scholarly journals Novel Scheme for Enhancing Storage Management and Performance and Saving Energy of Mobile Cloud Computing

2019 ◽  
Vol 15 (5) ◽  
pp. 648-663
Author(s):  
Ali A. Yassin ◽  
Abdulla J. Yassin ◽  
Abdullah Mohammed Rashid ◽  
Ahmed A. Alkadhmawee
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Youngjoo Shin ◽  
Junbeom Hur ◽  
Dongyoung Koo ◽  
Joobeom Yun

With the proliferation of new mobile devices, mobile cloud computing technology has emerged to provide rich computing and storage functions for mobile users. The explosive growth of mobile data has led to an increased demand for solutions that conserve storage resources. Data deduplication is a promising technique that eliminates data redundancy for storage. For mobile cloud storage services, enabling the deduplication of encrypted data is of vital importance to reduce costs and preserve data confidentiality. However, recently proposed solutions for encrypted deduplication lack the desired level of security and efficiency. In this paper, we propose a novel scheme for serverless efficient encrypted deduplication (SEED) in mobile cloud computing environments. Without the aid of additional servers, SEED ensures confidentiality, data integrity, and collusion resistance for outsourced data. The absence of dedicated servers increases the effectiveness of SEED for mobile cloud storage services, in which user mobility is essential. In addition, noninteractive file encryption with the support of lazy encryption greatly reduces latency in the file-upload process. The proposed indexing structure (D-tree) supports the deduplication algorithm and thus makes SEED much more efficient and scalable. Security and performance analyses prove the efficiency and effectiveness of SEED for mobile cloud storage services.


2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Saiyi Li ◽  
Hai Trieu Pham ◽  
M. Sajeewani Karunarathne ◽  
Yee Siong Lee ◽  
Samitha W. Ekanayake ◽  
...  

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment.


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