation rate of 90% compared to the basic genetic algorithm or mobile computing. The results show that the improved genetic algorithm and mobile computing have a certain ability to identify the optimal solution and greatly improve the work efficiency.

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Jadad, Hamid A., Abederezak Touzene, Khaled Day, Nasser Alziedi, and Bassel Arafeh. "Context-Aware Prediction Model for Offloading Mobile Application Tasks to Mobile Cloud Environments." International Journal of Cloud Applications and Computing 9, no. 3 (July 2019): 58–74. http://dx.doi.org/10.4018/ijcac.2019070104.

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Offloading intensive computation parts of the mobile application code to the cloud computing is a promising way to enhance the performance of the mobile device and save the battery consumption. Recent works on mobile cloud computing mainly focus on making a decision of which parts of application may be executed remotely, assuming that mobile and server processors have no other loads, mobile battery always full of charge, and have static network bandwidth. However, the mobile cloud environment parameters changes continuously. In this paper, the authors propose a new offloading approach which uses cost models to decide at runtime either to offload execution of the code to the remote cloud or not. This article considers the dynamic changes of the mobile cloud environment in the system cost models. Moreover, this article enhances the offloading process by considering parallel execution of application independent tasks in the cloud. The evaluation results show that the approach reduces the execution time and battery consumption by 75% and 55%, respectively, compared with existing offloading approaches.

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Jiang, Weijin, Yang Wang, Yirong Jiang, Jiahui Chen, Yuhui Xu, and Lina Tan. "Research on mobile Internet mobile agent system dynamic trust model for cloud computing." China Communications 16, no. 7 (July 2019): 174–94. http://dx.doi.org/10.23919/jcc.2019.07.014.

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K, Priyadarsini, Sri Lakshmi Chandana, Severo Simón Calderón Samaniego, Dr Megha Gupta Chaudhary, Dr Vipul Vekariya, and Mr Abhay Chaturvedi. "Intelligent Mobile Edge Computing Integrated with Blockchain Security Analysis for Millimetre-Wave Communication." International Journal of Communication Networks and Information Security (IJCNIS) 14, no. 3 (December 23, 2022): 100–122. http://dx.doi.org/10.17762/ijcnis.v14i3.5577.

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With the increase in number of devices enabled the Internet of Things (IoT) communication with the centralized cloud computing model. With the implementation of the cloud computing model leads to increased Quality of Service (QoS). The cloud computing model provides the edge computing technologies for the real-time application to achieve reliability and security. Edge computing is considered the extension of the cloud computing technology involved in transfer of the sensitive information in the cloud edge to increase the network security. The real-time data transmission realizes the interaction with the high frequency to derive improved network security. However, with edge computing server security is considered as sensitive privacy information maintenance. The information generated from the IoT devices are separated based on stored edge servers based on the service location. Edge computing data is separated based in edge servers for the guaranteed data integrity for the data loss and storage. Blockchain technologies are subjected to different security problem for the data integrity through integrated blockchain technologies. This paper developed a Voted Blockchain Elliptical Curve Cryptography (VBECC) model for the millimetre wave application. The examination of the blockchain model is evaluated based on the edge computing architecture. The VBECC model develop an architectural model based Blockchain technology with the voting scheme for the millimetre application. The estimated voting scheme computes the edge computing technologies for the estimation of features through ECC model. The VBECC model computes the security model for the data transmission in the edge computing-based millimetre application. The experimental analysis stated that VBECC model uses the data security model ~8% increased performance than the conventional technique.

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Ismail, Alshimaa H., Germien G. Sedhom, and Zainab H. Ali. "Enhanced Active Queue Management‑Based Green Cloud Model for 5G system using K-Means." International Journal of Wireless and Ad Hoc Communication 6, no. 2 (2023): 65–72. http://dx.doi.org/10.54216/ijwac.060206.

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The most unique and important design considerations in 5G cloud computing are the delay, energy consumption, and throughput. Therefore, most recent studies focused on boosting delay and energy consumption, and throughput using edge computing. The active queue management-based green cloud model (AGCM) is one of the most recent green cloud models that decreases the delay and sustains a stable throughput. Also, Mobile edge computing (MEC) is an essential cloud computing model for mobile users to meet the continuous growth of data requests. Thus, we offer a handoff scenario between the AGCM and MEC to assess the possible benefits of such collaboration and enhance its effects on the fundamental cloud restrictions such as delay and throughput. Accordingly, the proposed algorithm is named Enhanced Active queue management-based green cloud model (EAGCM). The proposed EAGCM regards incorporation between Kmeans and AGCM. The simulation results indicate that the proposed EAGCM serves mobile users efficiently, enhances the throughput, and reduces latency compared to AGCM and the cloud for 5G systems.

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Ding, Xinhui, and Wenjuan Zhang. "Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing." Mathematical Problems in Engineering 2021 (March 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/2163965.

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Due to the limited computing resources of the mobile edge computing (MEC) server, a massive Internet of things device computing unloading strategy using game theory in mobile edge computing is proposed. First of all, in order to make full use of the massive local Internet of things equipment resources, a new MEC system computing an unloading system model based on device-to-device (D2D) communication is designed and modeled, including communication model, task model, and computing model. Then, by using the utility function, the parameters are substituted into it, and the optimization problem with the goal of maximizing the number of CPU cycles and minimizing the energy consumption is constructed with the unloading strategy and power as constraints. Finally, the game theory is used to solve the problem of computing offload. Based on the proposed beneficial task offload theory, combined with the mobile user device computing offload task amount, transmission rate, idle device performance, and other factors, the computing offload scheme suitable for their own situation is selected. The simulation results show that the proposed scheme has better convergence characteristics, and, compared with other schemes, the proposed scheme significantly improves the amount of data transmission and reduces the energy consumption of the task.

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Longo, Mathias, Matías Hirsch, Cristian Mateos, and Alejandro Zunino. "Towards Integrating Mobile Devices into Dew Computing: A Model for Hour-Wise Prediction of Energy Availability." Information 10, no. 3 (February 26, 2019): 86. http://dx.doi.org/10.3390/info10030086.

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With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing/storage features and pervasiveness, but also due to their capability to render services for several hours, even days, without being plugged to the electricity grid. Nonetheless, misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew computing environments. Arguably, having accurate estimations of remaining battery would help to take better advantage of a device’s computing capabilities. In this paper, we propose a model to estimate mobile devices battery availability by inspecting traces of real mobile device owner’s activity and relevant device state variables. The model includes a feature extraction approach to obtain representative features/variables, and a prediction approach, based on regression models and machine learning classifiers. On average, the accuracy of our approach, measured with the mean squared error metric, overpasses the one obtained by a related work. Prediction experiments at five hours ahead are performed over activity logs of 23 mobile users across several months.

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Xu, Zhenpeng, Hairong Chen, and Weini Zeng. "The Consistence of Checkpointing and Rollback Recovery Scheme." International Journal of Advanced Pervasive and Ubiquitous Computing 7, no. 4 (October 2015): 1–12. http://dx.doi.org/10.4018/ijapuc.2015100101.

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For the traditional distributed computing system, the related message logging conditions had specified to keep the state consistence among the distributed process. Since many new characteristics are introduced in the mobile computing system, the new sufficient logging requirement for mobile computing has to be specified, to avoid the possible state inconsistence among the mobile nodes and the static nodes during the rollback recovery. Firstly, the related definitions of the inconsistence among the process state and the nondeterministic event were extended in the paper, independent of the specific log-based fault tolerant scheme. Finally, a novel particular logging consistency condition was derived based on the extended definitions and Piece-Wise Deterministic model for the mobile computing system. By contrast, the proposal is a practical and efficient constraint for mobile computing upon the possible failures.

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Song, Jun, Ya Hui Cheng, Xin Zhu Zhang, and Ya Fen Zhang. "The City Traffic Service Platform Based on MPI Model." Applied Mechanics and Materials 548-549 (April 2014): 1844–47. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1844.

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“Smart City” equals “Digital City” plus “The Internet of things” and “Cloud Computing”. Vehicle networking (IOV), cloud computing and digital communication constitute the core of the intelligent traffic. Vehicle networking (IOV) refers to a fact that objects (cars and trucks, cars and roads, cars and people, vehicles and sensors, RFID and other devices which communicates with vehicles) consitute a dynamic mobile communication system through network. It can collect and share information among cars and cars, cars and people, cars and roads, vehicles and environment. In this information network platform, the multi-source information will be collected for processing, computing, and sharing as well as providing professional multimedia and mobile Internet applications . And we use MPI model to compute the large amount of information data . In this project, we achieve all the work which will be mentioned in this passage and we aim to use these technologies to resolve the computing problem in data processing and apply these new resolutions in smart city.

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Wu, Xu. "Context-Aware Cloud Service Selection Model for Mobile Cloud Computing Environments." Wireless Communications and Mobile Computing 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/3105278.

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Mobile cloud computing (MCC) has attracted extensive attention in recent years. With the prevalence of MCC, how to select trustworthy and high quality mobile cloud services becomes one of the most urgent problems. Therefore, this paper focuses on the trustworthy service selection and recommendation in mobile cloud computing environments. We propose a novel service selection and recommendation model (SSRM), where user similarity is calculated based on user context information and interest. In addition, the relational degree among services is calculated based on PropFlow algorithm and we utilize it to improve the accuracy of ranking results. SSRM supports a personalized and trusted selection of cloud services through taking into account mobile user’s trust expectation. Simulation experiments are conducted on ns3 simulator to study the prediction performance of SSRM compared with other two traditional approaches. The experimental results show the effectiveness of SSRM.

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Brito, Carlos, Laécio Rodrigues, Brena Santos, Iure Fé, Tuan-Anh Nguyen, Dugki Min, Jae-Woo Lee, and Francisco Airton Silva. "Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System." Applied Sciences 11, no. 9 (April 29, 2021): 4088. http://dx.doi.org/10.3390/app11094088.

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Mobile Edge Computing (MEC) has emerged as a promising network computing paradigm associated with mobile devices at local areas to diminish network latency under the employment and utilization of cloud/edge computing resources. In that context, MEC solutions are required to dynamically allocate mobile requests as close as possible to their computing resources. Moreover, the computing power and resource capacity of MEC server machines can directly impact the performance and operational availability of mobile apps and services. The systems practitioners must understand the trade off between performance and availability in systems design stages. The analytical models are suited to such an objective. Therefore, this paper proposes Stochastic Petri Net (SPN) models to evaluate both performance and availability of MEC environments. Different to previous work, our proposal includes unique metrics such as discard probability and a sensitivity analysis that guides the evaluation decisions. The models are highly flexible by considering fourteen transitions at the base model and twenty-five transitions at the extended model. The performance model was validated with a real experiment, the result of which indicated equality between experiment and model with p-value equal to 0.684 by t-Test. Regarding availability, the results of the extended model, different from the base model, always remain above 99%, since it presents redundancy in the components that were impacting availability in the base model. A numerical analysis is performed in a comprehensive manner, and the output results of this study can serve as a practical guide in designing MEC computing system architectures by making it possible to evaluate the trade-off between Mean Response Time (MRT) and resource utilization.

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Jin, Jun, Chun Xiao Fan, and Yue Xin Wu. "Research of the Cloud Computing Applications in Smart Mobile Phone." Applied Mechanics and Materials 135-136 (October 2011): 759–65. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.759.

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Cloud computing as a new model of information technology, which is emerging as computing concept that promises provision of computational resources on demand and abstraction of technical details from the clients, in the IT sector has attracted more and more attention. Nowadays, with the popularity of smart mobile phones, the increasing functions of smart mobile phone could not meet the requirements of users. To solve this problem, cloud computing is applied in smart mobile phone. Finally, the detail of the current station of the application of cloud computing in smart mobile phone and some introductions of how to develop the application will be introduced.

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Kuang, Qiaobin, Jie Gong, Xiang Chen, and Xiao Ma. "Joint computation and bandwidth resources allocation for generalized computing model in mobile edge computing." IEICE Communications Express 9, no. 2 (2020): 42–47. http://dx.doi.org/10.1587/comex.2019xbl0146.

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Alseadoon, Ibrahim. "Motivations, challenges, and process support for the evolution of existing software to mobile computing platforms." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 5 (May 2021): 89–106. http://dx.doi.org/10.21833/ijaas.2021.05.011.

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Software maintenance and evolution support changes in the structure and behavior of existing software to change it as per the needs and demands of new requirements. The majority of the existing software systems lack features of mobile computing such as portability, context-awareness, connectivity, and high interactivity. The evolution of the existing software for mobile computing platforms can enable these systems to retain their core data and logic while acquiring new features that are compatible with mobile systems. The objectives of this research are to (i) systematically identify the motivations and challenges of software evolution for mobile computing, and (ii) develop and validate a process model that supports the evolution of existing software to a mobile computing platform. To conduct this research, an empirical software engineering approach has been adopted to investigate existing solutions (30 published studies from 1996 to 2019) and empirically derive a process model that supports software evolution for mobile computing. A case study-based approach is adopted to demonstrate the process-centric evolution of existing software as a mobile-enabled application. Case study-based demonstration highlights that the proposed process (i) supports an incremental evolution and (ii) allows user-decision support to guide the evolution process. Evaluation results highlight computation and energy efficiency along with enhanced usability of a mobile application when executed on resource-constrained mobile devices. The results of this research could help researchers and practitioners to rationalize motivations and challenges to utilize a process-based approach to evolve existing or aging software for mobile computing platforms. Future research is focused on providing patterns and tool support to automate and customize the evolution process.

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Journal articles on the topic 'MOBILE COMPUTING MODEL'

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