Mobile and Wireless Computing towards Sustainable and Ubiquitous Clouds Services

2013 ◽  
Vol 5 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Ching-Hsien Hsu ◽  
Emmanuel Udoh
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Critical Role ◽  
Cloud Services ◽  
Future Research ◽  
Mobile Cloud ◽  
Extended Version ◽  
Special Issue ◽  
Energy Aware ◽  
Wireless Computing

The ever-growing cloud computing and services provide dynamic intelligence and play an increasingly critical role in all aspects of our lives. By taking advantage of virtualized resources, cloud computing services presents an attractive means to address the challenges while realizing the potential of Mobile and Wireless Computing (MWC). The MWC paradigm can be generalized to include mobile devices, which not only incorporate sophisticated methods for users to interact with the online world through numerous applications in their devices, but are endowed with multiple sensors that enable them to contribute data as nodes in the IoT. In this context, mobile cloud services that enable widespread data collection through mobile devices and collaborative use of mobile devices to enhance existing and realize new applications are very much of interest. As such, the MWC has come to the picture seeking solutions for computing and IT infrastructures to be energy efficient and environmentally friendly. This special issue is in response to the increasing convergence between MWC and cloud services, while different approaches exist, challenges and opportunities are numerous in this context. The research papers selected for this special issue represent recent progresses in the field, including works on services computing and modeling, mobile cloud, U-Care cloud, vehicle networks, energy-aware architectures, and wireless sensor network technologies and applications. This special issue includes four extended version of the selected paper originally presented at the 17th Mobile Computing Workshop (MC 2012) and the 8th Workshop on Wireless, Ad Hoc and Sensor Networks (WASN 2012), held at Taipei, Taiwan; one extended version of the selected paper originally presented at the 4th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom 2012), held at Taipei, Taiwan; and one regular submission with 20% average acceptance rate for 2012 submissions in IJGHPC. The papers selected for this issue not only contribute valuable insights and results but also have particular relevance to the mobile, wireless and cloud computing community. All of them present high quality results for tackling problems arising from the ever-growing mobile and cloud services. We believe that this special issue provides novel ideas and state-of-the-art techniques in the field, and stimulates future research in the mobile and wireless services in clouds.

Mobile Cloud Computing

2018 ◽  
pp. 105-123
Author(s):  
Parkavi R ◽  
Priyanka C ◽  
Sujitha S. ◽  
Sheik Abdullah A
Keyword(s):  
Cloud Computing ◽  
Mobile Computing ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Mobile Service ◽  
Service Users ◽  
Security Issues ◽  
Mobile Computing Environment

Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry ring words and a major conversation thread in the IT world with an explosive development of the mobile applications and emerging of cloud computing idea, the MCC has become a possible technology for the mobile service users. The concepts of Cloud computing are naturally meshed with mobile devices to allow on-the-go functionalities and benefits. The mobile cloud computing is emerging as one of the most important branches of cloud computing and it is expected to expand the mobile ecosystems. As more mobile devices enter the market and evolve, certainly security issues will grow as well. Also, enormous growth in the variety of devices connected to the Internet will further drive security needs. MCC provides a platform where mobile users make use of cloud services on mobile devices. The use of MCC minimizes the performance, compatibility, and lack of resources issues in mobile computing environment.

Cloud and Mobile

2016 ◽  
pp. 1-20 ◽  
Author(s):  
Antonio Miguel Rosado da Cruz ◽  
Sara Paiva
Keyword(s):  
Cloud Computing ◽  
Mobile Computing ◽  
Mobile Devices ◽  
Mobile Apps ◽  
Mobile Technologies ◽  
Future Research ◽  
Mobile Cloud ◽  
Research Projects ◽  
Research Directions ◽  
Future Research Directions

Mobile computing and Cloud computing are two of the most growing technologies in number of users, practitioners and research projects. This chapter surveys mobile technologies and applications, along with cloud computing technologies and applications, presenting their evolution and characteristics. Then, building on mobile devices limitations and mobile apps increasing need of resources, and on the cloud computing ability to overcome those limitations, the chapter presents mobile cloud computing, and characterizes it by addressing approaches to augment mobile devices capabilities. The chapter is settled after some views about future research directions and some concluding remarks.

A Mobile Cloud Middleware to Support Mobility and Cloud Interoperability

2016 ◽  
Vol 7 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Khadija Akherfi ◽  
Hamid Harroud ◽  
Michael Gerndt
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Cloud Computing ◽  
Relevant Information ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Computing Power ◽  
Cloud Interoperability

With the recent advances in cloud computing and the improvement in the capabilities of mobile devices in terms of speed, storage, and computing power, Mobile Cloud Computing (MCC) is emerging as one of important branches of cloud computing. MCC is an extension of cloud computing with the support of mobility. In this paper, the authors first present the specific concerns and key challenges in mobile cloud computing. They then discuss the different approaches to tackle the main issues in MCC that have been introduced so far, and finally focus on describing the proposed overall architecture of a middleware that will contribute to providing mobile users data storage and processing services based on their mobile devices capabilities, availability, and usage. A prototype of the middleware is developed and three scenarios are described to demonstrate how the middleware performs in adapting the provision of cloud web services by transforming SOAP messages to REST and XML format to JSON, in optimizing the results by extracting relevant information, and in improving the availability by caching. Initial analysis shows that the mobile cloud middleware improves the quality of service for mobiles, and provides lightweight responses for mobile cloud services.

Fuzzy simplified swarm optimization for multisite computational offloading in mobile cloud computing

2020 ◽  
Vol 39 (6) ◽  
pp. 8285-8297
Author(s):  
V. Meena ◽  
Obulaporam Gireesha ◽  
Kannan Krithivasan ◽  
V.S. Shankar Sriram
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Swarm Optimization ◽  
Computational Offloading ◽  
Smart Mobile ◽  
Smart Mobile Devices

Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time.

scholarly journals Research gaps based virtualization in mobile cloud computing

2020 ◽  
Vol 10 (51) ◽  
pp. 212-222
Author(s):  
Boubakeur Annane ◽  
Adel Alti ◽  
Osman Ghazali
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Virtual Machines ◽  
Cloud Services ◽  
Security And Privacy ◽  
Battery Life ◽  
Mobile Cloud ◽  
Sensitive Data ◽  
Cloud Servers

Recently, mobile computing is known as a fast-growing utilization of people's daily life. However, the main is the limited mobile devices’ resources such as processing capability, storage space and battery life. With the development of cloud computing, mobile devices’ resources are improved with the help of cloud services, which resulted an emerged technology named Mobile Cloud Computing (MCC). Although the MCC has several advantages for mobile users, it is also challenged by many critical issues like security and privacy of the mobile user's data that offloaded on the cloud’ servers and processed on the virtual machines (VMs). In virtualization, various investigations showed that malicious users are able to break down the cloud security methods by spreading their VMs in order to alter or violate the user sensitive data that executed on cloud’ VMs. This paper deeply analyzes the recent MCC based virtualization approaches and methods by criticizing them. We found out that no approach protects the data from being stolen while distributed VMs that deployed on different cloud servers exchanging data. Hence, the paper provides practical gaps related to virtualization in MCC and future perspectives.

scholarly journals Intensive Energy Aware Mobile Computational Offloading using Machine Learning Strategies

2019 ◽  
Vol 8 (11S2) ◽  
pp. 346-354
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Mobile Devices ◽  
Learning Strategies ◽  
Cloud Services ◽  
General Interest ◽  
Energy Aware ◽  
Remote Server ◽  
Computational Offloading ◽  
Mobile Offloading

In the modern-world each and everyone needs a Smartphone to achieve their communication needs globally as well as an acceptable fact is mobile devices plays a vital role in individual's life. Smartphones now-a-days are fully consumeroriented, in which it contains a huge-variety of applications to provide service to its users. This case leads more computational power to the mobile devices as-well-as the processing ability of such devices are expected to be highly concentrated. The usage of multiple applications in simultaneous manner over mobile phones causes several issues to users such as poor-batterylifetime, speed-issues, mobile-heating and so on. In this system, a novel' and intelligent approach is proposed to solve the issues rising due to the computational abilities of mobile offloading as well as empirically proves the advantages of mobile offloading with remote servers. The term offloading explores a hidden meaning of remote accessibility, in which the mobile devices can process the storage mechanisms and computational-needs are in outside of the mobile device, so that the processing overhead of the mobile devices are highly reduced. This combination of Mobile Devices and Remote Server Manipulation is generally called as Mobile-Cloud-Computing. The term cloud refers the remote server, all the computational needs are performed over there and the resulting summaries are portrayed over the mobile devices within fraction of seconds.The accessing nature of cloud services usually follows an important strategy called MobileCrowdsensing, in which it also plays a major role in CloudService Selection procedures. In which the Mobile-Crowdsensing effectively sense the crowd ratio of mobile-devices and share the resources of cloud to their requirements as-well-as the MobileCrowdsensing also analyze and predict the application processes of general-interest. The advancement of Machine learning strategies gives hand to this nature of handling such difficult process like remote data handling and processing. This paper explores a new machine learning based approach called, Intensive Energy-aware Mobile Computational Offloading Model (IEMCOM), which concentrates more on mobile offloading issues such as huge-data transfers, complex-mobile application processing-scenarios, network-interruptions and so on. A final outcome empirically proves the integration of mobile and cloud computing results good battery-lifetime, enhanced offloading-process and security as well.

scholarly journals Enabling Efficient Decentralized and Privacy Preserving Data Sharing in Mobile Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiawei Zhang ◽  
Ning Lu ◽  
Teng Li ◽  
Jianfeng Ma
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Mobile Devices ◽  
Data Sharing ◽  
Mobile Cloud Computing ◽  
High Efficiency ◽  
Mobile Cloud ◽  
User Privacy ◽  
Access Policy ◽  
Fine Grained

Mobile cloud computing (MCC) is embracing rapid development these days and able to provide data outsourcing and sharing services for cloud users with pervasively smart mobile devices. Although these services bring various conveniences, many security concerns such as illegally access and user privacy leakage are inflicted. Aiming to protect the security of cloud data sharing against unauthorized accesses, many studies have been conducted for fine-grained access control using ciphertext-policy attribute-based encryption (CP-ABE). However, a practical and secure data sharing scheme that simultaneously supports fine-grained access control, large university, key escrow free, and privacy protection in MCC with expressive access policy, high efficiency, verifiability, and exculpability on resource-limited mobile devices has not been fully explored yet. Therefore, we investigate the challenge and propose an Efficient and Multiauthority Large Universe Policy-Hiding Data Sharing (EMA-LUPHDS) scheme. In this scheme, we employ fully hidden policy to preserve the user privacy in access policy. To adapt to large scale and distributed MCC environment, we optimize multiauthority CP-ABE to be compatible with large attribute universe. Meanwhile, for the efficiency purpose, online/offline and verifiable outsourced decryption techniques with exculpability are leveraged in our scheme. In the end, we demonstrate the flexibility and high efficiency of our proposal for data sharing in MCC by extensive performance evaluation.

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