Mobile client data security storage protocol based on multifactor node evaluation
- 34 Downloads
To improve calculation and storage capacity of mobile cloud data storage algorithm and solve the problem of low reliability and low energy utilization efficiency in remote server mode, the resampling mobile cloud data storage algorithm based on Gibbs probability allocation estimate is proposed. This algorithm firstly uses vote data allocation and vote data processing model to construct the calculation model for expected propagation time of resampling model in the condition of node failure probability and establishes vote dynamic network integrating energy efficiency and fault tolerance. Secondly, for the constructed dynamic network model, the storage path is optimized via sample probability allocation estimate. In addition, to improve process performance of allocation estimate, the Gibbs sampling process is used to realize high-dimensional coupling and non-supervised training for sample data in the process of allocation estimate. Lastly the effectiveness of proposed model algorithm was verified by experiment.
KeywordsMobile client side Cloud storage Mobile node Allocation estimate Mobile cloud Data storage
Authors acknowledge the Science and Technology Foundation of China National Textile and Apparel Council under Grant No. 2016066.
- 3.Malarkodi MP, Arunkumar N, Venkataraman V (2013) Gabor wavelet based approach for face recognition. Int J Appl Eng Res 8(15):1831–1840Google Scholar
- 4.Stephygraph LR, Arunkumar N (2016) Brain-actuated wireless mobile robot control through an adaptive human–machine interface. Adv Intell Syst Comput 397:537–549Google Scholar
- 5.Pan W, Chen S, Feng Z (2012) Investigating the collaborative intention and semantic structure among co-occurring tags using graph theory. In: International Enterprise Distributed Object Computing Conference. IEEE, Beijing, pp 190–195Google Scholar
- 6.Arunkumar N, Jayalalitha S, Dinesh S, Venugopal A, Sekar D (2012) Sample entropy based ayurvedic pulse diagnosis for diabetics. In: IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012, art. no. 6215973, pp 61–62Google Scholar
- 7.Arunkumar N, Ramkumar K, Hema S, Nithya A, Prakash P, Kirthika V (2013) Fuzzy Lyapunov exponent based onset detection of the epileptic seizures. In: 2013 IEEE Conference on Information and Communication Technologies, ICT 2013, art. no. 6558185, pp 701–706Google Scholar
- 10.Arunkumar N, Balaji VS, Ramesh S, Natarajan S, Likhita VR, Sundari S (2012) Automatic detection of epileptic seizures using independent component analysis algorithm. In: IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012, art. no. 6215903, pp 542–544Google Scholar
- 11.Yang D, Chen Y, Zhuang Y, Zhu C, Tang F, Huang J (2017) Probing nanostrain via a mechanically designed optical fiber interferometer. IEEE Photonics Technol Lett 29(2017):1348–1351Google Scholar
- 16.Mohammed MA, Ghani MKA, Arunkumar N, Hamed RI, Abdullah MK, Burhanuddin MA (2018) A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2018.07.022 Google Scholar