A survey of mobile cloud computing: architecture, applications, and approaches

Hoang, Dinh Thai; Lee, Chonho; Niyato, Dusit; Wang, Ping

doi:10.1002/wcm.1203

Cited by 1,927 publications

(894 citation statements)

References 97 publications

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“…The authors proposed the MobiCloud system, which was developed at Arizona State University to simplify the study and the analysis of MCC. The authors of [9] gave a survey of MCC's definitions, advantages, architectures, and applications (Mobile Commerce, Mobile Learning, Mobile Healthcare, and Mobile Gaming). They also described MCC issues (low bandwidth, availability, heterogeneity, computing offloading, contextaware mobile cloud services, security and enhancing the efficiency of data access) and listed the existing solutions.…”

Section: Related Workmentioning

confidence: 99%

Delay-aware power optimization model for mobile edge computing systems

Jararweh

Al‐Ayyoub

Al-Quraan

et al. 2017

Pers Ubiquit Comput

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Reducing the total power consumption and network delay are among the most interesting issues facing large-scale Mobile Cloud Computing (MCC) systems and their ability to satisfy the Service Level Agreement (SLA). Such systems utilize cloud computing infrastructure to support offloading some of user's computationally heavy tasks to the cloud's datacenters. However, the delay incurred by such offloading process lead the use of servers (called cloudlets) placed in the physical proximity of the users, creating what is known as Mobile Edge Computing (MEC). The cloudlet-based infrastructure has its challenges such as the limited capabilities of the cloudlet system (in terms of the ability to serve different request types from users in vast geographical regions). To cover the users demand for different types of services and in vast geographical regions, cloudlets cooperate among each other by passing user requests from one cloudlet to another. This cooperation affects both power consumption and delay. In this work, we present a mixed integer linear programming (MILP) optimization model for MEC systems with these two issues in mind. Specifically, we consider two types of cloudlets: local cloudlets and global cloudlets, which have higher capabilities. A user connects to a local cloudlet and sends all of its traffics to it. If the local cloudlet cannot serve the desired request, then the request is moved to another local cloudlet. If no local cloudlet can serve the request, then it is moved to a global cloudlet which can serve all service types. The process of routing requests through the hierarchical network of cloudlets increases power consumption and delay. Our model minimizes power consumption while incurring an acceptable amount of delay. We evaluate it under several realistic scenarios to show that it can indeed be used for power optimization of large-scale MEC systems without violating delay constraints.

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Section: Related Workmentioning

confidence: 99%

Delay-aware power optimization model for mobile edge computing systems

Jararweh

Al‐Ayyoub

Al-Quraan

et al. 2017

Pers Ubiquit Comput

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“…Cloud services can then be classified based on the amount of customizability and control of the underlying system available to the developer versus deployment speed and ease of maintenance (see Figure 1). In this paradigm, we recognize three major categories: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) [17].…”

Section: Mobile Cloud Computing and Healthcare Servicesmentioning

confidence: 99%

Improving Healthcare Team Collaboration in Hospital Transfers through Cloud-Based Mobile Systems

Neyem

Carrillo

Jerez

et al. 2016

Mobile Information Systems

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It is a clinical fact that better patient flow management in and between hospitals improves quality of care, resource utilization, and cost efficiency. As the number of patients in hospitals constantly grows, the need for hospital transfers is directly affected. Interhospital transfers can be required for several reasons but they are most commonly made when the diagnostic and therapeutic facilities required for a patient are not available locally. Transferring a critical patient between hospitals is commonly associated with risk of death and complications. This raises the question: How can we improve healthcare team collaboration in hospital transfers through the use of emerging information technology and communication services? This paper presents a cloud-based mobile system for supporting team collaboration and decision-making in the transportation of patients in critical condition. The Rapid Emergency Medicine Score (REMS) scale was used as an outcome variable, being a useful scale to assess the risk profile of critical patients requiring transfers between hospitals. This helps medical staff to adopt proper risk-prevention measures when handling a transfer and to react on time if any complications arise in transit.

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“…Specially, security and privacy of multimedia big data in mobile and cloud computing which is becoming a part of daily life to access different multimedia systems, services and applications is a serious issue, today. Moreover, protecting user privacy and multimedia data/application secrecy from adversary is a key to establish and maintain consumers' trust in the mobile and cloud platform [3,4,11,16]. However, it is challenging to achieve, as technology is changing at rapid speed and our systems turn into ever more complex.…”

Section: Introductionmentioning

confidence: 99%

Advances in Security and Privacy of Multimedia Big Data in Mobile and Cloud Computing

Gupta

Yamaguchi

Agrawal

2017

Multimed Tools Appl

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In recent years, the use of mobile and cloud Computing is rapidly growing. In other word, mobile and cloud Computing play an important role in everybody's life today. It carries rich computational resources to mobile users, network operators, as well as cloud computing providers. Moreover, the explosion of multimedia big data (image, video, 3D, etc.) in mobile and cloud computing have created unprecedented opportunities and fundamental security and privacy challenges as they are not just big in volume, but also unstructured and multi-modal. Therefore, the papers of this special issue address variety of security and privacy issues of multimedia big data in mobile and cloud computing and security of other aspects of mobile and cloud networks and also emphasizes many open questions. We anticipate that papers of this special issue will open new entrance for further research and technology improvements in this important area.

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A survey of mobile cloud computing: architecture, applications, and approaches

Cited by 1,927 publications

References 97 publications

Delay-aware power optimization model for mobile edge computing systems

Delay-aware power optimization model for mobile edge computing systems

Improving Healthcare Team Collaboration in Hospital Transfers through Cloud-Based Mobile Systems

Advances in Security and Privacy of Multimedia Big Data in Mobile and Cloud Computing

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