An Efficient and Secured Framework for Mobile Cloud Computing

Elgendy, Ibrahim A.; Zhang, Weizhe; Liu, Chuanyi; Hsu, Ching‐Hsien

doi:10.1109/tcc.2018.2847347

Cited by 68 publications

(45 citation statements)

References 22 publications

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“…The above figure, Fig-5 illustrates the delay analysis level of proposed system, which is evaluated with-the past implementation strategy called Energy-Efficient Offloading (EEO) [25], in which it estimates the data/packets transmission delay. It is designed with the consideration of buffering and data-transfer-time.…”

Section: Fig5end-to-end Delay Analysismentioning

confidence: 99%

“…T D = (P X S * P X R * P X TS)/(T X R+P N C) (1) Where T D indicates the Transmission Delay, P X S indicates the packet size, P X R indicates the Packet Transmission Rate, P X TS indicates the Packet Transmission Speed, T X R indicates the TransmissionRange and P N C indicates the present looping node count.The below figure, Fig-6 representsthe throughput analysis ratio of the proposed system, which is evaluated with-the past implementation strategy called Energy-Efficient Offloading (EEO) [25]. The throughput formulation is estimated by means of the following equation: The below figure, Fig-7 represents the energy-efficiency' analysis ratio of the proposed system, which is evaluated with the past implementation strategy called Energy-Efficient Offloading (EEO) [25].…”

Section: Fig5end-to-end Delay Analysismentioning

confidence: 99%

“…Where E Effi Aindicates the Energy-Efficiency Analysis-Ratio, P X S indicates the Packet Size, T X R indicates the Packet Transmission Range, P T F req indicates the Packet Transmission Frequency, P X S indicates the Packet TransmissionSpeed, P N C indicates the present looping node count and P XT R indicates the Packet Transmission Range. The below figure, Fig-8 symbolizes the energyconsumption' analysis ratio of the proposed system, which is evaluated with the past implementation strategy called Energy-Efficient Offloading (EEO) [25].…”

Section: ((P X S*t X R)+(p T F Req *P X S+p N C)/(p Xt R) (3)mentioning

confidence: 99%

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Intensive Energy Aware Mobile Computational Offloading using Machine Learning Strategies

2019

IJITEE

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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.

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Section: Fig5end-to-end Delay Analysismentioning

confidence: 99%

Section: Fig5end-to-end Delay Analysismentioning

confidence: 99%

Section: ((P X S*t X R)+(p T F Req *P X S+p N C)/(p Xt R) (3)mentioning

confidence: 99%

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Intensive Energy Aware Mobile Computational Offloading using Machine Learning Strategies

2019

IJITEE

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“…Several IoT devices offload the heavy computation workload to the MEC devices such as base stations, access points, and so forth [2]. Integrating EH and MEC techniques contribute to improving computation performance and open new possibilities for cloud computing [3,4].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

et al. 2020

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Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from 0.979 % and gradually decreases to 0.87 % . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.

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“…These new devices have various capabilities that are useful in developing complex software applications. Mobile devices can also be connected to cloud computing and offload tasks to be executed there, which saves power, memory, and battery [18][19][20]. The advantages of mobile technologies make them useful for accessing information from any place at any time and give PVI the opportunity to use smartphones in their daily activities [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies

2019

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People with visual impairment face various difficulties in their daily activities in comparison to people without visual impairment. Much research has been done to find smart solutions using mobile devices to help people with visual impairment perform tasks like shopping. One of the most challenging tasks for researchers is to create a solution that offers a good quality of life for people with visual impairment. It is also essential to develop solutions that encourage people with visual impairment to participate in social life. This study provides an overview of the various technologies that have been developed in recent years to assist people with visual impairment in shopping tasks. It gives an introduction to the latest direction in this area, which will help developers to incorporate such solutions into their research.

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An Efficient and Secured Framework for Mobile Cloud Computing

Cited by 68 publications

References 22 publications

Intensive Energy Aware Mobile Computational Offloading using Machine Learning Strategies

Intensive Energy Aware Mobile Computational Offloading using Machine Learning Strategies

Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies

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